基于改进粒子滤波的锂离子电池健康状态估计

State of Health Estimation of Lithium-Ion Battery Based on Improved Particle Filter

为了准确估计电池当前健康状态(SOH),提出一种基于改进粒子滤波算法的电池SOH在线评估方法。首先针对传统萤火虫优化算法的不足,提出了一种改进萤火虫算法替代传统粒子滤波的重采样,然后从锂离子电池工作时的可测参数中提取在线健康指标(HI),建立HI与SOH之间的映射模型,并将其应用于状态空间模型的观测。利用马里兰大学先进寿命周期工程中心(CALCE)公布的试验测量数据进行验证,结果表明,该方法对具有非线性和非高斯特性的锂离子电池降解过程的状态估计具有良好的适应性。

The State Of Health(SOH)estimation is one of the key functions of lithium-ion Battery Management System(BMS).In order to accurately estimate the current health state of battery,this paper proposes an improved firefly algorithm and replaces the resampling of the traditional particle filter in view of the shortcomings of the traditional firefly algorithm.And then the measurable parameters of lithium-ion battery are extracted on line health index Hi,the mapping model between Health Index(HI)and battery SOH is established and applied to the observation of state space model.An on-line evaluation method of battery SOH based on improved particle filter algorithm is proposed.Finally,based on a set of experimental data of battery capacity published by the University of Maryland Center for Advanced Life Cycle Engineering(CALCE),it is proved that the method has good adaptability to the degradation process of lithium-ion batteries with nonlinear and non-Gaussian characteristics.