State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing 400044, China.
College of Mechanical Engineering, Chongqing University, Chongqing 400044, China.
Sensors (Basel). 2021 Feb 5;21(4):1087. doi: 10.3390/s21041087.
The state of health (SOH) prediction of lithium-ion batteries (LIBs) is of crucial importance for the normal operation of the battery system. In this paper, a new method for cycle life and full life cycle capacity prediction is proposed, which combines the early discharge characteristics with the neural Gaussian process (NGP) model. The cycle data sets of commercial LiFePO(LFP)/graphite cells generated under different operating conditions are analyzed, and the power characteristic is extracted from the voltage and current curves of the early cycles. A Pearson correlation analysis shows that there is a strong correlation between and cycle life. Our model achieves 8.8% test error for predicting cycle life using degradation data for the 20th to 110th cycles. Based on the predicted cycle life, capacity degradation curves for the whole life cycle of the cells are predicted. In addition, the NGP method, combined with power characteristics, is compared with other classical methods for predicting the remaining useful life (RUL) of LIBs. The results demonstrate that the proposed prediction method of cycle life and capacity has better battery life and capacity prediction. This work highlights the use of early discharge characteristics to predict battery performance, and shows the application prospect in accelerating the development of electrode materials and optimizing battery management systems (BMS).
锂离子电池（LIB）的健康状态（SOH）预测对于电池系统的正常运行至关重要。本文提出了一种新的循环寿命和全生命周期容量预测方法，该方法将早期放电特性与神经高斯过程（NGP）模型相结合。分析了在不同工作条件下生成的商用 LiFePO（LFP）/石墨电池的循环数据集，并从早期循环的电压和电流曲线中提取出功率特性。皮尔逊相关性分析表明，与循环寿命之间存在很强的相关性。我们的模型使用第 20 到 110 个循环的降解数据预测循环寿命的测试误差为 8.8%。基于预测的循环寿命，预测了电池全生命周期的容量退化曲线。此外，还将结合功率特性的 NGP 方法与其他预测 LIB 剩余使用寿命（RUL）的经典方法进行了比较。结果表明，所提出的电池循环寿命和容量预测方法具有更好的电池寿命和容量预测能力。这项工作强调了利用早期放电特性来预测电池性能的重要性，并展示了在加速电极材料开发和优化电池管理系统（BMS）方面的应用前景。
