# Understanding Electric Vehicle Range and Charging Needs: Interactions Between Ambient Temperature, Commute Patterns, and State-of-Charge Usage

Charbel Mansour, Malo Benoit, Rabih Al Haddad, Namdoo Kim, Maroun Nemer, Natalia Zuniga, Joshua Auld

► To cite this version:

Charbel Mansour, Malo Benoit, Rabih Al Haddad, Namdoo Kim, Maroun Nemer, et al.. Understanding Electric Vehicle Range and Charging Needs: Interactions Between Ambient Temperature, Commute Patterns, and State-of-Charge Usage. Energies, 2026, 19, 10.3390/en19030709. [DOI: 10.3390/en19030709](https://doi.org/10.3390/en19030709)

HAL Id: hal-05485045

https://hal.science/hal-05485045v1

Submitted on 30 Jan 2026

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons CC BY 4.0 - Attribution - International License
Article

# Understanding Electric Vehicle Range and Charging Needs: Interactions Between Ambient Temperature, Commute Patterns, and State-of-Charge Usage

Charbel Mansour <fnref n="1" />*, Malo Benoit <fnref n="1" />, Rabih Al Haddad <fnref n="2" /> ID, Namdoo Kim <fnref n="1" /> ID, Maroun Nemer <fnref n="2" />, Natalia Zuniga <fnref n="1" /> and Joshua Auld <fnref n="1" />

## Abstract

Electric vehicle (EV) performance can vary substantially under real-world operating conditions, particularly due to ambient temperature effects on energy consumption, battery behavior, and thermal management requirements. This study quantifies how weather conditions, daily driving patterns, and State-of-Charge (SOC) usage strategies jointly influence EV driving range, charging frequency, and overall energy efficiency. A detailed and experimentally validated Autonomie vehicle model is developed, integrating a powertrain, a mono-zonal cabin model, and a battery electro-thermal model. Three battery sizes (200-, 300-, and 400-mile homologated ranges) are assessed across five commute profiles (20–200 miles) and six ambient temperatures (–18 °C to 50 °C), including scenarios with and without preconditioning. Results show that extreme temperatures could significantly decrease the maximum achievable range by up to 55% in cold conditions (–18 °C) and 40% in hot conditions (50 °C), relative to moderate conditions. Larger battery packs retain a greater fraction of their nominal range under thermal stress, while smaller packs experience sharper relative penalties due to the higher contribution of thermal loads to total energy demand. The analysis further demonstrates that limiting operation to partial SOC windows (e.g., 80–20%), a common real-world practice, significantly reduces achievable range and increases charging frequency, particularly in cold weather. Thermal preconditioning while plugged in is shown to mitigate these effects for short trips, reducing energy consumption by up to 31% in hot conditions and 7% in cold conditions. The findings demonstrate how climate, SOC usage behavior, and thermal management jointly shape the practical driving capability of EVs, highlighting the importance of efficient thermal management and realistic user charging strategies for ensuring reliable EV operation across diverse climatic scenarios.

**Keywords:** battery electric vehicles; driving range; energy consumption; range degradation; ambient temperature effects; cold-weather performance; hot-weather performance; thermal management systems; battery electro-thermal modeling; SOC usage strategy; battery heating and cooling; preconditioning; commute profiles; driving behavior; charging frequency; partial SOC operation; Autonomie simulation; HVAC load modeling; vehicle energy modeling

<figure><img src="image_2.png" /></figure>

Check for updates

Academic Editors: Vaidas Lukoševičius, Sergey Goolak, Robertas Keršys and Rolandas Makaras

Received: 19 December 2025

Revised: 20 January 2026

Accepted: 26 January 2026

Published: 29 January 2026

**Copyright:** © 2026 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the [Creative Commons Attribution (CC BY) license](https://creativecommons.org/licenses/by/4.0/).

<footnote n="1">¹ Vehicle and Mobility Systems Department, Argonne National Laboratory, 9700 S Cass Ave, Lemont, IL 60439, USA; mbenoit@anl.gov (M.B.); nkim@anl.gov (N.K.); nzuniga@anl.gov (N.Z.); jauld@anl.gov (J.A.)</footnote>

<footnote n="2">² Center for Energy, Environment, and Processes, Ecole des Mines de Paris, 21 All. des Marronniers, 78000 Versailles, France; rabih.al_haddad@minesparis.psl.eu (R.A.H.); maroun.nemer@minesparis.psl.eu (M.N.)</footnote>

<footnote>* Correspondence: cmansour@anl.gov</footnote>
# 1. Introduction

Electric vehicles (EVs) have become an increasingly visible component of the broader transportation landscape, supported by continuous advancements in battery technology, thermal management systems, and a growing diversity of market offerings. According to the IEA Global EV Outlook 2025, electric car sales in 2024 increased by more than 25% compared to the previous year to reach up to 17 million units worldwide, with more than 1.6 million units sold in the United States, accounting for 10% of new light-duty vehicle sales [1]. This expansion signals continued diversification of propulsion technologies and evolving consumer interest.

Yet, as EV adoption expands, important operational challenges persist. User satisfaction, which is critical to sustained growth, is strongly influenced by factors such as vehicle range, charging availability, and reliability under diverse real-world conditions. A key concern among drivers is range anxiety, which manifests both as uncertainty about reaching a destination (distance-related) and apprehension about the time required for charging (time-related) [2,3]. Although surveys indicate that EV owners generally report high satisfaction levels overall, concerns related to charging convenience, trip planning, and vehicle usability under variable weather conditions remain among the most frequently cited limitations [3]. These concerns tend to intensify during long-distance travel and in extreme temperatures, where increased thermal loads can substantially reduce effective driving range and alter charging behavior.

One of the most critical differences between EVs and ICE vehicles lies in thermal energy management [4]. ICE vehicles leverage engine waste heat to warm the cabin, while EVs must allocate additional battery energy to heat or cool both the cabin and the battery itself [4,5]. As a result, extreme ambient temperatures, especially below 0 °C or above 35 °C, can significantly increase energy consumption, reduce effective driving range, and modify charging behavior. Cabin and battery thermal management systems (TMS) play a central role in mitigating these impacts. In cold climates, resistive heating or heat pumps are required to maintain passenger comfort and battery health, while in hot climates, battery cooling and air conditioning loads increase [6,7]. These effects are not uniform: they vary based on vehicle configuration, battery capacity, commute distance, driving cycle, and battery state-of-charge (SOC) management strategy. As a result, EVs often experience substantial deviations from their nominal test-cycle range, particularly in regions with pronounced seasonal temperature variations.

Addressing these challenges requires an integrated understanding of how ambient temperature, driving behavior, and vehicle configuration interact to shape energy consumption and practical driving range. This study investigates the following research question: How do ambient temperature, battery capacity, commute distance, and driving behavior collectively influence energy consumption, driving range, and charging requirements in BEVs? Although prior research has examined the effects of ambient temperature, drive cycles, or battery configurations individually, few studies have considered the combined and interacting effects of these variables, along with SOC strategies and cabin preconditioning, within a unified analytical framework. To contextualize this research and highlight existing knowledge gaps, the following section presents a review of the relevant literature.

# 2. Literature Review

This section presents an overview of the most relevant studies in the literature that investigate the impact of ambient temperature, vehicle configuration, and user behavior on the energy use and operational performance of EVs. A broad selection of well-cited studies was reviewed to identify each study's focus, the factors examined, and the key results and insights achieved. These studies, which include both numerical simulations
and experimental validations, offer a thorough examination of the field. This section reviews the literature across three central themes: (i) the influence of temperature on energy consumption and range, (ii) temperature effects on charging behavior and grid demand, and (iii) the role of driving behavior, commuter types, and battery size. This synthesis highlights existing knowledge gaps and motivates the contributions of this study.

## 2.1. *Temperature Effects on EV Energy Consumption and Range*

Multiple studies have demonstrated that ambient temperature significantly influences EV energy consumption and driving range [8–11]. The reduction in range under cold conditions is largely attributed to additional energy demands for battery and cabin heating, while high temperatures increase HVAC and battery cooling requirements [12]. For example, Zhao et al. [13] showed a 42.8% range reduction in cold conditions, driven predominantly by cabin heating loads. Similarly, Deu et al. [14] validated an algorithm to simulate temperature effects, reporting a range increase of 29% at high temperatures and a decrease of 20% under cold conditions. Iora et al. [15], using an energy consumption model validated with Nissan Leaf data, confirmed the sharp rise in consumption at low temperatures due to diminished battery performance and increased thermal demands. Al Wreikat et al. [16] reinforced these findings using four years of real-world EV monitoring in the UK, observing increased specific energy consumption at low temperatures. These results were corroborated by Yuksel and Michalek [17], who reported a 15% increase in annual energy consumption in colder U.S. climates, underscoring the combined effect of ambient temperature and auxiliary loads on EV efficiency. Al Haddad et al. [18,19] further demonstrated that thermal loads in extreme cold can more than double energy consumption in production EVs, driven primarily by cabin heating and battery conditioning requirements. Together, these studies confirm that temperature-induced penalties are substantial and that range ratings often overstate real-world performance in thermally demanding environments.

## 2.2. *Charging Behavior and Grid Impact in Thermal Extremes*

Beyond its impact on energy use, ambient temperature also shapes real-world charging behavior, influencing when, how often, and how long drivers charge their vehicles. Senol et al. [8] examined the decline in lithium-ion battery performance at low temperatures and its consequences on charging behavior, highlighting slower charge rates and reduced energy throughput. Bayram [2] employed a Monte Carlo simulation to estimate additional generation capacity needed in cold weather, finding that charging 11 million EVs in winter could require 450 MW of extra power. This analysis emphasized the grid-side implications of climate-sensitive charging demand. Real-world data also demonstrate that thermal conditions shift charging behaviors. Jin et al. [3] analyzed operational data from 140,000 EVs in China and found that cold temperatures significantly alter charging frequency, patterns, and energy consumption, particularly in urban settings where infrastructure constraints and thermal loads compound. These studies highlight the systemic consequences of thermal extremes and the importance of accounting for regional climate variability in charging infrastructure planning.

Real-world field data also show that temperature extremes influence user charging practices. Observations from cold-weather fleet operations in Alaska [20] indicate that drivers tend to charge earlier and target higher SOC levels before or during extreme weather events to compensate for increased uncertainty in available range. Additionally, low ambient temperatures slow charging rates because batteries require pre-heating before accepting charge, leading to prolonged charging sessions. Because EV efficiency declines sharply in sub-zero temperatures, drivers must also charge more frequently, which can
increase local demand on charging infrastructure during cold spells. These behavioral and technical effects underscore the importance of considering user charging patterns and climate-sensitive operating conditions when assessing charging infrastructure needs and grid impacts.

## 2.3. Battery Size and Driving Behavior

In addition to environmental factors, user behavior and vehicle configuration influence the severity of thermal penalties. Driving patterns, trip length, and battery size all affect how thermal losses translate to practical range limitations. Hao et al. [21] evaluated seasonal energy consumption across different EV use cases, personal, taxi, and rideshare and reported higher charging needs and more pronounced range reduction for high-usage vehicles during winter. Jin et al. [3] showed that shorter trips in urban areas resulted in greater energy variability, largely due to the fixed energy required to ramp up HVAC systems. The literature also acknowledges trade-offs in battery sizing: larger batteries tend to offer a more consistent range under extreme conditions but come with higher cost, mass, and charging time, while smaller batteries are more efficient under moderate climates but suffer disproportionately in thermal extremes. Al Haddad et al. [19] further illustrated how differing battery capacities and thermal management architectures affect energy consumption and thermal resilience under extreme temperatures.

However, most existing work does not evaluate these trade-offs across a range of temperatures and user profiles in a unified framework. Moreover, most studies do not explore variations in drivers' SOC management. In practice, many EV users operate within partial SOC windows (e.g., 80-20%) due to concerns about battery longevity and range anxiety. The energy and range impacts of these SOC strategies, particularly under thermal stress, remain underexplored.

While the effects of temperature, battery size, and driving behavior have been individually addressed, there are clear gaps in the integration of these factors within a comprehensive, dynamic simulation environment. Most prior studies have focused on one or two parameters in isolation, such as temperature and range, or battery size and consumption, without capturing how these dimensions interact under realistic driving and charging scenarios. In particular, the interactive effects of ambient temperature, trip length, battery size, SOC strategy, and thermal preconditioning have not been jointly evaluated using a unified model.

Based on this synthesis of insights and identified gaps in literature, this study addresses these gaps by employing a validated vehicle simulation model in Autonomie [22] to evaluate the interactive effects of ambient temperature, battery capacity, driving cycle, commuter type, SOC strategy, and thermal preconditioning. The analysis covers three EV configurations (200, 300, and 400-mile nominal ranges), six ambient temperatures (−18 °C, −7 °C, 0 °C, 22 °C, 35 °C, 50 °C), five commute types (20, 50, 100, 150, and over 200 miles), and two SOC usage strategies (full depletion [100-0%] vs. partial use [80-20%]).

This study pursues the following specific objectives:

* Quantify the temperature-dependent energy split between propulsion and thermal management systems across SAE J1634 cycles [23].

* Assess the sensitivity of driving range to the interaction between ambient temperature and driving patterns (urban vs. highway).

* Evaluate the impact of user-defined SOC strategies (full vs. partial depletion) on charging frequency and trip feasibility.

* Determine the energy-saving potential of thermal preconditioning under extreme climatic conditions.
By quantifying the operational impacts of these interacting factors, this study provides new insights into how EV energy performance varies across realistic scenarios. The findings are intended to inform vehicle design trade-offs and help consumers identify EV configurations that align with their usage profiles. In doing so, this work contributes to a more nuanced understanding of EV performance and usability in diverse operational contexts and how EVs may be integrated into a transportation system where multiple technologies and user needs coexist.

