# Optimal Control of Electric Vehicles in Dynamic Wireless Charging Lanes Using Deep Q-Networks

Ahmed-Ramzi Houalef, Florian Delavernhe, Sidi-Mohamed Senouci, El-Hassane Aglzim

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Ahmed-Ramzi Houalef, Florian Delavernhe, Sidi-Mohamed Senouci, El-Hassane Aglzim. Optimal Control of Electric Vehicles in Dynamic Wireless Charging Lanes Using Deep Q-Networks. 2025 IEEE 102nd Vehicular Technology Conference (VTC2025-Fall), IEEE Vehicular Technology Society, Oct 2025, Chengdu, China. 10.1109/VTC2025-Fall65116.2025.11310467. hal-05094364

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# Optimal Control of Electric Vehicles in Dynamic Wireless Charging Lanes Using Deep Q-Networks

Ahmed-Ramzi Houalef, Florian Delavernhe, Sidi-Mohammed Senouci, El-Hassane Aglzim

Université Bourgogne Europe, DRIVE UR 1859, 58000 Nevers, France, email: {firstname.lastname}@ube.fr

***Abstract***—The increasing adoption of electric vehicles (EVs) presents new challenges in optimizing energy consumption, charging efficiency, and travel time. Dynamic Wireless Charging Lanes (DWCLs) offer a promising solution by allowing EVs to charge while driving, reducing dependency on static charging stations and minimizing downtime. However, optimizing the decision-making process for when and at what speed a vehicle should enter and exit a DWCL while maintaining an efficient driving strategy remains a complex problem. This work proposes a Deep Q-Network (DQN)-based reinforcement learning (RL) approach to dynamically control lane-switching and speed adjustments in a multi-lane road environment. The RL agent is designed to balance energy efficiency, travel time minimization, and charging maximization, while also considering real-world constraints such as road topology, accurate energy consumption modeling, and vehicle alignment with charging coils. By leveraging CARLA as a high-fidelity simulation environment, we implement and train a policy that autonomously decides lane-switching, speed adaptation, and charging strategies. The proposed framework not only improves the charging efficiency of EVs but also ensures smooth lane transitions while maintaining energy-optimal driving behavior. Experimental results show that the RL agent effectively learns the optimal use of DWCL, minimizing unnecessary lane switches and ensuring a highly efficient, and real-time deployable EV management system.

***Index Terms***—electric vehicles, Carla, energy efficiency, reinforcement learning, energy consumption, wireless charging, autonomous driving.

## I. INTRODUCTION

The growing adoption of EVs has increased the demand for efficient charging strategies that minimize downtime and maximize energy efficiency. Conventional static charging stations contribute to range anxiety, long queues, and extended trip durations. To address this, DWCL wirelessly transfers energy to EVs in motion using inductive power transfer. It consists of primary coils embedded in the road and secondary coils in the EV, enabling continuous charging without stops, improving range and efficiency. However, efficient DWCL utilization requires intelligent decision-making for optimal entry, exit, and speed control. Rule-based methods struggle with dynamic traffic and real-time energy demands, whereas RL adapts effectively, enabling scalable deployment. However, optimizing DWCL utilization requires intelligent decision-making for entry, exit, and speed control to ensure seamless integration into real-world traffic conditions. Rule-based approaches struggle to adapt to dynamic traffic patterns and fluctuating energy demands, whereas RL offers a more adaptive and scalable solution [1], enabling real-time optimization and efficient large-scale deployment. In this work, we propose

a RL-based approach using DQN to optimize lane-switching and speed control in a multi-lane road environment. The RL agent learns to decide when to enter and exit the DWCL based on the vehicle's State of Charge (SoC), estimated range, and remaining travel distance, while also adjusting its speed to balance energy efficiency and travel time. We implement the DQN agent in CARLA [2], a high-fidelity autonomous driving simulator, where the agent interacts with its environment, refines its decision-making process, and improves efficiency through continuous training. By integrating real-time vehicle telemetry, energy consumption models, and lane-change strategies, we develop an adaptive framework that enhances the effectiveness of DWCL-based EV transportation. To the best of our knowledge, this is the first study to apply RL for optimizing DWCL decisions in a high-fidelity simulation environment. Unlike previous works that rely on simplified models, our approach leverages advanced RL techniques to improve decision-making accuracy and scalability. The remainder of this paper is structured as follows: Section II discusses related works on DWCL projects and optimization. Section III details environment modeling, including wireless charging model formulation and EV's energy consumption model. Section IV discusses the Markov Decision Process (MDP) and the learning algorithm. Section V presents the simulation results and evaluation metrics and Section VI concludes with insights and future research directions.

