Dual-layer multi-mode energy management optimization strategy for electric vehicle hybrid energy storage systems

Abstract

Hybrid energy storage systems (HESSs) play a crucial role in enhancing the performance of electric vehicles (EVs). However, existing energy management optimization strategies (EMOS) have limitations in terms of ensuring an accurate and timely power supply from HESSs to EVs, leading to increased power loss and shortened battery lifespan. To ensure an accurate and timely power supply from HESSs to EVs, this paper proposes a dual-layer multi-mode (DLMM) EMOS. This strategy comprises two layers. The upper layer is a backpropagation neural network (BPNN) model enhanced by the particle swarm optimization (PSO) algorithm. It is used for real-time HESS power demand prediction. In the lower layer, a HESS operational mode determination process is formulated, and an objective optimization function is established based on HESS power loss. Under constraints designed according to the HESS state parameters, the PSO algorithm is utilized to search for the optimal power allocation ratio of the HESS in real time. The proposed DLMM-EMOS strategy is capable of providing optimal power reference values for the batteries and ultracapacitors of the HESS. The DLMM-EMOS is tested on an electrical experimental platform using US06, NEDC, and WLTP driving cycles. Results indicate that the DLMM-EMOS effectively reduces the HESS power loss while enhancing the driving range of the battery.