• Energy Research

To tackle the intricate challenges of nonlinearity and non-stationarity in lead-acid battery degradation data, this paper introduces the SG-LSTM model, an innovative approach to battery health prediction. This model uniquely integrates Singular Spectrum Analysis (SSA) and Grey Wolf Optimization (GWO) with Long Short-Term Memory (LSTM) networks, forming a sophisticated predictive framework. By targeting key degradation features, such as the charging time of multiple voltage rise segments from the charging curve, the model effectively captures critical battery health dynamics. SSA plays a vital role by filtering outliers from these feature sequences, ensuring high-quality data for analysis and enhancing the robustness and accuracy of predictions. The refined data are then processed by a GWO-optimized LSTM network, where GWO’s bio-inspired optimization fine-tunes the LSTM parameters for optimal performance. Experimental results demonstrate that the SG-LSTM model outperforms existing models in prediction accuracy and stability; specifically, SG-LSTM achieves 0.27 RMSE, outperforming LSTM (0.84), SSA-LSTM (0.4), and SSA-BP (0.6).

The failure of a battery may lead to a decline in the performance of electrical equipment, thus increasing the cost of use, so it is important to accurately evaluate the state of health (SOH) of the battery. Capacity degradation data for batteries are usually characterized by non-stationarity and non-linearity, which brings challenges for accurate prediction of battery health status. To tackle this problem, this paper proposes a battery prediction model based on singular spectrum analysis (SSA) and a transformer network. The model uses SSA to eliminate the effect of capacity regeneration, and a transformer network to automatically extract features from historical degraded data for the prediction. Specifically, the battery capacity sequence is used as the key index of performance degradation, which is decomposed by the SSA into trend components and periodic components. Then, the long-term dependence of capacity degradation is captured by the transformer network with a multi-head attention mechanism. Finally, two public lithium battery datasets were used to verify the validity of proposed model, and compared with mainstream models such as long-/short-term memory (LSTM) and convolutional neural networks (CNNs). The experimental results show that the proposed model has better prediction performance and extensive generalizability.

Lead–acid batteries are widely used, and their health status estimation is very important. To address the issues of low fitting accuracy and inaccurate prediction of traditional lead–acid battery health estimation, a battery health estimation model is proposed that relies on charging curve analysis using historical degradation data. This model does not require the assistance of battery mechanism models or empirical degradation models, instead, it is combined with improved deep learning algorithms. A long short-term memory (LSTM) regression model was established, and parameter optimization was performed using the bat algorithm (BA). The experimental results show that the proposed model can achieve an accurate capacity estimation of lead–acid batteries.