Abstract
Lithium-ion power batteries have been widely used in transportation due to their advantages of long life, high specific power, and energy. However, the safety problems caused by the inaccurate estimation and prediction of battery health state have attracted wide attention in academic circles. In this paper, the degradation mechanism and main definitions of state of health (SOH) were described by summarizing domestic and foreign literatures. The estimation and prediction methods of lithium-ion power battery SOH were discussed from three aspects: model-based methods, data-driven methods, and fusion technology methods. This review summarizes the advantages and disadvantages of the current mainstream SOH estimation and prediction methods. This paper believes that more innovative feature parameter extraction methods, multi-algorithm coupling, combined with cloud platform and other technologies will be the development trend of SOH estimation and prediction in the future, which provides a reference for health state estimation and prediction of lithium-ion power battery.
Highlights
Due to the characteristics of long cycle life, high specific energy, specific power, low self-discharge rate, high-temperature range, and the advantages of little pollution to the environment [1], lithium-ion power batteries are widely applied in various fields
The grey neural network was introduced for offline training, and the health index (HI) was taken as the input parameter, battery capacity degradation was taken as the output parameter of the grey neural network model, and battery state of health (SOH) was estimated by online construction of battery HI
A double extended Kalman filter (DEKF) joint estimation algorithm combined with the equivalent circuit model was proposed by [87], which is applicable for lithium-ion battery application scenarios by comparison with a single Kalman filtering (KF)
Summary
Due to the characteristics of long cycle life, high specific energy, specific power, low self-discharge rate, high-temperature range, and the advantages of little pollution to the environment [1], lithium-ion power batteries ( on referred to as lithium battery) are widely applied in various fields. The structure of this paper is as follows: Section 2 analyzes the degradation mechanism of lithium batteries and expounds on the current definition standards of SOH from the perspective of capacity and internal resistance of lithium batteries. The battery’s impedance characteristic is strengthened, and the ion diffusion coefficient at the electrode decreases, resulting in the rapid reduction of discharge voltage and the rapid attenuation of discharge capacity. This is not the case for some batteries, such as lithium titanate batteries, which may not produce an SEI film during operation.
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