The thermal-NDC model can be further improved to capture additional battery dynamics and aging effects by incorporating more detailed and accurate representations of the battery's behavior. One way to enhance the model is to include more sophisticated thermal dynamics that consider heat generation and dissipation mechanisms within the battery cells. This could involve modeling the thermal conductivity of different components, heat transfer between cells, and thermal runaway effects.
Additionally, incorporating aging effects into the model can provide insights into how the battery's performance changes over time. This could involve modeling degradation mechanisms such as capacity fade, impedance growth, and internal resistance increase. By integrating aging effects into the model, the control strategy can proactively adjust charging and thermal management to prolong the battery's lifespan and optimize its performance.
Furthermore, considering non-linear behaviors and uncertainties in the battery system can improve the model's accuracy and robustness. Advanced techniques such as adaptive control, machine learning, and data-driven modeling can be utilized to enhance the thermal-NDC model's predictive capabilities and enable more effective battery management strategies.