The study begins by outlining the methodology and the framework used, as presented in Section 3. This section also describes the characteristics of the electric vehicles selected for this study and the operational input conditions, including driving and weather conditions, as well as commuter types. Additionally, the modeling scheme used to calculate total energy consumption and driving range is explained, detailing the developed models for the cabin, battery, thermal management system, and powertrain. Finally, the validation of the models, the modeling approach, and the underlying assumptions are discussed based on previous work in [18,19]. The results of this analysis are presented in Section 4.

### 3. Methodology

This section presents the methodology adopted in analyzing the impact of ambient temperature on EV energy consumption, driving range, and charging needs. The analysis evaluates how these factors interact with different battery sizes, drive cycles, SOC strategies, and commuter types using a high-fidelity simulation framework developed in Autonomie [22].

Figure 1 outlines the simulation-based analytical framework used to investigate the combined effects of driving behavior, operating conditions, and vehicle configuration on EV energy efficiency. Driving behavior is characterized by using driving cycles encompassing urban and highway segments across a range of commuting distances (20 to 200+ miles). Operating conditions reflect key thermal stressors: ambient temperatures from -18 °C to 50 °C, driver charging behavior by varying initial and final SOC, and the presence or absence of battery thermal preconditioning. Technological specifications, such as battery size, motor power, vehicle mass, frontal area, and aerodynamic drag coefficient, are also incorporated to reflect real-world BEV variation.

At the core of the framework is a modular EV simulation model that integrates powertrain, battery, cabin, and thermal subsystems. Dedicated thermal models of the cabin and battery were developed to capture their respective thermal loads, and a comprehensive TMS was implemented to regulate subsystem temperatures. The overall modeling approach, including the battery electro-thermal model, cabin thermal model, and TMS control logic, has been previously adapted to several production EV case studies (Nissan Leaf, Chevrolet Bolt, and Tesla Model 3) and validated against experimental measurements reported in earlier work [18], ensuring that the integrated model reproduces temperature-dependent energy consumption with high fidelity. Performance metrics used in this study include subsystem-level energy consumption, total energy demand, required recharge energy, and effective driving range across all simulated scenarios. This end-to-end structure enables a holistic evaluation of BEV performance under diverse and realistic usage conditions. The remaining subsections describe the methodology in greater detail.
<figure><img src="image_3.png" /><figcaption>**Figure 1.** Framework for the study.</figcaption></figure>

### 3.1. *Definition of Vehicle Specifications, Operating Conditions, and Driving Behavior*

In the first step of the proposed framework, BEV configurations, operational settings, and driving behavior profiles were systematically defined. Driving behavior is parameterized by both trip length and drive cycle type (urban vs. highway). Operating conditions include ambient temperature, initial cabin and battery temperatures, charging behavior (full or partial depletion), and whether thermal preconditioning is activated before departure. These variables were selected to represent typical and extreme seasonal conditions encountered by EV users in the U.S. and to reflect charging and usage behavior found in studies.

#### 3.1.1. Battery Size Selection and Vehicle Configurations

The electric range of BEVs has steadily improved over the past decade, driven by advancements in battery technology, energy efficiency, and vehicle design. Projections suggest this upward trend will continue as automakers respond to evolving consumer expectations [24]. BEV range is determined through EPA certification, using the procedures of SAE J1634 that combine standardized lab cycles with approximate real-world energy use. The Urban Dynamometer Driving Schedule (UDDS) simulates stop-and-go urban traffic, while the Highway Fuel Economy Test (HWFET) reflects steady-state highway
conditions. Energy consumption is recorded at both high and low states of charge (SOC) and then weighted 55% city and 45% highway to form a composite range. To approximate real-world influences, such as HVAC loads, road grade, and driving style, the EPA applies a 0.7 adjustment factor to the calculated range. This correction yields the published EPA range, which provides consumers with a more realistic expectation than raw dynamometer values [24], though it does not explicitly capture temperature-dependent thermal penalties that can dominate in extreme conditions.

The average EPA-rated electric driving range has grown steadily [25]. In 2011, early-generation BEVs, such as the first-generation Nissan Leaf, offered around 73 miles of range. Technological innovations later in the decade led to significant increases in range, with vehicles like the Chevrolet Bolt demonstrating the viability of EVs with broader usability with a range of 238 miles in 2017. By the early 2020s, the average BEV range exceeded 200 miles, with a growing number of models surpassing 300 miles. Premium models such as Lucid Air and Tesla Model S have extended range ceilings beyond 400 miles, targeting consumers with high expectations for performance, convenience, and long-distance capability. This market shift toward longer-range vehicles is illustrated in Figure 2 [25].

<figure><img src="image_4.png" /><figcaption>**Figure 2.** Median and maximum range of EV offered for sales in the U.S. (2011–2024). Source: U.S. Department of Energy, Fact of the Week #1323; accessed on 13 July 2025.</figcaption></figure>

In this study, we focus on three categories to reflect key milestones in EV evolution:

* Short-range BEVs (200-mile EPA range), representing low-cost vehicles available in the U.S.

* Medium-range BEVs (300-mile EPA range), representing average vehicles to be sold in the coming years in the U.S.

* Long-range BEVs (400-mile EPA range), representing premium electric vehicles.

The representative vehicle modeled in this study is a compact-class EV, sized to reflect common industry offerings. Technical specifications of key components across the three categories are presented in Table 1, including battery energy capacity, electric motor power, and vehicle dynamics properties (mass, aerodynamics, rolling resistance, and inertia).
<table><caption>**Table 1.** Technology and specifications of the different BEVs chosen for this study.</caption><thead><tr><th>Component</th><th>Technology</th><th>Parameters</th><th>Value</th></tr></thead><tbody><tr><td rowspan="3">General</td><td></td><td>Gross Vehicle Weight</td><td>1370; 1506; 1706 kg *</td></tr><tr><td></td><td>Max Occupancy</td><td>4</td></tr><tr><td></td><td>EPA Range</td><td>200; 300; 400 miles *</td></tr><tr><td rowspan="2">Battery</td><td>Battery Pack</td><td>Lithium-ion</td><td>Geometry of cell<br/>Total Energy Capacity</td><td>Pouch<br/>46; 72; 109 kWh *</td></tr><tr><td>Electric Machine</td><td>Permanent magnet</td><td>Rated Power</td><td>96; 102; 120 kW *</td></tr><tr><td rowspan="5">Propulsion</td><td>Powertrain</td><td></td><td>Type</td><td>front-wheel drive</td></tr><tr><td>Transmission</td><td></td><td>Final Drive Ratio</td><td>8.139</td></tr><tr><td rowspan="3">Chassis</td><td></td><td>Frontal area</td><td>0.7488 m²</td></tr><tr><td></td><td>Drag coefficient</td><td>0.3038</td></tr><tr><td></td><td>Rolling resistance coef.</td><td>0.0095</td></tr><tr><td rowspan="2">Wheel</td><td></td><td>Radius</td><td>0.3175 m</td></tr><tr><td></td><td>Inertia</td><td>4 kg·m²</td></tr></tbody></table>

### 3.1.2. Driving and Charging Behavior

Building on the vehicle configurations defined in Section 3.1.1, this subsection outlines the representative commute distances used to evaluate EV performance under varying trip demands. These distances reflect the diversity of use cases for driving behavior that BEVs are expected to support, from short errands to extended daily or intercity travel.

Five commute distances are modeled: 20, 50, 100, 150, and over 200 miles. This range captures different driving patterns and long-distance scenarios that influence energy consumption, thermal load, and charging frequency. According to the U.S. Bureau of Transportation Statistics, over 50% of daily trips in the U.S. are less than three miles, highlighting the predominance of short-distance travel [16].

Although long commutes represent a smaller share of daily trips, they account for a disproportionate share of total vehicle miles traveled. Approximately 2% of daily trips in the U.S. exceed 50 miles, but these trips represent an important proportion of the passenger miles traveled [26]. This makes them particularly relevant for evaluating the adequacy of BEV range and the effectiveness of thermal management strategies in extended-use scenarios.

In addition, this study considers two distinct charging behaviors. The first scenario simulated full battery depletion, utilizing the maximum available capacity with SOC cycling between 100% and 0%. In contrast, the second behavior modeled a partial depletion strategy, restricting the SOC to a conservative window of 80% down to 20%. This strategy aligns with common manufacturer recommendations and user practices designed to maximize battery longevity by avoiding high voltage stress at 100% SOC and deep discharge degradation at low SOC [20,27,28]. Also, this 20% lower bound serves as a necessary safety margin, accounting for SOC estimation inaccuracies and driver range anxiety, particularly during long trips where charging infrastructure may be uncertain.

### 3.1.3. Ambient Temperature Scenarios

Building on the driving commutes and charging behavior framework outlined in Section 3.1.2, this subsection establishes the thermal boundary conditions essential for evaluating EV performance. Six ambient temperatures, chosen to represent seasonal extremes and typical temperate conditions, were derived from historical climate data across U.S. regions, including the January and July 2024 monthly averages.

* Baseline scenario (22 °C): Cabin and battery are within optimal operating range; no active heating or cooling required.

<footnote>* Data for short-range, medium-range, and long-range BEV, respectively.</footnote>
* Cold weather scenarios (0 °C and –7 °C): Heating is required for both cabin comfort and battery protection.

* Extreme cold scenario (–18 °C): High heating demand activates resistive heating and full thermal management operation.

* Summer scenario (35 °C): Cabin cooling is engaged; battery cooling may be activated based on thermal thresholds.

* Extreme summer scenario (50 °C): Maximum HVAC and battery cooling loads are expected.

Initial cabin and battery temperatures were set to match ambient conditions unless thermal preconditioning is applied. This approach ensured consistent comparison across scenarios by controlling thermal soak effects that influence early-trip energy consumption. Solar irradiance was applied to the cabin surface area during hot-weather scenarios based on standard clear-sky conditions to simulate realistic internal thermal loading. Direct solar irradiation ($I_{sol} = 850 \text{ W} \cdot \text{m}^{-2}$) was assumed when in cooling mode, while wind effects were excluded from the scope of this study. To capture latent cooling loads, a relative humidity of 50% was applied during hot-weather simulations.

## 3.2. *Vehicle Thermal Systems Modeling Approach*

EV energy consumption was simulated as a function of the operating conditions defined in Step 2 (Figure 1). A detailed model was developed in Simulink and integrated into Autonomie, comprising four subsystems: the cabin (Section 3.2.1), battery (Section 3.2.2), thermal management system (Section 3.2.3), and powertrain. A compact EV baseline from Autonomie was parametrized, and custom thermal sub-models for the cabin, battery, and TMS were integrated with the existing powertrain to form a unified framework. The following subsections summarize each subsystem and its modeling approach.

### 3.2.1. Cabin Model

One of the primary functions of the thermal management system is to maintain cabin temperature within a comfortable range under all ambient conditions. In this study, the cabin thermal model was built upon the framework introduced by the authors of [29] but was adapted and recalibrated to integrate with the present vehicle-level model developed in Autonomie. The objective is to accurately capture the transient thermal loads associated with various ambient temperatures and drive cycles. The model was implemented in Simulink and coupled with the overall vehicle model to enable dynamic interaction between cabin heating/cooling demands and vehicle energy consumption.

As illustrated in Figure 3, the adapted cabin model preserves the mono-zonal architecture proposed in [29], representing the cabin air as a single thermal node, but extends it by coupling HVAC power demand directly to the vehicle's thermal management and battery models. This allows evaluation of the cabin's thermal energy requirements as a function of driving conditions and ambient temperature. Parameter tuning was performed to reflect the compact-class BEV geometry and materials adopted in this study.

The cabin interior model simulates heating and cooling processes through interactions with the HVAC system and external environment, using a mono-zonal approach where cabin air is treated as a single thermal node with uniform temperature. It accounts for energy transfers between cabin air, interior surfaces (e.g., seats, dashboard), and heat emitted by passengers. Heat exchange with the outside environment is modeled via cabin walls, incorporating convective, radiative, and solar heat flows based on external air temperature, infrared environment temperature, and solar vector.
<figure><img src="image_5.png" /><figcaption>**Figure 3.** Schematic of the thermal model of the cabin.</figcaption></figure>

Dynamic thermal balances are applied to the cabin walls, factoring in thermal capaci-
tances, external conductance, and solar flux absorption. This enables the computation of
transient and steady-state thermal conditions while monitoring relative humidity changes
inside the cabin. The model requires detailed parameters of cabin walls, glazing, and
materials. The target cabin temperature is fixed at 22 °C, with initial conditions set to match
the exterior ambient temperature. All geometric and material parameters were updated to
match the compact BEV configuration considered in this work, as summarized in Table 2.

<table><caption>**Table 2.** Cabin geometry parameters.</caption>
  <thead>
    <tr>
      <th>Parameter</th>
      <th>Value</th>
      <th>Unit</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Glazing areas</td>
      <td>1.5094</td>
      <td>m<sup>2</sup></td>
    </tr>
    <tr>
      <td>Wall areas</td>
      <td>9.297</td>
      <td>m<sup>2</sup></td>
    </tr>
    <tr>
      <td>Lateral insulation thickness</td>
      <td>75</td>
      <td>mm</td>
    </tr>
    <tr>
      <td>Floor insulation thickness</td>
      <td>15</td>
      <td>mm</td>
    </tr>
    <tr>
      <td>Roof insulation thickness</td>
      <td>13</td>
      <td>mm</td>
    </tr>
    <tr>
      <td>Internal thermal capacity</td>
      <td>75</td>
      <td>kJ · K<sup>-1</sup></td>
    </tr>
    <tr>
      <td>Glazing transmissivity</td>
      <td>0.85</td>
      <td>[-]</td>
    </tr>
    <tr>
      <td>Wall outer absorptivity</td>
      <td>0.85</td>
      <td>[-]</td>
    </tr>
    <tr>
      <td>Cabin volume</td>
      <td>2.61</td>
      <td>[-]</td>
    </tr>
  </tbody>
</table>

3.2.2. Battery Model

A coupled electro-thermal battery model was developed to represent the behavior of a lithium-ion pouch-cell pack integrated within the vehicle-level simulation in Autonomie. The model dynamically captures the interactions among temperature, current, and SOC, enabling transient prediction of energy consumption and battery thermal loads under varying drive cycles and ambient conditions. The pack configuration (series-parallel layout) defines a nominal voltage range of 350–450 V, representative of compact-size BEVs.

