h i g h l i g h t sStudy on temperature, current, aging dependencies of maximum available energy. Study on the various factors dependencies of relationships between SOE and SOC. A quantitative relationship between SOE and SOC is proposed for SOE estimation. Estimate maximum available energy by means of moving-window energy-integral. The robustness and feasibility of the proposed approaches are systematic evaluated. Keywords: Battery management system (BMS) State of energy (SOE) State of charge (SOC) Maximum available energy a b s t r a c tThe battery state of energy (SOE) allows a direct determination of the ratio between the remaining and maximum available energy of a battery, which is critical for energy optimization and management in energy storage systems. In this paper, the ambient temperature, battery discharge/charge current rate and cell aging level dependencies of battery maximum available energy and SOE are comprehensively analyzed. An explicit quantitative relationship between SOE and state of charge (SOC) for LiMn 2 O 4 battery cells is proposed for SOE estimation, and a moving-window energy-integral technique is incorporated to estimate battery maximum available energy. Experimental results show that the proposed approaches can estimate battery maximum available energy and SOE with high precision. The robustness of the proposed approaches against various operation conditions and cell aging levels is systematically evaluated.
State of charge (SOC) is one of the most important parameters in battery management system (BMS). There are numerous algorithms for SOC estimation, mostly of model-based observer/filter types such as Kalman filters, closed-loop observers, and robust observers. Modeling errors and measurement noises have critical impact on accuracy of SOC estimation in these algorithms. This paper is a comparative study of robustness of SOC estimation algorithms against modeling errors and measurement noises. By using a typical battery platform for vehicle applications with sensor noise and battery aging characterization, three popular and representative SOC estimation methods (extended Kalman filter, PI-controlled observer, andH∞observer) are compared on such robustness. The simulation and experimental results demonstrate that deterioration of SOC estimation accuracy under modeling errors resulted from aging and larger measurement noise, which is quantitatively characterized. The findings of this paper provide useful information on the following aspects: (1) how SOC estimation accuracy depends on modeling reliability and voltage measurement accuracy; (2) pros and cons of typical SOC estimators in their robustness and reliability; (3) guidelines for requirements on battery system identification and sensor selections.
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