Reducing Fuel Consumption by Using Information from Connected and Automated Vehicle Modules to Optimize Propulsion System Control

Olin, Pete; Aggoune, Karim; Tang, Li; Confer, Keith; Kirwan, John; Deshpande, Shreshta Rajakumar; Gupta, Sidhant; Tulpule, Punit; Canova, Marcello; Rizzoni, Giorgio

doi:10.4271/2019-01-1213

Cited by 35 publications

(17 citation statements)

References 14 publications

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“…∆d s = d s+1 − d s ) calculated from the distance traveled (d s ) along the route at position s, and vs = vs+vs+1 2 is the average velocity. The state and action space are subject to a set of constraints, as described in Olin et al (2019).…”

Section: Discussionmentioning

confidence: 99%

Eco-Driving of Connected and Autonomous Vehicles with Sequence-to-Sequence Prediction of Target Vehicle Velocity

Gupta

Canova

2021

IFAC-PapersOnLine

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Section: Discussionmentioning

confidence: 99%

Eco-Driving of Connected and Autonomous Vehicles with Sequence-to-Sequence Prediction of Target Vehicle Velocity

Gupta

Canova

2021

IFAC-PapersOnLine

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“…PSU (Pennsylvania State University) has analyzed the platoon of heavy-duty vehicles without jeopardizing the air cooling of the engine [14] , MTU (Michigan Technology University) carried out the simulation and tests for the vehicles in running cycles [15] , SwRI (Southwest Research Institute) explored and practiced the bench test method [16] . OSU (Ohio State University) concerns the potential of various fuel-saving technologies [17] .…”

Section: Researches and Activities In Usmentioning

confidence: 99%

Research on the fuel consumption conservation potential of ADAS on passenger cars

Qian

2021

E3S Web Conf.

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Under the trend of automobile electrification, network connection, and intelligence, EU and USA have carried out fuel-saving research and initiatives on ADAS and CAV. The eCoMove project has aimed at economically optimal driving control and traffic management; MAVEN discusses the technical path of GLOSA (Green Light Optimal Speed Advisory) and ecological auto-driving EAD (Eco-Autonomous Driving) by smoothing the vehicle speed. The American NEXTCAR project contains multiple projects. When supplemented with DSF (Dynamic Skip Fire) and 48V technology, the road test led by Ohio State University resulted in a 15% fuel saving rate. Platoon and optimizing intersection signal lights can offer vehicles a more fuel-efficient condition; slope energy utilization, HEV SOC active management, cold storage evaporator, coasting, 48V and mDSF (miller cycle Dynamic Skip Fire) fuel-saving potential has been fully utilized.

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“…As concerns HEVs, a recurrent research topic involves developing velocity predictors that can improve the energy management strategy of the following vehicle through the information coming from the preceding vehicle. Different categories of longitudinal speed regulation logics have been developed in the literature (e.g., heuristic, instantaneous optimization, machine learning), and various HEV powertrain layouts have been considered, such as power-split [19], parallel P0 [20], parallel P2 [21] and series-parallel P1P4 [22] as an example. Regarding BEVs, the author of this paper proposed an optimal off-line velocity controller based on dynamic programming (DP) capable of minimizing the energy consumption of the following vehicle [23].…”

Section: Introductionmentioning

confidence: 99%

Optimization-Driven Powertrain-Oriented Adaptive Cruise Control to Improve Energy Saving and Passenger Comfort

Anselma

2021

Energies

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Assessing the potential of advanced driver assistance systems requires developing dedicated control algorithms for controlling the longitudinal speed of automated vehicles over time. In this paper, a multiobjective off-line optimal control approach for planning the speed of the following vehicle in adaptive cruise control (ACC) driving is proposed. The implemented method relies on the principle of global optimality fostered by dynamic programming (DP) and aims to minimize propelling energy consumption and enhance passenger comfort. The powertrain model and onboard control system are integrated within the proposed car-following optimization framework. The retained ACC approach ensures that the distance between the following vehicle and the preceding vehicle is always maintained within allowed limits. The flexibility of the proposed method is demonstrated here through ease of implementation on a wide range of powertrain categories, including a conventional vehicle propelled by an internal combustion engine solely, a pure electric vehicle, a parallel P2 hybrid electric vehicle (HEV) and a power-split HEV. Moreover, different driving conditions are considered to prove the effectiveness of the proposed optimization-driven ACC approach. Obtained simulation results suggest that up to 22% energy-saving and 48% passenger comfort improvement might be achieved for the ACC-enabled vehicle compared with the preceding vehicle by implementing the proposed optimization-driven ACC approach. Engineers may adopt the proposed workflow to evaluate corresponding real-time ACC approaches and assess optimal powertrain design solutions for ACC driving.

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Reducing Fuel Consumption by Using Information from Connected and Automated Vehicle Modules to Optimize Propulsion System Control

Cited by 35 publications

References 14 publications

Eco-Driving of Connected and Autonomous Vehicles with Sequence-to-Sequence Prediction of Target Vehicle Velocity

Eco-Driving of Connected and Autonomous Vehicles with Sequence-to-Sequence Prediction of Target Vehicle Velocity

Research on the fuel consumption conservation potential of ADAS on passenger cars

Optimization-Driven Powertrain-Oriented Adaptive Cruise Control to Improve Energy Saving and Passenger Comfort

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