MATLAB Implementation of a Generalized Battery Model

Implementing a generalized battery model in MATLAB is a critical component for electric vehicle and energy storage system simulations. A typical generalized battery model generally includes the following core modules: Equivalent Circuit Model: Utilizes resistor-capacitor (RC) networks to simulate the dynamic behavior of batteries, commonly implemented through Thevenin models or dual polarization models. The model describes transient responses and steady-state characteristics using series internal resistance and parallel RC components. In MATLAB code, this can be implemented using Simscape Electrical components or by defining state-space equations with functions like `ss()` for system representation. SOC (State of Charge) Estimation: Real-time calculation of remaining capacity based on ampere-hour integration methods or extended Kalman filters (EKF), requiring combination with open-circuit voltage (OCV)-SOC relationship curves to correct integration errors. Implementation typically involves creating functions for coulomb counting and EKF algorithms, using MATLAB's control system toolbox for filter design and data processing functions like `cumtrapz()` for numerical integration. Temperature Effect Compensation: Model parameters (such as internal resistance and capacity) vary with temperature, achievable through lookup tables or polynomial fitting for dynamic parameter adjustment. This can be coded using interpolation functions like `interp1()` or polynomial fitting functions like `polyfit()` to create temperature-dependent parameter models. For lithium-ion batteries, key parameters include: Nominal voltage (e.g., 3.7V) and capacity (e.g., 2.6Ah) RC network parameters (R1=0.01Ω, C1=2000F) OCV-SOC curve: Fitted from experimental data as piecewise linear or polynomial functions using curve fitting tools like `fit()` or `polyval()` functions In MATLAB, modular models can be built using Simulink, or scripts can be written utilizing ODE solvers like `ode45()` to simulate charge-discharge processes. Model validation requires comparison with measured data, adjusting parameters to match voltage response curves through optimization algorithms such as `fminsearch()` or parameter estimation tools. Extension directions include: Incorporating aging models (effects of cycle count on capacity) Combining machine learning techniques to optimize parameter identification processes using MATLAB's Statistics and Machine Learning Toolbox functions like `fitlm()` or neural network tools