ENHANCED OCV-SOC MODELLING USING MULTI-GENE GENETIC PROGRAMMING OF LI-ION BATTERIES

Accurate open-circuit voltage (OCV) modeling as a function of state-of-charge (SoC) is crucial for effective battery management and state estimation in lithium-ion batteries. This paper proposes a novel multi-gene genetic programming (MGGP)-based symbolic regression framework to derive an interpretable and generalized OCV-SoC model. Unlike traditional methods that rely on pre-defined model structures, MGGP automatically adapts to experimental data, implicitly capturing battery nonlinearities. The proposed model is validated using experimental charge-discharge and pulse-test data, demonstrating superior performance compared to conventional models, including Unnewehr, Shepherd, Nernst, and exponential models. Performance evaluation confirms the MGGP model's generalization capability across varying operating conditions. Results demonstrate that the MGGP model provides a more accurate and flexible representation of complex battery behavior, offering a promising approach for improved battery management systems.