Optimal Adaptive Fractional Order Integral Sliding Mode Controller‐Energy Management Strategy for Electric Vehicles Based on Bald Eagle Search Algorithm

Abstract

This research presents an optimal energy management system (EMS) for a lithium‐ion battery‐supercapacitor hybrid storage system used to power an electric vehicle. The storage systems are connected in parallel to the DC bus by bidirectional DC‐DC converters and feed a synchronous reluctance motor through an inverter. The proposed energy management strategy is built on the idea to take full benefits of two combined methods: the bald eagle search algorithm and fractional order integral sliding mode control. To evaluate the effectiveness of the suggested optimal energy management strategy, an urban dynamometer driving schedule (UDDS) driving cycle is considered. The obtained results are compared to a classical fractional order integral sliding mode control‐based energy management strategy in terms of voltage ripples, overshoots, and battery final state of charge. The ultimate results approve the ability of the proposed energy management system to enhance the power quality and enhance battery power consumption at the same time. Comprehensive processor‐in‐the‐loop (PIL) cosimulations were conducted on the electric vehicle using the C2000 launchxl‐f28379d digital signal processing (DSP) board to assess the practicability and effectiveness of the proposed EMS.