{'fraction_non_alphanumeric': 0.020602218700475437, 'fraction_numerical': 0.0, 'mean_word_length': 5.718085106382978, '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}}
Estimating Energy Consumption on the Basis of Microscopic Driving Parameters for Electric Vehicles

Energy, the environment, and global climate have been placed under great pressure recently by the rapid growth of gasoline- and diesel-powered vehicles. Battery-powered electric vehicles (EVs) have attracted attention because they produce less pollution and low noise and are highly energy efficient. However, limited battery capacity and a short cruising range have hindered the widespread use of EVs. This paper proposes an energy consumption estimation approach for EV route planning and dynamic route guidance, to provide EV drivers with optimal energy-efficient routes and fulfill recharging needs. The impacts of microscopic driving parameters (instantaneous speed and acceleration) and of vehicle-specific power on the EV energy consumption rate are fully analyzed. Energy consumption rate models for the various operation modes (accelerating, decelerating, cruising, and idling) are established on the basis of the data collected with the chassis dynamometer test according to the New European Driving Cycle. Finally, model validation demonstrates that the energy consumption rate models produce estimates relatively close to measured energy consumption.