Lithium

The advancements in lithium-ion battery technology have led to the development of new energy vehicles, smart grids, and other environment-friendly industries. However, lithium-ion batteries experience performance deterioration over time due to various factors, including battery manufacturing, operating conditions, and environmental conditions. This degradation can lead to uncontrolled combustions or explosions. Therefore, it is important to study the internal health states of lithium-ion batteries and develop accurate state estimation methods.

The aging process of lithium-ion batteries is complex and requires a model based on the battery’s aging mechanism for accurate life prediction. Calendar aging, which is the aging of batteries over a long period of time in a floating state, is particularly difficult to estimate due to the slow decay rate of the battery and the lack of measurable decay characteristics.

To address this issue, a particle filtering-based algorithm is proposed in this study for battery state-of-health (SOH) and remaining useful life (RUL) estimation. The algorithm takes into account the charging and discharging cycle process, which affects the battery’s aging. Battery capacity degradation is widely accepted as an indicator of battery aging. Once the battery capacity decays to a certain threshold, it is considered to have reached the end of its life and needs to be replaced.

There are different methods for predicting SOH, including direct measurement methods, model-based methods, and data-driven methods. Direct measurement methods involve simple testing of the battery’s SOH, such as the Coulomb counting method and internal-resistance-based estimation methods. These methods have limitations in accuracy and robustness.

Model-based methods, such as the equivalent circuit model, describe the relationship between internal resistance and available capacity. These models rely on accurate modeling and testing data. Closed-loop estimation algorithms, such as the extended Kalman filter and particle filter, have been introduced to improve prediction accuracy.

Data-driven methods, including machine learning and deep learning algorithms, have also been applied to SOH prediction tasks. These methods extract features from battery charging and discharging data to generate a set of feature vectors for prediction. Recurrent neural networks, such as long short-term memory and gated recurrent networks, have achieved good results in SOH prediction.

In conclusion, accurate estimation of the state-of-health of lithium-ion batteries is crucial for ensuring their safety and extending their lifespan. Various methods, including particle filtering algorithms and data-driven approaches, are being developed to improve the accuracy of SOH estimation and predict the remaining useful life of batteries.