Battery health management using physics-informed machine learning: Online degradation modeling and remaining useful life prediction
Battery health management using physics-informed machine learning: Online degradation modeling and remaining useful life prediction
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使用基于物理的机器学习进行电池健康管理：在线退化建模和剩余使用寿命预测
DOI：
10.1016/j.ymssp.2022.109347
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发表时间：
2022
影响因子：
8.4
通讯作者：
Junchuan Shi;Alexis Rivera;Dazhong Wu
中科院分区：
文献类型：
--
作者：
Junchuan Shi;Alexis Rivera;Dazhong Wu
在过去的十年中，锂离子电池已广泛用于为便携式电子产品、电动汽车和无人机提供动力。老化会降低锂离子电池的容量。因此，准确的剩余使用寿命 (RUL) 预测对于锂离子电池供电系统的可靠性、安全性和效率至关重要。然而，电池老化是一个复杂的电化学过程，受内部老化机制和操作条件（例如循环时间、环境温度和负载条件）的影响。本文提出了一种基于物理的机器学习方法来对锂离子电池的退化趋势进行建模并预测其 RUL，同时考虑电池的健康状况和工作条件。所提出的基于物理的长短期记忆 (PI-LSTM) 模型将基于物理的日历和周期老化 (CCA) 模型与 LSTM 层相结合。 CCA模型通过结合五个工作应力因子模型来衡量锂离子电池的老化效应。 PI-LSTM使用LSTM层来学习CCA模型确定的退化趋势与不同周期的在线监测数据（即电压、电流和电池温度）之间的关系。在通过 PI-LSTM 模型估计电池的退化模式后，使用另一个 LSTM 模型通过学习 PI-LSTM 模型估计的退化趋势来预测电池的未来退化和剩余使用寿命 (RUL)。使用十一个锂离子电池在不同工作条件下的监测数据来验证所提出的方法。实验结果表明，该方法可以准确地模拟锂离子电池的退化行为，并预测不同工作条件下锂离子电池的RUL。
Lithium-ion batteries have been extensively used to power portable electronics, electric vehicles, and unmanned aerial vehicles over the past decade. Aging decreases the capacity of Lithium-ion batteries. Therefore, accurate remaining useful life (RUL) prediction is critical to the reliability, safety, and efficiency of the Lithium-ion battery-powered systems. However, battery aging is a complex electrochemical process affected by internal aging mechanisms and operating conditions (e.g., cycle time, environmental temperature, and loading condition). In this paper, a physics-informed machine learning method is proposed to model the degradation trend and predict the RUL of Lithium-ion batteries while accounting for battery health and operating conditions. The proposed physics-informed long short-term memory (PI-LSTM) model combines a physics-based calendar and cycle aging (CCA) model with an LSTM layer. The CCA model measures the aging effect of Lithium-ion batteries by combining five operating stress factor models. The PI-LSTM uses an LSTM layer to learn the relationship between the degradation trend determined by the CCA model and the online monitoring data of different cycles (i.e., voltage, current, and cell temperature). After the degradation pattern of a battery is estimated by the PI-LSTM model, another LSTM model is then used to predict the future degradation and remaining useful life (RUL) of the battery by learning the degradation trend estimated by the PI-LSTM model. Monitoring data of eleven Lithium-ion batteries under different operating conditions was used to demonstrate the proposed method. Experimental results have shown that the proposed method can accurately model the degradation behavior as well as predict the RUL of Lithium-ion batteries under different operating conditions.