2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC) 2016
DOI: 10.1109/itsc.2016.7795793
|View full text |Cite
|
Sign up to set email alerts
|

Ecological Adaptive Cruise Control of a plug-in hybrid electric vehicle for urban driving

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
4
1

Citation Types

0
21
0

Year Published

2018
2018
2023
2023

Publication Types

Select...
6
1

Relationship

0
7

Authors

Journals

citations
Cited by 20 publications
(21 citation statements)
references
References 18 publications
0
21
0
Order By: Relevance
“…Based on ACC, literature [132] proposes an adaptive neuro‐fuzzy inference system, which uses the road data provided by ITS to control the vehicle speed adaptively. Literature [133] proposes an ecological ACC system which employs radar and traffic light‐to‐vehicle communication to predict the future route of the preceding vehicle and the information is used to drive the vehicle with ecological driving mode. The simulation results show that the energy cost of this system is decreased by about 17%, compared with the conventional tracking ACC.…”
Section: Emss For Hev/phev Under Itsmentioning
confidence: 99%
“…Based on ACC, literature [132] proposes an adaptive neuro‐fuzzy inference system, which uses the road data provided by ITS to control the vehicle speed adaptively. Literature [133] proposes an ecological ACC system which employs radar and traffic light‐to‐vehicle communication to predict the future route of the preceding vehicle and the information is used to drive the vehicle with ecological driving mode. The simulation results show that the energy cost of this system is decreased by about 17%, compared with the conventional tracking ACC.…”
Section: Emss For Hev/phev Under Itsmentioning
confidence: 99%
“…Predictive energy optimization for connected and automated PHEVs was reported to deliver a fuel saving of 10.1 % when considering the benefits of traffic light phasing [19]. 2) Studies for car-following scenarios [20]- [22] mainly address constraints of the movement of the preceding vehicle, to improve fuel economy, tracking safety, etc. For instance, a predictive car-following power management system for PHEVs was demonstrated to simultaneously coordinate battery state-of-charge (SoC) planning, inter-vehicle spacing, and power split in a cost-optimal manner [23].…”
Section: Introductionmentioning
confidence: 99%
“…2) Other studies highlight multi-objective co-optimization [22]- [24], addressing various needs including energy efficiency, tracking safety, ride comfort, traffic throughput, etc., especially in car-following and platooning scenarios. Previous studies converted the original CACC/ACC and EMS co-optimization with multiple objectives into a single-objective optimization problem by weighted-sum methods.…”
Section: Introductionmentioning
confidence: 99%
“…The obtained traffic information was used to predict the driving trajectory of its preceding vehicle and drive with an ecological driving pattern. The simulation results on a plug‐in HEV showed that 17% energy saving was improved compared with a regular car‐following ACC [31]. In summary, all these studies reveal that the combination of ACC and hybrid powertrain can enhance tracking ability, fuel economy, and driving comfort.…”
Section: Introductionmentioning
confidence: 99%