{'fraction_non_alphanumeric': 0.014191106906338695, 'fraction_numerical': 0.003784295175023652, 'mean_word_length': 5.530864197530864, '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}}
Probabilistic Analysis of Electric Vehicle Energy Consumption Using MPC Speed Control and Nonlinear Battery Model

This paper conducts a probabilistic analysis of energy consumption of electric vehicle. In particular, the vehicle speed is controlled by a model predictive control (MPC) to follow given reference speed while minimizing energy consumption, and the battery is modeled by nonlinear dynamic equations. Speed tracking accuracy and energy economy of MPC speed control is evaluated on EPA Federal Test Procedure driving cycle, which is commonly used for city driving testing and includes an approximate driving distance of 17.77 km. Furthermore, to conduct probabilistic analysis, Monte Carlo approach is taken to simulate a total number of ten thousands of synthetic FTP driving cycles, each with different profile. Numerical results and conclusion are then drawn to confirm the robustness of MPC speed control and the environmental friendliness of electric vehicle. Insights on battery operations for maximum energy efficiency are also discussed.