# Evaluation of Real-World Fuel Consumption of Hybrid-Electric Passenger Car Based on Speed-Specific Vehicle Power Distributions

Authors: Fei Peng; Ye Zhang; Guohua Song; Jianchang Huang; Zhiqiang Zhai; Lei Yu
Journal: Journal of Advanced Transportation
 (2023)

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## Abstract

Fuel consumption differs between the hybrid electric vehicle (HEV) and the conventional vehicle (CV). However, traditional fuel consumption models developed for CVs are commonly applied to HEVs, which leads to uncertainties in the quantitative evaluation of energy consumption for passenger cars in traffic road networks. Considering the internal combustion engine (ICE) operating modes of hybrid vehicles among varying vehicle specific power (VSP) demand, we present a method to incorporate the HEV ICE speed to develop speed-specific VSP distributions for real-world driving conditions. Using vehicle trajectory and fuel consumption data in real traffic conditions, the results of this study show that the application of methods developed for CVs leads to a significant underestimation of fuel consumption for HEVs when the average speed is in the high-speed range (over 50 km/h) and a significant overestimation of fuel consumption when the average speed is in the low-speed range (below 30 km/h). The average relative error of the measured fuel consumption factor in each speed bin is 7.1% compared with real-world observations, which is an unacceptably large error. This paper proposes a method to develop the speed-specific VSP distribution, considering whether the internal combustion engine (ICE) of HEVs is on or off. This approach reduces the average relative error of the obtained fuel consumption compared with real-world observations to 2.2%, and the measuring accuracy at different average speeds is significantly improved. This method enhances the functionality and applicability of the VSP theory-based traffic energy model for HEVs.


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## Body

## 1. Introduction

The energy and environmental problems associated with transportation are becoming increasingly serious, and there is a growing demand for improved vehicle fuel economy and energy efficiency. The transportation sector is a significant contributor to CO2 emissions, and reducing fuel consumption and carbon emissions during transportation is the key to achieving carbon neutrality. In the background of China’s carbon neutrality goal, it is important to measure vehicle fuel consumption and CO2 emissions in transportation for the effective formulation of traffic control policies for vehicle energy saving and emission reduction.In recent years, hybrid vehicles have become a transitional vehicle to the new energy vehicle field; for example, the share of the passenger car market in China has been increasing rapidly, with hybrid vehicles accounting for 7.9% of the Beijing passenger car market sales from January to July 2022, a significant increase compared to the 5.2% sales share in 2021. The hybrid electric vehicle (HEV) offers high fuel economy and low emission levels compared to the conventional vehicle (CV), as a strategy to improve energy efficiency and sustainability in transportation.However, it is not certain that the energy consumption evaluation methods developed for CVs are applicable to HEVs, due to the significant difference in fuel consumption between HEVs and CVs. HEVs select to use two power sources depending on driving conditions, including a conventional internal combustion engine (ICE) and a high-voltage battery. Powering by the battery reduces the demand for ICE operation to reduce fuel consumption and lower exhaust emissions [1]. HEVs generally consume 20–30% less fuel than CVs on urban roads, with the improvement being more pronounced on urban roads than highways [2]. The increase in the proportion of hybrid vehicles on roadways brings a degree of uncertainty and complexity to the evaluation of automotive vehicle fuel consumption in urban traffic road networks. Therefore, this study addresses two main questions: (1) How do we develop an accurate fuel consumption evaluation method for hybrid vehicles in real traffic conditions? (2) What are the implications of applying the traditional fuel consumption evaluation method to HEVs?


