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As the world moves toward greenhouse gas reduction, there is increasingly active work around Li-ion chemistry-based batteries as an energy source for electric vehicles (EVs), hybrid electric vehicles (HEVs) and smart grids. In these applications, the battery management system (BMS) requires an accurate online estimation of the state of charge (SOC) in a battery pack. This estimation is difficult, especially after substantial battery aging. In order to address this problem, this paper utilizes SOC estimation of Li-ion battery packs using a fuzzy-improved extended Kalman filter (fuzzy-IEKF) for Li-ion cells, regardless of their age. The proposed approach introduces a fuzzy method with a new class and associated membership function that determines an approximate initial value applied to SOC estimation. Subsequently, the EKF method is used by considering the single unit model for the battery pack to estimate the SOC for following periods of battery use. This approach uses an adaptive model algorithm to update the model for each single cell in the battery pack. To verify the accuracy of the estimation method, tests are done on a LiFePO4 aged battery pack consisting of 120 cells connected in series with a nominal voltage of 432 V.

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In response to increasing integration of renewable energy sources on electric grid systems, battery energy storage systems (BESSs) are being deployed world-wide to provide grid services, including fast frequency regulation. Without mitigating technologies, such as BESSs, highly variable renewables can cause operational and reliability problems on isolated grids. Prior to the deployment of a BESS, an electric utility company will typically perform modeling to estimate cost benefits and determine grid impacts. While there may be a comparison of grid operations before and after BESS installation, passive monitoring typically does not provide information needed to tune the BESS such that the desired services are maintained, while also minimizing the cycling of the BESS. This paper presents the results of testing from a live grid using a method that systematically characterizes the performance of a BESS. The method is sensitive enough to discern how changes in tuning parameters effect both grid service and the cycling of the BESS. This paper discusses the application of this methodology to a 1 MW BESS regulating the entire island of Hawaii (180 MW peak load) in-situ. Significant mitigation of renewable volatility was demonstrated while minimizing BESS cycling.