Health Indicator Development for Low-Voltage Battery Diagnostics and Prognostics in Electric Vehicles
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Abstract
Each electric vehicle (EV) requires a low-voltage (e.g., 12V) auxiliary battery to provide electric power to onboard electronic control units, lighting systems, and various sensors during power off. Therefore, when the low-voltage battery is in low state of health (SOH) or low state of charge (SOC), it may cause no-start events. The existing OnStar Proactive Alert service can effectively predict low SOC or low SOH events for low-voltage batteries in Internal Combustion Engine vehicles using cranking signals. However, it does not work for EVs since there is no cranking event. In this work, a diagnostic and prognostic solution for the low-voltage battery of EVs is proposed. Four novel health indicators (HIs) along with the decision-making system are developed based on equivalent circuit models. Furthermore, the selection process of appropriate HIs tailored to various operational states of the vehicle is described. The validation results based on GM test EV data have demonstrated the effectiveness and robustness of the proposed solution.
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Battery, Diagnostics
Du, X., & Zhang, Y. (2018). Development of Robust Fault Signatures for Battery and Starter Failure Prognosis. Annual Conference of the PHM Society. Vol. 10. No. 1.
Emadi, A., Williamson, S. S., & A. Khaligh. (2006). Power electronics intensive solutions for advanced electric hybrid electric and fuel cell vehicular power systems. IEEE Trans. Power Electron., 21(3), 567- 577.
G., S. M., & Nikdel, M. (2014). Various battery models for various simulation studies and applications. Renewable and Sustainable Energy Reviews, 32(1364-0321), 477-485.
Hasan, M. K., Mahmud, M., Habib, A. A., Motakabber, S., & Islam, S. (2021). Review of electric vehicle energy storage and management system: Standards, issues, and challenges. Journal of Energy Storag, 41, 102940.
Hou, R., Magne, P., Bilgin, B., & Emadi, A. (2015). A topological evaluation of isolated DC/DC converters for auxiliary power modules in electrified vehicle applications. Proc. IEEE Appl. Power Electron.Conf. . Expo.
Khan, S., & T. Yairi. (2018). A review on the application of deep learning in system health management. Mechanical Systems and Signal Processing, 107, 241-265.
Lam, L., Ozgun, H., Lim, O., Hamilton, J., Vu, L., Vella, D., & Rand, D. (1995). Pulsed-current charging of lead/acid batteries - a possible means for overcoming premature capacity loss. Journal of Power Sources, 215-228.
Mu, H., Liu, J., Ewing, R., & Li, J. (2021). Human Indoor Positioning via Passive Spectrum Monitoring. 2021 55th Annual Conference on Information Sciences and Systems (CISS), (pp. 1-6). Baltimore, MD.
Naha, A., Khandelwal, A., Agarwal, S., Tagade, P., Hariharan, K. S., Kaushik, A., . . . Oh, B. (2020). Internal short circuit detection in Li-ion batteries using supervised machine learning. Scientific Reports, 10(1), 1301.
Ng, S. S., Xing, Y., & Tsui, K. L. (2014). A naive Bayes model for robust remaining useful life prediction of lithium-ion battery. Applied Energy, 118(0306- 2619), 114-123.
Olabi, A., Abdelghafar, A. A., Soudan, B., Alami, A. H., Semeraro, C., Radi, M. A., . . . Abdelkareem, M. A. (2024). Artificial neural network driven prognosis and estimation of Lithium-Ion battery states: Current insights and future perspectives. Ain Shams Engineering Journal, 15(2), 102429.
Samanta, A., Chowdhuri, S., & Williamson, S. (2021). Machine Learning-Based Data-Driven Fault Detection/Diagnosis of Lithium-Ion Battery: A Critical Review. Electronics , 10, 1309.
Wang, C., Zheng, P., & J. Bauman. (2023). A Review of Electric Vehicle Auxiliary Power Modules: Challenges, Topologies, and Future Trends. IEEE Transactions on Power Electronics, 38(9), 11233- 11244.
Xing, Y., Ma, E. W., Tsui, K.-L., & Pecht, M. (2013). An ensemble model for predicting the remaining useful performance of lithium-ion batteries. Microelectronics Reliability, 53(6), 811-820.
Zhao, G., Zhang, G., & Ge, Q. (2016). Research Advances in Fault Diagnosis and Prognostic Based on Deep Learning. IEEE Prognostics and System Health Management Conference. Chengdu, China.
Zhou, W., Zheng, Y., Pan, Z., & Lu, Q. (2021). Review on the Battery Model and SOC Estimation Method. Processes, 9, 1685.
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