The accurate estimation of the state of charge, the state of health and the prediction of remaining useful life of lithium–ion batteries is an important component of battery management. It is of great significance to prolong battery life and ensure the reliability of the battery system. Many researchers have completed a large amount of work on battery state evaluation and RUL prediction methods and proposed a variety of methods. This paper first introduces the definition of the SOC, the SOH and the existing estimation methods. Then, the definition of RUL is introduced, and the main methods are classified and compared. Finally, the challenges of lithium–ion battery state estimation and RUL prediction are summarized, and the direction for future development is proposed.

The increasing adoption of lithium-ion batteries (LIBs) in low-carbon power systems is driven by their advantages, including long life, low self-discharge, and high-energy density. However, LIB failures degrade performance and cause fire hazards. Effective fault diagnosis is thus critical yet challenging. This article reviews LIB fault mechanisms, features, and methods with object of providing an overview of fault diagnosis techniques, emphasizing feature extraction’s critical role in detection via thresholds and isolation via multilevel strategies, and estimating detection quality. Several research gaps exist in current fault diagnosis techniques. Most techniques assume controlled conditions unlike complex real-world systems. Resource limitations often confine analytics to individual fault types, overlooking comprehensive approaches. Furthermore, many sensors lack the capability to detect precursor abnormalities, hampering early fault detection. To address these challenges, we advocate for advanced multiphysics and multiscale methods that incorporate sound, force, and thermal coupling to enhance fault diagnosis robustness in practical, complex systems. Multidimensional feature selection can prevent oversimplification in fault diagnosis. Overall, integrating analytics, sensing, and physics could enable comprehensive multidomain fault diagnosis under actual operating conditions.

The state of health and remaining useful life of lithium-ion batteries are important indicators to ensure the reliable operation of these batteries. However, because they cannot be directly measured and are affected by many factors, they are difficult to predict. This paper presents method of jointly predicting state of health and RUL based on the long short-term memory neural network and Gaussian process regression. This method extracts the batteries’ health factors from the charging curve, selects health factors with more relevance than the setting standard as the characteristic of capacity by the maximum information coefficient method, and establishes the battery aging and remaining useful life prediction models with Gaussian process regression. On this basis, the long short-term memory neural network is used to predict the trend of the change in health factors with the increase in cycles, and the results are input into a Gaussian process regression aging model to predict the state of health. Taking the health factors and state of health as the characteristics of remaining useful battery life, a battery remaining useful life model based on Gaussian process regression is established, and the change trend in the remaining useful life can be obtained by inputting the predicted health factors and state of health. In this study, four battery data sets with different depths of charge were used to verify the accuracy and adaptability of the algorithm. The results show that the proposed algorithm has high accuracy and reliability.