Shaojian Han

Lithium-ion batteries are widely used in energy storage and power systems due to their high energy density, long cycle life, and stability. Accurate prediction of the state of health (SOH) of batteries is critical to ensuring their safe and reliable operation. However, the prediction task remains challenging due to various complex factors. This paper proposes a hybrid TCN–Transformer–BiLSTM prediction model for battery SOH estimation. The model is first validated using the NASA public dataset, followed by further verification with dynamic operating condition simulation experimental data. Health features correlated with SOH are identified through Pearson analysis, and comparisons are conducted with existing LSTM, GRU, and BiLSTM methods. Experimental results demonstrate that the proposed model achieves outstanding performance across multiple datasets, with root mean square error (RMSE) values consistently below 2% and even below 1% in specific cases. Furthermore, the model maintains high prediction accuracy even when trained with only 50% of the data.

Due to the complex linear relationships within a battery, the prediction of state of charge (SOC) has become a significant challenge. This paper designs a three-layer backpropagation (BP) neural network to predict the battery’s SOC. To enhance the prediction accuracy, based on the charge–discharge mechanism of lithium batteries, the battery’s voltage, current, and temperature are selected as input variables, with the state of charge as the output variable. Under three different operating conditions, the BP neural network can achieve certain predictive effects. However, due to the instability and large error fluctuations of the BP neural network during the SOC prediction process, the BP neural network is optimized. The Sparrow Search Algorithm (SSA) can enhance the model’s ability to search for optimal values. By improving the Sparrow Search Algorithm, the uniform distribution of the initial population can be enhanced. The prediction results after optimizing the BP network show that, compared to the unimproved version, the stability of the prediction results can be improved, achieving more accurate predictions of SOC values, which has strong application value.

In low-temperature environments, both the electrochemical and thermodynamic performances of lithium-ion batteries are significantly affected, leading to a substantial decline in overall performance. This deterioration is primarily manifested in the inability of the battery to release its actual capacity effectively, a marked reduction in charge–discharge efficiency, and accelerated capacity degradation, directly undermining its power output capability under low-temperature conditions. This performance degradation severely restricts the application of lithium-ion batteries in scenarios requiring high power and extended range, such as EVs. This paper proposes an intelligent low-temperature DC discharge heating optimization strategy based on the PSO algorithm. The strategy aims to simultaneously optimize heating time and minimize capacity loss by employing the PSO algorithm to dynamically optimize discharge currents under varying ambient temperatures. This approach achieves the simultaneous optimization of battery heating efficiency and capacity loss. It effectively overcomes the limitation of traditional constant-current discharge methods, which struggle to dynamically adjust current intensity based on real operating conditions. By balancing heating efficiency and capacity degradation, the model significantly enhances energy utilization. Taking the weighting factor λ = 0.5 as an example, the battery is heated from −30 °C to 0 °C at a 90% initial SOC. Compared to preheating methods that directly use the minimum optimized dynamic current threshold, it reduces heating time by 48.71 s and increases the heating rate by more than twofold. In contrast to preheating methods using the maximum optimized dynamic current threshold, it decreases capacity degradation by 0.10 Ah after 1000 heating cycles. This strategy addresses the limitations of traditional heating methods, providing a novel solution for the efficient application of lithium-ion batteries in low-temperature environments.

In low-temperature environments, both the electrochemical and thermodynamic performance of lithium-ion batteries are significantly affected, leading to a substantial decline in overall performance. This deterioration is primarily manifested in the inability of the battery to release its actual capacity effectively, a marked reduction in charge-discharge efficiency, and accelerated capacity degradation, which directly undermines its power output capability under low-temperature conditions. Such performance degradation severely restricts the application of lithium-ion batteries in scenarios requiring high power and extended range, such as EVs. This paper proposes an intelligent low-temperature DC discharge heating optimization strategy based on the PSO algorithm. The strategy aims to simultaneously optimize heating time and minimize capacity loss by employing the PSO algorithm to dynamically optimize discharge currents under varying ambient temperatures. This approach achieves simultaneous optimization of battery heating efficiency and capacity loss. The proposed strategy effectively overcomes the limitation of traditional constant-current discharge methods, which struggle to dynamically adjust current intensity based on real operating conditions. By balancing heating efficiency and capacity degradation, the strategy significantly enhances energy utilization. The performance improvements achieved include a reduction in heating time by 48.71 seconds and an increase in heating rate by more than twofold. During 1,000 cycles of heating, the capacity loss was reduced by 0.10 Ah, effectively extending the battery's service life. This strategy addresses the limitations of traditional heating methods, providing a novel solution for the efficient application of lithium-ion batteries in low-temperature environments.