{'fraction_non_alphanumeric': 0.013282732447817837, 'fraction_numerical': 0.003795066413662239, 'mean_word_length': 5.211764705882353, '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}}
Predictive Energy Management for Hybrid Vehicles Based on Driving Cycle Recognition

This article presents a novel approach to design an optimum energy management for a series plug-in hybrid electric vehicle based on driving cycle recognition. Thanks to an intensive study of driver habits, an important part of cycles is predictable. Optimized energy management can be developed for representative cycles via DP. Those results can be applied to the same cycle with slight adaptations thereafter with quality factors above 98%.