The thermal formulation extends the framework presented in [18] and was adapted and recalibrated for the specific cell chemistry and geometry considered here. A two-dimensional transient heat-conduction model was implemented to simulate the temperature distribution across the pouch-cell surface, whose large area-to-thickness ratio justifies this dimensional reduction. Temperature gradients through the thickness were neglected, and conduction was assumed to dominate internal heat transfer.

The thermal model was coupled with a one-dimensional electrical equivalent-circuit model, capturing the dependence of internal resistance on SOC, temperature, and current rate. This bidirectional coupling, illustrated in Figure 4, allows heat generation to influence cell temperature, which in turn modifies electrical efficiency and resistance. Convective
heat transfer within the electrode-electrolyte domain was neglected because it is negligible compared with conduction.

<figure><img src="image_6.png" /><figcaption>**Figure 4.** Schematic representation of the 2D thermal and 1D electrical coupling in the pouch-cell battery model.</figcaption></figure>

All thermal and electrical parameters, such as heat capacity, conductivity, and internal resistance, were recalibrated to match the pouch-cell chemistry and nominal capacity used in this study. A summary of key thermal parameters used in the model is provided in Table 3. The model operated at the same co-simulation time step as the powertrain and thermal management subsystems, ensuring accurate dynamic interaction of battery temperature with vehicle energy flows.

<table><caption>**Table 3.** Thermal characteristics of different domains in a pouch cell.</caption><thead><tr><th>Parameter</th><th>Unit</th><th>Electrode</th><th>Tab Domain (Aluminum)</th><th>Tab Domain (Copper)</th><th>Casing</th></tr></thead><tbody><tr><td>rho</td><td>kg/m<sup>3</sup></td><td>2247</td><td>2719</td><td>8978</td><td>2700</td></tr><tr><td>Cp</td><td>kg·K/W</td><td>785</td><td>202.4</td><td>387.6</td><td>953</td></tr><tr><td>k</td><td>kg·K/W·m</td><td>30</td><td>871</td><td>381</td><td>155</td></tr></tbody></table>

Model behavior was cross-validated against experimental discharge and temperature data reported in [17,18], confirming its ability to reproduce cell-level thermal transients under varying load and ambient conditions. Detailed governing equations and numerical discretization schemes are available in [18].

### 3.2.3. Thermal Management System

The TMS was modeled to quantify cabin and battery thermal loads and their effect on vehicle energy consumption across ambient conditions. The TMS was implemented in Simulink and integrated with the vehicle-level simulation in Autonomie, enabling co-simulation of power and thermal energy flows. The architecture includes reversible heat-pump operation, PTC heating, chiller-assisted cooling, and radiator-based heat rejection.

For cabin thermal control (Figure 5), three heating modes and one cooling mode were defined based on ambient temperature:
* Heating mode 1 (0–15 °C): Heat-pump operation.

* Heating mode 2 (−10–0 °C): Combined heat-pump and positive temperature coefficient (PTC) heating.

* Heating mode 3 (<−10 °C): PTC-only heating through coolant loop and heater core.

* At ambient temperatures above 25 °C, a vapor compression cycle (VCC) operates in cooling mode using a scroll compressor, while between 15 °C and 25 °C, the system functions in ventilation mode through direct air circulation.

<figure><img src="image_7.png" /><figcaption>**Figure 5.** Thermal management system operating modes as a function of ambient temperature for vehicle cabin control.</figcaption></figure>

Battery thermal management operates in parallel using active coolant-based cooling technology, utilizing a fan and a chiller connected to the HVAC unit (Figure 6). Cooling is achieved through two strategies: radiator-assisted cooling (20–35 °C) and chiller-assisted active cooling (>35 °C), depending on thermal load and ambient temperature. At low temperatures (−20–15 °C), passive heating using the heat generated by the battery is mainly applied. However, PTC heating can be used if charging at those temperatures. This logic ensures stable operation while minimizing compressor use and parasitic energy demand.

<figure><img src="image_8.png" /><figcaption>**Figure 6.** Thermal management system operating modes as a function of ambient temperature for battery control. * Passive heating is used during driving, whereas PTC heating is activated during the charging phase.</figcaption></figure>

A detailed schematic of the complete TMS, covering the cabin, HVAC, and the battery systems, is illustrated in Figure 7, and the corresponding component specifications are summarized in Table 4.

<figure><img src="image_9.png" /><figcaption>**Figure 7.** Generic thermal management system architecture configuration.</figcaption></figure>
<table><caption>**Table 4.** Thermal management system specifications.</caption><thead><tr><th>Component</th><th>Technology</th><th>Parameters</th><th>Value</th></tr></thead><tbody><tr><td rowspan="10">TMS</td><td rowspan="3">Cabin<br>Reversible HP</td><td>Thermal comfort</td><td>19–23 °C</td></tr><tr><td>Max cooling unit power (AC)</td><td>5.6 kW</td></tr><tr><td>Max heating unit power (HP)</td><td>5.6 kW</td></tr><tr><td rowspan="2">Medium</td><td>Primary refrigerant</td><td>R134a</td></tr><tr><td>Secondary medium</td><td>air and glycol</td></tr><tr><td rowspan="2">Compressor</td><td>Type</td><td>Scroll</td></tr><tr><td>Discharge volume</td><td>27 cc/rev</td></tr><tr><td rowspan="3">Battery</td><td>PTC</td><td>Max power</td><td>4.5 kW</td></tr><tr><td rowspan="2">PTC</td><td>Temperature range</td><td>15–40 °C</td></tr><tr><td>Max power</td><td>2.5 kW</td></tr></tbody></table>

A simplified lumped-parameter thermodynamic model was applied to the HVAC cycle, computing heating and cooling capacity from the coefficient of performance (COP), which varies with ambient temperature [7]. In heating mode, condenser and evaporator temperatures are set by cabin and ambient conditions, respectively, while the inverse applies for cooling [18]. The HVAC control follows an on/off regulation scheme that maintains cabin temperature between 19 °C and 23 °C, adjusting airflow rate and compressor load automatically.

The instantaneous TMS power demand was evaluated as the sum of all active components shown in the following equation.

$$P_{TMS}(t) = P_{PTC}(t) + P_{Compressor}(t) + P_{fans}(t) + P_{Pumps}(t) \quad (1)$$

The integrated TMS model was calibrated against experimental data reported in [17,18], confirming its ability to reproduce steady-state and transient heating/cooling power demands within ±8% accuracy. This validation ensures that the model realistically represents the performance and control behavior of compact BEV thermal systems under the temperature scenarios considered in this study.

## 4. Results and Discussion

This section presents the main outcomes of the simulation analysis, focusing on how ambient temperature, driving conditions, battery capacity, and user behavior jointly influence EV energy consumption, usable driving range, and charging requirements. Results are organized to isolate the dominant physical and operational factors that govern EV performance under realistic usage scenarios, as follows:

* Section 4.1 quantifies the temperature-dependent energy consumption across powertrain and thermal management systems under winter and summer conditions, using the SAE J1634 procedure to characterize mixed urban-highway operation.

* Section 4.2 builds on these results to assess the sensitivity of energy consumption and range to ambient temperature across urban and highway drive cycles, demonstrating how low-speed, stop-and-go operation amplifies HVAC and battery-conditioning loads, whereas steady-speed highway driving moderates temperature penalties through aerodynamic and thermal dilution effects.

* Section 4.3 examines the influence of battery-usage strategies and charging behavior, comparing full depletion and partial SOC operation to evaluate their implications for achievable range and charging frequency under thermal extremes.

* Section 4.4 evaluates thermal preconditioning as a mitigation strategy, quantifying its effectiveness in reducing cold-start HVAC surges and improving overall efficiency for short and moderate commutes.
Together, these results clarify the interdependence between climatic conditions, vehicle configuration, and user charging practices, providing a comprehensive understanding of the factors that shape BEV performance in real-world operation.

## 4.1. *Impact on Energy Consumption Under Winter and Summer Conditions*

This section presents the results of the simulation analysis, quantifying the influence of ambient temperature and BEV configuration on energy consumption, range, and the operational demands of the TMS. The primary objective is to establish the fundamental energy split between propulsion and thermal loads across extreme weather conditions, following the standardized SAE J1634 procedure.

Specifically, the analysis addresses two core questions:

1. Energy Penalty: What is the relative contribution of TMS energy consumption to the total energy demand across a range of operational temperatures?

2. Operational Dynamics: How does the TMS power draw transition between the initial transient (cold/hot start) phase and the subsequent steady-state operation over the duration of a trip?

In this section, vehicle range and energy consumption are assessed using the Multi-Cycle Test (MCT) prescribed by SAE J1634. The composite driving sequence follows the standardized pattern UDDS-HWFET-UDDS, after which a Constant Speed Cycle (CSC) is introduced. As illustrated in Figure 8, the first constant-speed segment occurs immediately after the second UDDS and serves to impose a sustained highway load under controlled conditions. Importantly, the duration of this constant speed cycle is not predetermined; instead, it is SOC-dependent. Following SAE J1634, its length is calculated from the vehicle's estimated remaining range at the end of the preceding transient cycles and is selected such that CSC contributes no more than 80% of the expected residual driving distance. This requirement ensures that the test remains predominantly governed by the transient behavior captured in the UDDS and HWFET cycles, while still incorporating a representative steady-state segment.

<figure><img src="image_10.png" /><figcaption>**Figure 8.** SAE J1634 driving procedure.</figcaption></figure>

A second constant-speed phase is present at the end of the sequence to drive the battery to its charge-depleting condition. In contrast to the first CSC, the last one is intentionally shorter and must represent no more than 20% of the total distance, preventing excessive weighting of steady-speed operation in the final depletion stage. Consequently, both CSC segments exhibit variable durations that depend on the SOC trajectory, thereby aligning the procedure with realistic electric vehicle depletion behavior.

Results are organized by climatic conditions, examining the responses of the short-, medium-, and long-range BEV configurations using the following key parameters: distance
travel, total consumption, powertrain consumption, AC/HP consumption, PTC heating consumption, and battery conditioning consumption.

### 4.1.1. Impact on Energy Consumption Split Under Winter Conditions

This subsection investigates the energy partition among the primary consumers in EVs under challenging winter conditions. The analysis specifically quantifies the energy demands at two cold ambient temperatures: -18 °C, and -7 °C.

The initial results presented here focus on the short-range EV configuration, establishing a baseline for the thermal penalty. Subsequent discussion will address the medium- and long-range BEV configurations by highlighting the key differences in their overall energy penalties and the corresponding TMS behavior. The complete trip consumption, partitioned across the powertrain, AC/HP system, PTC heater, and battery conditioning, is detailed in Tables 5 and 6 (consumption across the J1634 depletion test) for ambient temperatures of -18 °C and -7 °C, respectively. In addition, the distance traveled and total energy consumption of each cycle in the J1634 depletion test is shown at 22 °C for comparison in both tables. Supplementary tables encompassing the full data matrix for all temperatures and vehicle configurations are provided in Appendix A.

<table><caption>**Table 5.** Energy consumption partition for a short-range vehicle at ambient temperature of -18 °C.</caption><thead><tr><th rowspan="2">Cycle</th><th colspan="3">Ambient Temperature: 22 °C</th><th colspan="4">Ambient Temperature: -18 °C</th></tr><tr><th>Distance [Miles]</th><th>Total Cons [Wh/Mile]</th><th>Distance [Miles]</th><th>Total Cons [Wh/Mile]</th><th>Powertrain Cons [Wh/Mile]</th><th>AC/HP Cons [Wh/Mile]</th><th>PTC Cons [Wh/Mile]</th><th>Battery Thermal Cons [Wh/Mile]</th></tr></thead><tbody><tr><td>UDDS 1</td><td>7.4</td><td>217.7</td><td>7.4</td><td>544.0</td><td>247.5</td><td>0.0</td><td>296.4</td><td>0.0</td></tr><tr><td>HWFET 1</td><td>10.3</td><td>189.0</td><td>10.3</td><td>341.2</td><td>227.5</td><td>0.0</td><td>113.7</td><td>0.0</td></tr><tr><td>UDDS 2</td><td>7.4</td><td>142.4</td><td>7.4</td><td>459.2</td><td>200.0</td><td>0.0</td><td>259.2</td><td>0.0</td></tr><tr><td>Steady state</td><td>213.8</td><td>197.2</td><td>71.7</td><td>333.3</td><td>240.1</td><td>0.0</td><td>93.1</td><td>0.0</td></tr><tr><td>UDDS 3</td><td>7.4</td><td>142.3</td><td>7.4</td><td>456.9</td><td>200.2</td><td>0.0</td><td>256.8</td><td>0.0</td></tr><tr><td>HWFET 2</td><td>10.3</td><td>184.2</td><td>10.3</td><td>335.0</td><td>227.9</td><td>0.0</td><td>107.0</td><td>0.0</td></tr><tr><td>UDDS 4</td><td>7.4</td><td>143.0</td><td>7.4</td><td>459.2</td><td>202.7</td><td>0.0</td><td>256.5</td><td>0.0</td></tr><tr><td>Steady state</td><td>7.8</td><td>215.6</td><td>8.4</td><td>360.3</td><td>264.5</td><td>0.0</td><td>95.7</td><td>0.0</td></tr></tbody></table>

