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Summary To achieve accurate state‐of‐charge (SoC) estimation for LiFePO4 batteries, the effects of temperature, hysteresis, and thermal evolution are elaborately modeled. Open‐circuit voltage is regarded as the sum of electromotive force and hysteresis potential (Vh), where electromotive force is constructed as the function of SoC and temperature and Vh is reproduced with a geometrical model. By simulating battery heat generation and dissipation, a thermal evolution model is established and exploited for open‐circuit voltage and parameter identification. Then, on the basis of a second‐order equivalent circuit model, 2 SoC estimation schemes are proposed: One scheme uses the recursive least square with forgetting factor algorithm and off‐line equivalent circuit model parameters derived by the differential evolution algorithm; the other scheme resorts to the adaptive extended Kalman filter (EKF) and online tuned parameters. Experiments validate the effectiveness of the hysteresis model and the thermal evolution model. In contrast to a joint EKF estimator, experimental results under different temperatures and initial states suggest that both the proposed estimators are superior to the joint EKF estimator. Benefiting from the online updated parameters, the adaptive EKF estimator behaves best for giving consistent SoC‐tracking performance under different conditions.

To prevent the leakage phenomenon and reduce the thermal contact resistance of composite phase change material (CPCM) boards in battery thermal management (BTM) applications, we develop a kind of composite board by enclosing a CPCM plate with a thermal conductive silica gel (TCSG) shell. The compact and flexible TCSG shell effectively prevents the leakage phenomenon and provides a flexible contact interface between the boards and cells to reduce the thermal contact resistance and buffer the compressive stress accumulation. Consequently, the CPCM enclosed with TCSG (TCSG-CPCM) delivers an excellent antileakage performance under high temperatures, even up to 140 °C, and demonstrates a much better temperature-control performance in comparison to traditional CPCM boards in BTM applications. For example, during cyclic charge−discharge tests at the rates of 3C-3C, the maximum temperature and temperature difference of the battery module cooled by TCSG-CPCM boards can be controlled below 52.6 and 4.4 °C, respectively, much lower than those of the battery module cooled by rigid CPCM boards. In addition, no leakage trace, which appears seriously in the bare CPCM board, is detected on the TCSG-CPCM board.

Summary The available power of vehicle‐mounted batteries needs to be real‐time predicted to accommodate prospective driving demands of overtaking, gradient climbing, constant‐speed cruising, and regenerative braking. Generally, battery power capabilities are limited by multiple constraints, for example, terminal voltage, current, state‐of‐charge (SoC), and State‐of‐Energy (SoE). This paper constructs SoC and SoE estimators resorting to the square‐root central difference Kalman filter (SR‐CDKF) and an equivalent circuit model with online updated parameters. In addition, a battery thermal evolution model is formulated, whereby the ordinarily ignored temperature constraint is taken into account. Based on above achievements, a battery power prediction scheme conforming to multiple constraints is realized. Finally, experimental verifications are conducted on a LiFePO4 battery pack subject to consecutive Federal Urban Driving Schedule profiles. The SR‐CDKF‐based SoC and SoE estimators give accurate results under different conditions. In contrast to the conventional PNGV‐HPPC method, the proposed method behaves with more reliability and robustness at different time horizons, temperatures, and aging levels. The assessment results justify the effectiveness of the battery modeling and algorithm utilization efforts.

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The conventional methods used to evaluate battery state-of-charge (SOC) cannot accommodate the chemistry nonlinearities, measurement inaccuracies and parameter perturbations involved in estimation systems. In this paper, an impedance-based equivalent circuit model has been constructed with respect to a LiFePO battery by approximating the electrochemical impedance spectrum (EIS) with RC circuits. The efficiencies of approximating the EIS with RC networks in different series-parallel forms are first discussed. Additionally, the typical hysteresis characteristic is modeled through an empirical approach. Subsequently, a methodology incorporating an H-infinity observer designated for open-circuit voltage (OCV) observation and a hysteresis model developed for OCV-SOC mapping is proposed. Thereafter, evaluation experiments under FUDS and UDDS test cycles are undertaken with varying temperatures and different current-sense bias. Experimental comparisons, in comparison with the EKF based method, indicate that the proposed SOC estimator is more effective and robust. Moreover, test results on a group of Li-ion batteries, from different manufacturers and of different chemistries, show that the proposed method has high generalization capability for all the three types of Li-ion batteries.

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