Low Purchase Willingness for Battery Electric Vehicles: Analysis and Simulation Based on the Fault Tree Model

Purchase intention is the key to popularizing battery electric vehicles (BEVs) and to developing the industry. This study combines classical theoretical and qualitative research, and applies fault tree analysis (FTA) methods to study factors that hinder BEV purchase, and identify the logical relationship between top fault events and basic events, by calculating minimal cut sets and minimal path sets. Activity based classification analysis was used to investigate the key basic event and key event combination (i.e., minimal cut sets) that hinders purchase intention, with the effectiveness and feasibility of the proposed method verified by Monte Carlo simulation. The results indicate (1) there were 26 minimal cut sets and 18 minimal path sets in the fault tree model, and the fault tree was defined by four key event combinations and five key basic events; and (2) by reducing key events’ failure probability, the probability of fault tree cumulative occurrence was reduced from 0.86021 to 0.57406 over 100,000 Monte Carlo simulations, i.e., the willingness to purchase BEVs was significantly increased. Thus, the proposed FTA method was feasible and effective for addressing low purchase intentions. Consequently, some policy implications are suggested.