Development of Deep Reinforcement Learning Traction Controllers for Front and Rear Wheel Drive Electrified Vehicles

Traction control plays a key role in improving vehicle safety, especially for driving scenarios involving low levels of tire-road friction. Over the past 30 years, academic and industrial research in traction controllers has mainly favored deterministic approaches. This paper introduces a traction control strategy based on a deep reinforcement learning agent tailored for straight-line acceleration maneuvers from standstill in low-friction conditions. The proposed agent is trained on two different electric vehicles, a front-wheel drive city car (from EU vehicle segment A), and a rear-wheel drive sedan (from EU vehicle segment D). The paper presents a deep reinforcement learning agent formulation suitable for training on different vehicles, assesses the performance of the resulting controllers in comparison with a benchmarking integral sliding mode controller, and evaluates their response to changes in vehicle mass, powertrain parameters and tire-road friction conditions. The assessment uses a high-fidelity co-simulation model, combining AVL VSM and Simulink, developed as part of the Horizon Europe project EM-TECH. Results highlight the capability of the deep reinforcement learning agent to create traction controllers for the different vehicle configurations by only changing the weights of a single term of the reward function.