HYBRID DATA-DRIVEN AND PHYSICS-BASED MODELING FOR ENHANCING BATTERY SAFETY AND RELIABILITY IN ELECTRIC VEHICLES

Chinta srikiran

doi:10.36676/urr.v10.i4.1574

Authors

Chinta srikiran Research scholar , Kalinga university

DOI:

https://doi.org/10.36676/urr.v10.i4.1574

Keywords:

HYBRID DATA-DRIVEN AND PHYSICS-BASED MODELING, BATTERY SAFETY, ELECTRIC VEHICLES

Abstract

Making sure lithium-ion battery systems are safe and dependable has become crucial as electric vehicles (EVs) proliferate. This study explores a hybrid modelling approach for early defect detection, thermal runaway prediction, and health diagnostics that blends physics-based models with data-driven machine learning (ML) methods. In order to improve forecast accuracy under actual driving situations, the research assesses the combination of electrochemical models, Long Short-Term Memory (LSTM) neural networks, and Gaussian Process Regression (GPR). The safe functioning of EV battery packs is greatly enhanced by results from simulated and experimental datasets, which show increased fault classification accuracy, greater generalisation to unknown conditions, and quicker reaction times. Lithium-ion battery systems' safety, dependability, and operational efficiency have become critical determinants of user trust and long-term sustainability as electric vehicles (EVs) continue to expand in the worldwide automotive industry. Because of the intricate electrochemical behaviour and vulnerability of these batteries to deterioration and failure, conventional battery management techniques often fail to correctly identify early-stage defects or anticipate crucial events like thermal runaway.

References

Ali, M., Li, M., & Li, Y. (2022). Embedded artificial intelligence for battery management systems: Challenges and opportunities. IEEE Transactions on Transportation Electrification , 8 (2), 2215–2230. [https://doi.org/10.1109/TTE.2021.3129517](https://doi.org/10.1109/TTE.2021.3129517)

Chen, X., Zhang, Y., & Wang, Y. (2021). Fault diagnosis of lithium-ion batteries under mechanical abuse and environmental stress using hybrid models. Journal of Energy Storage , 44 , 103491. [https://doi.org/10.1016/j.est.2021.103491](https://doi.org/10.1016/j.est.2021.103491)

Fu, C., Guo, Y., Liu, K., & Zhang, C. (2020). Real-time implementation of data-driven battery health estimation on automotive-grade microcontrollers. IEEE Access , 8 , 194173–194184. [https://doi.org/10.1109/ACCESS.2020.3032575](https://doi.org/10.1109/ACCESS.2020.3032575)

Janek, J., & Zeier, W. G. (2016). A solid future for battery development: Solid-state batteries challenge the status quo. Nature Energy , 1 , 16141. [https://doi.org/10.1038/nenergy.2016.141](https://doi.org/10.1038/nenergy.2016.141)

Liu, C., Bian, X., Wang, J., & Yang, Y. (2023). Hybrid modeling for battery fault diagnostics under diverse environmental conditions. Applied Energy , 342 , 120996. [https://doi.org/10.1016/j.apenergy.2023.120996](https://doi.org/10.1016/j.apenergy.2023.120996)

Pan, H., Hu, Y. S., & Chen, L. (2020). Room-temperature stationary sodium-ion batteries for large-scale electric energy storage. Nature Reviews Chemistry , 4 , 27–43. [https://doi.org/10.1038/s41570-019-0097-0](https://doi.org/10.1038/s41570-019-0097-0)

Rasheed, A., San, O., & Kvamsdal, T. (2020). Digital twin: Values, challenges, and enablers from a modeling perspective. IEEE Access , 8 , 21980–22012. [https://doi.org/10.1109/ACCESS.2020.2970143](https://doi.org/10.1109/ACCESS.2020.2970143)

Wang, Z., Liu, P., Zhang, Y., & Wang, Y. (2021). A review on real-world battery management system testing and validation using HIL and vehicle-level environments. Renewable and Sustainable Energy Reviews , 148 , 111254. [https://doi.org/10.1016/j.rser.2021.111254](https://doi.org/10.1016/j.rser.2021.111254)

Zhao, J., Zhang, C., Wang, C., & Tian, H. (2022). Adaptation of battery management strategies to solid-state battery characteristics: Challenges and perspectives. Energy Storage Materials , 44 , 616–628. [https://doi.org/10.1016/j.ensm.2021.10.014](https://doi.org/10.1016/j.ensm.2021.10.014)