BATTERY MANAGEMENT SYSTEM (BMS): STATE OF HEALTH ESTIMATION USED IN MATLAB)/SIMULINK

A MATLAB/Simulink model combining a Simscape Electrical equivalent-circuit representation, a Simscape Thermal lumped network, sensor-noise injection, and masked SOC/R0 and SOH estimator subsystems has been described and exercised to study on-line State-of-Health estimation for a Li-ion cell. A survey of twenty representative studies — spanning Kalman-filter/observer methods, incremental-capacity and differential-voltage feature methods, data-driven machine-learning methods, and existing MATLAB/Simulink and digital-twin implementations — was used to position the model and to justify combining a Coulomb-counting-with-correction SOC estimator, an on-line resistance identifier, an explicit thermal subsystem, and a blended capacity/resistance SOH index within a single co-simulated block diagram. The model tracks SOC to within about 1% at steady state, identifies internal-resistance growth consistent with the published aging trends surveyed in Section 2, and produces a blended SOH indicator that gives an earlier and more conservative end-of-life warning than a capacity-only metric.

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