Multiobjective co-optimization of cooperative adaptive cruise control and energy management strategy for PHEVs

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Multiobjective co-optimization of cooperative adaptive cruise control and energy management strategy for PHEVs. / He, Yinglong; Zhou, Quan; Makridis, Michail; Mattas, Konstantinos; Li, Ji; Williams, Huw; Xu, Hongming.

In: IEEE Transactions on Transportation Electrification, Vol. 6, No. 1, 9000888, 03.2020, p. 346 - 355.

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He, Yinglong ; Zhou, Quan ; Makridis, Michail ; Mattas, Konstantinos ; Li, Ji ; Williams, Huw ; Xu, Hongming. / Multiobjective co-optimization of cooperative adaptive cruise control and energy management strategy for PHEVs. In: IEEE Transactions on Transportation Electrification. 2020 ; Vol. 6, No. 1. pp. 346 - 355.

Bibtex

@article{62a84bc1a0fd406d82a6f6253d3251c8,
title = "Multiobjective co-optimization of cooperative adaptive cruise control and energy management strategy for PHEVs",
abstract = "Electrification, automation, and connectivity in the automotive and transport industries are gathering momentum, but there are escalating concerns over their need for co-optimization to improve energy efficiency, traffic safety, and ride comfort. Previous approaches to these multiobjective co-optimization problems often overlook tradeoffs and scale differences between the objectives, resulting in misleading optimizations. To overcome these limitations, this article proposes a Pareto-based framework that demonstrably optimizes the system parameters of the cooperative adaptive cruise control (CACC) and the energy management strategy (EMS) for plug-in hybrid electric vehicles (PHEVs). The high-level Pareto knowledge assists in finding a best compromise solution. The results of this article suggest that the energy and the comfort targets are harmonious, but both conflict with the safety target. Validation using real-world driving data shows that the Pareto optimum for CACC and EMS systems, relative to the baseline, can reduce energy consumption (by 7.57%) and tracking error (by 68.94%) while simultaneously satisfying ride comfort needs. In contrast to the weighted-sum method, the proposed Pareto method can optimally balance and scale the multiple-objective functions. In addition, sensitivity analysis proves that the vehicle reaction time impacts significantly on tracking safety, but its effect on energy saving is trivial.",
keywords = "cooperative adaptive cruise control (CACC), energy consumption, energy management strategy (EMS), multiobjective co-optimization, tracking safety",
author = "Yinglong He and Quan Zhou and Michail Makridis and Konstantinos Mattas and Ji Li and Huw Williams and Hongming Xu",
year = "2020",
month = mar,
doi = "10.1109/TTE.2020.2974588",
language = "English",
volume = "6",
pages = "346 -- 355",
journal = "IEEE Transactions on Transportation Electrification",
issn = "2332-7782",
publisher = "Institute of Electrical and Electronics Engineers (IEEE)",
number = "1",

}

RIS

TY - JOUR

T1 - Multiobjective co-optimization of cooperative adaptive cruise control and energy management strategy for PHEVs

AU - He, Yinglong

AU - Zhou, Quan

AU - Makridis, Michail

AU - Mattas, Konstantinos

AU - Li, Ji

AU - Williams, Huw

AU - Xu, Hongming

PY - 2020/3

Y1 - 2020/3

N2 - Electrification, automation, and connectivity in the automotive and transport industries are gathering momentum, but there are escalating concerns over their need for co-optimization to improve energy efficiency, traffic safety, and ride comfort. Previous approaches to these multiobjective co-optimization problems often overlook tradeoffs and scale differences between the objectives, resulting in misleading optimizations. To overcome these limitations, this article proposes a Pareto-based framework that demonstrably optimizes the system parameters of the cooperative adaptive cruise control (CACC) and the energy management strategy (EMS) for plug-in hybrid electric vehicles (PHEVs). The high-level Pareto knowledge assists in finding a best compromise solution. The results of this article suggest that the energy and the comfort targets are harmonious, but both conflict with the safety target. Validation using real-world driving data shows that the Pareto optimum for CACC and EMS systems, relative to the baseline, can reduce energy consumption (by 7.57%) and tracking error (by 68.94%) while simultaneously satisfying ride comfort needs. In contrast to the weighted-sum method, the proposed Pareto method can optimally balance and scale the multiple-objective functions. In addition, sensitivity analysis proves that the vehicle reaction time impacts significantly on tracking safety, but its effect on energy saving is trivial.

AB - Electrification, automation, and connectivity in the automotive and transport industries are gathering momentum, but there are escalating concerns over their need for co-optimization to improve energy efficiency, traffic safety, and ride comfort. Previous approaches to these multiobjective co-optimization problems often overlook tradeoffs and scale differences between the objectives, resulting in misleading optimizations. To overcome these limitations, this article proposes a Pareto-based framework that demonstrably optimizes the system parameters of the cooperative adaptive cruise control (CACC) and the energy management strategy (EMS) for plug-in hybrid electric vehicles (PHEVs). The high-level Pareto knowledge assists in finding a best compromise solution. The results of this article suggest that the energy and the comfort targets are harmonious, but both conflict with the safety target. Validation using real-world driving data shows that the Pareto optimum for CACC and EMS systems, relative to the baseline, can reduce energy consumption (by 7.57%) and tracking error (by 68.94%) while simultaneously satisfying ride comfort needs. In contrast to the weighted-sum method, the proposed Pareto method can optimally balance and scale the multiple-objective functions. In addition, sensitivity analysis proves that the vehicle reaction time impacts significantly on tracking safety, but its effect on energy saving is trivial.

KW - cooperative adaptive cruise control (CACC)

KW - energy consumption

KW - energy management strategy (EMS)

KW - multiobjective co-optimization

KW - tracking safety

UR - http://www.scopus.com/inward/record.url?scp=85079616228&partnerID=8YFLogxK

U2 - 10.1109/TTE.2020.2974588

DO - 10.1109/TTE.2020.2974588

M3 - Article

VL - 6

SP - 346

EP - 355

JO - IEEE Transactions on Transportation Electrification

JF - IEEE Transactions on Transportation Electrification

SN - 2332-7782

IS - 1

M1 - 9000888

ER -