Lithium-ion batteries have become the primary electrical energy storage device in commercial and industrial applications due to their high energy/power density, high reliability, and long service life. It is essential to estimate the state of health (SOH) of batteries to ensure safety, optimize better energy efficiency and enhance the battery life-cycle management. This paper presents a comprehensive review of SOH estimation methods, including experimental approaches, model-based methods, and machine learning algorithms. A critical and in-depth analysis of the advantages and limitations of each method is presented. The various techniques are systematically classified and compared for the purpose of facilitating understanding and further research. Furthermore, the paper emphasizes the prospect of using a knowledge graph-based framework for battery data management, multi-model fusion, and cooperative edge-cloud platform for intelligent battery management systems (BMS).

In the operation process of a power battery pack, the inconsistency among lithium-ion cells may seriously restrict the pack’s capacity, power capability and lifetime, which may bring hidden danger to the use of electric vehicles. Equalization management systems (EMSs) are crucial to alleviate such inter-cell inconsistency, whose performance such as accuracy and stability, mainly depends on the setting of equalization control strategies. This paper proposes an equalization strategy aimed at throughput maximization of series battery in the whole life cycle based on Model Prediction Control (MPC). In this paper, a Mean-plus-difference model (M+D) model is selected as the series battery model and the parameters are identified by Recursive Least Squares (RLS). Based on the model predictive control theory, the control model of series battery pack is established and the objective function of maximizing the throughput in the whole life cycle is derived. At the end of the paper, the simulation results show that the proposed equalization strategy can achieve greater life cycle throughput compared with the traditional SOC equalization strategy, which verifies the guiding significance of the equilibrium strategy proposed in this paper.