For guaranteeing the performance and safety of battery systems, a valid fault diagnostic method is quite essential. This article presents a systematic fault diagnostic scheme based on a hybrid system for the typical faults of lithium-ion battery packs, including sensor faults and relay faults. The automata are established based on a hybrid system theory to simultaneously capture the continuous dynamics and discrete dynamics of the battery pack. The distributed diagnostic structure is adopted to avoid constructing the global model of the battery pack so that the computation burden can be reduced. A dual extended Kalman filter algorithm is developed to estimate the parameters, terminal voltages, and state of charges (SOC) of each cell in the battery pack. The current, terminal voltage residual, and SOC residual are used to implement the distinguishability analysis. The diagnostic scheme is implemented based on both the observation of the events and the distinguishability analysis of the continuous dynamics. The performance of the diagnostic method is validated for the battery pack that contains two batteries in series connection and two branches in parallel connection through the Federal Urban Driving Schedule driving cycle.

中文翻译：

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基于混合系统和双重扩展卡尔曼滤波算法的锂离子电池组故障诊断

为了保证电池系统的性能和安全性，有效的故障诊断方法非常重要。本文针对锂离子电池组的典型故障（包括传感器故障和继电器故障）提出了一种基于混合系统的系统故障诊断方案。基于混合系统理论建立自动机，以同时捕获电池组的连续动态和离散动态。采用分布式诊断结构，避免构造电池组的全局模型，从而减轻了计算负担。开发了双扩展卡尔曼滤波器算法，以估计电池组中每个电池的参数，端电压和电荷状态（SOC）。电流，端子电压残留，利用SOC残差和SOC残差进行区分性分析。诊断方案是基于事件的观察和连续动态的可分辨性分析来实现的。对于通过联邦城市驾驶计划行驶周期包含两个串联连接的电池和两个并联连接的电池组的电池组，该诊断方法的性能得到了验证。