A Novel Adaptive Cruise Control Strategy for Electric Vehicles Based on a Hierarchical Framework

Conclusive evidence has demonstrated the critical importance of adaptive cruise control (ACC) in relieving traffic congestion. To improve the performance of the ACC system, this paper proposes a novel ACC strategy for electric vehicles based on a hierarchical framework. Three main efforts have been...

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Autores principales:	Yanwu Xu, Liang Chu, Di Zhao, Cheng Chang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	adaptive cruise control recursive least squares model predictive control hierarchical framework iterative learning Mechanical engineering and machinery TJ1-1570
Acceso en línea:	https://doaj.org/article/7c1cd19fdec44689bbd71078d4543176
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spelling	oai:doaj.org-article:7c1cd19fdec44689bbd71078d45431762021-11-25T18:12:07ZA Novel Adaptive Cruise Control Strategy for Electric Vehicles Based on a Hierarchical Framework10.3390/machines91102632075-1702https://doaj.org/article/7c1cd19fdec44689bbd71078d45431762021-10-01T00:00:00Zhttps://www.mdpi.com/2075-1702/9/11/263https://doaj.org/toc/2075-1702Conclusive evidence has demonstrated the critical importance of adaptive cruise control (ACC) in relieving traffic congestion. To improve the performance of the ACC system, this paper proposes a novel ACC strategy for electric vehicles based on a hierarchical framework. Three main efforts have been made to distinguish our work from the existing research. Firstly, a sliding acceleration identification model is established based on the recursive least squares algorithm with multiple forgetting factors (MFF-RLS). Secondly, with vehicle following, economy, and comfort as the optimization objectives, the upper-level controller is developed based on the model predictive control (MPC) algorithm. Benefit from the identification of the sliding acceleration, the MPC controller holds better capability in accommodating environmental changes. Thirdly, an iterative learning lower-level controller is designed to control the driving and braking systems. Considering the efficiency of regenerative braking, the braking force distribution strategy is also designed in the lower-level controller. Simulation results show that, compared with the conventional MPC-based ACC strategy, the proposed strategy has similar performance in vehicle following, but it makes great improvements in comfort and economy. The specific features are that the vehicle acceleration and speed fluctuation are significantly reduced, and the energy consumption is also reduced by 2.05%.Yanwu XuLiang ChuDi ZhaoCheng ChangMDPI AGarticleadaptive cruise controlrecursive least squaresmodel predictive controlhierarchical frameworkiterative learningMechanical engineering and machineryTJ1-1570ENMachines, Vol 9, Iss 263, p 263 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	adaptive cruise control recursive least squares model predictive control hierarchical framework iterative learning Mechanical engineering and machinery TJ1-1570
spellingShingle	adaptive cruise control recursive least squares model predictive control hierarchical framework iterative learning Mechanical engineering and machinery TJ1-1570 Yanwu Xu Liang Chu Di Zhao Cheng Chang A Novel Adaptive Cruise Control Strategy for Electric Vehicles Based on a Hierarchical Framework
description	Conclusive evidence has demonstrated the critical importance of adaptive cruise control (ACC) in relieving traffic congestion. To improve the performance of the ACC system, this paper proposes a novel ACC strategy for electric vehicles based on a hierarchical framework. Three main efforts have been made to distinguish our work from the existing research. Firstly, a sliding acceleration identification model is established based on the recursive least squares algorithm with multiple forgetting factors (MFF-RLS). Secondly, with vehicle following, economy, and comfort as the optimization objectives, the upper-level controller is developed based on the model predictive control (MPC) algorithm. Benefit from the identification of the sliding acceleration, the MPC controller holds better capability in accommodating environmental changes. Thirdly, an iterative learning lower-level controller is designed to control the driving and braking systems. Considering the efficiency of regenerative braking, the braking force distribution strategy is also designed in the lower-level controller. Simulation results show that, compared with the conventional MPC-based ACC strategy, the proposed strategy has similar performance in vehicle following, but it makes great improvements in comfort and economy. The specific features are that the vehicle acceleration and speed fluctuation are significantly reduced, and the energy consumption is also reduced by 2.05%.
format	article
author	Yanwu Xu Liang Chu Di Zhao Cheng Chang
author_facet	Yanwu Xu Liang Chu Di Zhao Cheng Chang
author_sort	Yanwu Xu
title	A Novel Adaptive Cruise Control Strategy for Electric Vehicles Based on a Hierarchical Framework
title_short	A Novel Adaptive Cruise Control Strategy for Electric Vehicles Based on a Hierarchical Framework
title_full	A Novel Adaptive Cruise Control Strategy for Electric Vehicles Based on a Hierarchical Framework
title_fullStr	A Novel Adaptive Cruise Control Strategy for Electric Vehicles Based on a Hierarchical Framework
title_full_unstemmed	A Novel Adaptive Cruise Control Strategy for Electric Vehicles Based on a Hierarchical Framework
title_sort	novel adaptive cruise control strategy for electric vehicles based on a hierarchical framework
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/7c1cd19fdec44689bbd71078d4543176
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A Novel Adaptive Cruise Control Strategy for Electric Vehicles Based on a Hierarchical Framework

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