Xueyuan Wang

Accurate state-of-health (SOH) estimation of lithium iron phosphate (LiFePO₄) batteries is critical for ensuring the safety and performance of electric vehicles, particularly under extreme operating conditions. This study presents a data-driven SOH prediction framework based on high-frequency electrochemical impedance spectroscopy (EIS) measurements conducted at −5 °C across various states of charge (SOCs). Feature parameters were extracted from the impedance spectra using equivalent circuit modeling. These features were optimized through Bayesian weighting and subsequently fed into three machine learning models: Random Forest (RF), Gradient Boosting (GB), and Extreme Gradient Boosting (XGB). To mitigate SOC-dependent variations, the models were trained, validated, and tested using features from different SOC levels for each aging cycle. This work provides a practical and interpretable approach for battery health monitoring using high-frequency EIS data, even under sub-zero temperature and partial-SOC conditions. The findings offer valuable insights for developing SOC-agnostic SOH estimation models, advancing the reliability of battery management systems in real-world applications.

The cold start issue in low temperature environments significantly impacts the durability of proton exchange membrane fuel cells (PEMFCs). As a powerful tool, electrochemical impedance spectroscopy (EIS) can effectively characterize the internal state of PEMFCs, especially for internal water content assessment. However, impedance applications in PEMFC cold start processes remain largely unexplored. In this article, an onboard impedance measurement system for cold start scenarios of PEMFC is proposed. First, extensive dynamic impedance spectra and their evolution mechanism are obtained and revealed, to further select the key characteristic frequencies of 1 kHz, 1 Hz, and 0.5 Hz. Besides, a four-phase interleaved dual boost dc/dc converter prototype with integrated impedance measurement function is developed. Based on these, the dc/dc converter prototype can achieve high impedance measurement accuracy, with the average root mean square error (RMSE), mean absolute error (MAE) of 0.02767 and 0.02600 (real part), 0.00549 and 0.00475 (imaginary part), and 0.00060 and 0.00042 (magnitude), respectively. Furthermore, the system can achieve online impedance measurement of key characteristic frequencies for PEMFC cold start scenarios within 11 s. Experiments have been carried out on the prototype to verify the effectiveness of the proposed onboard impedance measurement system.

The multistage constant current (MCC) charging protocol for lithium‐ion batteries is commonly used to balance lithium plating and charging time. Traditional methods depend on a pre‐defined charging map without considering the feedback of lithium plating and subsequent self‐regulation of the charging rate. To tackle this problem, an adaptive MCC charging method is proposed, which is based on expansion force feedback to detect lithium plating. By integrating experiments with simulations, the results indicate that when lithium plating occurs, the force experiences an abnormal, accelerated increase. If the charging rate is reduced until lithium plating ceases, the force decreases. Correspondingly, three thresholds, V1, V2, and V3, in the derivative of force (dF/dSOC), are identified. Utilizing these thresholds, the charging rate can be self‐regulated. The results demonstrate that charging speed can be increased by 50% without causing irreversible lithium plating. The proposed method holds great promise for integration into intelligent battery management systems, thereby enhancing the performance of MCC fast charging.

Lithium plating may occur during charging, especially at high rates or overcharging conditions for lithium-ion batteries (LIBs), which would cause battery capacity degradation and even trigger thermal runaway. Thus, it is essential to detect lithium plating onset during the charging processes. Electrochemical impedance can reveal the dynamic electrode properties of the battery, which is promising for use in battery management systems for the online detection of lithium plating onset. In this article, the impedance at 1 Hz is measured during the over-discharge and fast discharge processes using lithium–graphite half-cells. For half-cells, the variation in graphite electrode potential vs. Li/Li⁺ during discharging is directly recorded. An equivalent circuit model is proposed and adopted to estimate the real lithium plating reaction overpotential, which is deemed the thermodynamic indicator of lithium plating and is used as validation for the detection of lithium plating onset. Through the auxiliary validation of the estimation of lithium plating overpotential and the shape of incremental capacity curves, the relationship between impedance changes at specific frequency and the lithium plating onset is revealed. The results lay a good foundation for proposing the online diagnostic method of lithium plating onset based on the in situ impedance.

The electrification of transportation is experiencing rapid development. Electric bicycles (e-bikes) are commonly employed as convenient modes of transportation. Thanks to the advantages of long life and high energy density, lithium-ion batteries (LIBs) are widely used in e-bikes. In certain business models, e-bikes can utilize rental LIBs, which are centrally managed at charging stations. The low-temperature charging and discharging performance of the LIB system poses a significant challenge during usage. Among various heating methods, alternating current (AC) heating has garnered attention due to its high efficiency and has been applied to quickly warm up the LIB system. To address this issue, an AC heating model was established to determine the appropriate frequency and magnitude of the current, and a prototype AC heating system for the LIB modules used in e-bikes was designed. A full-bridge topology system model was established, and an experimental platform was constructed to test the effectiveness of the proposed AC heating topology and thermoelectric model under different AC heating frequencies and currents. The results show that the proposed AC heating system can heat an 18650 battery module within 20 min. Under an ambient temperature of −20 °C, using a 10 A, a 100 Hz excitation current achieves a heating rate of 1.3 °C per minute, with minimum power losses. The prototype also has a fast response time of only 70 ms. Finally, the strategies of LIB heating and insulation are proposed for the scenario of a battery swapping station. This research holds great significance in resolving the problem of low-temperature heating for e-bikes in cold regions.

Advanced sensors based on electrochemical impedance spectrum (EIS) are essential for fuel cell system state estimation, fault diagnosis, and health management. However, traditional EIS methods are unsuitable for real-time state management due to high costs, large sizes, and long acquisition times, limiting their online industrial application. To enhance onboard EIS application, this study proposes a novel multistage and multisine injection method to improve wideband frequency sweeping, balancing EIS quality, and measurement time using a four-phase floating-interleaving parallel dc–dc converter prototype with EIS injection functions. In addition, a fuel cell coupled with a four-phase floating-interleaving parallel dc–dc equivalent circuit model is established for comprehensive performance analysis in impedance calculation with different excitation methods. Experimental validation showed an impedance magnitude error within 3.2% from 1 kHz to 0.1 Hz, and the implementation reduces EIS measurement time from 900 s to approximately 150 s. Furthermore, a case study on fuel cell EIS sensitivity under dynamic conditions indicates that the proposed prototype can capture dynamic EIS variations using local multifrequency points.