On the Robustness of Adaptive Nonlinear Model Predictive Cruise Control

Šantin, Ondřej; Beran, Jaroslav; Mikuláš, Ondřej; Pekař, Jaroslav; Michelini, John; Szwabowski, Steven; Mohan, Shankar; Filev, Dimitar; Jing, Junbo; Özgüner, Ü.

doi:10.4271/2018-01-1360

Cited by 4 publications

(3 citation statements)

References 14 publications

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“…An advanced algorithm that consists slope information processing, named ANLMPC (Adaptive Nonlinear Model Predictive Controller), is adopted by OSU. A further Fuel saving 4.33% is achieved by this algorithm in a typical test road, since the gear shift operation of the vehicle was reduced to 38, a far cry from the number which occurs in the CACC control, which is 90 [19] .…”

Section: Road Slope Energymentioning

confidence: 97%

“…In 2018, OSU announced two research results, one is of the performance improvement of coasting in N gear [18] ,and the other is about the nonlinear simulation of predictive cruise control [19] . By modifying 2010 Prius, CSU(Colorado State University) inquired several research topics, including the speed prediction by V2V-based technology vehicle to improve HEV fuel economy [20] , ADAS to improve vehicle fuel economy [21] , and improve the accuracy of prediction on fuel economy by control optimize [22] .…”

Section: Researches and Activities In Usmentioning

confidence: 99%

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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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Section: Road Slope Energymentioning

confidence: 97%

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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“…During a real-world test on a sport utility vehicle (SUV), ANLMPC improved fuel economy up to 2.4% on average compared to a production cruise controller with the same time of arrival. The works of References [14,15] incorporated quadratic programming to handle nonlinearity in improving fuel economy while cruising. Since these nonlinear MPC (NMPC) is difficult to design and can take a lot of CPU power, an approach to train a deep neural network (DNN) controller architecture on data from well-designed NMPC was proposed [16].…”

Section: Introductionmentioning

confidence: 99%

Autonomous Vehicle Fuel Economy Optimization with Deep Reinforcement Learning

et al. 2020

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The ever-increasing number of vehicles on the road puts pressure on car manufacturers to make their car fuel-efficient. With autonomous vehicles, we can find new strategies to optimize fuels. We propose a reinforcement learning algorithm that trains deep neural networks to generate a fuel-efficient velocity profile for autonomous vehicles given road altitude information for the planned trip. Using a highly accurate industry-accepted fuel economy simulation program, we train our deep neural network model. We developed a technique for adapting the heterogeneous simulation program on top of an open-source deep learning framework, and reduced dimension of the problem output with suitable parameterization to train the neural network much faster. The learned model combined with reinforcement learning-based strategy generation effectively generated the velocity profile so that autonomous vehicles can follow to control itself in a fuel efficient way. We evaluate our algorithm’s performance using the fuel economy simulation program for various altitude profiles. We also demonstrate that our method can teach neural networks to generate useful strategies to increase fuel economy even on unseen roads. Our method improved fuel economy by 8% compared to a simple grid search approach.

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