Development of Deep Neural Network-Decision Tree Hybrid Control Strategy for Regenerative Braking in Electric Vehicles

Abstract

Optimizing regenerative braking in dual-motor electric vehicles (EVs) is critical for extending driving range but presents a complex high-speed control problem. This study proposes a novel, real-time control strategy by training a hybrid deep neural network-decision tree (DNN-DT) model on an optimal dataset generated by offline dynamic programming (DP) considering seven key characteristic variables: road grade, friction coefficient, vehicle load distribution, velocity, braking rate, battery state of charge, and total braking torque. This hybrid methodology combines the high-accuracy, non-linear mapping of DNNs with the interpretability of DTs. The model was validated in a 14-DOF Simulink environment against two reference strategies (fixed-ratio and baseline) across four different scenarios (UDDS, NYCC, WLTP), including interpolation and extrapolation tests. Key experimental results show the hybrid model accurately tracks the DP-optimal torques (average ) and consistently outperforms the reference methods, achieving a 1.26% to 5.06% reduction in net SOC loss. This energy saving translates to a practical gain of 90-383 meters per cycle. Crucially, the model's average inference time of 2.3 ms confirms its computational efficiency and feasibility for real-time implementation on a standard vehicle control unit (VCU).