## II. RELATED WORK

Several DWCL systems have been tested in real-world pilot projects, showcasing the feasibility of dynamic wireless charging for electric vehicles. Notable early examples include KAIST's Online Electric Vehicle bus in South Korea, which pioneered inductive power transfer to extend vehicle range without stationary charging stops. Another significant demonstration is Qualcomm Halo's Dynamic Electric Vehicle Charging system, which featured a 100-meter test track in France, capable of wirelessly delivering 20 kW of power to vehicles traveling at highway speeds. However, these prototypes revealed efficiency limitations, with trials in France achieving only 60% end-to-end efficiency at 50 km/h. More recently, Electreon deployed DWCL technology on public roads, including a 1.65 km electrified road in Gotland, Sweden, demonstrating increasing industry and governmental interest.

Despite proving the feasibility of in-motion charging, real-world trials highlight efficiency and scalability challenges. Precise coil alignment is critical, as even slight misalignment
reduces power transfer efficiency [3]. Current DWCL systems suffer higher losses than plug-in charging, and achieving near-wired efficiency remains a challenge. Safety and interoperability must also be addressed, requiring vehicle detection mechanisms to activate coils only when necessary. Moreover, large-scale DWCL deployment demands significant infrastructure investment and grid upgrades, limiting current implementations to short test stretches. Academic research has primarily addressed DWCL optimization from the perspective of vehicle decision-making and speed control, rather than focusing exclusively on hardware improvements. Several studies have tackled infrastructure placement as an optimization problem, aiming to identify optimal road segments for electrification to maximize coverage and minimize battery size requirements [4]. At the vehicle level, strategies analyzing the impact of speed on energy transfer efficiency have also been explored; faster speeds decrease charging duration but can reduce energy transfer efficiency, whereas slower speeds improve charging but potentially disrupt traffic flow. For instance, [5] combined Dynamic Wireless Charging with Variable Speed Limit control to improve both efficiency and safety. Additionally, adaptive speed control, often referred to as eco-driving, was proposed in [6] as a solution to balance energy efficiency with traffic fluidity. Due to its adaptability to dynamic environments, RL has been explored in various applications; however, only a few studies have leveraged it in the context of DWCL. For example, [7] proposed an RL-based method to optimize charging decisions, although their approach assumed electric vehicles remained within DWCLs without addressing lane-switching or speed regulation. Most DWCL studies rely on SUMO or MATLAB for simulation. While effective for traffic modeling and optimization, these tools lack realistic vehicle dynamics, sensor models, and physics-based interactions. SUMO efficiently simulates large-scale traffic but oversimplifies energy consumption and real-time decision-making. MATLAB, meanwhile, depends on theoretical frameworks that overlook real-world uncertainties and complex driving behaviors. CARLA, in contrast, provides a high-fidelity simulation environment, incorporating realistic vehicle dynamics, detailed traffic interactions, and accurate energy modeling. This makes it particularly suitable for training RL agents in DWCL scenarios, enabling adaptive decision-making for lane-switching and speed control. While some studies examine network-wide impacts [8], our research takes a foundational approach by first focusing on a single EV interacting with a DWCL. This allows a rigorous analysis of core decision-making logic, including optimal lane entry timing and speed control. Establishing this baseline is crucial for validating RL-based control strategies before expanding to multi-vehicle coordination and traffic interactions.

### III. ENVIRONMENT MODELING

In this section, we discuss how we model the environment and its different key components as it can be seen in Figure 1.

<figure><img src="image_1.png" /><figcaption>Fig. 1. Framework Architecture</figcaption></figure>

#### A. Dynamic Wireless Charging Lanes

DWCLs can be implemented in various layouts [9]: (a) Full Coverage, where the entire road segment is equipped with charging lanes; (b) Partial Flexible, with selectively positioned charging segments; (c) Intersection-focused, optimizing charging near intersections; and (d) Multi-lane, where only one lane is designated for wireless charging. We adopt a multi-lane layout, designating the far-right lane as the DWCL—an optimal choice for highways and suburban roads, enabling low-SoC vehicles to recharge without obstructing traffic flow. Coil placement follows two main approaches [10]: (a) a single large coil spanning the lane or (b) segmented coils, where every three coils are independently powered. We use the segmented approach to minimize electromagnetic emissions by activating coils only when a vehicle is detected. Our environment model is illustrated in Figure 2. The vehicle’s speed varies depending on its location: inside the DWCL, it ranges between 15 and 50 km/h, while outside, it falls between 60 and 90 km/h.

