TY - JOUR A2 - Mrugalski, Marcin AU - Zhang, Ming AU - Wang, Kai AU - Zhou，Yan-ting PY - 2020 DA - 2020/01/10 TI -在线使用粒子基于过滤器的锂电池电荷状态估计混合滤波方法SP - 8231243六世- 2020 AB -基于过滤的电荷状态(SOC)估计一般以等效电路模型扩展到锂离子电池(锂)电动汽车(EV)或类似的energy storage applications. During the last several decades, different implementations of online parameter identification such as Kalman filters have been presented in literature. However, if the system is a moving EV during rapid acceleration or regenerative braking or when using heating or air conditioning, most of the existing works suffer from poor prediction of state and state estimation error covariance, leading to the problem of accuracy degeneracy of the algorithm. On this account, this paper presents a particle filter-based hybrid filtering method particularly for SOC estimation of Li-ion cells in EVs. A sampling importance resampling particle filter is used in combination with a standard Kalman filter and an unscented Kalman filter as a proposal distribution for the particle filter to be made much faster and more accurate. Test results show that the error on the state estimate is less than 0.8% despite additive current measurement noise with 0.05 A deviation. SN - 1076-2787 UR - https://doi.org/10.1155/2020/8231243 DO - 10.1155/2020/8231243 JF - Complexity PB - Hindawi KW - ER -