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| Online Estimation of Lithium-Ion Battery Capacity Based on Polarization Decay Feature and Hybrid Neural Network with Channel Attention |
| Xu Zhicheng1,2, Yang Da1,2, Zhang Chuang1,2, Chen Zhanqun3, Zhang Xian1,2 |
1. State Key Laboratory of Intelligence Power Distribution Equipment and System Hebei University of Technology Tianjin 300130 China;
2. Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability of Hebei Province Hebei University of Technology Tianjin 300130 China;
3. Baoding UNT Electric Co. Ltd Baoding 071051 China |
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Abstract The capacity of a lithium-ion battery is a key indicator of the state of health (SOH) and remaining useful life (RUL) of the battery, as well as the state of charge (SOC) and equalization management within the battery pack. Therefore, accurate estimation of battery capacity is crucial for the battery management system (BMS). However, in practice, the battery capacity often cannot be measured directly, and needs to be estimated indirectly by combining relevant methods and accessible parameters. Therefore, this paper extracts multidimensional features reflecting the polarization strength of the battery based on a data-driven approach, and constructs a hybrid neural network with adaptive channel focusing capability, so as to realize online accurate estimation of lithium-ion battery capacity.
First, considering that the microscopic manifestation of battery capacity decay is the increase of battery polarization internal resistance, and the constant voltage charging and voltage relaxation processes are directly related to the battery depolarization reaction, the depolarization characteristics of constant voltage current and relaxation voltage decay are utilized to extract the multidimensional practical features that are not affected by the charging starting point. Data preprocessing of the original multidimensional features is performed by correlation coefficient method and principal component analysis (PCA) to complete the screening and dimensionality reduction and fusion of the features, and finally four indirect features are identified as model inputs. Next, the squeeze-excitation (SE) module is integrated into each hidden layer of the deep belief network (DBN) to construct an enhanced DBN network with channel attention capability, and the higher-order feature information extracted from the DBN is used to perform time-series prediction using a long-and short-term memory (LSTM) network to capture the mapping relationship between the battery capacity and the input features. Finally, six sets of simulation experiments are designed for different data structures, prediction models and input features to verify the effectiveness and accuracy of the method proposed in this paper in all aspects.
The simulation results show that the method proposed in this paper can effectively improve the accuracy of capacity estimation. Under different training and prediction data structures, the mean absolute percentage error (MAPE) and root mean square percentage error (RMSPE) of capacity estimation are controlled within 1.2% and 1.5%, respectively, and the model has the ability to simultaneously estimate the battery capacity under different charging conditions with good generalization. For different prediction models, the fusion of DBN network, SE mechanism and LSTM network effectively improves the accuracy of capacity estimation, especially the SE module contributes more significantly to the stability and accuracy of the model, but the LSTM network is equally indispensable, and the synergistic effect of both of them improves the performance of the model by more than 25%. Meanwhile, compared with other advanced algorithms, the model proposed in this paper demonstrates better stability while ensuring higher estimation accuracy, which verifies the effectiveness of the model improvement strategy in this paper. Compared with the original input features, the capacity estimation accuracy is improved by about 30% by combining the complementary features of constant pressure and relaxation dual depolarization processes and data preprocessing methods, which validates the effectiveness of the work done on feature engineering in this paper.
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Received: 28 July 2024
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2025, Vol. 40 
