For guaranteeing the performance and safety of battery systems, a valid fault diagnostic method is quite essential. This paper presents a systematic fault diagnostic scheme based on hybrid system for the typical faults of lithium-ion battery packs, including sensor faults and relay faults. The automata are established based on 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. Dual extended Kalman filter algorithm is developed to estimate the parameters, terminal voltages and state of charges of each cell in the battery pack. The current, terminal voltage residual and state of charge 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 which contains 2 batteries in series connection and 2 branches in parallel connection through the Federal Urban Driving Schedule driving cycle.

Multifault diagnosis of a battery pack is a difficult issue, especially when different faults are with similar characteristics. This article presents a multifault diagnosis approach based on a hybrid system for diagnosing cell parametric faults, sensor faults, and relay faults in a Li-ion battery pack. The battery pack is defined as a hybrid system with hierarchical and decentralized diagnostic structure in order to reduce model complexity and improve diagnosability. The multifault detection and isolation is achieved by integrating an equivalent circuit model (ECM) into hybrid automata and tracing continuous/discrete dynamics from cell level to module level. By using a parameter identification-based method, cell parametric faults are distinguished and diagnosed from sensor faults in cell level. Sensor faults and relay faults are diagnosed through the hybrid system mode estimation and discrete event system diagnosis method in module level. The results of similar fault isolation test and random multifault injection experiments on a real Li-ion battery pack show that the different faults are detected and isolated in reasonable time with high accuracy.

The reliability of battery fault diagnosis depends on an accurate estimation of the state of charge and battery characterizing parameters. This paper presents a fault diagnosis method based on an adaptive unscented Kalman filter to diagnose the parameter bias faults for a Li-ion battery in real time. The first-order equivalent circuit model and relationship between the open circuit voltage and state of charge are established to describe the characteristics of the Li-ion battery. The parameters in the equivalent circuit model are treated as system state variables to set up a joint state and parameter space equation. The algorithm for fault diagnosis is designed according to the estimated parameters. Two types of fault of the Li-ion battery, including internal ohmic resistance fault and diffusion resistance faults, are studied as a case to validate the effectiveness of the algorithm. The experimental results show that the proposed approach in this paper has effective tracking ability, better estimation accuracy, and reliable diagnosis for Li-ion batteries.

目的 通过对万米深海环境中的高压和低温环境的模拟,研究深海环境对水下潜行器中动力电池性能所产生的影响以及该影响对电池剩余电量(SoC)估计精度的影响。