## 2. Literature Review

The main methods for measuring energy consumption and emissions in urban transportation are divided into “top-down” and “bottom-up” approaches [3, 4]. The “top-down” approach measures transport emissions based on region-wide transport sector energy consumption data multiplied by fuel emission factors. The “bottom-up” approach measures transport emissions based on the activity level (e.g., miles traveled) of various vehicles multiplied by the emission factor for the corresponding activity level. Many researchers have developed a series of “bottom-up” models that evaluate the energy consumption and emissions of various vehicles in transportation from micro to macro levels., and there are many widely used models, such as the Motor Vehicle Emissions Simulator (MOVES) [5], the Computer Programme to calculate Emissions from Road Transportation (COPERT) [6], International Vehicle Emission Model (IVE) [7], the Comprehensive Modal Emission Model (CMEM) [8], and the Virginia Tech Microscopic Emission Model (VT-Micro) [9].Macro-level models focus on estimating the fuel consumption of an entire area by the average speed of vehicles in a certain driving area and can be used to predict the total amount of fuel and the development trend of traffic in a city or country, the representative model is CORPERT [6], which provides options for driving cycles to represent vehicle driving characteristics, while many studies have found large discrepancies between driving cycles and vehicle driving characteristics on real roads. Micro-level models are based on the derivation of the relationship between the instantaneous fuel consumption of a vehicle and the driving parameters, and these models are suitable for estimating the quantitative fuel consumption of a vehicle through a certain road section or intersection, the representative models are CMEM and VT-micro. Scora and Barth developed the CMEM model based on a microscopic perspective, which calculates the fuel emissions of a vehicle second by second from the engine operating state parameters of the vehicle in different driving modes [8]. Rakha et al. proposed the VT-micro model to illustrate the relationship between vehicle speed, acceleration, and fuel consumption through driving tests [10]. Although the input parameters and modeling scale of these models are different, they are all structured with the coupled characterization of motor vehicle operating conditions and energy consumption factors as the core module.Meso-level models are suitable for measuring the fuel consumption and emissions of vehicles in a local area, such as a traffic road network. MOVES is a comprehensive emissions model developed by the US Environmental Protection Agency (EPA) that offers macro and meso dimensions [5]. In the meso-level model, the traffic road network energy consumption is measured as an energy consumption factor coupled with vehicle miles traveled [11]. Within MOVES, the Vehicle Specific Power (VSP) distributions are used to characterize the fuel consumption and emission of vehicles in different operating conditions [9]. The VSP distribution changes with the traffic condition, and the speed-specific VSP distribution is developed to calculate the energy consumption factor of the vehicle at different trip speeds [12–14]. Current energy consumption models based on VSP theory (e.g., MOVES and IVE) use VSP as a parameter in the modeling process to characterize the relationship between the fuel consumption of motor vehicles and its driving conditions. This relationship is usually described as the fuel consumption per unit time of motor vehicle driving, or the fuel consumption rate (FCR). The MOVES calculation of the VSP equation contains the four main components of kinetic energy, potential energy, rolling resistance, and aerodynamic drag, which represent the instantaneous power required to move a vehicle per unit mass [5], [15]. The VSP is directly related to the engine load, so it is a better indicator of the relationship with fuel consumption than speed and acceleration. The VSP has a clear physical meaning and a good correlation with fuel consumption, and the scientific validity of the approach has been well documented in many studies [16, 17]. However, energy consumption models based on the VSP distribution theory lack separate modeling for hybrid/plug-in hybrid vehicles. Traditional measurement methods include these hybrids in the same energy emission standards as CVs and measure energy emissions together with CVs.Many software and models currently support the fuel consumption evaluation of hybrid vehicle powertrains in the automotive engineering field, such as the future automotive systems technology simulator (FASTSim) and ADVISOR developed by the National Renewable Energy Laboratory (NREL), or autonomie developed by the Argonne National Laboratory [18]. FASTSim models a variety of vehicle powertrains and fuel converter types: electric drive vehicles (hybrid, plug-in hybrid, and all-electric) [19]. Autonomie is a state-of-the-art vehicle system simulation tool capable of evaluating the fuel consumption and performance impact of various advanced vehicle technologies (including conventional to hybrid vehicles) [20]. These existing research focuses on vehicle performance analysis to evaluate the impact of advanced vehicle technologies on energy consumption and costs, as well as the optimization of energy management control strategies to reduce hybrid vehicle energy consumption. Although these energy consumption models for vehicle design that measures fuel consumption are highly accurate, the data input requirements for individual vehicle parameters are very detailed. Given these requirements, these modeling approaches are unsuitable for interfacing with large amounts of real traffic operation data, making it difficult to support the quantification of traffic energy consumption from micro (road sections and intersections) to macro (road network) levels in urban transportation. The models based on the VSP distribution can characterize the vehicle activity and can accurately estimate the fuel consumption in real driving conditions, and some previous studies utilize VSP-based HEV fuel consumption models. For example, Zhai optimizes an emission model for hybrid vehicles based on VSP and ICE speed setting rules for identifying ICE-on/ICE-off [21]. Robinson introduced a variable to portray the power-split relationship between the electric motor and the internal combustion engine to improve the hybrid vehicle CO2 emission measurement method and verified that considering the road grade in VSP can accurately modeling vehicle emissions [1]. Meanwhile, the VSP demand distribution for hybrid vehicles involves the vehicle powertrain, and there are three common types of HEVs according to their powertrain configurations: series hybrids, parallel hybrids, and serie-–parallel hybrids [22]. The series configuration enables the ICE to be completely decoupled from the wheel, and the ICE’s mechanical output is converted to electrical energy through a generator. In parallel configurations, the traction power and torque produced by the ICE can be transferred directly to the drive wheels. The electric drive is provided as an auxiliary power for transient or continuous operation, particularly under special torque and power requirements [23]. The power-split configurations allow for both series and parallel hybrid modes, with engines capable of operating at the theoretically optimal operating point. Typical series-parallel hybrid systems include toyota hybrid systems [24]. Therefore, it can be found that for each type, the fuel consumption of HEV is closely related to the ICE operation, and determining whether the VSP demand is powered by the battery or the internal combustion engine becomes the key to modeling, while judging the ICE on and off can be done by the engine speed data obtained from the on-board diagnostic (OBD) system.The following main findings can be summarized from this review of current research in various fields. (1) Recent studies have developed VSP distributions by collecting a large amount of real-world driving data for energy consumption and emission measurement for CVs, while studies have shown that the development of VSP distributions at different speeds can describe the fuel consumption levels of motor vehicles in traffic road networks. (2) There is a lack of methods for constructing VSP distributions specifically for hybrid vehicles, and most of the existing studies on the fuel consumption evaluation of hybrid vehicles focus on energy management strategies in automotive engineering. Traffic road network energy modeling research for hybrid vehicles all use the same fuel consumption factor calculation method as CVs, which generates a certain degree of uncertainty. Therefore, this study addresses the VSP distribution based on a large amount of real driving trajectory data, while coupling the second-by-second OBD data to optimize the VSP distribution and to achieve the fuel consumption factor measurements of hybrid vehicles at different speeds. This work presents an effective method for the evaluation of fuel consumption and carbon emissions of motor vehicles in the traffic road network, along with the evaluation of the energy saving and emission reduction effects of hybrid vehicles among varied traffic conditions.


## 3. Methodology

The framework of this study is shown in Figure1. This study utilized a large amount of real-world driving data and fuel consumption data of HEVs and CVs. Speed-specific VSP distribution was developed based on vehicle activity data categorized into average speed bins, and the FCR corresponding to each VSP bin was calculated. Three methods were considered in the process of developing the VSP distribution, all three of which were performed with real-world driving data. The first method replicates the VSP distribution as developed for CVs, the second distinguishes the HEV ICE on and off modes according to the ICE engine speed, and the third method further divides the HEV ICE-on mode into power recovery and electric drive assist by probabilistic fitting. The objective of the study is to identify which method can more accurately obtain the HEV fuel consumption factor for various average speeds.Figure 1
Framework of the study.


