Prognostics of Lithium-ion Batteries Based on IMM-UPF
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摘要:
提出了一种基于交互式多模型（Interacting Multiple Model，IMM）和无迹粒子滤波算法（Unscented Particle Filter，UPF）的锂电池健康状态（State of Health，SOH）估计方法，针对目前SOH估计方法需求样本量大、不适用于全寿命周期结果跟踪等问题，建立了基于多项式模型、双指数模型和集成模型的IMM，通过UPF解决了重采样过程中粒子贫化的问题，根据滤波的结果对锂电池的SOH进行预测，实现了锂电池全寿命周期内的SOH精确估计. 讨论了IMM的选型依据和建模方法，给出了详细的SOH估计算法，并通过仿真和实验对不同模型进行对比. 仿真和实验结果表明，所提出的基于IMM-UPF的锂电池SOH估计结果的概率密度函数标准偏差仅为19，实现了高估计精度.
Abstract:
Aiming at the problem that the current SOH estimation method requires a large sample size and is not suitable for tracking the results of the whole life cycle，this paper proposed a lithium battery health state estimation method based on Interacting Multiple Model（IMM） and Unscented Particle Filter（UPF） algorithm. Through the establishment of IMM model based on polynomial model，double exponential model and integrated model and the use of UPF filter to solve the problem of particle dilution in resampling process，the SOH of lithium battery was predicted according to the results of filter，and the accurate estimation of SOH in the whole life cycle of lithium battery was realized. In this paper，the selection basis and modeling method of IMM were discussed，the detailed SOH estimation algorithm was given，and the different models were compared by simulation and experiment. The simulation and experiment results show that the standard deviation of probability density function of the proposed IMM-UPF based SOH estimation result of lithium battery is only 19，which achieves high estimation accuracy.
