Energy-Efficient Maneuvering of Connected and Automated Vehicles: NEXTCAR Phase II Results

<div class="section abstract"><div class="htmlview paragraph">Onboard sensing and Vehicle-to-Everything (V2X) connectivity enhance a vehicle's situational awareness beyond direct line-of-sight scenarios. A team led by Southwest Research Institute (SwRI) demonstrated 20% energy savings by leveraging these information streams on a 2017 Prius Prime as part of the first phase of the ARPA-E-funded NEXTCAR program. Combining this technology with automation can improve vehicle safety and enhance energy efficiency further. In the second phase, SwRI demonstrated 30% energy savings over the baseline. This paper summarizes the efforts to achieve 30% savings on a 2021 Honda Clarity PHEV. The vehicle was outfitted with the SwRI Ranger automated driving suite for perception and localization. Model-based control schemes with selective interrupt and control (SIC) were used to override stock vehicle controls and actuate the accelerator, brake, and electric power steering system, enabling drive-by-wire and steer-by-wire functionalities. Key algorithms contributing to the 30% savings include Eco-driving, Eco-routing, Plugin Hybrid Electric Vehicle (PHEV) Powertrain mode selection, and cooperative maneuvers such as Eco-merge, and Platooning. These algorithms were tested through large-scale simulations using a high-fidelity forward-looking powertrain model, dynamic and stochastic traffic simulations (calibrated based on real-world corridor data), and real-world trip data. Statistical significance was established for simulation results, and a clustering and downlselection routine was used to select representative scenarios for dynamometer evaluation. This paper presents an overview of the contributing algorithms, the development of the simulation framework, the experiments designed to test the effectiveness of algorithms in simulations, an overview of the scenario downselection routine, and results from simulations and dynamometer tests.</div></div>

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SAE technical papers on CD-ROM/SAE technical paper series
Published
2025-03-31
DOI
https://doi.org/10.4271/2025-01-8385
Primary Topic
Vehicle Dynamics and Control Systems
Type
article
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article

Energy-Efficient Maneuvering of Connected and Automated Vehicles: NEXTCAR Phase II Results

Joshua Alden, Scott Hotz, Joseph Drallmeier, Jayant Sarlashkar et al.
SAE technical papers on CD-ROM/SAE technical paper series
Vehicle Dynamics and Control Systems
article

Energy-Efficient Maneuvering of Connected and Automated Vehicles: NEXTCAR Phase II Results

Joshua Alden, Scott Hotz, Joseph Drallmeier, Jayant Sarlashkar, Shuvodeep Bhattacharjya, Stanislav Gankov, Piyush Bhagdikar, Shreshta Rajakumar Deshpande, Kartik Adsule, Sankar Rengarajan, D.N. D’Souza
article en

Abstract

<div class="section abstract"><div class="htmlview paragraph">Onboard sensing and Vehicle-to-Everything (V2X) connectivity enhance a vehicle's situational awareness beyond direct line-of-sight scenarios. A team led by Southwest Research Institute (SwRI) demonstrated 20% energy savings by leveraging these information streams on a 2017 Prius Prime as part of the first phase of the ARPA-E-funded NEXTCAR program. Combining this technology with automation can improve vehicle safety and enhance energy efficiency further. In the second phase, SwRI demonstrated 30% energy savings over the baseline. This paper summarizes the efforts to achieve 30% savings on a 2021 Honda Clarity PHEV. The vehicle was outfitted with the SwRI Ranger automated driving suite for perception and localization. Model-based control schemes with selective interrupt and control (SIC) were used to override stock vehicle controls and actuate the accelerator, brake, and electric power steering system, enabling drive-by-wire and steer-by-wire functionalities. Key algorithms contributing to the 30% savings include Eco-driving, Eco-routing, Plugin Hybrid Electric Vehicle (PHEV) Powertrain mode selection, and cooperative maneuvers such as Eco-merge, and Platooning. These algorithms were tested through large-scale simulations using a high-fidelity forward-looking powertrain model, dynamic and stochastic traffic simulations (calibrated based on real-world corridor data), and real-world trip data. Statistical significance was established for simulation results, and a clustering and downlselection routine was used to select representative scenarios for dynamometer evaluation. This paper presents an overview of the contributing algorithms, the development of the simulation framework, the experiments designed to test the effectiveness of algorithms in simulations, an overview of the scenario downselection routine, and results from simulations and dynamometer tests.</div></div>

SAE technical papers on CD-ROM/SAE technical paper seriesVol. 1
Southwest Research Institute (US)
Openalex Percentile: Top 94%
Vehicle Dynamics and Control Systems
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