### 3.1. Data Sources and Preparation

Real-world driving data were collected on 23 Toyota Corolla HEVs and 50 CVs in Beijing from August to October 2020, using OBD scan tools and global positioning system (GPS) receivers. The Toyota Corolla HEV is powered by a 1.8-liter Toyota Hybrid Systems (THS). The 50 CVs represent different manufacturers (Volkswagen, Ford, Toyota, Nissan, etc.) and were all equipped with 1.6-liter gasoline ICEs. Over 67 million seconds of real-world driving data were recorded in seconds, including speed, longitude, and latitude from the GPS receiver and ICE engine speed (in revolutions per minute, or RPM) and FCR from the OBD.The VSP quantifies the road load coefficients during vehicle driving, including road gradient, rolling resistance, and aerodynamic drag acting on the vehicle and calculates the VSP based on the instantaneous speed and acceleration of vehicles. For every second of vehicle activity trajectory data and fuel consumption data, the VSP was calculated as(1)VSP=A⋅v+B⋅v2+C⋅v3m+a+gsinθ⋅v,where VSP is in kW/ton, v is the vehicle speed in m/s, a is the vehicle acceleration in m/s2, and A, B, and C are road load coefficients associated with rolling resistance, mechanical rotational resistance, and aerodynamic drag, in units of kW·s/m, kW·s2/m2, and kW·s3/m3, respectively. These parameters of the hybrid vehicles in this study are 0.156461, 0.00200193, and 0.000492646 [26]. m is the mass for the specific vehicle type in metric tons, g is the gravitational acceleration, and θ is the road grade.In order to evaluate the correlation between VSP variables and instantaneous FCR, a binning method was used, wherein the VSP was divided into different intervals (defined as “VSP Bin”), as shown in (2). This study defines an interval of 1 kW/ton to cluster the VSP and analyses the instantaneous FCR in the VSP bin.(2)VSPBin=i,∀:VSP∈i−0.5,i+0.5.Based on the above clustering method to determine each VSP bin, the average FCR was calculated using(3)FCRi,j=∑n=1NFCRn,i,jN,where FCRi,j (in mL/s) is the average FCR for the vehicle state j and VSP bin i. FCRn,i,j is the value of the instantaneous FCR for the vehicle state j and VSP bin i. N is the number of observations for vehicle state j and VSP bin i.


### 3.2. Development of Speed-Specific VSP Distributions and Fuel Consumption Factors

Vehicle trajectories that are sourced from real-world CVs and HEVs driving data are divided into 180-second intervals and classified into speed-specific trajectory pools based on the average speed (v¯) of each trajectory. The speed bin is also divided in 1 km/h average speed intervals.(4)v¯=1180⋅∑t=1180vm,SpeedBin=k∀:v¯∈k,k+1,where v¯ is the average speed of the trajectory in km/h, and vm is the speed at time m in km/h.The speed-specific VSP distribution is developed based on the percentage of VSP bin in each trajectory pool:(5)Ri,j,k=Ni,j,kNj,k,where Ri,j,k is the percentage of VSP Bin i in the vehicle ICE operating mode j and speed bin k; Nj,k is the number of total VSP records in the vehicle ICE operating mode j and speed bin k; and Ni,j,k is the number of VSP Bin i records in the vehicle ICE operating mode j and speed bin k.The speed-specific VSP distribution in each pool was used to characterize the driving behavior of the vehicle based on the vehicle activity data. Different fuel consumption rates (FCRi,j) in each VSP bin were coupled by using the VSP as an intermediate variable. The calculation of the fuel consumption factor FCk at each average speed is (6)FCk=∑i=−20i=20∑j=1SRi,j,k⋅FCRi,jVk⋅3600,where FCk (in L/km) is the fuel consumption factor when the average speed is k in km/h.


### 3.3. Classification of HEV ICE Operating Mode

This study analysed the hybrid powertrain operating modes and delineated three HEV operating modes, as shown in Figure2. The HEV in this study is equipped with the THS, which is a full hybrid system using a series parallel design with two motors (MG1 and MG2) capable of operating as an electric motor and generator (MG). The power split device (PSD) consisting of two sets of planetary gears to balance the ICE and the PSD to balance the power output from the ICE and both motors to the wheels, and the power control unit (PCU) to control the current input and output from the high-voltage battery [24, 25]. Depending on whether fuel consumption and exhaust emissions are generated, the HEV state includes the electric drive mode and hybrid drive mode, where the ICE is turned off in the electric drive mode, and no fuel consumption or exhaust emissions are generated. Considering the fuel consumption levels, the hybrid drive mode is divided into two states, power recovery and electric drive assist, according to the series parallel design and the purpose of ICE operation [1]. Therefore, this study divides the HEV state into three ICE operating modes, as shown in Figure 2.Figure 2
HEV power flow for different ICE operation modes.
(a)(b)(c)


#### 3.3.1. ICE-Off Operating Mode

In this mode, the ICE is turned off and the HEV has no fuel consumption. The HEV is powered by MG1 and MG2 through the high-voltage battery. Normally, MG2 acts as the electric motor alone to provide the power source for the whole vehicle in the starting and low-speed range (Figure2(a), green solid line). When accelerating sharply and during climbing conditions, the high-voltage battery also provides power to MG1, and through the planetary gearing, MG1 and MG2 act as a single electric motor with providing more torque (Figure 2(a), green dashed line). During deceleration and braking, the wheels drive the motor while MG2 has negative torque, and MG2 acts as a generator, with the generated electricity stored in the battery through the PCU (Figure 2(a), yellow solid line).


