Lithium (Li)-ion batteries have become the mainstream energy storage solution for many applications, such as electric vehicles (EVs) and smart grids. However, various faults in a Li-ion battery system (LIBS) can potentially cause performance degradation and severe safety issues. Developing advanced fault diagnosis technologies is becoming increasingly critical for the safe operation of LIBS. This article provides a comprehensive review of the mechanisms, features, and diagnosis of various faults in LIBSs, including internal battery faults, sensor faults, and actuator faults. Future trends in the development of fault diagnosis technologies for a safer battery system are presented and discussed.

State of health (SOH) is a key parameter to assess lithium-ion battery feasibility for secondary usage applications. SOH estimation based on machine learning has attracted great attention in recent years, and holds potentials for battery informatization and cloud battery management techniques. In this paper, a comprehensive study of the data-driven SOH estimation methods is conducted. A new classification for health indicators (HIs) is proposed where the HIs are divided into the measured variables and calculated variables. To illustrate the significance of data preprocessing, four noise reduction methods are assessed in the HIs extraction process; different feature selection methods, including filter-based method, wrapper-based method, and fusion-based method, are applied to select HIs subsets. The four widely used machine learning algorithms, including artificial neural network, support vector machine, relevance vector machine, and Gaussian process regression, are applied and compared. In order to evaluate the estimation performance in potential real usages under future big data era, the three HIs selection methods and four machine learning methods are evaluated using three public data sets and two estimation strategies. The results show that the combination of the fusion-based selection method and Gaussian process regression has an overall superior estimation performance in terms of both accuracy and computational efficiency.

This paper describes the experimental dataset of lithium-ion battery cells subjected to a typical electric vehicle discharge profile and periodically characterized through diagnostic tests. Data were collected at the Stanford Energy Control Laboratory, at Stanford University. The INR21700-M50T battery cells with graphite/silicon anode and Nickel-Manganese-Cobalt cathode were tested over a period of 23 months according to the Urban Dynamometer Driving Schedule (UDDS) discharge driving profile and the Constant Current (CC)-Constant Voltage (CV) charging protocol designed at different charging rates – ranging from C/4 to 3C. Ten (10) cells are tested in a temperature-controlled environment (23${}^{\circ }$C). A periodic assessment of battery degradation during life testing is accomplished via Reference Performance Tests (RPTs) comprising of capacity, Hybrid Pulse Power Characterization (HPPC), and Electrochemical Impedance Spectroscopy (EIS) tests. The dataset allows for the characterization of battery aging under real-driving scenarios, enabling the development of models and management strategies in electric vehicle applications.