What is it about?
This article proposes, designs and implements an online framework for estimating the state of charge (SoC) of battery cells using system identification methods. The methods combine two modified nonlinear optimization algorithms (modified Genetic Algorithm and modified Levenberg Marquardt) that are adapted to estimate the battery cell parameters. Then a linear recursive Kalman filter is used to estimate the state parameters of the battery cell. Furthermore, a new statistical approach is developed to deal with the hysteresis effects in the cell. The SoC estimation in the electric vehicle (EV) is challenging because the battery can have hundreds of cells with varying load currents and short time requirements for SoC estimation to prolong the battery pack lifetime. Therefore, accurately estimating the SoC of the cells in a battery pack is crucial for their effective use. The framework is robust, optimal and feasible in time-constrained environment with reasonable accuracy.
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Why is it important?
The battery SOC is a vital parameter that reflects the battery's performance. Accurate SOC estimation can protect the battery from overcharging or discharging, enhance the battery life, and enable the application to make rational control strategies for energy-saving purposes.
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This page is a summary of: An online framework for state of charge determination of battery systems using combined system identification approach, Journal of Power Sources, January 2014, Elsevier, DOI: 10.1016/j.jpowsour.2013.07.092.
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