#### 3.3.2. ICE-On Operating Mode 1

The purpose of ICE-on operation in this mode is power recovery, with MG1 and MG2 acting as generators to charge the battery. The ICE works in series with MG1 for optimal efficiency, and the power generated by MG1 through the ICE is used to charge the battery (Figure2(b), blue solid line). During deceleration (coasting or braking), there is also energy recovery, MG2 has negative torque, and the power generated is stored in the battery (Figure 2(b), yellow solid line). During the high-speed operation, the power generated by MG2 can also be recirculated to MG1; instead of being used as a high-voltage battery charge, MG1 then operates as an electric motor to control the PSD (Figure 2(b), blue dotted line).


#### 3.3.3. ICE-On Operating Mode 2

The purpose of ICE-on operation in this mode is for electric drive assist. The torque generated by ICE is distributed to the driving wheels and MG1 through PSD, MG1 generates electricity to power MG2, which then acts as an electric motor and drives the wheels together with ICE in parallel through PSD transmission (Figure2(c), red solid line). The excess power generated by MG1 is used to charge the battery; during periods of high-power demand, the power generated by the maximum power of MG1 does not meet the power demand of MG2, and the battery can provide power to MG2 to assist with the additional power (Figure 2(c), red dashed line).


## 3.1. Data Sources and Preparation

Real-world driving data were collected on 23 Toyota Corolla HEVs and 50 CVs in Beijing from August to October 2020, using OBD scan tools and global positioning system (GPS) receivers. The Toyota Corolla HEV is powered by a 1.8-liter Toyota Hybrid Systems (THS). The 50 CVs represent different manufacturers (Volkswagen, Ford, Toyota, Nissan, etc.) and were all equipped with 1.6-liter gasoline ICEs. Over 67 million seconds of real-world driving data were recorded in seconds, including speed, longitude, and latitude from the GPS receiver and ICE engine speed (in revolutions per minute, or RPM) and FCR from the OBD.The VSP quantifies the road load coefficients during vehicle driving, including road gradient, rolling resistance, and aerodynamic drag acting on the vehicle and calculates the VSP based on the instantaneous speed and acceleration of vehicles. For every second of vehicle activity trajectory data and fuel consumption data, the VSP was calculated as(1)VSP=A⋅v+B⋅v2+C⋅v3m+a+gsinθ⋅v,where VSP is in kW/ton, v is the vehicle speed in m/s, a is the vehicle acceleration in m/s2, and A, B, and C are road load coefficients associated with rolling resistance, mechanical rotational resistance, and aerodynamic drag, in units of kW·s/m, kW·s2/m2, and kW·s3/m3, respectively. These parameters of the hybrid vehicles in this study are 0.156461, 0.00200193, and 0.000492646 [26]. m is the mass for the specific vehicle type in metric tons, g is the gravitational acceleration, and θ is the road grade.In order to evaluate the correlation between VSP variables and instantaneous FCR, a binning method was used, wherein the VSP was divided into different intervals (defined as “VSP Bin”), as shown in (2). This study defines an interval of 1 kW/ton to cluster the VSP and analyses the instantaneous FCR in the VSP bin.(2)VSPBin=i,∀:VSP∈i−0.5,i+0.5.Based on the above clustering method to determine each VSP bin, the average FCR was calculated using(3)FCRi,j=∑n=1NFCRn,i,jN,where FCRi,j (in mL/s) is the average FCR for the vehicle state j and VSP bin i. FCRn,i,j is the value of the instantaneous FCR for the vehicle state j and VSP bin i. N is the number of observations for vehicle state j and VSP bin i.


## 3.2. Development of Speed-Specific VSP Distributions and Fuel Consumption Factors

Vehicle trajectories that are sourced from real-world CVs and HEVs driving data are divided into 180-second intervals and classified into speed-specific trajectory pools based on the average speed (v¯) of each trajectory. The speed bin is also divided in 1 km/h average speed intervals.(4)v¯=1180⋅∑t=1180vm,SpeedBin=k∀:v¯∈k,k+1,where v¯ is the average speed of the trajectory in km/h, and vm is the speed at time m in km/h.The speed-specific VSP distribution is developed based on the percentage of VSP bin in each trajectory pool:(5)Ri,j,k=Ni,j,kNj,k,where Ri,j,k is the percentage of VSP Bin i in the vehicle ICE operating mode j and speed bin k; Nj,k is the number of total VSP records in the vehicle ICE operating mode j and speed bin k; and Ni,j,k is the number of VSP Bin i records in the vehicle ICE operating mode j and speed bin k.The speed-specific VSP distribution in each pool was used to characterize the driving behavior of the vehicle based on the vehicle activity data. Different fuel consumption rates (FCRi,j) in each VSP bin were coupled by using the VSP as an intermediate variable. The calculation of the fuel consumption factor FCk at each average speed is (6)FCk=∑i=−20i=20∑j=1SRi,j,k⋅FCRi,jVk⋅3600,where FCk (in L/km) is the fuel consumption factor when the average speed is k in km/h.


