Battery SoC Estimation Methods Compared: Coulomb Counting, Kalman Filter & Neural Networks in MATLAB
Battery SoC Estimation Methods Compared: Coulomb Counting, Kalman Filter & Neural Networks in MATLAB is most useful as a research topic when the simulation is treated as an experiment rather than a demonstration. The central objective is battery state-of-charge estimation using model-based and data-driven estimators. A strong study fixes the plant and test conditions, defines a baseline, changes one research factor at a time and reports numerical evidence alongside plots.
For doctoral and postgraduate work, the model should make every assumption visible: rated values, data sources, solver settings, controller sampling, initial conditions, boundary conditions and disturbance definitions. This makes the results easier to defend in a thesis, reproduce later and convert into a publication-oriented comparison.
A reproducible modelling and validation plan
- Choose a battery equivalent-circuit or table-based reference model.
- Generate current profiles covering charge, discharge and dynamic drive cycles.
- Implement Coulomb counting, Kalman-filter family and neural-network estimators.
- Use the same initial SOC uncertainty and measurement noise conditions.
- Validate against reference SOC over temperature and ageing scenarios.
- Compute error metrics and execution/complexity trade-offs.
What the thesis or paper should measure
Use numerical metrics that map directly to the research objective. Recommended outputs for this topic include:
- SOC RMSE
- maximum absolute error
- convergence time
- noise sensitivity
- temperature robustness
- computation cost
Move beyond a basic implementation
To turn this topic into a stronger research contribution, start with one baseline and one proposed method, then extend the validation using EKF/UKF comparison, LSTM/Transformer estimator, SOH-aware SOC estimation. The final results section should explain why the proposed method changes the engineering behaviour, not only whether the output curve looks smoother. Include failure cases or operating limits when they reveal the boundary of the method.
- EKF/UKF comparison
- LSTM/Transformer estimator
- SOH-aware SOC estimation
- online parameter identification
Need the model adapted to your research objective?
We can help with model architecture, parameterisation, controller/algorithm implementation, scenario design, plots and research-oriented result interpretation.
