The precision of battery modeling is usually determined by the identification of model parameters, which is dependent on the measured outside characteristic data of batteries. However, there is a lot of noise because of the environment noise and measurement error, leading to poor estimation accuracy of model parameters. This paper proposes a stochastic theory response reconstruction (STRR) method to reconstruct the measured battery voltage data, which can eliminate the noise interference and ensure high-precision model parameter identification. The relationship between the battery voltage and current is established based on the the second-order equivalent circuit model (ECM) by the convolution theorem, and the impulse function is calculated by the correlation function between the measured voltage and current. Then, the battery voltage is reconstructed and used to identify model parameters with the recursive least squares (RLS) algorithm. All data for model parameter identification is produced through the pseudo random binarysequence (PRBS) excitation signal. Finally, the Urban Dynamometer Driving Schedule (UDDS) and Federal Urban Driving Schedule (FUDS) tests are conducted to validate the performance of the proposed method. Experimental results show that when compared with the traditional solution using low-pass filter, the proposed method can eliminate the noise interference more effectively and has higher identification accuracy.

Accurate estimation of battery state of health (SOH) is critical to the efficient operation of energy storage battery systems. Furthermore, precise SOH estimation methods can significantly reduce resource waste by extending the battery service life and optimizing retirement strategies, which is compatible with the sustainable development of energy systems under carbon neutrality goals. Conventional methods struggle to comprehensively characterize the health degradation properties of batteries. To address that limitation, this study proposes a data-driven model based on multi-feature analysis using a hybrid convolutional neural network and long short-term memory (CNN-LSTM) architecture, which synergistically extracts multi-dimensional degradation features to enhance SOH estimation accuracy. The framework begins by systematically collecting the voltage, current, and other parameters during charge–discharge cycles to construct a temporally resolved multi-dimensional feature matrix. A correlation analysis employing Pearson correlation coefficients subsequently identifies key health indicators strongly correlated with SOH degradation. At the same time, the K-means clustering method was adopted to identify and process the outliers of CALCE data, which ensures the high quality of data and the stability of the model. Then, CNN-LSTM hybrid neural network architecture was constructed. The experimental results demonstrated that the absolute value of MBE for the dataset provided by CALCE was less than 0.2%. The MAE was less than 0.3%, and the RMSE was less than 0.4%. Furthermore, the proposed method demonstrated a strong performance on the dataset provided by NASA PCoE. The experimental results indicated that the proposed method significantly reduced the estimation error of SOH across the entire battery lifecycle, and they fully verified the superiority and engineering applicability of the algorithm in battery SOH estimation.

Reliable and accurate state of charge (SOC) estimation is pivotal to enhancing the performance and safety of lithium-ion batteries for battery management system. Extended Kalman filter (EKF) and smooth variable structure filter (SVSF) has alreadly been used for estimating the SOC of lithium-ion batteries. However, either of the two methods only considers the accuracy or robustness of SOC estimation, and cannot give attention to both aspects simultaneously. To this end, this paper proposes a particle swarm optimized combined method that leverages their complementary strengths fully, enhancing the accuracy and robustness of SOC estimation. The effectiveness and superiority of the proposed method are verified by battery test experiments conducted at different ambient temperatures and battery aging states, and comparing to EKF, SVSF, and manual tuning combined method. Experimental results demonstrate that the root mean square error of the proposed method is kept below 6% across all conditions, highlighting its application potential for SOC estimation in complex and variable operational conditions.