## 3.3. Classification of HEV ICE Operating Mode

This study analysed the hybrid powertrain operating modes and delineated three HEV operating modes, as shown in Figure2. The HEV in this study is equipped with the THS, which is a full hybrid system using a series parallel design with two motors (MG1 and MG2) capable of operating as an electric motor and generator (MG). The power split device (PSD) consisting of two sets of planetary gears to balance the ICE and the PSD to balance the power output from the ICE and both motors to the wheels, and the power control unit (PCU) to control the current input and output from the high-voltage battery [24, 25]. Depending on whether fuel consumption and exhaust emissions are generated, the HEV state includes the electric drive mode and hybrid drive mode, where the ICE is turned off in the electric drive mode, and no fuel consumption or exhaust emissions are generated. Considering the fuel consumption levels, the hybrid drive mode is divided into two states, power recovery and electric drive assist, according to the series parallel design and the purpose of ICE operation [1]. Therefore, this study divides the HEV state into three ICE operating modes, as shown in Figure 2.Figure 2
HEV power flow for different ICE operation modes.
(a)(b)(c)


### 3.3.1. ICE-Off Operating Mode

In this mode, the ICE is turned off and the HEV has no fuel consumption. The HEV is powered by MG1 and MG2 through the high-voltage battery. Normally, MG2 acts as the electric motor alone to provide the power source for the whole vehicle in the starting and low-speed range (Figure2(a), green solid line). When accelerating sharply and during climbing conditions, the high-voltage battery also provides power to MG1, and through the planetary gearing, MG1 and MG2 act as a single electric motor with providing more torque (Figure 2(a), green dashed line). During deceleration and braking, the wheels drive the motor while MG2 has negative torque, and MG2 acts as a generator, with the generated electricity stored in the battery through the PCU (Figure 2(a), yellow solid line).


### 3.3.2. ICE-On Operating Mode 1

The purpose of ICE-on operation in this mode is power recovery, with MG1 and MG2 acting as generators to charge the battery. The ICE works in series with MG1 for optimal efficiency, and the power generated by MG1 through the ICE is used to charge the battery (Figure2(b), blue solid line). During deceleration (coasting or braking), there is also energy recovery, MG2 has negative torque, and the power generated is stored in the battery (Figure 2(b), yellow solid line). During the high-speed operation, the power generated by MG2 can also be recirculated to MG1; instead of being used as a high-voltage battery charge, MG1 then operates as an electric motor to control the PSD (Figure 2(b), blue dotted line).


### 3.3.3. ICE-On Operating Mode 2

The purpose of ICE-on operation in this mode is for electric drive assist. The torque generated by ICE is distributed to the driving wheels and MG1 through PSD, MG1 generates electricity to power MG2, which then acts as an electric motor and drives the wheels together with ICE in parallel through PSD transmission (Figure2(c), red solid line). The excess power generated by MG1 is used to charge the battery; during periods of high-power demand, the power generated by the maximum power of MG1 does not meet the power demand of MG2, and the battery can provide power to MG2 to assist with the additional power (Figure 2(c), red dashed line).


## 3.3.1. ICE-Off Operating Mode

In this mode, the ICE is turned off and the HEV has no fuel consumption. The HEV is powered by MG1 and MG2 through the high-voltage battery. Normally, MG2 acts as the electric motor alone to provide the power source for the whole vehicle in the starting and low-speed range (Figure2(a), green solid line). When accelerating sharply and during climbing conditions, the high-voltage battery also provides power to MG1, and through the planetary gearing, MG1 and MG2 act as a single electric motor with providing more torque (Figure 2(a), green dashed line). During deceleration and braking, the wheels drive the motor while MG2 has negative torque, and MG2 acts as a generator, with the generated electricity stored in the battery through the PCU (Figure 2(a), yellow solid line).


## 3.3.2. ICE-On Operating Mode 1

The purpose of ICE-on operation in this mode is power recovery, with MG1 and MG2 acting as generators to charge the battery. The ICE works in series with MG1 for optimal efficiency, and the power generated by MG1 through the ICE is used to charge the battery (Figure2(b), blue solid line). During deceleration (coasting or braking), there is also energy recovery, MG2 has negative torque, and the power generated is stored in the battery (Figure 2(b), yellow solid line). During the high-speed operation, the power generated by MG2 can also be recirculated to MG1; instead of being used as a high-voltage battery charge, MG1 then operates as an electric motor to control the PSD (Figure 2(b), blue dotted line).


## 3.3.3. ICE-On Operating Mode 2

The purpose of ICE-on operation in this mode is for electric drive assist. The torque generated by ICE is distributed to the driving wheels and MG1 through PSD, MG1 generates electricity to power MG2, which then acts as an electric motor and drives the wheels together with ICE in parallel through PSD transmission (Figure2(c), red solid line). The excess power generated by MG1 is used to charge the battery; during periods of high-power demand, the power generated by the maximum power of MG1 does not meet the power demand of MG2, and the battery can provide power to MG2 to assist with the additional power (Figure 2(c), red dashed line).


