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Sequential Monte Carlo based technique for SOC estimation of LiFePO4 battery pack for electric vehicles

机译:基于序列蒙特卡罗的电动汽车LiFePO4电池组SOC估算技术

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Battery energy storage management for electric vehicles and hybrid electric vehicles is the most critical enabling technology. A battery system is a complex electrochemical phenomenon whose performance degrades with ageing and the existence of varying material design. Moreover, it is very tedious and computationally very complex for monitoring and controlling the internal state of battery's electrochemical systems. This paper presents a particle filtering method for state of charge estimation of LiFePO4 battery. Using different open circuit voltages the state of charge is estimated with respect to the estimation accuracy and convergence. It was demonstrated that particle filter can be utilized to estimate the SOC for Li ion battery.
机译:电动汽车和混合动力汽车的电池能量存储管理是最关键的使能技术。电池系统是一种复杂的电化学现象,其性能会随着老化和材料设计的变化而降低。而且,监视和控制电池电化学系统的内部状态非常繁琐并且计算非常复杂。本文提出了一种用于LiFePO4电池充电状态估计的粒子滤波方法。使用不同的开路电压,就估计精度和收敛性估计了电荷状态。结果表明,可以利用粒子滤波器估算锂离子电池的SOC。

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