Hybrid energy storage system for intelligent electric vehicles incorporating improved PSO algorithm

Abstract

Existing energy storage system is difficult to balance the energy distribution and dynamic response efficiency issues of lithium-ion batteries and supercapacitor, resulting in low energy utilization. Therefore, the study proposes a hybrid energy storage system for intelligent electric vehicles incorporating improved particle swarm optimization. The study analyzes the relationship between vehicle driving speed and power demand through equivalent model, constructs an objective function containing power demand and state of charge, and uses an improved algorithm for optimization and solution. The performance test results indicated that the proposed improved algorithm exhibited the fastest convergence speed by rapidly decreasing the objective function value and approximating the optimal solution within the first 20 iterations in both single-peak and multi-peak functions. The simulation experiments were validated under urban working conditions and highway working conditions, respectively. The results indicated that the energy efficiency in both working conditions was improved to 92.5% and 94.9%, respectively. In addition, good results were achieved in the contribution of supercapacitor, which were 27.2% and 29.6%, respectively. In the test results based on HIL environment, the system proposed by the research institute can also maintain energy efficiency of over 80% under extreme conditions. The findings support the optimal design of intelligent electric vehicle energy storage systems both theoretically and practically, showing that the study’s revised algorithm performs well in both energy allocation efficiency and dynamic response performance.