The Bayesian framework for EV battery capacity fade modeling
Department of Electrical and Computer Engineering, Data Science
In this study, we present a Bayesian Networks (BNs) approach for the electric vehicle (EV) battery degradation modeling. Battery aging is caused by factors that carry heavy uncertainty, such as battery usage depending on driver behavior, temperature profile depending on location and thermal management system, etc. with all these variations complicating the battery aging modeling with traditional frameworks. That is why we propose that the modeling should be carried out in a Bayesian Network framework that is capable of incorporating uncertainty and causality. The battery aging model is developed in the Bayesian framework and set of training and test data are used to validate the model. Results show that the BN model has a promising performance in the battery aging modeling. The model is also used to estimate the battery capacity loss in real driving cycles.
2018 IEEE Transportation Electrification Conference and Expo
The Bayesian framework for EV battery capacity fade modeling.
2018 IEEE Transportation Electrification Conference and Expo.
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/993