Lithium-ion battery (LIB) power systems have been commonly used for energy storage in electric vehicles. However, it is quite challenging to implement a robust real-time fault diagnosis and protection scheme to ensure battery safety and performance. This paper presents a resilient framework for real-time fault diagnosis and protection in a battery-power system. Based on the proposed system structure, the self-initialization scheme for state-of-charge (SOC) estimation and the fault-diagnosis scheme were tested and implemented in an actual 12-cell series battery-pack prototype. The experimental results validated that the proposed system can estimate the SOC, diagnose the fault and provide necessary protection and self-recovery actions under the load profile for an electric vehicle.

This paper introduces a pseudo three-dimensional electrochemical-thermal coupled battery model for a cylindrical Lithium Iron Phosphate battery. The model comprises a pseudo two-dimensional electrochemical cell model coupled with three-dimensional lumped thermal model. The cell is disassembled to obtain the physical dimensions of the cell components. The thermal characteristics of the cell are studied during the discharge process over a range of temperatures and discharge rates. The validity of the numerical model is demonstrated experimentally via a 26650 cylindrical Lithium Iron Phosphate/graphite battery cylindrical cell.Instead of infrared thermal images, series of regression models are utilized to quantify the thermal behavior at various depth of discharge under various discharge rates. The results demonstrated that the battery cell performs differently at a lower ambient temperature and lower discharge rate where the exothermic reactions are milder.

This paper presents an integrated state-of-charge (SOC) estimation model and active cell balancing of a 12-cell lithium iron phosphate (LiFePO4) battery power system. The strong tracking cubature extended Kalman filter (STCEKF) gave an accurate SOC prediction compared to other Kalman-based filter algorithms. The proposed groupwise balancing of the multiple SOC exhibited a higher balancing speed and lower balancing loss than other cell balancing designs. The experimental results demonstrated the robustness and performance of the battery when subjected to current load profile of an electric vehicle under varying ambient temperature.

Ambient temperature affects the performance of a battery power system and its accuracy in state-of-charge (SOC) estimation for electric vehicles and smart grid systems. This paper proposes a battery model that considered ambient temperature, cell temperature, hysteresis voltage and thermal aging on capacity due to multiple charging and discharging. The SOC is then estimated using an extended Kalman filter. Several forms of validation were tested on an actual cell battery under specific ambient temperatures to verify the battery cell model, terminal voltage and SOC estimation performance. The SOC estimation results show an improvement in root-mean-squared error as compared to Extended Kalman Filter (EKF) without considering the temperature dependency. The proposed battery temperature-dependent model gave a smaller root-mean square error in SOC and terminal voltage at 5 °C, 15 °C and 45 °C.

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