Dual-Motor-BEV Controls Optimization

In this paper we will discuss different techniques of controls optimization for a dual motor BEV model and look at the most optimal solution with an objective to minimize motor losses or maximize efficiency. In a dual motor model, torque split ratio plays a vital role in deciding the operating points for each motor and hence the losses or inefficiency in the system. It is important to find the optimal torque split ratio to improve range and performance. Three techniques covered in this paper include ECMS (Equivalent Consumption Minimization Strategy, DOE (Design of Experiment) and Empirical relation-based approach. ECMS is a local optimization technique. A combined ECMS and DOE based approach was performed to analyze with an eventual goal of creating a map which relates TSR (Torque Split Ratio) to the operating points in the form of vehicle acceleration and vehicle speed. A quadratic empirical relation between vehicle acceleration, speed and torque split ratio was set up and coefficients were optimized using genetic algorithm with an objective to minimize motor losses and hence increase range. We will analyze and compare these three techniques on the basis of optimal solution, computational effectiveness and final vehicle range increment as compared to baseline model. In the end we will also briefly discuss how Machine Learning can help enhance the model for controls development and digital twin applications to improve runtime without compromising on the accuracy.