Meta-Learning-Based Predictive Energy Management for 4WD Battery Electric Vehicles

The predictive energy management (PEM) of four-wheel drive (4WD) battery electric vehicles (BEVs) requires high accuracy in the driving information forecast of velocity and road gradient. In this article, a meta-learning-based driving information prediction (DIP) method is proposed aiming to improve the performances of real-time PEM of 4WD-BEVs with a dual-electric machine (EM) driving system in each wheel. The proposed meta-learning-based DIP method is demonstrated to have higher accuracy, better robustness and faster computation speed by testing on collected real-world driving cycles. Then, a novel PEM method is proposed based on the model predictive controller (MPC), the reference projection of which is generated by the proposed meta-learning predictor. Our PEM strategy applies dynamic programming (DP) to solve the optimal control problem. The practicability of the proposed PEM method is analyzed in regard to the model accuracy, robustness to disturbances, generalization ability and real-time applicability. The proposed PEM method takes both the power distribution between the front and rear axles and that between two motors in each wheel into consideration. The simulation results reveal that the state-of-charge (SOC) declination of the proposed PEM method is better than the benchmark by 2.66% to 3.64%.