## 4. Results and Discussion

### 4.1. Comparative Analysis of CVs HEVs

All CV and HEV real-world driving data were used to develop the VSP distribution, as illustrated in Figure3. For more than 99% of the driving data in this study, the VSP range was between 20 and 20 kW/ton. The VSP distribution of HEVs established by introducing ICE speed contains two operating modes, ICE-on and ICE-off, with about 64% of the data in the ICE-off operating mode. The HEV operating mode in this study was categorized as “ICE-on” when the ICE speed exceeded 500 r/min [21]. The most essential difference between HEV and CV during driving is that an HEV has zero fuel consumption when ICE-off, while a CV remains in ICE-on.Figure 3
VSP distribution of HEVs and CVs. (a) The real-world driving data of HEVs, (b) the real-world driving data of CVs.
(a)(b)Since HEVs do not consume fuel in ICE-off mode, as distinct from CVs, we compared the difference in fuel consumption between these two vehicle types in ICE-on mode only and calculated the proportions of FCR in each VSP bin (FCR values are accurate to 0.1 mL/s). The results show two distinct peaks in the different FCR distributions for HEVs, reflecting the two ICE-on modes of operation, as shown in Figure4. In the ICE-on mode 1, the ICE is kept at the most economical charging efficiency to charge the battery and the FCR of HEVs is relatively stable. In the ICE-on mode 2, the ICE of HEVs provides the vehicle driving power and the FCR and VSP are clearly related. It is generally apparent that VSP bin <0 is associated with ICE-on mode 1 and in ICE-on mode 2 when VSP Bin ≥6.Figure 4
Instantaneous fuel consumption distribution of CVs and HEVs. (a) The FCR of HEVs, (b) the FCR of CVs.
(a)(b)It has been studied that the VSP-based fuel consumption distribution of CVs follows the Gaussian distribution [27], while this property was verified in this study. For hybrid vehicles, the different ICE-on operating modes are determined by the vehicle energy management strategy, and it is not possible to further distinguish whether the ICE-on state is mode 1 or mode 2 based on VSP and engine speed. Therefore, we developed the Gaussian mixture model (GMM) to fit the FCR probability distribution curve in ICE-on for hybrid vehicles:(7)yk=y0+Ak2πσkexp−x−μk22σk2.We selected the range from VSP bin = 0 to VSP bin = 6 for fitting, as these were closely associated with ICE-on mode 1 and mode 2. The probability distribution curve fitting achieved good results, with the CV and HEV fitting parametersR2 exceeding 0.86 and 0.96, respectively, as shown in Tables 1 and 2.Table 1
Gaussian distribution fitting parameters for CV VSP bins.
VSP bin012345y00.010.010.010.010.010.01μ10.290.420.540.650.760.86σ10.270.450.670.810.900.98α10.790.650.720.730.720.72R20.950.870.860.880.890.90Table 2
Gaussian distribution fitting parameters for HEV VSP bins.
VSP bin012345y00.010.010.010.000.010.01μ10.260.250.240.240.250.26σ10.170.200.220.220.220.21α10.300.290.280.200.130.08μ20.640.830.920.991.041.09σ20.230.520.560.580.580.60α20.540.380.590.690.740.76R20.980.960.970.980.980.98For CVs, the relationship between instantaneous fuel consumption and VSP is clear. The distribution of instantaneous fuel consumption rate in each VSP bin follows a Gaussian distribution, and the parameterFCk used to calculate the FCRi,j has a positive linear relationship with the VSP bin, as shown in Figure 5. In addition, there is only one ICE-on mode for CVs, while there are two for HEVs. We fit the distribution of instantaneous FCR in each VSP bin during each HEV ICE-on mode. The results show that the instantaneous fuel consumption distribution of both HEV and ICE-on operating modes conforms to a Gaussian distribution. As VSP increases, the proportion of HEV ICE-on mode 2 increases (green shading in Figure 6), while the proportion of HEV ICE-on operating mode 1 decreases (orange shading in Figure 6). The instantaneous fuel consumption of ICE-on Mode 1 is stable in the range of 0.1–0.4 ml/s, while the instantaneous fuel consumption associated with ICE-on mode 2 exceeds 0.5 ml/s. These thresholds were used to calculate the FCRi,j in HEV ICE-on, and obtain the FCRi,j of ICE-on mode 1 and mode 2 for HEVs.Figure 5
Distribution of CV fuel consumption rate in each VSP bin.Figure 6
Distribution of HEV fuel consumption rates in each VSP bin.


### 4.2. Development of the VSP Distribution and FCR

In order to measure the accurate fuel consumption factor for different speed ranges of HEVs, three methods were chosen to establish the VSP distribution and calculate the corresponding FCR. The VSP distribution was developed based on more than 14 million seconds of HEV activity data categorized into average speed bins, with each speed bins containing over 30,000 seconds of data, and each second of data were differentiated by ICE operating mode, as shown in Figure7. The results show that the proportion of ICE-Off operating mode decreases as the average travel speed increases, while the proportions of ICE-On operating mode and mode1 are consistent with the average speed change, both increasing with the growth of the average travel speed. The mode 2 is almost unaffected by the average speed change, and its occurrence is closely related to the remaining battery charge.Figure 7
Data size in HEV ICE operating mode.The VSP distributions of hybrid vehicles are shown for average speeds at 20, 40, 60, and 80 km/h, as shown in Figure8. Each method is based on the different ICE operating modes for hybrid vehicles: (1) Method 1: As with the CV measurement method, the VSP distribution for HEVs utilizes only speed and acceleration parameters, and the average FCR is calculated without considering the ICE operating mode. (2) Method 2: The VSP distribution is developed by introducing the ICE speed for HEVs, considering the ICE-on and off, while calculating the ICE-on is the corresponding FCR for fuel consumption factor calculation. (3) Method 3: Following from method 2, the two ICE-on operating modes of hybrid vehicles are considered, and the VSP distribution is developed by fitting the probabilities relationship between the two ICE-on modes in each VSP bin. The FCR corresponding to ICE-on mode 1 (0.1–0.4 ml/s) and mode 2 (exceed 0.5 ml/s) in each VSP bin are also calculated for fuel consumption factor calculation.Figure 8
The HEV VSP distribution with method 1 through 3.The average FCR was additionally calculated for each HEV ICE mode, as shown in Figure9. The FCR of each mode corresponds to the VSP distribution developed by the different methods. The average FCR used for method 1 includes the ICE-on and off modes in each VSP bin; the average FCR calculated in method 2 is for the ICE-on mode; and the average FCR used for method 3 is for the ICE-on operating mode 1 (0.1–0.4 ml/s) and mode 2 (exceed 0.5 ml/s), respectively. The specific average FCR used depends on whether the VSP value depends upon the HEV ICE operating mode and calculates the fuel consumption factor of HEVs according to (6).Figure 9
The FCR in different HEV ICE modes.


