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