{'fraction_non_alphanumeric': 0.027712264150943397, 'fraction_numerical': 0.003537735849056604, 'mean_word_length': 5.060714285714286, '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}}
Personalized Energy Consumption Prediction of CAEVs With Personal Driving Cycle Selection

For connected and autonomous electric vehicles (CAEVs), drivers must choose a power-efficient route because the energy source of a CAEV is a battery. The driver can find the power-efficient route and reduce the risk of the battery running out if the CAEV navigation system can accurately predict the battery’s state of charge (SOC) based on the power consumption prediction for the selected route. As the driver’s driving pattern and the vehicle’s battery capacity are different for individual vehicles, it is important to predict the power consumption and battery SOC for the selected route. In this paper, the system model of CAEV to collect on-board diagnostics (OBD), OBD data cloud (ODC) to manage the OBD data, and the sequence of message exchange between them is proposed. Further, we propose an algorithm that can learn power consumption based on machine learning (ML) in the ODC. It predicts the battery SOC at the end of a trip using the current SOC when the onboard navigation system requests prediction for a specific route of the CAEV. The algorithm’s performance has been evaluated by the OBD dataset of personal vehicles collected in Michigan based on three road types: downtown, urban road, and highway. The results confirmed that the proposed MLR+DNN model could predict battery consumption with an average accuracy of 92%. Additionally, an average accuracy of 94% was obtained in selecting the most similar personal driving cycle (PDC) using hierarchical grey relational analysis (GRA), which is about 15% higher than the accuracy obtained by using the dynamic time warping (DTW) method.