原文信息：
Battery state of health estimation under dynamic operations with physics-driven deep learning
原文链接：
https://www.sciencedirect.com/science/article/pii/S0306261924010158
Highlights
•提出了一种可微调的编码器-解码器深度学习框架。
•特征工程技术被有效地用于压缩物理信息，并获得具有很强泛化能力的融合特征。
•在动态运行中的各种充电状态间隔下，实现了高精度的 SOH 估算。
•通过微调解码器权重，可对不同电池的SOH进行估算。
Abstract
Accurate assessment of battery aging is crucial for the effectiveness of electrochemical energy storage systems. This study focuses estimation of the state of health for lithium-ion batteries under multi-dynamic operations, leveraging data from different states of charge interval. The approach begins with online identification of the battery model to swiftly gather aging-related physical information. This physical information, when combined with feature engineering techniques, is transformed into fused features capable of generalizing multi-dynamic operations. This is adopted as an input to a recurrent neural network based on an encoder-decoder framework, which establishes a mapping relationship between the fused features and the state of health (SOH). Notably, the framework showcases universality and flexibility, with an encoder that integrates with most recurrent neural networks, bypassing the need for intricate structures to deliver estimation with excellent accuracy. Specifically, with five common RNNs, the battery with a rated capacity of 5.0 Ah has a root mean square error of less than 0.68%. Moreover, by simply fine-tuning the weights, the decoder facilitates SOH estimation across different battery types. This methodology underscores the efficacy of merging physical information with a universal deep learning framework, enabling precise SOH estimations under multi-dynamic operations.
Keywords
State of health；
Multi-dynamic operations；
Physical information；
Transfer learning；
Recurrent neural network；
Graphics
Fig. 4. SOH estimation framework.
Fig. 6. Features extracted from partial discharge information of NMC cell.
Fig. 7. Fused feature extraction process.
Fig. 11. (a) A fine-tunable RNN-based encoder-decoder framework. (b) LSTM internal working schematic diagram.
Fig. 16. LFP cell estimation results and errors.
Fig. 18. LFP cell estimation results and errors with artificially introduced noise.
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