A novel multiobjective charging optimization method of power lithium‐ion batteries based on charging time and temperature rise. (26th July 2019) - Sun, Jinlei; Ma, Qian; Liu, Ruihang; Wang, Tianru; Tang, Chuanyu
Summary: Power lithium‐ion batteries have been widely utilized in energy storage system and electric vehicles, because these batteries are characterized by high energy density and power density, long cycle life, and low self‐discharge rate. However, battery charging always takes a long time, and the high current rate inevitably causes great temperature rises, which is the bottleneck for practical applications. This paper presents a multiobjective charging optimization strategy for power lithium‐ion battery multistage charging. The Pareto front is obtained using multiobjective particle swarm optimization (MOPSO) method, and the optimal solution is selected using technique for order of preference by similarity to ideal solution (TOPSIS) method. This strategy aims to achieve fast charging with a relatively low temperature rise. The MOPSO algorithm searches the potential feasible solutions that satisfy two objectives, and the TOPSIS method determines the optimal solution. The one‐order resistor‐capacitor (RC) equivalent circuit model is utilized to describe the model parameter variation with different current rates and state of charges (SOCs) as well as temperature rises during charging. And battery temperature variations are estimated using thermal model. Then a PSO‐based multiobjective optimization method for power lithium‐ion battery multistage charging is proposed to balance charging speed and temperature rise, and the best charging stage currents are obtained using the TOPSIS method. Finally, the optimal results are experimentally verified with a power lithium‐ion battery, and fast charging is achieved within 1534 s with a 4.1°C temperature rise. Abstract : A multiobjective charging optimization strategy for power lithium‐ion battery multistage charging is presented. This strategy aims to achieve a balance between charging time and temperature rise while fulfilling the maximum charging current limitation. The Pareto front is obtained using Multi‐Objective Particle Swarm Optimization (MOPSO) method and the optimal solution is selected using Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method.