{'fraction_non_alphanumeric': 0.024720423778693348, 'fraction_numerical': 0.007651559741024131, 'mean_word_length': 5.367041198501872, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Implications of Modeling Range and Infrastructure Barriers to Adoption of Battery Electric Vehicles

Compared with traditional vehicles, light-duty battery electric vehicles (BEVs) currently have price premiums and noncost limitations, such as reduced range, sparse public recharging infrastructure, and long recharge times. These additional limitations can be captured in different ways in a consumer choice model. Three approaches are implemented to noncost barrier modeling, and results are compared. A penalty approach quantifies limitations as additional costs to the consumer, and two threshold approaches determine BEV suitability by the frequency that daily driving distance exceeds the vehicle range. GPS-based trip data are used to form ensemble distributions of low-, medium-, and high-intensity driving distances to support the analysis. All approaches show limited (5%) adoption of BEVs by 2050, and the BEV mileage fraction trails the stock fraction because of the use of substitute vehicles for high-mileage trips and adoption biased toward lower driving intensity segments. In fact, a majority of the electrified miles driven stem from plug-in hybrid electric vehicles, not BEVs. Of the BEVs, the powertrains offering 150- to 250-mi ranges are responsible for more than 50% of sales. Results also hint that longer-range BEVs act as primary household vehicles, but lower-range BEVs serve as secondary household vehicles. A parametric exploration shows that mechanisms to mitigate the hardship of the noncost barriers can significantly increase adoption rates but that reducing battery price alone does not. However, these mechanisms can be different for different modeling approaches.