### 4.3. Method Validation and Error Analysis

Three different methods were implemented to develop speed-specific VSP distributions for HEVs. The fuel consumption factors generated by these methods were compared with the real-world observations, and the relative errors were calculated, as shown in Figure10. The average relative errors associated with methods 1 through 3 were 7.1%, 2.2%, and 5.9%, respectively. The fuel consumption factor obtained from method 2 (ICE-on) best approximates the observed values. The HEV consumption factor obtained with the same method as the CV (Method 1) decreased with increasing average speed, producing a large relative error, especially in the low-speed range (below 30 km/h) and high-speed range (over 50 km/h). This method fails to estimate the fuel consumption characteristics of HEVs at different average speeds. Additionally, the estimates produced by method 3, which considers two separate HEV ICE-on operating modes are not satisfactory. The proportion of the two modes is idealized and fixed during the process of developing the VSP distribution; however, the proportion of the two modes in the real-world driving conditions is more complicated. These mischaracterizations introduce a greater estimation error than seen in the simpler method 2, which only considers ICE-on and ICE-off. In summary, these results indicate that HEV fuel consumption modeling is more accurate when information on ICE-on or ICE-off mode is incorporated when developing the speed specific VSP distribution, as opposed to modeling using an analogous process to CVs. However, adding model complexity to an attempt to characterize ICE-on variability may introduce problematic uncertainty.Figure 10
Comparison of validation and error in three methods. (a) Fuel consumption in speed bin, (b) relative error in speed bin.
(a)(b)


## 4.1. Comparative Analysis of CVs HEVs

All CV and HEV real-world driving data were used to develop the VSP distribution, as illustrated in Figure3. For more than 99% of the driving data in this study, the VSP range was between 20 and 20 kW/ton. The VSP distribution of HEVs established by introducing ICE speed contains two operating modes, ICE-on and ICE-off, with about 64% of the data in the ICE-off operating mode. The HEV operating mode in this study was categorized as “ICE-on” when the ICE speed exceeded 500 r/min [21]. The most essential difference between HEV and CV during driving is that an HEV has zero fuel consumption when ICE-off, while a CV remains in ICE-on.Figure 3
VSP distribution of HEVs and CVs. (a) The real-world driving data of HEVs, (b) the real-world driving data of CVs.
(a)(b)Since HEVs do not consume fuel in ICE-off mode, as distinct from CVs, we compared the difference in fuel consumption between these two vehicle types in ICE-on mode only and calculated the proportions of FCR in each VSP bin (FCR values are accurate to 0.1 mL/s). The results show two distinct peaks in the different FCR distributions for HEVs, reflecting the two ICE-on modes of operation, as shown in Figure4. In the ICE-on mode 1, the ICE is kept at the most economical charging efficiency to charge the battery and the FCR of HEVs is relatively stable. In the ICE-on mode 2, the ICE of HEVs provides the vehicle driving power and the FCR and VSP are clearly related. It is generally apparent that VSP bin <0 is associated with ICE-on mode 1 and in ICE-on mode 2 when VSP Bin ≥6.Figure 4
Instantaneous fuel consumption distribution of CVs and HEVs. (a) The FCR of HEVs, (b) the FCR of CVs.
(a)(b)It has been studied that the VSP-based fuel consumption distribution of CVs follows the Gaussian distribution [27], while this property was verified in this study. For hybrid vehicles, the different ICE-on operating modes are determined by the vehicle energy management strategy, and it is not possible to further distinguish whether the ICE-on state is mode 1 or mode 2 based on VSP and engine speed. Therefore, we developed the Gaussian mixture model (GMM) to fit the FCR probability distribution curve in ICE-on for hybrid vehicles:(7)yk=y0+Ak2πσkexp−x−μk22σk2.We selected the range from VSP bin = 0 to VSP bin = 6 for fitting, as these were closely associated with ICE-on mode 1 and mode 2. The probability distribution curve fitting achieved good results, with the CV and HEV fitting parametersR2 exceeding 0.86 and 0.96, respectively, as shown in Tables 1 and 2.Table 1
Gaussian distribution fitting parameters for CV VSP bins.
VSP bin012345y00.010.010.010.010.010.01μ10.290.420.540.650.760.86σ10.270.450.670.810.900.98α10.790.650.720.730.720.72R20.950.870.860.880.890.90Table 2
Gaussian distribution fitting parameters for HEV VSP bins.
VSP bin012345y00.010.010.010.000.010.01μ10.260.250.240.240.250.26σ10.170.200.220.220.220.21α10.300.290.280.200.130.08μ20.640.830.920.991.041.09σ20.230.520.560.580.580.60α20.540.380.590.690.740.76R20.980.960.970.980.980.98For CVs, the relationship between instantaneous fuel consumption and VSP is clear. The distribution of instantaneous fuel consumption rate in each VSP bin follows a Gaussian distribution, and the parameterFCk used to calculate the FCRi,j has a positive linear relationship with the VSP bin, as shown in Figure 5. In addition, there is only one ICE-on mode for CVs, while there are two for HEVs. We fit the distribution of instantaneous FCR in each VSP bin during each HEV ICE-on mode. The results show that the instantaneous fuel consumption distribution of both HEV and ICE-on operating modes conforms to a Gaussian distribution. As VSP increases, the proportion of HEV ICE-on mode 2 increases (green shading in Figure 6), while the proportion of HEV ICE-on operating mode 1 decreases (orange shading in Figure 6). The instantaneous fuel consumption of ICE-on Mode 1 is stable in the range of 0.1–0.4 ml/s, while the instantaneous fuel consumption associated with ICE-on mode 2 exceeds 0.5 ml/s. These thresholds were used to calculate the FCRi,j in HEV ICE-on, and obtain the FCRi,j of ICE-on mode 1 and mode 2 for HEVs.Figure 5
Distribution of CV fuel consumption rate in each VSP bin.Figure 6
Distribution of HEV fuel consumption rates in each VSP bin.


