A Method for Estimating the State of Health of Lithium-Ion Batteries Based on Hybrid Neural Network Model
TL;DR
The results demonstrate that the proposed model significantly outperforms other comparison methods in terms of MAE, MAPE, and RMSE for three battery cells under both 80% and 60% training set ratios, confirming its comprehensive superiority in estimation accuracy, robustness, and generalization capability with limited samples.
Abstract
Accurate estimation of the state of health (SOH) of lithium ion batteries is a fundamental prerequisite for the safe and reliable operation of battery management systems. To address the issues of insufficient feature representativeness, manually dependent hyperparameter tuning, and limited generalization under small sample conditions in existing SOH estimation methods, this paper proposes a hybrid SOH estimation approach based on a convolutional neural network and bidirectional gated recurrent unit optimized by the RIME optimization algorithm. Unlike existing CNN GRU/LSTM models that rely on unidirectional recurrent structures and can only utilize forward temporal information, the proposed CNN-BiGRU architecture captures bidirectional contextual dependencies inherent in battery degradation, enabling more comprehensive characterization of aging dynamics from limited cycle data. Firstly, 13 health indicators (HIs) related to capacity degradation are extracted from the incremental capacity (IC) curves, and Spearman’s rank correlation coefficient is employed to select the optimal feature subset with the highest correlation. Secondly, a CNN BiGRU hybrid architecture is constructed, where CNN extracts local degradation features and BiGRU captures bidirectional temporal dependencies. More importantly, instead of relying on manual trial and error or grid search for hyperparameter determination, the RIME algorithm is introduced to automatically and globally optimize the core hyperparameters of the model. Finally, ablation and comparative experiments are conducted on the public NASA dataset. The results demonstrate that the proposed model significantly outperforms other comparison methods in terms of MAE, MAPE, and RMSE for three battery cells under both 80% and 60% training set ratios, confirming its comprehensive superiority in estimation accuracy, robustness, and generalization capability with limited samples.