LAPSE:2023.2081
Published Article

LAPSE:2023.2081
Study on Speed Planning of Signalized Intersections with Autonomous Vehicles Considering Regenerative Braking
February 21, 2023
Abstract
In order to reduce the energy consumption caused by the frequent braking of vehicles at signalized intersections, an optimized speed trajectory control method is proposed, based on braking energy recovery efficiency (BERE) in connection with an automated system for vehicle real-time interaction with roadside facilities and regional central control. Our objectives were as follows; firstly, to establish the simulation model of the hybrid energy regenerative braking system (HERBS) and to verify it by bench test. Secondly, to build up the genetic algorithm (GA) optimization model for the deceleration stopping of the HERBS. Then, to obtain signal light status and timing information to be the constraints; the BERE is to be the optimized objective, resulting in optimization for the speed trajectory under the deceleration stopping condition of a single signalized intersection. Finally, vehicle simulations in ADVISOR software are utilized to validate the optimization results. The results show that the BERE during deceleration stopping at a single signalized intersection after the speed trajectory optimization is 36.21% higher than that of inexperienced drivers, and 7.82% higher than that of experienced drivers.
In order to reduce the energy consumption caused by the frequent braking of vehicles at signalized intersections, an optimized speed trajectory control method is proposed, based on braking energy recovery efficiency (BERE) in connection with an automated system for vehicle real-time interaction with roadside facilities and regional central control. Our objectives were as follows; firstly, to establish the simulation model of the hybrid energy regenerative braking system (HERBS) and to verify it by bench test. Secondly, to build up the genetic algorithm (GA) optimization model for the deceleration stopping of the HERBS. Then, to obtain signal light status and timing information to be the constraints; the BERE is to be the optimized objective, resulting in optimization for the speed trajectory under the deceleration stopping condition of a single signalized intersection. Finally, vehicle simulations in ADVISOR software are utilized to validate the optimization results. The results show that the BERE during deceleration stopping at a single signalized intersection after the speed trajectory optimization is 36.21% higher than that of inexperienced drivers, and 7.82% higher than that of experienced drivers.
Record ID
Keywords
connected automated vehicle, regenerative braking, signalized intersection, speed trajectory
Subject
Suggested Citation
Li N, Yang J, Jiang J, Hong F, Liu Y, Ning X. Study on Speed Planning of Signalized Intersections with Autonomous Vehicles Considering Regenerative Braking. (2023). LAPSE:2023.2081
Author Affiliations
Li N: School of Intelligent Manufacture, Taizhou University, Taizhou 318000, China
Yang J: School of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310014, China
Jiang J: Upower Automotive Technology (Shanghai) Co., Ltd., Shanghai 200030, China
Hong F: School of Continuing Education, Zhejiang University of Science and Technology, Hangzhou 310014, China
Liu Y: School of Intelligent Manufacture, Taizhou University, Taizhou 318000, China [ORCID]
Ning X: School of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310014, China
Yang J: School of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310014, China
Jiang J: Upower Automotive Technology (Shanghai) Co., Ltd., Shanghai 200030, China
Hong F: School of Continuing Education, Zhejiang University of Science and Technology, Hangzhou 310014, China
Liu Y: School of Intelligent Manufacture, Taizhou University, Taizhou 318000, China [ORCID]
Ning X: School of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310014, China
Journal Name
Processes
Volume
10
Issue
7
First Page
1414
Year
2022
Publication Date
2022-07-20
ISSN
2227-9717
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Original Submission
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PII: pr10071414, Publication Type: Journal Article
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LAPSE:2023.2081
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https://doi.org/10.3390/pr10071414
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Feb 21, 2023
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