Real-Time Degradation Abatement Framework for Energy Storage System in Automotive Application using Data-Driven Approaches

Author

Laxman TimilsinaFollow

Date of Award

5-2024

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Electrical and Computer Engineering (Holcomb Dept. of)

Committee Chair/Advisor

Christopher S. Edrington

Committee Member

Johan H. Enslin

Committee Member

Yingjie Lao

Committee Member

Behnaz Papari

Committee Member

Gokhan Ozkan

Abstract

The increasing popularity of electric vehicles (EVs) is driven by their compatibility with sustainable energy goals. However, the decline in the performance of energy storage systems, such as batteries, due to their degradation puts EVs and hybrid electric vehicles (HEVs) at a disadvantage compared to traditional internal combustion engine (ICE) vehicles. The batteries used in these vehicles have limited life. The degradation of the battery is accelerated by the operating conditions of the vehicle, which further reduces its life and increases the reliability and economic concerns for the vehicle’s operation. The aging mechanism inside a battery cannot be eliminated but can be minimized depending on the vehicle’s operating conditions and different control mechanisms that can alter the operating conditions. Different operating conditions affect the aging mechanism differently. Knowing the factors and how they impact battery capacity is crucial for minimizing degradation. This dissertation presents the detailed degradation mechanism inside the battery and the major factors responsible for the degradation, along with their effects on the battery during the operation of EVs. Then, to abate the degradation mechanism, a prognostic-based control framework (PBCF) for HEVs is proposed. Also, this framework reduces the overall cost of operating HEVs by taking into account the degradation of the energy storage systems. The strategy utilizes a degradation forecasting model of energy storage systems to predict their degradation paths. Analytical and data-driven approaches are used to find the degradation path of the energy storage systems as follows: Markov Chain Model and Neural Network Model. These two models employ distinct datasets to validate the feasibility of the proposed strategy. The predicted degradation rate is then used to control the HEV via its energy management (EM) system in order to reduce the degradation of energy storage systems. During the simulation, three distinct operating scenarios are developed to observe their effects on battery degradation and the response of the proposed control strategy PBCF.

Recommended Citation

Timilsina, Laxman, "Real-Time Degradation Abatement Framework for Energy Storage System in Automotive Application using Data-Driven Approaches" (2024). All Dissertations. 3648.
https://open.clemson.edu/all_dissertations/3648

Author ORCID Identifier

0000-0002-0081-7702