<table><caption>**Table 6.** Energy consumption partition for a short-range vehicle at ambient temperature of -7 °C.</caption><thead><tr><th rowspan="2">Cycle</th><th colspan="3">Ambient Temperature: 22 °C</th><th colspan="4">Ambient Temperature: -7 °C</th></tr><tr><th>Distance [Miles]</th><th>Total Cons [Wh/Mile]</th><th>Distance [Miles]</th><th>Total Cons [Wh/Mile]</th><th>Powertrain Cons [Wh/Mile]</th><th>AC/HP Cons [Wh/Mile]</th><th>PTC Cons [Wh/Mile]</th><th>Battery Thermal Cons [Wh/Mile]</th></tr></thead><tbody><tr><td>UDDS 1</td><td>7.4</td><td>217.7</td><td>7.4</td><td>412.7</td><td>246.0</td><td>95.3</td><td>71.4</td><td>0.0</td></tr><tr><td>HWFET 1</td><td>10.3</td><td>189.0</td><td>10.3</td><td>274.6</td><td>214.9</td><td>34.1</td><td>25.6</td><td>0.0</td></tr><tr><td>UDDS 2</td><td>7.4</td><td>142.4</td><td>7.4</td><td>327.4</td><td>185.3</td><td>81.2</td><td>60.8</td><td>0.0</td></tr><tr><td>Steady state</td><td>213.8</td><td>197.2</td><td>111.6</td><td>278.4</td><td>227.7</td><td>29.0</td><td>21.7</td><td>0.0</td></tr><tr><td>UDDS 3</td><td>7.4</td><td>142.3</td><td>7.4</td><td>327.6</td><td>185.9</td><td>81.0</td><td>60.7</td><td>0.0</td></tr><tr><td>HWFET 2</td><td>10.3</td><td>184.2</td><td>10.3</td><td>272.3</td><td>214.8</td><td>32.9</td><td>24.6</td><td>0.0</td></tr><tr><td>UDDS 4</td><td>7.4</td><td>143.0</td><td>7.4</td><td>328.9</td><td>187.1</td><td>81.1</td><td>60.7</td><td>0.0</td></tr><tr><td>Steady state</td><td>7.8</td><td>215.6</td><td>12.1</td><td>294.0</td><td>242.7</td><td>29.3</td><td>22.0</td><td>0.0</td></tr></tbody></table>

The simulation results at -18 °C, shown in Table 5, demonstrate a severe cold-start penalty for the short-range EV, driven primarily by cabin heating demand. It is important to note that the system relies on self-heating as active battery conditioning shows zero consumption, a key design characteristic that minimizes direct auxiliary load but may slow the battery's approach to optimal operating temperature. The total energy consumption for the initial UDDS cycle surged to 544.0 Wh/mile, representing a 149% increase compared to the 22 °C reference condition. This increase is primarily due to the PTC heater consumption
(296.4 Wh/mile), which accounted for 54.4% of the total energy in UDDS1, confirming that the AC/HP system was unable to meet the required cabin conditions. Heat pumps struggle to operate efficiently at such a low temperature because it significantly reduces the density of the refrigerant vapor, preventing the system from circulating sufficient mass flow to meet the high cabin heating demand. Consequently, the compressor must work harder to achieve the necessary pressure ratio, causing the COP to drop below 1 and making the resistance-based PTC heater the more effective option for delivering immediate, high-power heat. Furthermore, the analysis reveals a 13.7% increase in powertrain consumption itself, caused by reduced battery efficiency, higher aerodynamic resistance due to the increase in air density, and lower regenerative capability at this extreme temperature. As the trip progressed, the PTC consumption fell to 256.5 Wh/mile by the final UDDS cycle, a quantifiable 13.5% reduction, mainly attributable to the steady-state operation of the HVAC system after reaching the target set temperature.

The primary distinction across the medium- and long-range BEV configurations com-
pared to the short-range variant lies in the resulting higher powertrain consumption and
the overall driving range extension under extreme cold conditions. The baseline consump-
tion at the reference temperature of 22 °C increased proportionally with battery size: the
medium-range and long-range vehicles exhibited consumption rates of 230.7 Wh/mile
and 257.9 Wh/mile, respectively, compared to the 217.7 Wh/mile rate for the short-range
model for the first UDDS cycle. This variance primarily reflects the increased vehicle mass
associated with the larger battery pack, coupled with drivetrain efficiency losses. Conse-
quently, the share of total energy consumption attributed to the PTC heater decreases to
51.8% for the medium-range and 48.3% for the long-range vehicle. This indicates that while
the absolute thermal load remains nearly constant across models, the larger denominator
(total consumption) from the increased mass causes a slight reduction in the proportional
thermal penalty. The most significant consequence of increasing battery capacity is the
increase in total energy consumption, along with the extension of the total distance traveled
during the J1634 depletion simulation. The increased energy reserve dominates the slight
increase in powertrain consumption. The short-range vehicle achieved 130 miles compared
to 210 miles and 329 miles for the medium and long-range vehicles.

At -7 °C, the TMS transitions to another operation mode where, for short-range
EVs, as shown in Table 6, the HP and PTC operate together in order achieve the required
cabin load with a COP greater than 1, thereby increasing the overall system efficiency
compared to the purely PTC-driven operation at -18 °C. During the cold-start (UDDS
1 cycle), the TMS consumption drops in comparison to -18 °C and accounts for 40.4% of
the total consumption. This decrease in consumption is governed by two main factors:
(i) the reduced total consumption due to the combined operation of the HP and the PTC,
leading to a greater COP, and (ii) the reduced thermal load at -7 °C compared to -18 °C.
Steady-state operation further reduces this consumption, where the total TMS consumption
is reduced by 14.3% compared to the transient cold-start phase. The same analysis holds
when comparing short-range, medium-range, and long-range vehicles, as presented earlier.
However, the total distance covered at -7 °C becomes 174 miles, 273 miles, and 418 miles,
respectively, for the three vehicles.

4.1.2. Impact on Energy Consumption Split Under Summer Conditions

This subsection investigates the energy partition among the primary consumers in
EVs under summer conditions. The analysis specifically quantifies the energy demands
at two hot ambient temperatures: 35 °C and 50 °C. The same simulating conditions were
followed as in Section 4.1.1.
The simulation results at 35 °C for the short-range EV on the J1634 procedure shown in Table 7 reveal a moderate thermal penalty, distinctly different in magnitude and behavior from the winter scenarios. The total energy consumption for the initial UDDS cycle rose to 288.4 Wh/mile, representing a 32.5% increase compared to the 22 °C reference condition. This increase is driven by the AC/HP system, which consumed 65.5 Wh/mile and accounted for 22% of the total energy in the first UDDS cycle. Unlike the winter scenario where the COP was reduced, the AC system operates efficiently at 35 °C. The high initial consumption is mainly a transient load required to rapidly cool the cabin thermal mass (soak effect).

<table><caption>**Table 7.** Energy consumption partition for a short-range vehicle at ambient temperature of 35 °C.</caption><thead><tr><th rowspan="2">Cycle</th><th colspan="4">Ambient Temperature = 22 °C</th><th colspan="4">Ambient Temperature = 35 °C</th></tr><tr><th>Distance [Miles]</th><th>Total Cons [Wh/Mile]</th><th>Distance [Miles]</th><th>Total Cons [Wh/Mile]</th><th>Powertrain Cons [Wh/Mile]</th><th>AC/HP Cons [Wh/Mile]</th><th>PTC Cons [Wh/Mile]</th><th>Battery Thermal Cons [Wh/Mile]</th></tr></thead><tbody><tr><td>UDDS 1</td><td>7.4</td><td>217.7</td><td>7.4</td><td>288.4</td><td>224.9</td><td>63.5</td><td>0.0</td><td>0.0</td></tr><tr><td>HWFET 1</td><td>10.3</td><td>189.0</td><td>10.3</td><td>186.5</td><td>181.3</td><td>5.1</td><td>0.0</td><td>0.0</td></tr><tr><td>UDDS 2</td><td>7.4</td><td>142.4</td><td>7.4</td><td>153.2</td><td>141.7</td><td>11.4</td><td>0.0</td><td>0.0</td></tr><tr><td>Steady state</td><td>213.8</td><td>197.2</td><td>175.9</td><td>196.2</td><td>192.4</td><td>3.8</td><td>0.0</td><td>0.0</td></tr><tr><td>UDDS 3</td><td>7.4</td><td>142.3</td><td>7.4</td><td>153.1</td><td>141.8</td><td>11.2</td><td>0.0</td><td>0.0</td></tr><tr><td>HWFET 2</td><td>10.3</td><td>184.2</td><td>10.3</td><td>184.1</td><td>179.5</td><td>4.5</td><td>0.0</td><td>0.0</td></tr><tr><td>UDDS 4</td><td>7.4</td><td>143.0</td><td>7.4</td><td>153.4</td><td>142.1</td><td>11.3</td><td>0.0</td><td>0.0</td></tr><tr><td>Steady state</td><td>7.8</td><td>215.6</td><td>42.5</td><td>201.5</td><td>194.9</td><td>3.9</td><td>0.0</td><td>2.8</td></tr></tbody></table>

The transition to steady-state operation yields a reduction in auxiliary consumption. As the trip progressed to the last UDDS cycle, the AC/HP consumption dropped to 11.4 Wh/mile, representing an 82% reduction from the initial cool-down phase. This highlights that maintaining the cabin setpoint against a 35 °C ambient temperature requires significantly less energy than heating against sub-freezing conditions. Moreover, the powertrain consumption during the first highway cycle decreased slightly compared to the reference, at 181.4 vs. 189.0 Wh/mile. This phenomenon is attributed to the reduced air density at higher ambient temperatures, which lowers aerodynamic drag at higher speeds. Regarding the battery, active battery conditioning (2.8 Wh/mile) appeared only in the final steady-state phase, indicating that the system initially utilizes the battery's thermal inertia to absorb heat before active liquid cooling is required to maintain safety thresholds.

The comparative analysis across the medium- and long-range BEV configurations at 35 °C follows the established trend of mass-dependent energy scaling. These vehicles exhibited higher baseline powertrain consumption due to increased curb mass, which dilutes the relative impact of the AC cooling load. Consequently, the thermal penalty percentage for these larger vehicles was lower than the 22% observed in the short-range model. However, the most critical metric remains the total covered range. The reduced thermal stress of summer compared to winter allows for significantly better range retention. The short-range vehicle achieved 268.7 miles, while the greater energy reserves of the medium- and long-range vehicles allowed them to cover significantly longer distances of 409 miles and 583 miles, respectively, leveraging the lower aerodynamic drag and efficient AC operation to maximize the utility of their larger battery capacities.

The 50 °C simulation revealed a significant thermal penalty observed in this study, as shown in Table 8. The total energy consumption during the initial urban driving cycle escalated to 550.8 Wh/mile, a staggering 153% increase over the reference baseline. This surge was driven by the AC/HP system, which consumed 275 Wh/mile, accounting for 50% of the total vehicle energy. This disproportionately high cooling demand is not solely attributable to the sensible heat load (lowering the air temperature). A significant portion
of this energy is consumed to overcome the latent heat load. At high ambient temperatures, the AC system must perform substantial dehumidification to maintain cabin comfort. The phase change of water vapor during condensation on the evaporator requires significant additional compressor work, drastically increasing the overall electrical load compared to the dry cooling conditions of moderate climates.

<table><caption>**Table 8.** Energy consumption partition for a short-range vehicle at ambient temperature of 50 °C.</caption><thead><tr><th rowspan="2">Cycle</th><th colspan="4">Ambient Temperature: 22 °C</th><th colspan="3">Ambient Temperature: 50 °C</th></tr><tr><th>Distance [Miles]</th><th>Total Cons [Wh/Mile]</th><th>Distance [Miles]</th><th>Total Cons [Wh/Mile]</th><th>Power-Train Cons [Wh/Mile]</th><th>AC/HP Cons [Wh/Mile]</th><th>PTC Cons [Wh/Mile]</th><th>Battery Thermal Cons [Wh/Mile]</th></tr></thead><tbody><tr><td>UDDS 1</td><td>7.4</td><td>217.7</td><td>7.4</td><td>550.8</td><td>224.5</td><td>275.1</td><td>0.0</td><td>51.2</td></tr><tr><td>HWFET 1</td><td>10.3</td><td>189.0</td><td>10.3</td><td>218.9</td><td>168.9</td><td>32.4</td><td>0.0</td><td>17.6</td></tr><tr><td>UDDS 2</td><td>7.4</td><td>142.4</td><td>7.4</td><td>213.3</td><td>124.6</td><td>51.4</td><td>0.0</td><td>37.4</td></tr><tr><td>steady state</td><td>213.8</td><td>197.2</td><td>109.3</td><td>206.8</td><td>183.5</td><td>17.3</td><td>0.0</td><td>6.0</td></tr><tr><td>UDDS 3</td><td>7.4</td><td>142.3</td><td>7.4</td><td>200.5</td><td>137.4</td><td>48.8</td><td>0.0</td><td>14.2</td></tr><tr><td>HWFET 2</td><td>10.3</td><td>184.2</td><td>10.3</td><td>195.1</td><td>174.9</td><td>20.2</td><td>0.0</td><td>0.0</td></tr><tr><td>UDDS 4</td><td>7.4</td><td>143.0</td><td>7.4</td><td>191.5</td><td>142.6</td><td>48.9</td><td>0.0</td><td>0.0</td></tr><tr><td>steady state</td><td>7.8</td><td>215.6</td><td>76.5</td><td>207.4</td><td>186.5</td><td>17.3</td><td>0.0</td><td>3.7</td></tr></tbody></table>

Furthermore, unlike the 35 °C scenario where battery conditioning was delayed, the 50 °C condition forced the immediate activation of the battery thermal management system to operate the battery within the target temperature range of 15 °C and 40 °C. The data shows an immediate 51.2 Wh/mile consumption rate for battery conditioning during UDDS 1, accounting for 9.2% of the total consumption. The system engages the chiller, a heat exchanger that couples the battery coolant loop with the AC refrigeration loop to actively refrigerate the battery. This chiller operation adds an additional parasitic load to the AC compressor, further explaining the massive spike in auxiliary consumption.

While steady-state operation eventually reduced the AC consumption to 17.3 Wh/mile and battery conditioning to 6 Wh/mile, the cumulative effect of the initial thermal soak was a sensible reduction in range. The short-range EV covered 236 miles at 50 °C, a nearly 13% reduction from its nominal range, highlighting that extreme heat poses a challenge to EV efficiency comparable to—and in transient phases, exceeding—that of extreme cold.

