Ooi Chia Ai, Khan Neha, Desa Mohd Khairunaz Bin Mat, Ishak Mohamad Khairi, Ammar Khalid
School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Engineering campus, Nibong Tebal, Penang, 14300, Penang, Malaysia.
Department of Electrical and Computer Engineering, College of Engineering and Information Technology, Ajman University, Ajman, United Arab Emirates.
Sci Rep. 2025 May 6;15(1):15764. doi: 10.1038/s41598-025-96581-8.
In series and parallel strings connected Lithium-ion (Li-ion) battery modules or packs, it is essential to equalise each Li-ion cell to enhance the power delivery performance and usable capacity, otherwise, it is restricted by the worst cell in the string. An active cell balancing algorithm based on Charging State-of-Power (CSoP) and Discharging State-of-Power (DSoP) derived from the dynamically estimated State-of-Charge (SoC) or State-of-Health (SoH) is proposed to handle the problem of cell imbalance during both charging and discharging operation. Compared with the voltage-based and SoC-based cell equalization algorithms, the proposed algorithm determines cell imbalance using State-of-Power (SoP) invariance among cells in the battery pack, which allows the Battery Management System (BMS) to regulate the power flow of the Electric Vehicle (EV) with minimum balancing efforts and fully charge/discharge each cell in the battery pack. This ensures the better performance of the proposed cell balancing as compared to other (Voltage/SoC-based) balancing in maximizing the battery pack capacity and minimizing balancing losses. To validate the efficacy of the novel SoP-based cell equalization algorithm, a simulation is conducted in which a Li-ion battery model is built in MATLAB/Simulink platform. The simulation results show that the usable capacity using the proposed SoP-based method is improved by 16% as compared to the usable capacity of the battery pack without-balancing. An experimental setup using four Li-ion cells is also executed to explore the stability, robustness, and precision of the proposed cell balancing algorithm. The parameters of cells differ in capacity and initial SoC from each other to resemble the imbalance among the cells in the battery pack.
在串联和并联连接的锂离子（Li-ion）电池模块或电池组中，均衡每个锂离子电池以提高功率传输性能和可用容量至关重要，否则，它会受到电池组中最差电池的限制。提出了一种基于从动态估计的充电状态（SoC）或健康状态（SoH）导出的充电功率状态（CSoP）和放电功率状态（DSoP）的有源电池均衡算法，以解决充电和放电操作期间的电池不平衡问题。与基于电压和基于SoC的电池均衡算法相比，该算法利用电池组中各电池之间的功率状态（SoP）不变性来确定电池不平衡，这使得电池管理系统（BMS）能够以最小的均衡努力调节电动汽车（EV）的功率流，并对电池组中的每个电池进行完全充电/放电。与其他（基于电压/SoC）的均衡相比，这确保了所提出的电池均衡在最大化电池组容量和最小化均衡损耗方面具有更好的性能。为了验证基于SoP的新型电池均衡算法的有效性，在MATLAB/Simulink平台上建立了锂离子电池模型进行仿真。仿真结果表明，与未进行均衡的电池组可用容量相比，采用所提出的基于SoP的方法时可用容量提高了16%。还进行了一个使用四个锂离子电池的实验装置，以探索所提出的电池均衡算法的稳定性、鲁棒性和精度。各电池的参数在容量和初始SoC方面彼此不同，以模拟电池组中电池之间的不平衡。
