In order to solve the problem of lower available capacity and shorter cycle life due to the barrel effect of series-connected batteries, as well as the problem of pseudo-equalization caused by battery aging, this paper proposes a modified modular multilevel converter (MMC) reconfigurable equalization scheme with difference of voltage variation (DOVV) as the equalization variable. The equalization topology consists of an MMC circuit and a voltage regulator, which effectively solves the problems of low total available capacity, inefficient energy transfer, and slow equalization by reducing the number of switches and achieving independent control of the equalization and voltage regulator modules. A control strategy based on the Oxford aging dataset is proposed with DOVV as the equalization variable, and a fuzzy logic control algorithm is introduced according to the distribution characteristics of DOVV data. This equalization control strategy overcomes the pseudo-equalization phenomenon due to battery aging. The simulation results show that compared with the traditional DC–DC energy transfer equalization topology, the energy transfer efficiency of the proposed equalization topology is improved by 62.15% and the equalization time is reduced by about 16.36%, and the pseudo-equalization phenomenon caused by the aging of the battery pack during the equalization process is well overcome. The feasibility of the proposed equalization scheme is verified.

In recent years, the number of safety accidents in new-energy electric vehicles due to lithium-ion battery failures has been increasing, and the lithium-ion battery fault diagnosis technology is particularly important to ensure the safe operation of electric vehicles. This paper proposes a method for lithium-ion battery fault diagnosis based on the historical trajectory of lithium-ion battery remaining discharge capacity in medium and long time scales. The method first utilizes the sparrow search algorithm (SSA) to identify the parameters of the second-order equivalent circuit model of the lithium-ion battery, and then estimates the state of charge (SOC) of the lithium-ion battery using the extended Kalman filter (EKF). The remaining discharge capacity is estimated according to the SOC, and finally the feature vectors are used to diagnose the faults using box plots on the medium and long time scales. Experimental results verify that the root mean squared error (RSME) and mean absolute error (MAE) of the proposed SOC estimation method are 0.0049 and 0.0034, respectively. This method can accurately identify the faulty single cell in a battery pack with low-capacity single cells and promptly detect any abnormalities in the single cell when a micro-short circuit fault occurs.

Accurate estimation of the State of Charge (SOC) of lithium-ion batteries is one of the core functions of a battery management system and is of great significance for ensuring the safe operation of electric vehicles and optimizing energy utilization. However, due to the strong nonlinearity, time-varying characteristics, and interference from complex operating conditions within the battery, high-precision SOC estimation faces severe challenges. To address the problems that a single data-driven method lacks physical constraints and a single model-driven method struggles to characterize complex nonlinearities, this paper proposes a series-connected LSTM-UKF fusion estimation method. This method first utilizes a Long Short-Term Memory network to learn the dynamic characteristics of the battery from historical voltage and current data, capturing the long-term dependencies of SOC changes to achieve an initial prediction. Subsequently, using this predicted value as the observation input, an Unscented Kalman Filter based on a second-order RC equivalent circuit model is introduced for optimal state correction, effectively suppressing model uncertainty and measurement noise. Simulation validation under various dynamic conditions, such as constant current discharge and FUDS, shows that compared to single LSTM or UKF algorithms, the proposed fusion method has significant advantages in estimation accuracy, convergence speed, and robustness. Its root mean square error is reduced to 0.0031, and it maintains stable estimation performance under different operating conditions. This study provides an effective data-model fusion solution for high-precision SOC estimation of lithium-ion batteries under complex operating conditions.

This study focuses on the safety and reliability issues of lithium-ion batteries, proposing a fault diagnosis strategy that leverages dual-feature extraction from both the time and frequency domains. Additionally, by modifying the traditional autoencoder, the study proposes a feature-guided autoencoder as an unsupervised model for extracting features in the time domain. Initially, wavelet packet decomposition and its energy-denoising treatment are employed to refine fault information within battery voltage signals. Subsequently, the reconstruction error outputted by the Feature-Guided Autoencoder is utilized as the time-domain fault feature, while the cosine similarity of the energy of signals in various frequency bands obtained after wavelet packet decomposition serves as the frequency-domain fault feature. Ultimately, this article selects the Isolation Forest algorithm for two-dimensional outlier detection of time and frequency features. Experimental results demonstrate that the feature-guided autoencoder proposed in this study not only enhances the sensitivity of time-domain fault features compared to traditional autoencoders and their variants but also optimizes issues related to training time and computational load. The effectiveness of the proposed dual-feature fault diagnosis method in both the time and frequency domains is validated through data from two actual vehicles, showing superior early fault detection capability relative to single-feature fault diagnosis methods.

With the rapid development of new energy vehicles, a large number of lithium batteries have been produced, used, and then retired. The full utilization and safe use of the whole life cycle of the batteries have become a hot topic in the research field. Compared to brand-new batteries, retired power batteries exhibit significant inconsistency and safety risks, thus necessitating effective battery equalization and safety monitoring methods. In this article, an active equalization method for cascade utilization lithium battery pack with online measurement of electrochemical impedance spectroscopy is proposed to actively equalize the retired battery pack and alleviate the inconsistency of the battery pack. Besides, the electrochemical impedance spectrum of the single battery is measured online without adding additional hardware circuits so as to realize real-time safety monitoring and solve the safety problem of the battery. Finally, in order to verify the feasibility of the active equalization and electrochemical impedance spectrum monitoring scheme designed in this article, a simulation model is built based on the matlab-Simulink platform. The simulation results show that the six batteries in the proposed scheme model complete the active equalization in about 710 s, 850 s, and 740 s, respectively, in the balance mode, charge mode, and discharge mode, and the electrochemical impedance spectrum in the frequency range of 1–20 KHz can be successfully measured.

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.