UC Davis

The dissertation focuses on understanding the low-speed vehicle (LSV) markets and theirimpacts on China’s future energy use and emissions. I focused on four main areas: 1) current markets status of LSVs, including sales and population, vehicle characteristics, main OEMs, and related policies; 2) vehicle travel intensity of different LSVs and conducting data analytics on real-world LSEV GPS data to understand their different travel patterns; 3) total cost ownership analysis to compare the cost benefits of different vehicle types, conducting sensitivity analysis to understand the variability of levelized costs; 4) energy and emission analysis in different provinces of China to explore the geospatial and technological variations. In chapter 2, I examined key market information, including key sales statistics and stocks, manufacturers and models, technology development, and government’s major policies for LSVs including low-speed electric vehicles (LSEVs), rural vehicles (RVs) and gasoline/electrified twowheelers (G2Ws, E2Ws). I found that despite LSVs facing obstacles such as fierce competitions from car industries and stringent government policies, the LSV industries are developing rapidly and account for a stable market share of new vehicle sales. In chapter 3, I collected by-second GPS data of LSEVs and conducted data analysis to understand the heterogeneity of travel behaviors such as VKT distributions and travel frequencies. I visualized and calculated daily vehicle travel distributions, number of daily trips, travel behaviors differences between weekdays and weekends, and travel behaviors before and during the COVID-19 pandemic. It is found that LSEVs can provide comparable mobility level with E2Ws, RVs and G2Ws. It is also found that the stay-at-home orders and stricter regulations on LSEVs have discouraged LSEV users from operating their vehicles during the COVID-19 pandemic.iii In chapter 4, I developed a comprehensive total cost of ownership model for different low-speed vehicles and their replacement options by considering the impact of factors such as monetary factors and consumer behaviors. Sensitivity analysis such as Monte Carlo simulation were applied to find the stochastic dominance between different vehicles in terms of total costs and levelized costs. It is found that EVs have lower cost of ownership compared with their gasoline or diesel counterparts and the biggest cost component for gasoline/diesel vehicles is the fuel cost while the biggest cost component for EVs is the purchase cost. For 2/3W comparison, the levelized cost is about 0.5 RMB/km for gasoline 3W motorcycles and 3W rural vehicles, while it is about 0.37 RMB/km for gasoline 2Ws and the about 0.2 RMB/km for electrified 2Ws and 3Ws. For 4W comparison, the levelized cost for compact gasoline car and BEVs with 500km range are both around 2 RMB/km, and about 1.5 RMB/km for the BEVs with 300km range and compact PHEVs, while LSEVs have the lowest levelized cost about 0.75 RMB/km. It is also found that LSVs such as LSEVs have very similar cost compared with their counterparts such as Micro EVs due to the higher lead-acid battery cost for LSEVs, implying that replacing lead-acid batteries with lithium-ion batteries will not increase the cost of ownership. In chapter 5, I conducted a well-to-wheel energy and emission analysis of various vehicle types and utilized data on vehicle energy efficiency coupled with a high-resolution grid emission rate data. By considering the technological and geospatial heterogeneity, the energy use and carbon emissions were compared for different provinces, and it is found that the greener grid will enhance the GHG reduction benefits with electrification, for example provinces such as Qinghai, Sichuan with a lower coal-based electricity generation percentage have a larger potential of GHG reduction.