## 4.2. Development of the VSP Distribution and FCR

In order to measure the accurate fuel consumption factor for different speed ranges of HEVs, three methods were chosen to establish the VSP distribution and calculate the corresponding FCR. The VSP distribution was developed based on more than 14 million seconds of HEV activity data categorized into average speed bins, with each speed bins containing over 30,000 seconds of data, and each second of data were differentiated by ICE operating mode, as shown in Figure7. The results show that the proportion of ICE-Off operating mode decreases as the average travel speed increases, while the proportions of ICE-On operating mode and mode1 are consistent with the average speed change, both increasing with the growth of the average travel speed. The mode 2 is almost unaffected by the average speed change, and its occurrence is closely related to the remaining battery charge.Figure 7
Data size in HEV ICE operating mode.The VSP distributions of hybrid vehicles are shown for average speeds at 20, 40, 60, and 80 km/h, as shown in Figure8. Each method is based on the different ICE operating modes for hybrid vehicles: (1) Method 1: As with the CV measurement method, the VSP distribution for HEVs utilizes only speed and acceleration parameters, and the average FCR is calculated without considering the ICE operating mode. (2) Method 2: The VSP distribution is developed by introducing the ICE speed for HEVs, considering the ICE-on and off, while calculating the ICE-on is the corresponding FCR for fuel consumption factor calculation. (3) Method 3: Following from method 2, the two ICE-on operating modes of hybrid vehicles are considered, and the VSP distribution is developed by fitting the probabilities relationship between the two ICE-on modes in each VSP bin. The FCR corresponding to ICE-on mode 1 (0.1–0.4 ml/s) and mode 2 (exceed 0.5 ml/s) in each VSP bin are also calculated for fuel consumption factor calculation.Figure 8
The HEV VSP distribution with method 1 through 3.The average FCR was additionally calculated for each HEV ICE mode, as shown in Figure9. The FCR of each mode corresponds to the VSP distribution developed by the different methods. The average FCR used for method 1 includes the ICE-on and off modes in each VSP bin; the average FCR calculated in method 2 is for the ICE-on mode; and the average FCR used for method 3 is for the ICE-on operating mode 1 (0.1–0.4 ml/s) and mode 2 (exceed 0.5 ml/s), respectively. The specific average FCR used depends on whether the VSP value depends upon the HEV ICE operating mode and calculates the fuel consumption factor of HEVs according to (6).Figure 9
The FCR in different HEV ICE modes.


## 4.3. Method Validation and Error Analysis

Three different methods were implemented to develop speed-specific VSP distributions for HEVs. The fuel consumption factors generated by these methods were compared with the real-world observations, and the relative errors were calculated, as shown in Figure10. The average relative errors associated with methods 1 through 3 were 7.1%, 2.2%, and 5.9%, respectively. The fuel consumption factor obtained from method 2 (ICE-on) best approximates the observed values. The HEV consumption factor obtained with the same method as the CV (Method 1) decreased with increasing average speed, producing a large relative error, especially in the low-speed range (below 30 km/h) and high-speed range (over 50 km/h). This method fails to estimate the fuel consumption characteristics of HEVs at different average speeds. Additionally, the estimates produced by method 3, which considers two separate HEV ICE-on operating modes are not satisfactory. The proportion of the two modes is idealized and fixed during the process of developing the VSP distribution; however, the proportion of the two modes in the real-world driving conditions is more complicated. These mischaracterizations introduce a greater estimation error than seen in the simpler method 2, which only considers ICE-on and ICE-off. In summary, these results indicate that HEV fuel consumption modeling is more accurate when information on ICE-on or ICE-off mode is incorporated when developing the speed specific VSP distribution, as opposed to modeling using an analogous process to CVs. However, adding model complexity to an attempt to characterize ICE-on variability may introduce problematic uncertainty.Figure 10
Comparison of validation and error in three methods. (a) Fuel consumption in speed bin, (b) relative error in speed bin.
(a)(b)


## 5. Conclusions

This paper presents a fuel consumption factor evaluation method for hybrid vehicles. The VSP distribution developed herein can be used to characterize the driving condition and ICE operating characteristics for hybrid vehicles in real-world traffic conditions, to obtain the fuel consumption factor across a range of average speed intervals. This approach was utilized to determine the differences in fuel consumption caused by different ICE operating modes between HEVs and CVs. In this study, we developed VSP distributions that consider the operation modes of ICE-on and ICE-off, vehicle driving trajectory, and OBD data from real-world data obtained in Beijing. This information was used to quantify the fuel consumption factors of hybrid vehicles driving at variable speeds, while reflecting the fuel consumption changes in different traffic conditions. This method produced more accurate estimates than applying standard calculation methods for CVs to HEVs.The results of this study confirm that the inclusion of HEVs into a standard CV model structure to quantify fuel consumption in the traffic road network results in large errors, typically underestimating fuel consumption for HEVs driving at average speeds above 60 km/h and overestimating fuel consumption when driving at average speeds below 30 km/h. The method proposed in this paper reduces this error, and the average relative error of fuel consumption measurement in each speed bin is 2.2%.This study enhances the functionality and applicability of the VSP theory-based traffic energy consumption model for hybrid vehicles, avoiding the complexity and uncertainty of road network energy consumption measurement when the original method is used for hybrid vehicles. Furthermore, research will explore the fuel consumption factor of hybrid vehicles for different road types (including expressways and nonexpressways) and apply the model to the traffic network energy consumption evaluation system. These efforts will contribute to the important goals of assessing energy efficiency, reducing motor vehicle emissions, and effectively formulating traffic control measures to improve fuel economy [26, 27].



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*Source: 9016510-2023-02-27.xml*