Torque Distribution Optimization for a Dual-Motor Electric Vehicle Using Adaptive Network-Based Fuzzy Inference System

Abstract

The development of electric vehicles (EVs) has been considered one of the most efficient ways to reduce the carbon footprint of the transportation system. Among battery EV designs, a dual-motor configuration is introduced as a promising solution to improve dynamic performance and energy efficiency. In this study, a novel energy management strategy framework based on an Adaptive Network-based Fuzzy Inference System (ANFIS) is proposed for torque distribution optimization between two different motors. At first, Dynamic Programming (DP) is employed to find global optimization of the torque distribution. After training with the DP-obtained data set, the ANFIS model can execute a torque distribution online. By minimizing the battery energy consumption, the motor torque-speed solution pair is found under representative driving cycles (only three cycles). In addition, the best ANFIS model has been selected using a clustering technique, goodness-of-fit metrics, and sensitivity analysis. This distinguishes the optimization problem in this study from previously published literatures. The simulation results show that over an unknown Urban Dynamometer Driving Schedule (UDDS) cycle, the torque prediction using this ANFIS model achieves 98.3% of the benchmark DP result. As a result, the overall efficiency of the proposed strategy is increased to 73.3%, which is 3.4% higher than that of the rule-based method. Furthermore, the signal hardware-in-the-loop (S-HIL) experiments validate the real-time prediction of the ANFIS-based approach.