Comparative analysis across the three vehicle configurations identifies the battery thermal management system load as the primary differentiator under extreme heat conditions 50 °C. Specifically, during the hot-start phase, the energy required for battery cooling scaled by factors of 1.5 and 2 for the medium- and long-range vehicles, respectively, relative to the short-range baseline. This increase in cooling demand is directly attributable to the upscaling of the battery pack, which introduces greater thermal mass and a higher aggregate internal heat generation rate. However, despite the higher absolute energy consumption required to cool the larger packs, the previously established conclusion regarding range retention remains valid: the substantial increase in available energy capacity in the medium- and long-range vehicles effectively mitigates the relative impact of the thermal penalty, ensuring superior range performance despite the parasitic losses. Comprehensive datasets supporting this analysis for all three vehicles under all investigated temperatures are provided in Appendix A.

Having quantified the magnitude and origin of thermal loads in Section 4.1, the next section translates these energy penalties into operational terms by examining how they interact with different commute lengths and SOC usage strategies to determine effective driving range and charging requirements.
## 4.2. Impact on Range Reduction Under Urban and Highway Conditions

Based on the results in Section 4.1, this section analyzes the energy consumption and range reduction at different ambient temperatures for the three vehicles under the UDDS and HWFET cycles. The purpose is to quantify and compare the expected range reduction across weather conditions for two representative driving patterns. The UDDS cycle simulates low-speed, stop-and-go city driving with frequent idling, low average speeds, and limited convective cooling, conditions that directly intensify thermal system workload. The HWFET cycle simulates sustained-speed driving with minimal idling and limited stop-and-go behavior. Highway conditions introduce distinct thermal and aerodynamic dynamics compared to urban driving, making them critical for understanding BEV energy performance across longer-distance use cases.

To construct representative cycle-level consumptions consistent with SAE J1634, the post-processing of the MCT determines energy consumption for each UDDS and HWFET segment. A central element is the UDDS weighting, which accounts for the pronounced “first-cycle effect” in the J1634 standard, characterized by high initial SOC, limited regenerative braking, and non-optimal component temperatures. To quantify this effect, we used a weight $K_1$ as the fraction of the total discharged energy during the full-depletion run that is drawn in the first UDDS cycle. This weight captures test-specific conditions such as thermal state, SOC, and accessory loads. The remaining weight, $1 - K_1$, was distributed evenly across the three subsequent UDDS passes, giving $K_2 = (1 - K_1)/3$. The representative UDDS consumption was then calculated as the weighted average of the four UDDS cycles using $K_1$ and $K_2$.

Highway consumption was obtained more directly by taking the arithmetic mean of the two HWFET passes, reflecting their relatively stable behavior across repeats and aligning with common J1634 practice. The combined city/highway consumption was then computed following the EPA convention of 55% UDDS and 45% HWFET. This procedure yields a single representative value suitable for reporting and comparison, while the separate UDDS and HWFET analyses isolate vehicle performance in low-speed stop-and-go versus steady-speed cruising.

Unlike EPA certification range values, the 0.7 adjustment factor was not applied in this analysis, as thermal loads and temperature-dependent losses were explicitly modeled within the simulation framework. This approach enabled a direct, physics-based assessment of range sensitivity to ambient conditions, rather than reliance on an empirical correction.

Figure 9 presents a U-shaped energy consumption trend, centered around 22 °C, across ambient temperatures for urban, highway, and combined driving behavior. At 22 °C (baseline scenario), for the UDDS, energy consumption ranged from 168 Wh/mile for the short-range BEV to 208 Wh/mile for the long-range BEV, lower results than the HWFET consumption due to reduced propulsion loads and lower aerodynamic drag. However, this small advantage at 22 °C is quickly offset in colder climates, where thermal system demand rises sharply.

This behavior is strongly linked to trip duration and speed. At the lower average speeds characteristic of the UDDS cycle, the vehicle spends significantly more time per mile traveled than under highway conditions. Since the cabin and battery temperatures evolve with time rather than distance, the thermal management system must operate over a longer duration to maintain the same thermal setpoints, even for an identical trip length. This extended operating time increases the thermal load per unit distance, so that, on a per-mile basis, more energy is directed to cabin conditioning and battery temperature control in urban driving than in highway driving. This results in a marked spike in consumption, which directly causes a drop in range. Urban driving therefore exposes EVs to the most severe thermal performance penalties, both in absolute energy use and relative range
degradation, with short-range vehicles most affected because HVAC loads represent the largest fraction of their total energy demand.

<figure><img src="image_11.png" /><figcaption>**Figure 9.** Energy consumption vs. ambient temperature for BEVs across (a) UDDS, (b) HWFET, and (c) combined driving cycles.</figcaption></figure>

Figure 10 shows the corresponding percentage range reduction under the UDDS, HWFET, and combined cycles. Range was computed consistently across cases by dividing the measured usable battery energy from the depletion run by the representative cycle-level energy consumption. Specifically, urban range was obtained using the weighted UDDS consumption, while highway range was derived using the representative HWFET consumption. The range reduction in this section is calculated based on Equation (2).

$$ \text{Range Reduction (\%)} = \frac{\text{abs}(\text{Range}_{T_{\text{Reference}}} - \text{Range}_{T_{\text{ambient}}})}{\text{Range}_{T_{\text{Reference}}}} \times 100 \quad (2) $$

Cold-induced range losses are substantially more severe under urban driving conditions than under highway operation. At −18 °C, range reductions reach approximately 65% for short- and medium-range BEVs and 60% for long-range BEVs. Even at 0 °C, range penalties remain pronounced, particularly for short-range vehicles. High-temperature operation also leads to notable degradation, with range reductions of approximately 55%, confirming that HVAC loads dominate energy consumption when propulsion demands are low.

Although urban trips are typically shorter in absolute distance, their high energy intensity per mile under thermal extremes highlights the importance of efficient thermal management solutions. These results underscore the need for high-COP heat pumps within an integrated TMS, localized cabin conditioning (e.g., seat/steering heating), and predictive thermal control strategies tailored to urban-focused BEV operations.
<figure><img src="image_12.png" /><figcaption>**Figure 10.** Range reduction vs. ambient temperature for BEVs across (a) UDDS, (b) HWFET, and (c) combined driving cycles.</figcaption></figure>

Highway driving significantly mitigates temperature sensitivity through the thermal-dilution effect and improved convective cooling. However, this mitigation does not eliminate thermal vulnerability. Under cold conditions, highway range still decreases by approximately 40%, particularly for short-range BEVs. While summer highway operation is more favorable due to efficient cooling and reduced aerodynamic drag, winter highway travel continues to impose significant constraints, emphasizing the need for effective pre-conditioning and adaptive thermal control strategies. As a result, steady-speed BEV travel is less volatile than urban driving conditions under thermal extremes but still requires careful thermal energy management to maintain predictable range.

For the combined driving cycle, the short-range BEV exhibited a maximum range reduction of approximately 58% at -18 °C, compared with 56% and 52% for the medium- and long-range vehicles, respectively. Under hot conditions (50 °C), the combined-cycle range reduction converged to approximately 40% across all three configurations.

These results confirm that cold-weather operation represents the most critical condition for BEV efficiency, leading to the largest range reductions due to sustained heating demand and reduced battery performance. Extreme hot conditions also impose measurable penalties, particularly in urban driving, where air conditioning and battery cooling loads increase energy consumption. Short-range BEVs are the most affected, as thermal loads represent a larger fraction of usable energy. Overall, these findings show that certification-based range values derived under moderate, HVAC-off conditions do not capture the magnitude of real-world thermal penalties, highlighting the need for climate-aware performance metrics.

### 4.3. Impact on Charging Needs Considering Commute Length and SOC Usage Behavior

While Sections 4.1 and 4.2 quantified the effects of ambient temperature and driving patterns on BEV energy use and range, this section extends the analysis by evaluating how driver SOC usage practices and commute length jointly shape charging needs. In real-world operation, drivers rarely exploit the entire battery capacity (100-0% SOC); instead, most operate within narrower windows to balance convenience, battery longevity, and range confidence. Two representative usage windows are therefore evaluated:
* Full-use window (100–0% SOC): Assumes the entire battery capacity is available for propulsion, representing the technical maximum achievable range.

* Partial-use window (80–20% SOC): Reflects common real-world practice in which approximately 40% of the nominal capacity is withheld to minimize degradation, reduce fast-charging time, and maintain a reserve against range anxiety.

These SOC strategies are not purely behavioral; they are closely linked to charging infrastructure context. Home charging typically enables partial SOC operation, as drivers routinely “top-up” overnight without time pressure, often stopping around 70–80% SOC to reduce battery wear and because daily commutes do not require the full pack. By contrast, full-use SOC (charging to almost 100% and allowing deep discharge) is more common during public DC fast charging, where drivers aim to maximize segment length and reduce the number of charging stops, particularly during long-distance travel or under temperature-induced range constraints. Therefore, SOC window selection is inherently shaped by both user preference and the type of charging infrastructure available, and its interaction with ambient temperature is essential for understanding real-world charging needs.

Comparing these two scenarios allows us to separate physical constraints (temperature- and drive-cycle-dependent energy use) from behavioral constraints (user-defined SOC limits and infrastructure-driven practices). The analysis explores how the interaction of temperature, commute distance, and SOC window selection affects both trip feasibility and charging frequency for the three BEV configurations considered in this study.

Section 4.3.1 presents results for the full-use case, quantifying the attainable range and required charging stops across climates and commute distances. Section 4.3.2 then examines how the adoption of a partial-use SOC strategy reshapes these outcomes, revealing the extent to which driver behavior influences BEV performance. Both sections’ results are evaluated under the combined driving cycle.

### 4.3.1. Full Battery Depletion (100–0% SOC)

Figure 11 presents the number of required charging stops as a function of ambient temperature and commute distance for the three vehicle classes under full-depletion conditions. This “best-case” scenario represents the theoretical maximum driving capability for each configuration.

<figure><img src="image_13.png" /><figcaption>**Figure 11.** Number of charging stops per trip as a function of ambient temperature and commute distance for three different BEVs: (a) short, (b) medium, and (c) long range under full-depletion scenario [100–0%].</figcaption></figure>
Under this scenario, the short-range BEV (200-mile nominal range) demonstrated sufficient performance only in moderate temperatures. At 22 °C, it completed a 200-mile trip on a single charge and maintained comparable range at 35 °C. However, once thermal loads rose, its usable range declined to roughly 160 miles at 50 °C, insufficient for the 200+ miles journeys with one single charge. Cold conditions were not favorable either: at 0 °C, the vehicle barely completed a 175-mile trip, and at -7 °C, it barely reached 145 miles on one single charge. At -18 °C, range decreased by nearly 60%, to about 110 miles, limiting operation to short urban commutes on a single charge.

The medium-range BEV (300-mile nominal range) demonstrated higher thermal resiliency, completing all trips up to 200 mi under all conditions except -18 °C, where an intermediate charge became necessary after approximately 170 miles. The long-range BEV (400 miles) consistently completed all commute scenarios without recharging across the entire temperature spectrum.

These findings confirm that battery capacity is a primary moderator of thermal vulnerability by diluting fixed HVAC and heating loads over a larger energy reserve. However, larger battery packs are not a universal solution, as this resilience is offset by higher baseline energy consumption, greater mass and cost, and longer charging duration or equivalently a stronger reliance on high-power fast-charging infrastructure to fully exploit the vehicle's usable range.

### 4.3.2. Partial Battery Depletion (80–20% SOC)

Figure 12 illustrates the same analysis under partial-depletion operation, revealing a clear threshold in charging needs once the usable battery window narrows. Limiting the SOC window effectively removes 40% of total usable energy, which translates to roughly a one-third reduction in achievable trip distance before recharging becomes necessary.

<figure><img src="image_14.png" /><figcaption>**Figure 12.** Number of charging stops per trip as a function of ambient temperature and commute distance for three different BEVs: (a) short, (b) medium, and (c) long range under full-depletion scenario [80–20%].</figcaption></figure>

For the short-range BEV, the 22 °C baseline provided the most favorable conditions, enabling trips of up to roughly 150 miles on a single charge before reaching the 20% SOC threshold. As temperature decreased, thermal losses became dominant: at 0 °C, usable range contracted sharply, and by -7 °C, the vehicle required a recharge after approximately 85 miles. At -18 °C, usable range dropped to 25% of the nominal value, limiting operation
to short urban commutes. In contrast, high-temperature conditions (35–50 °C) imposed
moderate to severe penalties, as air conditioning and battery cooling loads increased. While
the vehicle retained much of its 22 °C range at 35 °C, cooling penalties intensified markedly
at 50 °C, leading to a pronounced reduction in usable range.

The medium- and long-range BEVs exhibited similar qualitative trends but with lower sensitivity to temperature and SOC constraints. Under moderate climates (22–35 °C), both configurations maintained ample usable range (≥200 miles), completing most modeled commutes without interruption. Cold-weather conditions (≤0 °C) introduced substantial but manageable losses, with one or more intermediate charging stops required for the longest 200-mile trips, depending on temperature severity and battery size.

Each additional charging stop in Figure 12 represents not only increased energy de-
mand but also a logistical burden, requiring drivers to locate, access, and potentially queue
for available fast chargers. For all BEV ranges, limiting operation to an 80–20% SOC win-
dow can lead to multiple charging stops on long trips under cold conditions. In these
scenarios, expanding the usable SOC window toward full capacity becomes necessary to
limit interruptions. However, charging beyond 80% SOC occurs at significantly reduced
power due to fast-charging taper, meaning that the final 20% of charge adds dispropor-
tionately to charging time relative to the additional miles gained. As a result, operating
in a full-use SOC window may reduce the total number of charging stops but does not
necessarily minimize overall trip time. For example, it is important to note that while the
long-range BEV requires fewer stops, the duration of each charging event is longer than for
the short-range BEV due to the larger energy required. Consequently, while the frequency
of interruption is lower, the total travel time may not decrease proportionally, particularly
if charging power is thermally constrained.

