刘新天　张恒　何耀　郑昕昕　曾国建

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.

关键词：锂电池;健康状态;经验模型;交互式多模型;无迹粒子滤波

中图分类号：TM912                               文献标志码：A

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.

Key words：lithium battery;state of health;empirical models;interacting multiple model;unscented particle filter

鋰电池因其具有高能量比、高效率、循环寿命长等显著特点，而成为未来电子市场的首选电源[1]. 与铅酸电池和镍氢电池相比，锂电池因为可高温存储、快速充电、输出功率大且没有记忆效应等优点在车辆和固定式动力系统中得到广泛应用[2].

作为电池管理系统（Battery Management System，BMS）的核心环节之一[3-4]，健康状态（State of Health，SOH）因为能够提供准确的数据，达到延长电池使用寿命的目的，因此在电池使用方面发挥着至关重要的作用. 然而，由于电池使用期间伴随着复杂的物理和化学反应，锂电池的性能在使用一定时间后以非线性的形式恶化[5]，这就给锂电池状态的预测带来了很大的不确定性[6].

目前，锂……