To address the limitations of conventional electrochemical impedance spectroscopy (EIS) testing, we propose an efficient rapid EIS testing system. This system utilizes an AC pulse excitation signal combined with an “intelligent fast fourier transform (IFFT) optimization algorithm” to achieve rapid “one-to-many” impedance data measurements. This significantly enhances the speed, flexibility, and practicality of EIS testing. Furthermore, the conventional model-fitting approach for EIS data often struggles to resolve the issue of overlapping impedance arcs within a limited frequency range. To address this, the present study employs the Regularization Distributed Relaxation Time (RDRT) method to process EIS data obtained under AC pulse conditions. This approach avoids the workload and analytical uncertainties associated with assuming equivalent circuit models. Finally, the practical utility of the proposed testing system and the RDRT impedance analysis method is demonstrated through the estimation of battery state of health (SOH). In summary, the method proposed in this study not only addresses the issues associated with conventional EIS data acquisition and analysis but also broadens the methodologies and application scope of EIS impedance testing. This opens up new possibilities for its application in fields such as lithium-ion batteries (LIBs) energy storage.

Impedance spectroscopy can characterize a variety of states within a battery and shows great potential for application in safety warning and condition assessment, but there is a lack of an effective online monitoring method. Therefore, this article proposes a method for online measurement of impedance spectroscopy based on harmonic injection of equalization currents, which superimposes different frequency sinusoidal perturbation signals on the original equalization currents through process reconstruction and collaborative control, and realizes real-time impedance spectroscopy monitoring at the level of single cell without additional hardware. In order to verify the validity of the method proposed in this article, a hardware experimental platform was constructed. First, a four-switch buck-boost (FSBB) converter is used to perform energy equalization of a six-cell Li-ion battery pack under different working conditions, and the experimental results show that the Li-ion battery pack can be equalized by the active equalization circuit under the states of resting, charging, and discharging in about 60 min, 75 min, and 100 min, respectively. Then, the impedance spectroscopy in the frequency range of 0.2 Hz–800 Hz is achieved through the collaborative control of active equalization and impedance spectroscopy. Finally, in order to verify the reliability of the measured data, the measured impedance spectroscopy data were evaluated by the Kramers–Kronig (K-K) transforms, and the data measured at the initial stage were compared and analyzed with the data measured by a professional electrochemical workstation, which verified the validity of the method proposed in this article.

State-of-energy (SOE) estimation helps to enhance the safety of battery operation and predict vehicle range. However, the voltage plateau of the LiFePO4 (LFP) battery presents a significant challenge for SOE estimation. Therefore, this paper introduces a significantly varying mechanical force feature to tackle the flat voltage curve in the mid-SOE region. A fusion model that integrates a bidirectional long short-term memory (BiLSTM) network, particle swarm optimization (PSO), and Kalman filter (KF) algorithm is proposed for SOE estimation. The BiLSTM is applied to fully capture the temporal dependencies from inputs to output over both local and long cycles. Subsequently, PSO is employed to optimize the parameters of KF, which is utilized to smooth the results of the BiLSTM network, thereby achieving highly accurate SOE estimation. Experimental results across different operating conditions and temperatures reveal that the introduction of mechanical force significantly improves SOE estimation accuracy. Compared to models using only traditional electrical and thermal features, the model with the introduction of mechanical force achieves average improvements of 67.06%, 66.38%, and 66.46% for the root mean square error (RMSE), maximum absolute error (MAXE), and mean absolute error (MAE), respectively. Moreover, the generalizability and robustness of the proposed method are further confirmed by the comparison of different models and preload forces.