Numerical Energy Analysis of In-Wheel Motor Driven Autonomous Electric Vehicles

Shen, Kang; Ke, Xinyou; Yang, Fan; Wang, Weibo; Zhang, Cheng; Yuan, Chris

doi:10.1109/tte.2023.3236894

IEEE Trans. Transp. Electrific.

2023

DOI: 10.1109/tte.2023.3236894

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Numerical Energy Analysis of In-Wheel Motor Driven Autonomous Electric Vehicles

Kang Shen

Xinyou Ke

Fan Yang

et al.

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“…Energy consumption influences CO 2 emissions (in the near term), indicating that higher energy consumption might result in increased CO 2 emissions, and conversely (in the extended period). The [202] paper mentioned that current autonomous electric vehicles, which are based on conventional propulsion systems, have limitations in terms of energy efficiency and power transmission. These limitations may hinder large-scale adoption in the future.…”

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confidence: 99%

Localization and Mapping for Self-Driving Vehicles: A Survey

Charroud,

El Moutaouakil,

Palade

et al. 2024

Machines

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The upsurge of autonomous vehicles in the automobile industry will lead to better driving experiences while also enabling the users to solve challenging navigation problems. Reaching such capabilities will require significant technological attention and the flawless execution of various complex tasks, one of which is ensuring robust localization and mapping. Recent surveys have not provided a meaningful and comprehensive description of the current approaches in this field. Accordingly, this review is intended to provide adequate coverage of the problems affecting autonomous vehicles in this area, by examining the most recent methods for mapping and localization as well as related feature extraction and data security problems. First, a discussion of the contemporary methods of extracting relevant features from equipped sensors and their categorization as semantic, non-semantic, and deep learning methods is presented. We conclude that representativeness, low cost, and accessibility are crucial constraints in the choice of the methods to be adopted for localization and mapping tasks. Second, the survey focuses on methods to build a vehicle’s environment map, considering both the commercial and the academic solutions available. The analysis proposes a difference between two types of environment, known and unknown, and develops solutions in each case. Third, the survey explores different approaches to vehicles’ localization and also classifies them according to their mathematical characteristics and priorities. Each section concludes by presenting the related challenges and some future directions. The article also highlights the security problems likely to be encountered in self-driving vehicles, with an assessment of possible defense mechanisms that could prevent security attacks in vehicles. Finally, the article ends with a debate on the potential impacts of autonomous driving, spanning energy consumption and emission reduction, sound and light pollution, integration into smart cities, infrastructure optimization, and software refinement. This thorough investigation aims to foster a comprehensive understanding of the diverse implications of autonomous driving across various domains.

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mentioning

confidence: 99%

Localization and Mapping for Self-Driving Vehicles: A Survey

Charroud,

El Moutaouakil,

Palade

et al. 2024

Machines

View full text Add to dashboard Cite

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Life cycle assessment of lithium ion battery from water-based manufacturing for electric vehicles

Shen,

Zhai,

et al. 2023

Resources, Conservation and Recycling

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Numerical Energy Analysis of In-Wheel Motor Driven Autonomous Electric Vehicles

Cited by 2 publications

References 45 publications

Localization and Mapping for Self-Driving Vehicles: A Survey

Localization and Mapping for Self-Driving Vehicles: A Survey

Life cycle assessment of lithium ion battery from water-based manufacturing for electric vehicles

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