TITLE:
Dynamic Cell Modeling for Accurate SOC Estimation in Autonomous Electric Vehicles
AUTHORS:
Qasim Ajao, Lanre Sadeeq
KEYWORDS:
Autonomous Electric Vehicle, Modeling, Battery Model, Battery Management Systems (BMS), Lithium Polymer, State of Charge, Kalman-Filter
JOURNAL NAME:
Journal of Power and Energy Engineering,
Vol.11 No.8,
August
14,
2023
ABSTRACT: 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 8 Ah, 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.