<figure><img src="image_2.png" /><figcaption>Fig. 2. Environment Modeling</figcaption></figure>

#### B. Wireless Power Transfer Modeling

Wireless power transfer efficiency in electric vehicles is highly dependent on coil alignment, coupling efficiency, and charging characteristics. The amount of electrical power transferred to the vehicle, denoted as $P_{ev}$, along with the total energy charged, $E_{ch}$, over a given time interval $\Delta t$ (in seconds), can be expressed as:
$$P_{ev} = P_{ev,max} \cdot \eta_{tr} \cdot \eta_{ch}, \quad E_{ch} = \frac{P_{ev} \cdot \Delta t}{3600} \quad (1)$$

where $P_{ev,max}$ is the maximum transferable power (80 kW in our case), $\eta_{tr}$ is the alignment-dependent transfer efficiency, and $\eta_{ch}$ is battery charging efficiency. The alignment factor ($f_{align}$), combining lateral, longitudinal, and angular misalignments, is defined as:

$$f_{align} = f_{tat} \cdot f_{long} \cdot f_{ang} \quad (2)$$

Individual misalignment factors are computed as:

$$f_{tat} = e^{-(\frac{d_y}{d_{max}})^2}, \quad f_{long} = e^{-(\frac{d_x}{l_{max}})^2} \quad (3)$$

$$f_{ang} = |\cos(\theta_{mis}) + \frac{1}{2} \sin(\theta_{mis})| \quad (4)$$

Here, $d_y$ and $d_x$ represent lateral and longitudinal misalignments, respectively, while $d_{max}$ and $l_{max}$ denote their permissible maximum values. The term $\theta_{mis}$ is the angular misalignment between coils. The coupling coefficient ($k$) and the resulting power transfer efficiency ($\eta_{tr}$) are:

$$k = k_0 \cdot f_{align}, \quad \eta_{tr} = \frac{k^2 Q_1 Q_2}{1 + k^2 Q_1 Q_2} \quad (5)$$

In these equations, $k_0$ represents the nominal coupling coefficient for ideal alignment, and $Q_1, Q_2$ indicate the quality factors of primary and secondary coils, respectively.

### C. Energy Consumption Modeling

For energy consumption, we used a hybrid energy model trained on a Nissan Leaf. This model incorporates critical factors including regenerative braking, road slope, speed, and acceleration. A detailed explanation of the model's training and performance can be found in our previous work [11]. The total energy and (SOC) of the battery are updated as follows:

$$E_{total} = E_{con} - E_{char}, \quad SOC = \left( \frac{E_{total}}{C_{bat}} \right) \times 100 \quad (6)$$

where $E_{con}$ is the energy consumed, $E_{char}$ is the energy recharged by DWCL in kWh, and $C_{bat}$ is the total battery capacity.

## IV. MARKOV DECISION PROCESS FORMULATION

The problem of optimal lane selection and speed control in DWCL is modeled as a MDP:

$$\mathcal{M} = (\mathcal{S}, \mathcal{A}, P, R, \gamma) \quad (7)$$

where $\mathcal{S}$ is the states space, $\mathcal{A}$ the actions space, $P(s'|s, a)$ the transition probability, $R(s, a)$ the reward function, and $\gamma$ the discount factor.

### A. States and Actions Space

The agent's state $s_t$ is:

$$s_t = [\text{SoC}, \text{SoC}_{\text{req}}, \text{ETA}, d_{\text{DWCL}}, \text{lane}, V_{\text{target}}] \quad (8)$$

where: SoC is the battery level, SoC_{req} the required charge to reach the destination, ETA the estimated time of arrival, $d_{DWCL}$ the distance to the end of DWCL (same as distance

to destination in our case), lane the current lane (0: outside DWCL, 1: inside DWCL), and $V_{target}$ the vehicle's target speed.

