{'fraction_non_alphanumeric': 0.023686158401184307, 'fraction_numerical': 0.0022205773501110288, 'mean_word_length': 5.76, '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}}
Addressing human factors in electric vehicle design: Building an integrated computational human-electric vehicle framework

Electric vehicle (EV) has been developed rapidly and predicting the lifetime of Li-ion batteries in EVs has become an important issue. Characteristics of human driver and the battery configuration interact and both play important roles in determining EV battery lifetime. However, few of existing studies was able to integrate both driver and EV battery into one framework. To address this problem, the current work proposed the first integrated computational human-electric vehicle framework (ICHEV) and analyzed the effects of driver differences (including personality, decision making reference (DMR), charging strategy, driving profile, and living schedule) and battery configuration on the lifetime of the battery. The battery life can be predicted based on and battery configuration. Software was developed according to the framework. ICHEV can be used to: 1) Predict battery life given the fixed driver characteristics and fixed battery configuration; 2) Obtain the optimal battery configuration given the fixed drivers' characteristics and target lifetime of battery; and 3) Propose the optimal driving behavior and charging strategy given the fixed battery configuration, target lifetime of battery and living schedule