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This paper presents findings on dynamic cell modeling for state-of-charge(SOC) estimation in an autonomous electric vehicle (AEV). The studied cells areLithium-Ion Polymer-based with a nominal capacity of around 8Ah, optimized forpower-needy applications. The AEV operates in a harsh environment with raterequirements up to +/-25C and highly dynamic rate profiles, unlikeportable-electronic applications with constant power output and fractional Crates. SOC estimation methods effective in portable electronics may not sufficefor the AEV. Accurate SOC estimation necessitates a precise cell model. Theproposed SOC estimation method utilizes a detailed Kalman-filtering approach.The cell model must include SOC as a state in the model state vector. Multiplecell models are presented, starting with a simple one employing "Coulombcounting" as the state equation and Shepherd's rule as the output equation,lacking prediction of cell relaxation dynamics. An improved model incorporatesfilter states to account for relaxation and other dynamics in closed-circuitcell voltage, yielding better performance. The best overall results areachieved with a method combining nonlinear autoregressive filtering and dynamicradial basis function networks. The paper includes lab test results comparingphysical cells with model predictions. The most accurate models obtained havean RMS estimation error lower than the quantization noise floor expected in thebattery-management-system design. Importantly, these models enable precise SOCestimation, allowing the vehicle controller to utilize the battery pack's fulloperating range without overcharging or undercharging concerns.

Dynamic Cell Modeling of Li-Ion Polymer Batteries for Precise SOC Estimation in Power-Needy Autonomous Electric Vehicles