The discrete action space is:

$$\mathcal{A} = \{a_0, a_1, a_2, a_3, a_4, a_5\} \quad (9)$$

where: $a_0$: Move to DWCL (if outside), $a_1$: Exit DWCL (if inside), $a_2$: Accelerate inside DWCL, $a_3$: Decelerate inside DWCL, $a_4$: Maintain speed inside DWCL, $a_5$: Stay out of DWCL. Furthermore, we opted for Carla built-in auto-driver outside the DWCL.

### B. Reward Function

The reward function balances charging efficiency, speed regulation, lane-switching penalties, and travel time. The total reward is:

$$R(s, a) = r_{\text{charge}} + r_{\text{speed}} + r_{\text{control}} + r_{\text{progress}} + r_{\text{time}} + r_{\text{invalid}} \quad (10)$$

Charging is encouraged when SoC is below the required threshold $SoC_{req}$, but penalized when SoC exceeds 80% inside the DWCL to prevent unnecessary charging. Speed regulation penalizes deviations from the optimal range (15–50 km/h) to balance efficiency and safety inside DWCL. Control rewards maintaining SoC above 20% while penalizing excessive depletion. Progress penalizes frequent lane switching using a quadratic penalty $-\lambda(\text{switches})^2$ and rewards reaching the destination with sufficient SoC. Travel efficiency is encouraged by rewarding trips completed within 120% of the ETA. Finally, invalid actions, such as attempting to enter the DWCL while already in, are penalized to improve decision-making.

### C. Objective Function and Learning Algorithm

RL optimizes an agent's decision-making process by maximizing the expected cumulative discounted reward:

$$J = \mathbb{E} \left[ \sum_{t=0}^{T} \gamma^t R(s_t, a_t) \right] \quad (11)$$

where $\gamma \in (0, 1]$ is the discount factor that prioritizes long-term gains while ensuring short-term responsiveness, and $R(s_t, a_t)$ represents the immediate reward received at time step $t$. To efficiently learn optimal policies, we employ DQN, a neural network-based extension of Q-learning that approximates the Q-function:

$$Q(s, a; \theta) \approx Q^*(s, a) \quad (12)$$

where $\theta$ represents the network parameters. Unlike Q-tables, which are infeasible for continuous state spaces such as SoC, speed, and ETA, DQN enables function approximation, allowing the agent to generalize across unseen states while avoiding the curse of dimensionality. To stabilize learning, we integrate Experience Replay, a technique that stores past transitions $(s, a, r, s')$ in a replay buffer. Instead of updating the network sequentially, the agent samples experiences randomly, which breaks correlations in training data, enhances sample efficiency, and smooths training updates for faster convergence.
Through DQN with Experience Replay, the agent efficiently learns to optimize lane-switching and speed decisions.

## V. SIMULATION & RESULTS

In this section, we discuss the simulation process used to train the model, followed by an analysis of the results.

### A. Training Process

We conducted our simulation in the environment described in section III (Figure 2), consisting of a 600-meter, multi-lane road, where the far-right lane is designated as the DWCL. The agent was trained to optimize its decisions for reaching the destination while maintaining a minimum SoC of 20% as it's important for State of Health (SoH) preservation [12]. To challenge the agent, we initialized the SoC at 19%, forcing it to strategically manage energy consumption and charging behavior. Training spanned approximately 4000 episodes, each terminating either upon reaching the destination or when the travel time exceeded $2 \times ETA$. As shown in Figure 3, the epsilon value, which regulates the exploration-exploitation tradeoff, gradually decays over episodes. This approach allows the agent to explore extensively in early training stages before shifting toward exploitation to refine its policy. The agent's

<figure><img src="image_3.png" /><figcaption>Fig. 3. Epsilon Decay Over Episodes</figcaption></figure>

behavior over episodes is shown in Figure 4, where reward increases while loss decreases. The loss stabilizes after episode 1000, mainly due to reducing the discount factor $\gamma$ from 0.99 to 0.9 at episode 800 to mitigate overestimation bias. Between episodes 3000 and 3500, the agent started avoiding the DWCL, leading to low SoC at arrival. To correct this, we increased the $r_{charge}$ threshold by 20%. at episode 3500, encouraging the agent to maintain at least 20% SoC. This adjustment resulted in a noticeable reward spike. Notably, the number of invalid actions and lane switches decreases over episodes, as shown in Figure 5. This indicates that the agent has learned to minimize unnecessary lane changes. At the beginning of training, the agent performed approximately 20 lane switches per episode, whereas towards the end, it executes only one or two lane switches, which is optimal. A count of two lane switches suggests that the agent enters the DWCL for recharging and exits once it has acquired sufficient energy.

