Optimal Energy Management Strategy Based on Driving Pattern Recognition for a Dual-Motor Dual-Source Electric Vehicle

This article introduces a novel approach in electric vehicle technology by combining dual-motor coupling with a hybrid energy storage system (HESS) using batteries and supercapacitors. This innovation enhances vehicle performance and prolongs battery life. An energy management strategy (EMS) based on Pontryagin's minimum principle (PMP) is used to optimize power distribution within the HESS. To improve PMP performance, the proposal integrates driving pattern recognition (DPR) and co-state variable ($\lambda $) control. DPR employs an adaptive network-based fuzzy inference system (ANFIS) for real-time pattern recognition. The process involves creating a sample driving cycle, employing subtractive clustering to establish the original fuzzy inference system (FIS), and fine-tuning FIS parameters through neural network training. $\lambda $ values are updated based on recognition results to adapt control actions for various driving styles. Real-time simulations on Opal-RT reveal significant improvements compared to EMS without DPR. Battery current root mean square and standard deviation decrease by 11.4% and 29.4%, respectively, during the unknown in advance Federal Test Procedure (FTP) cycle. This adaptable DPR method offers versatility for various EMSs and clarifies the impact of disturbances like supercapacitor size, state of charge variations, and off-road conditions on HESS performance, aiding researchers in designing more efficient systems.