The lithium-ion batteries retired from electric vehicles (EVs) and hybrid EVs have been exponentially utilized in battery energy storage systems (BESSs) for 2nd use due to their economic and environmentally friendly benefits. Therefore, research on their aging mechanism and state of health (SOH) has attracted increasing amounts of attention across the world. However, few studies focus on optimizing the economic operation of BESSs that are built by retired batteries with various SOHs. This paper proposes an economic operation optimization method for BESSs comprised of retired batteries with different SOHs, which provides a way for the BESS to operate with new and retired battery systems (BSs) together. An operation cost model is put forward that considers the cost increase caused by aging. This method aims to minimize the operating cost in a time step based on the particle swarm optimization method. To validate the feasibility of the economic operation optimization method, a case was studied using a BESS consisting of four BSs with different SOHs under peak load shifting. Compared with the traditional method, which allocated power according to the available peak power of each BS, the proposed method has advantages in the scheduled number and cost.

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.

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