Optimizing battery and supercapacitor management in electric vehicles: A hybrid approach for enhanced performance and reduced harmonics

Integrating super-capacitors and batteries requires optimizing energy flow, designing robust controllers for different driving conditions, managing voltage ranges and power fluctuations, and thermal management to ensure system longevity and performance. According to studies, hybrid battery and supercapacitor (SCAP) energy management is best for electric vehicles (EVs). The PIDA-PCA-KF-ISGO hybrid technique uses the Proportional Integral Derivative Accelerator (PIDA), Principle Component Analysis with Kernel Function (PCA-KF), and Improved Snow Geese Optimization (ISGO). An online low-frequency battery power demand prediction approach uses a PIDA-PCA-KF algorithm trained using load power need to match hybrid energy storages dataset features. The SCAP receives high-frequency power requirements simultaneously. The ISGO method regulates SCAP voltage within a reasonable range. The method targets battery peak power and power variation. First, the SCAP's reference voltage is derived using real-time load and vehicle dynamics, motor parameters, driving environment, and regenerative braking systems. The approach uses input elements such load current, battery current, and SCAP State of Charge (SOC) to reduce battery power variance and loss. The suggested method is tested in MATLAB against numerous benchmarks. The recommended solution reduces battery current total harmonic distortion (THD) to 10.49 %, compared to 77.39 % by the standard differential flatness strategy and 34.52 % by the PSO strategy.