Optimal Sizing and Improved Low-Pass Filter Energy Management for Hybrid Energy Storage Electric Vehicles

The global promotion of electric vehicle (EV) usage is a response to the challenges of energy crises and climate change. However, significant drawbacks remain, particularly related to the high costs and limitations of onboard energy storage. To mitigate these issues, the concept of Hybrid Energy Storage Systems (HESS), which integrate batteries with complementary energy storage solutions, has gained attention. This study focuses on the optimal sizing and energy management strategy of HESS, which are critical for enhancing the performance and viability of these systems in EVs. An Improved Low-Pass Filter (ILPF) is introduced to address limitations of conventional low-pass filters (LPF), including phase shift effects and state of charge limitations while optimizing the cut-off frequency and ensuring adaptability to varying driving conditions. Particle Swarm Optimization (PSO) is employed to achieve optimal sizing of the HESS and to develop an efficient energy management strategy (EMS). The performance of the ILPF is validated against a Fuzzy Logic Controller (FLC) and a conventional-LPF across various driving cycles. Simulation results demonstrate that the proposed ILPF significantly enhances performance, achieving up to an 8.345% improvement in city driving conditions compared to battery-only electric vehicles.