Hierarchical Bayesian Model for Probabilistic Analysis of Electric Vehicle Battery Degradation

TL;DR

This paper introduces a hierarchical Bayesian model for probabilistic estimation of electric vehicle battery capacity fade, accounting for variable aging factors and uncertainties, achieving over 95% accuracy after training.

Contribution

The paper presents a novel hierarchical Bayesian network model that incorporates multiple external variables and uncertainties for more accurate battery degradation analysis.

Findings

Achieves over 95% accuracy in capacity fade estimation.

Demonstrates model flexibility across different driving and climate conditions.

Uses MCMC sampling for posterior distribution estimation.

Abstract

This paper proposes a hierarchical Bayesian model for probabilistic estimation of the electric vehicle battery capacity fade. Since the battery aging factors such as temperature, current, and state of charge are not fixed, and they change in different times, locations and by the different users, deterministic models with constant parameters cannot accurately evaluate the battery capacity fade. Therefore, a probabilistic presentation of the capacity fade including uncertainties of the measurements or observations of the variables can be a proper solution. We have developed a hierarchical Bayesian Network model for the electric vehicle battery capacity fade considering multiple external variables. The mathematical expression of the model is extracted based on Bayes theorem, the probability distributions for all variables and their dependencies are carefully chosen where the Metropolis Hastings Markov Chain Monte Carlo sampling method is applied to generate the posterior distributions. The model is trained with 85 percent of experimental data to obtain its unseen parameters and tested with other 15 percent of data to prove its accuracy. Also, three case studies for different drivers, different grid services frequencies, and different climates are explored to show model flexibility with different input data. The developed model needs training data for parameter tuning in different conditions. However, after training, it has more than 95 percent precision in estimating the battery capacity fade percentage.