### B. Validation Process

To validate our approach, We compare its performance with a rule-based model that enters the DWCL when the SoC falls below the required SoC plus 20%—ensuring arrival with a minimum of 20% SoC—drives at a reduced speed of 40 km/h, and exits once the threshold is exceeded. The comparison

<figure><img src="image_4.png" /><figcaption>Fig. 4. Reward & Loss Over Episodes</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>Fig. 5. Invalid Actions and Lane Switches Over Episodes</figcaption></figure>

includes two scenarios where the agent starts with an initial SoC of 17.2% and 20.7%, strategically selected to assess the RL agent's adaptability in real-world scenarios. 17.2% represents a critical low-energy case, forcing the agent to prioritize charging decisions efficiently, while 20.7% simulates a near-threshold scenario where unnecessary DWCL usage must be avoided. These values ensure a robust evaluation by testing whether the trained model generalizes beyond the 19% training SoC, highlighting its ability to balance energy sufficiency, charging efficiency, and travel time. We conducted 10 test episodes for both the rule-based model and our trained agent, ensuring a fair evaluation under identical conditions. As shown in Figure 7, our agent successfully reaches the destination in both cases while maintaining a minimum SoC of 20%. In the *first scenario* initial SoC of 17.2%, the RL agent reduced its speed inside the DWCL to maximize energy intake, which is reflected in the total travel time that slightly exceeded the ETA, since our reward was designed to give more importance to SoC preservation. Additionally, it performed only one lane switch, meaning it entered the DWCL and remained there until arrival. In contrast, the rule-based model followed a similar strategy of staying in the DWCL but did not adapt its speed dynamically. Instead, it simply reduced its speed to a fixed 40 km/h, which prevented it from optimizing the charging efficiency. As a result, it did not charge as effectively as the RL agent.
Interestingly, as shown in Figure 6, the RL agent optimizes charging by alternating acceleration and deceleration inside the DWCL. It slows down over charging coils and speeds up over gaps between them. Once sufficiently charged, it maintains a constant speed until arrival. This behavior demonstrates the agent's ability to extract and exploit complex patterns for efficient energy management. In the *second scenario* initial

<figure><img src="image_6.png" /><figcaption>Fig. 6. DQN Agent Speed Inside DWCL</figcaption></figure>

SoC of 20.7%, both models successfully arrived with 20% SoC. However, the RL agent achieved this with a shorter total travel time and less time spent inside the DWCL, thanks to its adaptive speed control. The agent performed only two lane switches—entering the DWCL to recharge for five seconds before exiting. In contrast, the rule-based model repeatedly entered and exited the DWCL, leading to increased travel time and higher overall energy consumption. While the SoC threshold in the rule-based model accounts for future energy needs, it lacks adaptability to real-time conditions such as speed, and charging efficiency. As a result, it reacts rigidly to SoC changes, frequently switching lanes instead of optimizing charging duration and travel time, unlike the RL agent, which dynamically balances these factors.

<figure><img src="image_7.png" /><figcaption>Fig. 7. RL vs Rule Based Approaches Average Performance Comparison</figcaption></figure>

## VI. CONCLUSION

This study introduced a RL-based framework for optimizing lane selection and speed control in DWCLs. The proposed model dynamically adjusts its decisions based on real-time energy demands, striking an optimal balance between energy efficiency and travel time. Empirical results demonstrate that the RL agent effectively learns to navigate DWCLs while ensuring a minimum SoC threshold at the destination.

Furthermore, its ability to generalize to previously unseen initial conditions underscores the robustness of the learned policy. Future research will extend this work by investigating multi-agent RL approaches for cooperative decision-making in DWCL environments. Additionally, integrating real-world driving constraints, such as traffic regulations and stochastic vehicle interactions, along with vehicle-to-infrastructure (V2I) communication, will further enhance the system's applicability and efficiency in real-world scenarios.

## ACKNOWLEDGMENT

This paper is supported by the OPEVA project that has received funding within the Chips Joint Undertaking (Chips JU) from the European Union's Horizon Europe Program and the National Authorities (France, Czechia, Italy, Portugal, Turkey, Switzerland), under grant agreement 101097267. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or Chips JU. Neither the European Union nor the granting authority can be held responsible for them.

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