Variability of Battery Wear in Light Duty Plug-In Electric Vehicles Subject to Ambient Temperature, Battery Size, and Consumer Usage

Battery wear in plug-in electric vehicles (PEVs) is a complex function of ambient temperature, battery size, and disparate usage, and is not well understood. Battery life simulation scenarios that capture varying ambient temperature profiles, battery sizes, and driving patterns are of great value to battery manufacturers and vehicle original equipment manufacturers. This study seeks to improve understanding of battery wear in PEVs by implementing a predictive battery wear model, developed by the National Renewable Energy Laboratory, that is capable of capturing the effects of multiple cycling and storage conditions in a representative lithium chemistry. In particular, this paper explores the sensitivity of battery wear rates to ambient conditions, maximum allowable depth of discharge, and vehicle miles traveled. The analysis focuses on two midsize vehicle platforms: a battery electric vehicle (BEV) with a nominal range of 75 mi (121 km) and a plug-in hybrid electric vehicle (PHEV) with 40 mi (64 km) of nominal charge-depleting range. Current U.S. hybrid electric vehicle populations are used to focus analysis on markets where consumers have shown a tendency towards early adoption of advanced vehicle technology. Both cross-sectional and longitudinal driving distance distributions are implemented to represent the variability of vehicle use, both vehicle-to-vehicle and day-to-day. In the scenarios examined, battery wear over an 8-year period was found to be dominated by ambient conditions for the BEV with capacity fade ranging from 19% to 32% while the PHEV was most sensitive to maximum allowable depth of discharge with capacity fade ranging from 16% to 24%. In addition, the BEV and PHEV were found to be comparable in terms of petroleum displacement potential after 8 years of service due to the BEV's limited utility for accomplishing long trips. Future work may include incorporating the effects of temperature on pack internal resistance/available capacity and analyzing a range of vehicle-to-grid scenarios.