Novel iterative Ragone plot-based optimization of low pass filter for hybrid power sources electric vehicles

Abstract

The growing demand for eco-friendly transportation has led to the emergence of electric vehicles (EVs). To meet driving requirements and enhance the performance of EVs, researchers combine Energy Storage Systems (ESS) to form Hybrid-ESS (HESS). Efficient power distribution within HESS relies on an effective Energy Management Strategy (EMS). While various EMS methods exist, practical implementation is often hindered by computational complexity. The low-pass filter (LPF) approach is one of the easiest and implementable EMS methods. However, the pivotal challenge lies in determining the optimal cut-off frequency for LPF, a crucial factor influencing energy distribution. Traditional optimization methods, such as Particle Swarm Optimization (PSO) face significant limitations when applied to intricate ESS and DC-DC converter models. Therefore, the iterative method based on the Ragone plot is proposed. Its performance is compared with LPF tuned by PSO. The test results show that the proposed method has a faster process by up to 83.51 % while maintaining comparable performance levels. Compared to battery-only EVs, it is superior in terms of the delta-State of Charge of the battery (delta-SoCB), energy consumption, maximum battery current, and Battery Current Root Means Square (BCRMS). Compared to the Fuzzy Logic Controller (FLC), it outperforms in maximum battery current and BCRMS, with slightly lower delta-SoCB and energy consumption. This highlights the practicality and efficiency of the proposed method, offering a solution for optimizing battery life and power distribution in HESS EVs. Hence advancing the sustainability and performance of eco-friendly transportation systems.

Keywords

• Electric vehicle
• Energy management
• Filter
• Optimization
• Particle swarm optimization