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原文信息：
Long-range battery state-of-health and end-of-life prediction with neural networks and feature engineering
原文链接：
https://www.sciencedirect.com/science/article/pii/S030626192301125X
Highlights
• 新的特征和损耗工程方法相结合，改进了长期电池寿命预测。
• 使用充放电电压曲线并进行特征分析，以确定与老化最相关的特征。
• 新方法对 SOH 和 EOL 预测平均百分比误差分别是 5.49% 和-1.27%。
Abstract
Determining the state of health (SOH) and end of life (EOL) represents a critical challenge in battery management. This study introduces an innovative neural network-based methodology that forecasts both the SOH and EOL, utilizing features engineered from charge-discharge voltage profiles. Specifically, long-short-term memory (LSTM) and gated-recurrent unit (GRU) neural networks are trained against fast-charging datasets with novel loss function that emphasizes SOH regression while penalizing its decay. The devised models yield low average errors in SOH and EOL predictions (5.49% and -1.27%, respectively, for LSTM), over extended horizons encompassing 80% of the forecast battery lifespan. From a combined evaluation using Pearson's correlation and saliency analysis, it is found that voltages most strongly associated with aging occur after the initial constant current rate step. In short, this study offers a new perspective on the precise prediction of SOH and EOL by integrating feature engineering with neural networks.
Keywords
Lithium-ion batteries；
Automated feature extraction；
Deep learning；
State of health；
End of life;
Graphics
Fig. 2. Schematic representation of the LSTM and GRU neural networks.
Fig. 4. (a) Representation of experimental SOH and modeled test data of three batteries using LSTM (a) and GRU (c) models to predict EOL with a fixed NKP/EOL ratio of 20% and fixed training windows. The black and blue dots represent the experimental SOH, while the orange lines represent the modeled test data. Experimental versus forecast EOL are shown for LSTM (b) and GRU (d). (For interpretation of the references to colour in this
figure legend, the reader is referred to the web version of this article.)
Fig. 5. (a) Representation of experimental SOH and modeled test data of three batteries using LSTM (a) and GRU (c) models to predict EOL with a fixed NKP/EOL ratio of 20% and variable training windows. The black and blue dots represent the experimental SOH, while the orange lines represent the modeled test data.
Experimental versus forecast EOL are shown for LSTM (b) and GRU (d). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Fig. 6. (a) Features correlation map from Eq. (8). (b) Visualization of the features selected using PC. (c) Saliency scores based on saliency analysis using LSTM. (d) Visualization of the features selected by saliency analysis using LSTM. Herein, variable training windows were considered.
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