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

Abstract

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).