Addressing human factors in electric vehicle system design: Building an integrated computational humaneelectric vehicle framework

The electric vehicle (EV) has been developing rapidly and predicting the lifetime of Li-ion batteries in EVs has become an important issue. Characteristics of human drivers and the battery configuration interact and both play important roles in determining EV battery lifetime. Moreover, due to the relatively high cost of real EVs and long testing time for battery life of EVs, it is important to integrate the human driver and EV battery into one framework and implement it in a driving simulator test-bed. To address this problem, the current work proposes the first integrated computational humaneelectric vehicle framework (ICHEV) and implemented in a STISIM driving simulator. ICHEV can be used to: 1) Analyze the effects of driver differences (including driver characteristics, charging strategy, and driving schedule) and battery configuration on battery lifetime in saving real EV test cost and time; 2) Predict the battery lifetime given the driver characteristics, driving schedule, and battery configuration; and 3) Obtain the optimal battery configuration, the optimal driving patterns and charging strategy for the purpose of maximal battery lifetime. According to the ICHEV, software was developed and further applications of the ICHEV were also discussed. 2012 Elsevier B.V. All rights reserved.