Date of Award

Spring 5-1-2022

Document Type

Dissertation

Degree Name

Doctor of Philosophy in Technology Management

Department

College of Technology

Abstract

Electric vehicles (EVs) are emerging as a component of the global solution to combat climate change. However, in North America, particularly in the United States and Canada, the transition away from internal combustion engines (ICE) has been slow. North America faces unique challenges due to its geographical size and population in comparison to other continents. The good news is that EV adoption is increasing within North America. Along with increased EV adoption, governments and public companies are constructing charging infrastructure to support increased consumer EV purchases. Despite increased adoption, many future and current owners throughout North American society have concerns about an electric vehicles’ key feature: the battery. Many EV owners are concerned about the battery's State of Health (SOH) – how to keep batteries healthy and use best practices to keep their range at maximum capacity. SOH is influenced by five key factors: (1) temperature, (2) charge/discharge rate, (3) charge/discharge depth, (4) cyclic charging, and (5) ending State of Charge (SOC). This study primarily focuses on data centered around charging. This dissertation examines data generated by everyday EV users and uses it to predict how charging habits affect batteries over time. Charging effects include decreasing battery SOH and capacity degradation. Lowering the SOH reduces the battery's viability for continuous use; at approximately 70% SOH the battery is 'typically' deemed End of Life (EoL). The overall range of the EV is affected by capacity degradation; as batteries degrade, the total km (or miles) iv available decreases. This study uses regression analysis to examine relationships and predictors of SOH, temperature, levels of charging, and SOC. The data collected and analyzed determine best practices for charging batteries at home and abroad for consumers. There were two methods for analyzing data: (1) Using EV generated data (SOH, Charger Type) saved in CSV files via a smartphone application, and (2) Analyzing consumed energy in a large dataset using a segmentation process based on equivalent SOC differences between two points in time. The current study makes use of one of the largest datasets of "real world" data ever collected from EVs in the United States and Canada, with over one million lines. Eighteen models of EVs are used to make comparisons for amounts of degradation over one year. A discussion of how these findings affect EV owners’ usage of models from 2010-2020 is included. Multiple recommendations for future studies are provided.

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