These findings underscore that, for long-distance travel, SOC usage strategy plays a
central role, balancing the number of stops against the time required to reach higher SOC
levels. A comprehensive comparison of total trip time, including both driving and charging,
would therefore be essential for evaluating 80–20% versus 100–0% SOC operation. While
such analysis lies beyond the scope of this study, it represents an important avenue for
future work on long-distance BEV usability.

4.4. *Impact of Preconditioning*

Building on the findings from Sections 4.1–4.3, which demonstrated that extreme ambient temperatures significantly increase the thermal management load and reduce driving range, this section evaluates preconditioning as a practical strategy to mitigate these penalties.

As demonstrated in Section 4.1, extreme cold and hot weather impose substantial
transient loads on the cabin HVAC system and, to a lesser extent, on battery conditioning.
These start-up loads are particularly pronounced at the beginning of a trip, when both cabin
air and interior mass (cold-soak or heat-soak) must be brought to their target temperatures.
Preconditioning—heating or cooling the cabin and battery while the vehicle is plugged
into the grid—shifts this initial thermal demand from the onboard battery to external
electricity, thereby improving comfort and reducing early-trip energy spikes. This benefit
is most significant for short trips, where thermal loads represent a large share of total
energy consumption.

For this reason, two representative UDDS trip lengths are examined: a short 20-mile
trip and a longer 100-mile trip, selected based on Section 4.2 results showing the high
thermal sensitivity of urban driving.

Figure 13 shows that preconditioning yields considerable energy savings for short trips under extreme temperatures. Without preconditioning, consumption reaches 491 Wh/mile
at -18 °C and 309 Wh/mile at 50 °C, while preconditioning reduces these values to 459 Wh/mile (-7%) and 214 Wh/mile (-31%), respectively. At 50 °C, the high cooling load of the cabin and battery cooling demand is alleviated by preconditioning, as the cabin and battery begin operation within its optimal temperature range. In addition, at moderate temperatures (22 °C), the benefit is negligible since HVAC systems remain mostly inactive. For intermediate conditions (-7 °C, 0 °C, and 35 °C), savings range from 4% to 7%, primarily due to reduced cabin thermal ramp-up time. It is also important to note that in the non-preconditioned case, the battery was not actively heated during driving. If onboard heating was applied to raise battery temperature while driving, the relative energy savings achieved through preconditioning in cold conditions would be even greater.

<figure><img src="image_15.png" /><figcaption>**Figure 13.** Energy consumption vs. ambient temperature for a short-range BEV with and without preconditioning (20-mile trip, UDDS driving cycle).</figcaption></figure>

Figure 14 illustrates that the energy-saving effect diminishes for longer trips. At -18 °C, savings fell to 3%, and at 50 °C to 11%. Under temperate conditions (22-35 °C), the benefit becomes marginal, as propulsion energy dominates overall trip consumption, and the initial HVAC spike becomes diluted over distance. In effect, preconditioning reduces transient startup loads, but steady-state behavior governs total energy use for long trips.

<figure><img src="image_16.png" /><figcaption>**Figure 14.** Energy consumption vs. ambient temperature for a short-range BEV with and without preconditioning (100-mile trip, UDDS driving cycle).</figcaption></figure>
Overall, the results confirm that preconditioning is most effective for short to moderate trips under extreme temperatures, where thermal loads would otherwise constitute a significant portion of total consumption. By bringing the cabin and battery to their target setpoints before departure, the TMS operates less intensively during the early phase of the trip, yielding measurable efficiency gains. For longer trips, however, propulsion energy becomes dominant, and the relative benefit decreases, although preconditioning still offers comfort advantages and reduces peak thermal demand.

## 5. Conclusions

This study quantified how ambient temperature, drive-cycle characteristics, battery capacity, commute length, and SOC usage strategies jointly influence the energy consumption, usable range, and charging needs of battery electric vehicles. Using a detailed, experimentally validated Autonomie model that couples the powertrain with cabin and battery electro-thermal models and a representative TMS, three BEV configurations (200-, 300-, and 400-mile nominal ranges) were evaluated across six ambient temperatures (−18 °C to 50 °C), multiple commute distances (20–200+ miles), and two SOC usage windows (100–0% and 80–20%), with and without thermal preconditioning.

Across the SAE J1634 mixed urban–highway procedure, ambient temperature emerged as a first-order driver of consumption and range. Under extreme cold (−18 °C), total energy consumption increased by approximately 128% relative to 22 °C, resulting in range reductions of about 64% under urban driving and 42% under highway conditions for the 300-mile BEV. These penalties were dominated by cabin heating loads, particularly when heat pump performance degraded and PTC heating became the primary source of thermal power. High ambient temperatures also imposed substantial penalties: at 50 °C, total energy consumption increased by up to 71%, with pronounced transient spikes at trip start driven by combined sensible and latent cooling loads, as well as active battery thermal management. The analysis further confirmed that urban, low-speed, stop-and-go operation represents the most thermally demanding use case, as longer travel times per mile require sustained operation of the thermal management system. By contrast, highway driving benefits from higher propulsion energy share and improved convective heat transfer, partially diluting but not eliminating temperature-induced penalties.

Battery capacity strongly moderates the severity of these thermal penalties. Short-range BEVs (200-mile nominal range) experienced sharp reductions in usable range under cold conditions, often requiring additional charging stops for commutes ≥ 100–150 miles even under full-use SOC operation. Medium- and long-range configurations (300 and 400 miles) provided much more stable range across temperatures, completing most daily commutes (≤200 miles) without interruption except under the most extreme cold, where a single intermediate charge could be required. This demonstrates that larger packs offer greater thermal resilience by diluting HVAC and battery-conditioning energy over a larger energy reservoir. However, this resilience comes at the cost of higher baseline consumption, added mass, longer charging times, and more expensive cars, indicating that simply oversizing batteries is an inefficient response to thermal challenges.

User SOC usage strategies were found to be equally important in determining practical range and charging frequency. When the full 100–0% SOC window was used, the three vehicles achieved their technical maximum range, and long-range configurations could reliably support long trips across all climates. Under the more common 80–20% SOC strategy, which withholds about 40% of nominal capacity, effective trip distance before recharge fell by roughly one-third. For the short-range BEV, 22 °C operation allowed 130-mile trips on a single charge, but cold conditions compressed this to 50 miles at −7 °C and below 25% of nominal range at −18 °C, often requiring multiple fast-charging events
for 150–200-mile trips. Medium- and long-range BEVs were less sensitive but still showed non-negligible reductions in usable range and added charging events in winter. These results highlight a key trade-off: restricting the SOC window reduces degradation risk and fast-charging time per session but increases charging frequency. Expanding toward full SOC reduces the number of stops but, due to fast-charging taper above 80% SOC, adds disproportionately more time per charge. A full evaluation of total trip time (driving plus charging) for different SOC strategies and thermal conditions is therefore a critical next step and lies beyond the scope of this study.

Preconditioning was shown to be an effective mitigation strategy for short and moderate trips under thermal extremes. For 20-mile urban (UDDS) trips in a short-range BEV, preconditioning while plugged in reduced energy consumption by up to 7% at –18 °C and 31% at 50 °C by shifting cabin and battery warm-up/cool-down from the onboard battery to the grid and reducing early-trip HVAC peaks. For 100-mile UDDS trips, the relative benefit decreased (to 3–11%), as propulsion energy dominated total trip consumption and the initial thermal transient was diluted over distance. Preconditioning therefore offers its largest efficiency gains and user-comfort benefits for short commuters and thermally severe conditions, especially when combined with high-efficiency heat pumps and intelligent TMS control.

Taken together, the results have several implications for stakeholders. For vehicle manufacturers, the results underscore the importance of designing EVs that are not only efficient under ideal conditions but resilient under thermal stress. They emphasize that robust TMS design (high-COP heat pumps, improved insulation, localized heating/cooling strategies, and predictive control) is at least as important as battery sizing for ensuring winter usability. Reliance on large battery packs alone is neither cost-optimal nor energy-optimal. For consumers, the findings illustrate how climate, commute distance, SOC usage habits, and preconditioning decisions combine to determine real-world range and charging needs, and can inform the choice of vehicle range class and charging strategy that best matches individual usage profiles. For charging infrastructure planners, the work underscores the need for accessible, reliable high-power charging networks in cold regions, where thermal penalties and SOC-conservative behavior can significantly increase charging frequency and clustering of demand during extreme weather.

Finally, the study highlights important directions for future research. First, integrating charging power profiles and taper behavior to compare total trip time under 80–20% versus 100–0% SOC strategies would provide a more complete picture of long-distance usability and optimal charging practices. Second, scaling this vehicle-level framework to fleet and grid levels across climate zones and including medium- and heavy-duty electric vehicles would enable assessment of seasonal peaks in charging demand, preconditioning-driven load patterns, and their implications for power-system planning. Third, while this study focused on a fresh battery pack, future research should investigate how battery aging (State of Health degradation) interacts with these thermal penalties, as reduced capacity would likely amplify the charging frequency requirements identified here. Fourth, extending the present framework to incorporate additional real-world usage datasets across regions, vehicle segments, and user groups would broaden their applicability and support the development of more targeted design, infrastructure, and policy strategies. Future work may also compare these baseline results against advanced thermal control strategies (e.g., model predictive control) to quantify the potential energy savings over standard rule-based logic. Addressing these questions will be essential to narrow the gap between certified and real-world EV performance and to design vehicles, infrastructure, and policies that remain robust under increasingly variable climatic conditions.
**Author Contributions:** Conceptualization, C.M. and J.A.; Methodology, C.M., M.B., R.A.H., N.K. and M.N.; Software, M.B., R.A.H. and N.K.; Validation, R.A.H. and N.Z.; Formal analysis, M.B. and R.A.H.; Resources, N.Z. and J.A.; Writing—original draft, M.B. and R.A.H.; Writing—review & editing, C.M., R.A.H., N.K., M.N., N.Z. and J.A.; Supervision, C.M., M.N. and J.A.; Project administration, J.A.; Funding acquisition, C.M. and J.A. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was sponsored by the U.S. Department of Energy (DOE) Vehicle Technologies Office (VTO) under the Integrated Transportation and Energy Cross-Sectoral System of Systems at Scale (ITE-S4) initiative of the Energy Efficient Mobility Systems (EEMS) Program.

**Data Availability Statement:** The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.

**Acknowledgments:** Melissa Rossi of the DOE's Office of Critical Minerals and Energy Innovation (CMEI) played an important role in shaping the project concept, supporting its implementation, and providing programmatic guidance. The submitted manuscript has been created by the UChicago Argonne, LLC, Operator of Argonne National Laboratory (Argonne). Argonne, a U.S. Department of Energy Office of Science laboratory, operated under Contract No. DE-AC02-06CH11357. The U.S. Government retains for itself, and others acting on its behalf, a paid-up nonexclusive, irrevocable worldwide license in said article to reproduce, prepare derivative works, distribute copies to the public, and perform publicly and display publicly, by or on behalf of the Government.

**Conflicts of Interest:** The authors declare no conflict of interest.

## Appendix A. Energy Consumption Partition for Different Ambient Temperatures and Vehicles

### Appendix A.1. Short-Range Vehicle

<table><thead><tr><th rowspan="2">Cycle</th><th colspan="3">Ambient Temperature: 22 °C</th><th colspan="4">Ambient Temperature: 0 °C</th></tr><tr><th>Distance [Miles]</th><th>Total Cons [Wh/Mile]</th><th>Distance [Miles]</th><th>Total Cons [Wh/Mile]</th><th>Powertrain Cons [Wh/Mile]</th><th>AC/HP Cons [Wh/Mile]</th><th>PTC Cons [Wh/Mile]</th><th>Battery Thermal Cons [Wh/Mile]</th></tr></thead><tbody><tr><td>UDDS 1</td><td>7.4</td><td>217.7</td><td>7.4</td><td>336.9</td><td>246.9</td><td>60.7</td><td>29.3</td><td>0.0</td></tr><tr><td>HWFET 1</td><td>10.3</td><td>189.0</td><td>10.3</td><td>242.2</td><td>208.0</td><td>23.1</td><td>11.1</td><td>0.0</td></tr><tr><td>UDDS 2</td><td>7.4</td><td>142.4</td><td>7.4</td><td>257.5</td><td>176.9</td><td>54.4</td><td>26.2</td><td>0.0</td></tr><tr><td>Steady state</td><td>213.8</td><td>197.2</td><td>143.3</td><td>248.7</td><td>220.2</td><td>19.3</td><td>9.3</td><td>0.0</td></tr><tr><td>UDDS 3</td><td>7.4</td><td>142.3</td><td>7.4</td><td>257.4</td><td>176.7</td><td>54.5</td><td>26.3</td><td>0.0</td></tr><tr><td>HWFET 2</td><td>10.3</td><td>184.2</td><td>10.3</td><td>240.1</td><td>207.5</td><td>22.0</td><td>10.6</td><td>0.0</td></tr><tr><td>UDDS 4</td><td>7.4</td><td>143.0</td><td>7.4</td><td>256.6</td><td>177.4</td><td>53.4</td><td>25.8</td><td>0.0</td></tr><tr><td>Steady state</td><td>7.8</td><td>215.6</td><td>10.2</td><td>266.5</td><td>237.1</td><td>19.8</td><td>9.6</td><td>0.0</td></tr></tbody></table>

