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 tyre-road friction. Over the past 30 years, academic and industrial research in traction controllers has mainly favoured deterministic approaches. This paper introduces a traction control strategy based on a deep reinforcement learning agent tailored for straight-line acceleration manoeuvres 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, and evaluates their response to changes in vehicle mass, powertrain parameters and tyre-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.