### Appendix A.2. Medium-Range Vehicle

<table><thead><tr><th rowspan="2">Cycle</th><th colspan="3">Ambient Temperature: 22 °C</th><th colspan="4">Ambient Temperature: -18 °C</th></tr><tr><th>Distance [Miles]</th><th>Total Cons [Wh/Mile]</th><th>Distance [Miles]</th><th>Total Cons [Wh/Mile]</th><th>Powertrain Cons [Wh/Mile]</th><th>AC/HP Cons [Wh/Mile]</th><th>PTC Cons [Wh/Mile]</th><th>Battery Thermal Cons [Wh/Mile]</th></tr></thead><tbody><tr><td>UDDS 1</td><td>7.4</td><td>230.7</td><td>7.4</td><td>571.7</td><td>275.3</td><td>0.0</td><td>296.4</td><td>0.0</td></tr><tr><td>HWFET 1</td><td>10.3</td><td>202.2</td><td>10.3</td><td>343.5</td><td>229.8</td><td>0.0</td><td>113.7</td><td>0.0</td></tr><tr><td>UDDS 2</td><td>7.4</td><td>158.6</td><td>7.4</td><td>462.2</td><td>203.0</td><td>0.0</td><td>259.2</td><td>0.0</td></tr><tr><td>Steady state</td><td>346.8</td><td>202.1</td><td>145.9</td><td>335.0</td><td>242.1</td><td>0.0</td><td>92.9</td><td>0.0</td></tr><tr><td>UDDS 3</td><td>7.4</td><td>148.4</td><td>7.4</td><td>459.3</td><td>203.1</td><td>0.0</td><td>256.2</td><td>0.0</td></tr><tr><td>HWFET 2</td><td>10.3</td><td>189.7</td><td>10.3</td><td>337.7</td><td>230.2</td><td>0.0</td><td>107.5</td><td>0.0</td></tr><tr><td>UDDS 4</td><td>7.4</td><td>149.0</td><td>7.4</td><td>460.7</td><td>204.5</td><td>0.0</td><td>256.2</td><td>0.0</td></tr><tr><td>Steady state</td><td>13.4</td><td>213.6</td><td>14.2</td><td>351.2</td><td>256.7</td><td>0.0</td><td>94.5</td><td>0.0</td></tr></tbody></table>
<table><thead><tr><th colspan="4">Ambient Temperature: 22 °C</th><th colspan="4">Ambient Temperature: -7 °C</th></tr><tr><th>Cycle</th><th>Distance [Miles]</th><th>Total Cons [Wh/Mile]</th><th>Distance [Miles]</th><th>Total Cons [Wh/Mile]</th><th>Powertrain Cons [Wh/Mile]</th><th>AC/HP Cons [Wh/Mile]</th><th>PTC Cons [Wh/Mile]</th><th>Battery Thermal Cons [Wh/Mile]</th></tr></thead><tbody><tr><td>UDDS 1</td><td>7.4</td><td>230.7</td><td>7.4</td><td>442.5</td><td>275.8</td><td>95.3</td><td>71.4</td><td>0.0</td></tr><tr><td>HWFET 1</td><td>10.3</td><td>202.2</td><td>10.3</td><td>280.1</td><td>220.4</td><td>34.1</td><td>25.6</td><td>0.0</td></tr><tr><td>UDDS 2</td><td>7.4</td><td>158.6</td><td>7.4</td><td>332.0</td><td>190.0</td><td>81.2</td><td>60.8</td><td>0.0</td></tr><tr><td>Steady state</td><td>346.8</td><td>202.1</td><td>202.1</td><td>281.6</td><td>231.0</td><td>28.9</td><td>21.7</td><td>0.0</td></tr><tr><td>UDDS 3</td><td>7.4</td><td>148.4</td><td>7.4</td><td>329.8</td><td>190.1</td><td>79.8</td><td>59.8</td><td>0.0</td></tr><tr><td>HWFET 2</td><td>10.3</td><td>189.7</td><td>10.3</td><td>277.4</td><td>219.0</td><td>33.4</td><td>25.0</td><td>0.0</td></tr><tr><td>UDDS 4</td><td>7.4</td><td>149.0</td><td>7.4</td><td>332.1</td><td>191.1</td><td>80.6</td><td>60.4</td><td>0.0</td></tr><tr><td>Steady state</td><td>13.4</td><td>213.6</td><td>20.2</td><td>292.2</td><td>240.9</td><td>29.3</td><td>22.0</td><td>0.0</td></tr></tbody></table>

<table><thead><tr><th colspan="4">Ambient Temperature: 22 °C</th><th colspan="4">Ambient Temperature: 0 °C</th></tr><tr><th>Cycle</th><th>Distance [Miles]</th><th>Total Cons [Wh/Mile]</th><th>Distance [Miles]</th><th>Total Cons [Wh/Mile]</th><th>Powertrain Cons [Wh/Mile]</th><th>AC/HP Cons [Wh/Mile]</th><th>PTC Cons [Wh/Mile]</th><th>Battery Thermal Cons [Wh/Mile]</th></tr></thead><tbody><tr><td>UDDS 1</td><td>7.4</td><td>230.7</td><td>7.4</td><td>357.4</td><td>267.4</td><td>60.7</td><td>29.3</td><td>0.0</td></tr><tr><td>HWFET 1</td><td>10.3</td><td>202.2</td><td>10.3</td><td>251.0</td><td>216.8</td><td>23.1</td><td>11.1</td><td>0.0</td></tr><tr><td>UDDS 2</td><td>7.4</td><td>158.6</td><td>7.4</td><td>262.8</td><td>182.1</td><td>54.4</td><td>26.2</td><td>0.0</td></tr><tr><td>Steady state</td><td>346.8</td><td>202.1</td><td>248.2</td><td>252.7</td><td>224.2</td><td>19.3</td><td>9.3</td><td>0.0</td></tr><tr><td>UDDS 3</td><td>7.4</td><td>148.4</td><td>7.4</td><td>262.7</td><td>182.3</td><td>54.3</td><td>26.2</td><td>0.0</td></tr><tr><td>HWFET 2</td><td>10.3</td><td>189.7</td><td>10.3</td><td>244.5</td><td>211.9</td><td>21.9</td><td>10.6</td><td>0.0</td></tr><tr><td>UDDS 4</td><td>7.4</td><td>149.0</td><td>7.4</td><td>263.6</td><td>183.1</td><td>54.3</td><td>26.2</td><td>0.0</td></tr><tr><td>Steady state</td><td>13.4</td><td>213.6</td><td>16.4</td><td>263.7</td><td>235.0</td><td>19.3</td><td>9.3</td><td>0.0</td></tr></tbody></table>

<table><thead><tr><th colspan="4">Ambient Temperature: 22 °C</th><th colspan="4">Ambient Temperature: 35 °C</th></tr><tr><th>Cycle</th><th>Distance [Miles]</th><th>Total Cons [Wh/Mile]</th><th>Distance [Miles]</th><th>Total Cons [Wh/Mile]</th><th>Powertrain Cons [Wh/Mile]</th><th>AC/HP Cons [Wh/Mile]</th><th>PTC Cons [Wh/Mile]</th><th>Battery Thermal Cons [Wh/Mile]</th></tr></thead><tbody><tr><td>UDDS 1</td><td>7.4</td><td>230.7</td><td>7.4</td><td>304.4</td><td>240.9</td><td>63.5</td><td>0.0</td><td>0.0</td></tr><tr><td>HWFET 1</td><td>10.3</td><td>202.2</td><td>10.3</td><td>199.6</td><td>194.5</td><td>5.1</td><td>0.0</td><td>0.0</td></tr><tr><td>UDDS 2</td><td>7.4</td><td>158.6</td><td>7.4</td><td>161.1</td><td>149.6</td><td>11.4</td><td>0.0</td><td>0.0</td></tr><tr><td>Steady state</td><td>346.8</td><td>202.1</td><td>290.4</td><td>200.9</td><td>197.1</td><td>3.8</td><td>0.0</td><td>0.0</td></tr><tr><td>UDDS 3</td><td>7.4</td><td>148.4</td><td>7.4</td><td>159.5</td><td>148.4</td><td>11.2</td><td>0.0</td><td>0.0</td></tr><tr><td>HWFET 2</td><td>10.3</td><td>189.7</td><td>10.3</td><td>189.9</td><td>185.3</td><td>4.6</td><td>0.0</td><td>0.0</td></tr><tr><td>UDDS 4</td><td>7.4</td><td>149.0</td><td>7.4</td><td>159.9</td><td>148.6</td><td>11.3</td><td>0.0</td><td>0.0</td></tr><tr><td>Steady state</td><td>13.4</td><td>213.6</td><td>68.7</td><td>202.9</td><td>199.0</td><td>3.9</td><td>0.0</td><td>0.0</td></tr></tbody></table>

<table><thead><tr><th colspan="4">Ambient Temperature: 22 °C</th><th colspan="4">Ambient Temperature: 50 °C</th></tr><tr><th>Cycle</th><th>Distance [Miles]</th><th>Total Cons [Wh/Mile]</th><th>Distance [Miles]</th><th>Total Cons [Wh/Mile]</th><th>Powertrain Cons [Wh/Mile]</th><th>AC/HP Cons [Wh/Mile]</th><th>PTC Cons [Wh/Mile]</th><th>Battery Thermal Cons [Wh/Mile]</th></tr></thead><tbody><tr><td>UDDS 1</td><td>7.4</td><td>230.7</td><td>7.4</td><td>605.8</td><td>254.5</td><td>275.1</td><td>0.0</td><td>76.2</td></tr><tr><td>HWFET 1</td><td>10.3</td><td>202.2</td><td>10.3</td><td>227.1</td><td>168.3</td><td>32.4</td><td>0.0</td><td>26.4</td></tr><tr><td>UDDS 2</td><td>7.4</td><td>158.6</td><td>7.4</td><td>223.2</td><td>115.4</td><td>51.4</td><td>0.0</td><td>56.4</td></tr><tr><td>Steady state</td><td>346.8</td><td>202.1</td><td>186.0</td><td>210.9</td><td>187.7</td><td>17.3</td><td>0.0</td><td>5.9</td></tr><tr><td>UDDS 3</td><td>7.4</td><td>148.4</td><td>7.4</td><td>197.8</td><td>148.7</td><td>49.1</td><td>0.0</td><td>0.0</td></tr><tr><td>HWFET 2</td><td>10.3</td><td>189.7</td><td>10.3</td><td>200.6</td><td>180.5</td><td>20.1</td><td>0.0</td><td>0.0</td></tr><tr><td>UDDS 4</td><td>7.4</td><td>149.0</td><td>7.4</td><td>207.7</td><td>137.7</td><td>49.4</td><td>0.0</td><td>20.6</td></tr><tr><td>Steady state</td><td>13.4</td><td>213.6</td><td>76.5</td><td>211.7</td><td>191.3</td><td>17.4</td><td>0.0</td><td>3.1</td></tr></tbody></table>
<table><caption>Appendix A.3. Long-Range Vehicle</caption><thead><tr><th colspan="4">Ambient Temperature: 22 °C</th><th colspan="4">Ambient Temperature: -18 °C</th></tr><tr><th>Cycle</th><th>Distance [Miles]</th><th>Total Cons [Wh/Mile]</th><th>Distance [Miles]</th><th>Total Cons [Wh/Mile]</th><th>Powertrain Cons [Wh/Mile]</th><th>AC/HP Cons [Wh/Mile]</th><th>PTC Cons [Wh/Mile]</th><th>Battery Thermal Cons [Wh/Mile]</th></tr></thead><tbody><tr><td>UDDS 1</td><td>7.4</td><td>257.9</td><td>7.4</td><td>612.8</td><td>316.4</td><td>0.0</td><td>296.4</td><td>0.0</td></tr><tr><td>HWFET 1</td><td>10.3</td><td>223.8</td><td>10.3</td><td>358.0</td><td>244.3</td><td>0.0</td><td>113.7</td><td>0.0</td></tr><tr><td>UDDS 2</td><td>7.4</td><td>232.2</td><td>7.4</td><td>474.7</td><td>215.5</td><td>0.0</td><td>259.2</td><td>0.0</td></tr><tr><td>Steady state</td><td>537.4</td><td>213.1</td><td>257.7</td><td>344.4</td><td>251.6</td><td>0.0</td><td>92.8</td><td>0.0</td></tr><tr><td>UDDS 3</td><td>7.4</td><td>162.3</td><td>7.4</td><td>472.3</td><td>215.8</td><td>0.0</td><td>256.5</td><td>0.0</td></tr><tr><td>HWFET 2</td><td>10.3</td><td>202.1</td><td>10.3</td><td>348.3</td><td>240.7</td><td>0.0</td><td>107.7</td><td>0.0</td></tr><tr><td>UDDS 4</td><td>7.4</td><td>162.8</td><td>7.4</td><td>473.0</td><td>216.6</td><td>0.0</td><td>256.4</td><td>0.0</td></tr><tr><td>Steady state</td><td>19.1</td><td>222.5</td><td>21.2</td><td>356.4</td><td>262.6</td><td>0.0</td><td>93.8</td><td>0.0</td></tr></tbody></table><table><thead><tr><th colspan="4">Ambient Temperature: 22 °C</th><th colspan="4">Ambient Temperature: -7 °C</th></tr><tr><th>Cycle</th><th>Distance [Miles]</th><th>Total Cons [Wh/Mile]</th><th>Distance [Miles]</th><th>Total Cons [Wh/Mile]</th><th>Powertrain Cons [Wh/Mile]</th><th>AC/HP Cons [Wh/Mile]</th><th>PTC Cons [Wh/Mile]</th><th>Battery Thermal Cons [Wh/Mile]</th></tr></thead><tbody><tr><td>UDDS 1</td><td>7.4</td><td>257.9</td><td>7.4</td><td>471.5</td><td>304.7</td><td>95.3</td><td>71.4</td><td>0.0</td></tr><tr><td>HWFET 1</td><td>10.3</td><td>223.8</td><td>10.3</td><td>300.4</td><td>240.7</td><td>34.1</td><td>25.6</td><td>0.0</td></tr><tr><td>UDDS 2</td><td>7.4</td><td>232.2</td><td>7.4</td><td>346.7</td><td>204.7</td><td>81.2</td><td>60.8</td><td>0.0</td></tr><tr><td>Steady state</td><td>537.4</td><td>213.1</td><td>337.9</td><td>291.8</td><td>241.2</td><td>28.9</td><td>21.7</td><td>0.0</td></tr><tr><td>UDDS 3</td><td>7.4</td><td>162.3</td><td>7.4</td><td>344.5</td><td>203.6</td><td>80.6</td><td>60.3</td><td>0.0</td></tr><tr><td>HWFET 2</td><td>10.3</td><td>202.1</td><td>10.3</td><td>288.2</td><td>230.2</td><td>33.1</td><td>24.8</td><td>0.0</td></tr><tr><td>UDDS 4</td><td>7.4</td><td>162.8</td><td>7.4</td><td>345.9</td><td>204.3</td><td>81.0</td><td>60.6</td><td>0.0</td></tr><tr><td>Steady state</td><td>19.1</td><td>222.5</td><td>29.6</td><td>299.3</td><td>248.7</td><td>29.0</td><td>21.7</td><td>0.0</td></tr></tbody></table><table><thead><tr><th colspan="4">Ambient Temperature: 22 °C</th><th colspan="4">Ambient Temperature: 0 °C</th></tr><tr><th>Cycle</th><th>Distance [Miles]</th><th>Total Cons [Wh/Mile]</th><th>Distance [Miles]</th><th>Total Cons [Wh/Mile]</th><th>Powertrain Cons [Wh/Mile]</th><th>AC/HP Cons [Wh/Mile]</th><th>PTC Cons [Wh/Mile]</th><th>Battery Thermal Cons [Wh/Mile]</th></tr></thead><tbody><tr><td>UDDS 1</td><td>7.4</td><td>257.9</td><td>7.4</td><td>384.1</td><td>294.0</td><td>60.7</td><td>29.3</td><td>0.0</td></tr><tr><td>HWFET 1</td><td>10.3</td><td>223.8</td><td>10.3</td><td>274.1</td><td>240.0</td><td>23.1</td><td>11.1</td><td>0.0</td></tr><tr><td>UDDS 2</td><td>7.4</td><td>232.2</td><td>7.4</td><td>291.5</td><td>210.8</td><td>54.4</td><td>26.2</td><td>0.0</td></tr><tr><td>Steady state</td><td>537.4</td><td>213.1</td><td>403.0</td><td>263.2</td><td>234.7</td><td>19.3</td><td>9.3</td><td>0.0</td></tr><tr><td>UDDS 3</td><td>7.4</td><td>162.3</td><td>7.4</td><td>275.7</td><td>195.7</td><td>54.0</td><td>26.0</td><td>0.0</td></tr><tr><td>HWFET 2</td><td>10.3</td><td>202.1</td><td>10.3</td><td>257.6</td><td>224.1</td><td>22.6</td><td>10.9</td><td>0.0</td></tr><tr><td>UDDS 4</td><td>7.4</td><td>162.8</td><td>7.4</td><td>274.4</td><td>195.9</td><td>52.9</td><td>25.5</td><td>0.0</td></tr><tr><td>Steady state</td><td>19.1</td><td>222.5</td><td>23.3</td><td>272.3</td><td>243.5</td><td>19.4</td><td>9.4</td><td>0.0</td></tr></tbody></table><table><thead><tr><th colspan="4">Ambient Temperature: 22 °C</th><th colspan="4">Ambient Temperature: 35 °C</th></tr><tr><th>Cycle</th><th>Distance [Miles]</th><th>Total Cons [Wh/Mile]</th><th>Distance [Miles]</th><th>Total Cons [Wh/Mile]</th><th>Powertrain Cons [Wh/Mile]</th><th>AC/HP Cons [Wh/Mile]</th><th>PTC Cons [Wh/Mile]</th><th>Battery Thermal Cons [Wh/Mile]</th></tr></thead><tbody><tr><td>UDDS 1</td><td>7.4</td><td>257.9</td><td>7.4</td><td>331.7</td><td>268.2</td><td>63.5</td><td>0.0</td><td>0.0</td></tr><tr><td>HWFET 1</td><td>10.3</td><td>223.8</td><td>10.3</td><td>223.9</td><td>218.8</td><td>5.1</td><td>0.0</td><td>0.0</td></tr><tr><td>UDDS 2</td><td>7.4</td><td>232.2</td><td>7.4</td><td>226.8</td><td>215.4</td><td>11.5</td><td>0.0</td><td>0.0</td></tr><tr><td>Steady state</td><td>537.4</td><td>213.1</td><td>455.8</td><td>212.1</td><td>207.7</td><td>3.8</td><td>0.0</td><td>0.6</td></tr><tr><td>UDDS 3</td><td>7.4</td><td>162.3</td><td>7.4</td><td>173.0</td><td>161.8</td><td>11.2</td><td>0.0</td><td>0.0</td></tr><tr><td>HWFET 2</td><td>10.3</td><td>202.1</td><td>10.3</td><td>202.1</td><td>197.5</td><td>4.6</td><td>0.0</td><td>0.0</td></tr><tr><td>UDDS 4</td><td>7.4</td><td>162.8</td><td>7.4</td><td>173.5</td><td>162.2</td><td>11.3</td><td>0.0</td><td>0.0</td></tr><tr><td>Steady state</td><td>19.1</td><td>222.5</td><td>76.5</td><td>214.2</td><td>210.4</td><td>3.8</td><td>0.0</td><td>0.0</td></tr></tbody></table>
<table><thead><tr><th rowspan="2">Cycle</th><th colspan="4">Ambient Temperature: 22 °C</th><th colspan="4">Ambient Temperature: 50 °C</th></tr><tr><th>Distance [Miles]</th><th>Total Cons [Wh/Mile]</th><th>Distance [Miles]</th><th>Total Cons [Wh/Mile]</th><th>Powertrain Cons [Wh/Mile]</th><th>AC/HP Cons [Wh/Mile]</th><th>PTC Cons [Wh/Mile]</th><th>Battery Thermal Cons [Wh/Mile]</th></tr></thead><tbody><tr><td>UDDS 1</td><td>7.4</td><td>257.9</td><td>7.4</td><td>673.1</td><td>296.4</td><td>275.1</td><td>0.0</td><td>101.6</td></tr><tr><td>HWFET 1</td><td>10.3</td><td>223.8</td><td>10.3</td><td>247.2</td><td>179.0</td><td>32.4</td><td>0.0</td><td>35.7</td></tr><tr><td>UDDS 2</td><td>7.4</td><td>232.2</td><td>7.4</td><td>240.9</td><td>112.3</td><td>51.4</td><td>0.0</td><td>77.2</td></tr><tr><td>Steady state</td><td>537.4</td><td>213.1</td><td>298.5</td><td>222.1</td><td>198.9</td><td>17.2</td><td>0.0</td><td>5.9</td></tr><tr><td>UDDS 3</td><td>7.4</td><td>162.3</td><td>7.4</td><td>233.6</td><td>131.5</td><td>49.2</td><td>0.0</td><td>52.9</td></tr><tr><td>HWFET 2</td><td>10.3</td><td>202.1</td><td>10.3</td><td>213.5</td><td>192.3</td><td>20.0</td><td>0.0</td><td>1.2</td></tr><tr><td>UDDS 4</td><td>7.4</td><td>162.8</td><td>7.4</td><td>211.8</td><td>162.9</td><td>48.9</td><td>0.0</td><td>0.0</td></tr><tr><td>Steady state</td><td>19.1</td><td>222.5</td><td>76.5</td><td>223.9</td><td>202.1</td><td>17.3</td><td>0.0</td><td>4.5</td></tr></tbody></table>

## References

1. IEA. Global EV Outlook 2025; IEA: Paris, France, 2025.

2. Bayram, S. Impacts of Electric Vehicle Charging under Cold Weather on Power Networks. In Proceedings of the 2021 56th International Universities Power Engineering Conference (UPEC): Powering Net Zero Emissions, Virtual event, 21 August–3 September 2021.

3. Jin, L.; Wang, S.; He, H. Real-World Performance of Battery Electric Passenger Cars in China: Energy Consumption, Range, And Charging Patterns; International Council on Clean Transportation: Washington, DC, USA, 2023.

4. Dan, D.; Zhao, Y.; Wei, M.; Wang, X. Review of Thermal Management Technology for Electric Vehicles. Energies **2023**, *16*, 4693. [CrossRef]

5. Al Haddad, R.; Basma, H.; Mansour, C. Analysis of heat pump performance in battery-electric buses. In Proceedings of the ECOS 2019—32nd International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy System, Wroclaw, Poland, 23–28 June 2019.

6. Al Haddad, R.; Basma, H.; Mansour, C. Modeling and control of heat pump system for battery electric buses. Proc. Inst. Mech. Eng. Part D J. Automob. Eng. **2022**, *236*, 3201–3220. [CrossRef]

7. Lemort, V.; Olivier, G.; De Pelsemaeker, G. Thermal Energy Management in Vehicles; Wiley: Hoboken, NJ, USA, 2023.

8. Senol, M.; Bayram, I.S.; Naderi, Y.; Galloway, S. Electric Vehicles Under Low Temperatures: A Review on Battery Performance, Charging Needs, and Power Grid Impacts. IEEE Access **2023**, *11*, 39879–39912. [CrossRef]

9. Ramsey, D.; Bouscayrol, A.; Boulon, L.; Desreveaux, A.; Vaudrey, A. Flexible Simulation of an Electric Vehicle to Estimate the Impact of Thermal Comfort on the Energy Consumption. IEEE Trans. Transp. Electrif. **2022**, *8*, 2288–2298. [CrossRef]

10. Basciotti, D.; Dvorak, D.; Gellai, I. A novel methodology for evaluating the impact of energy efficiency measures on the cabin thermal comfort of electric vehicles. Energies **2020**, *13*, 3872. [CrossRef]

11. Steinstraeter, M.; Heinrich, T.; Lienkamp, M. Effect of low temperature on electric vehicle range. World Electr. Veh. J. **2021**, *12*, 115. [CrossRef]

12. Jia, C.; Liu, W.; Chau, K.T.; He, H.; Zhou, J.; Niu, S. Passenger-aware reinforcement learning for efficient and robust energy management of fuel cell buses. eTransportation **2026**, *27*, 100537. [CrossRef]

13. Zhao, C.; Li, Y.; Yang, Y.; Wan, S.; Yu, F.; Yu, C.; Deng, C.; Zhou, A.; Shen, X. Research on electric vehicle range under cold condition. Adv. Mech. Eng. **2022**, *14*, 16878132221087083. [CrossRef]

14. Armenta-Déu, C.; Giorgi, B. Analysis of the Influence of Variable Meteorological Conditions on the Performance of the EV Battery and on the Driving Range. Future Transp. **2023**, *3*, 626–642. [CrossRef]

15. Iora, P.; Tribioli, L. Effect of ambient temperature on electric vehicles’ energy consumption and range: Model definition and sensitivity analysis based on Nissan Leaf data. World Electr. Veh. J. **2019**, *10*, 2. [CrossRef]

16. Al-Wreikat, Y.; Serrano, C.; Sodré, J.R. Effects of ambient temperature and trip characteristics on the energy consumption of an electric vehicle. Energy **2022**, *238*, 122028. [CrossRef]

17. Yuksel, T.; Michalek, J.J. Effects of regional temperature on electric vehicle efficiency, range, and emissions in the united states. Environ. Sci Technol. **2015**, *49*, 3974–3980. [CrossRef] [PubMed]

18. Al Haddad, R.; Mansour, C.; Kim, N.; Seo, J.; Nemer, M. Comprehensive Thermal Modeling and Analysis of a 2019 Nissan Leaf Plus for Enhanced Battery Electric Vehicle Performance; SAE Technical Papers; SAE International: Warrendale, PA, USA, 2024. [CrossRef]

19. Haddad RAI Mansour, C.; Kim, N.; Seo, J.; Stuttenberg, K.; Nemer, M. Comparative analysis of thermal management systems in electric vehicles at extreme weather conditions: Case study on Nissan Leaf 2019 Plus, Chevrolet Bolt 2020 and Tesla Model 3 2020. Energy Convers. Manag. **2025**, 332, 119706. [CrossRef]
20. United European Car Carriers. UECC Electric Vehicle Guideline. Available online: https://www.uecc.com/media/1524/uecc-electric-vehicle-guideline-v1.pdf (accessed on 25 January 2026).

21. Hao, X.; Wang, H.; Lin, Z.; Ouyang, M. Seasonal effects on electric vehicle energy consumption and driving range: A case study on personal, taxi, and ridesharing vehicles. *J. Clean. Prod.* **2020**, *249*, 119403. [CrossRef]

22. Argonne National Laboratory. Autonomie. 2023. Available online: https://vms.taps.anl.gov/tools/autonomie/ (accessed on 15 November 2023).

23. SAE Standard J1634_202104; SAE International Recommended Practice, Battery Electric Vehicle Energy Consumption and Range Test Procedure. SAE International: Warrendale, PA, USA, 2021. [CrossRef]

24. US Environmental Protection Agency. Fuel Economy and Electric Vehicle Range Testing. n.d. Available online: https://www.epa.gov/greenvehicles/fuel-economy-and-ev-range-testing (accessed on 13 July 2025).

25. US Department of Energy. Top Range of Model Year 2023 EVs Was 516 Miles on a Single Charge. 2024. Available online: https://www.energy.gov/eere/vehicles/articles/fotw-1323-january-1-2024-top-range-model-year-2023-evs-was-516-miles-single (accessed on 13 July 2025).

26. Oak Ridge National Laboratory. 2022 National Household Travel Survey. Share of All US Vehicle Trips by Length (Miles) n.d. Available online: https://afdc.energy.gov/data/10318 (accessed on 13 July 2025).

27. Argue, C. How Long Do Electric Car Batteries Last? The Updated Guide to Real-World EV Battery Health. Geotab n.d. Available online: https://www.geotab.com/blog/ev-battery-health/ (accessed on 18 January 2026).

28. Etengoff, A. What Is an EV Battery State of Charge (SOC)? EV Engineering & Infrastructure n.d. Available online: https://www.evengineeringonline.com/what-is-an-ev-battery-state-of-charge-soc/ (accessed on 18 January 2026).

29. Brèque, F.; Nemer, M. Cabin Thermal Needs: Modeling and Assumption Analysis. In Proceedings of the 12th International Modelica Conference, Prague, Czech Republic, 15–17 May 2017; Linköping University Electronic Press: Linköping, Sweden, 2017; Volume 132, pp. 771–781. [CrossRef]

**Disclaimer/Publisher's Note:** The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.