Model predictive control of eco-driving for transit using V2I communication

Zheng, Xiuzheng; Zhang, Liguo; Kholodov, Yaroslav

doi:10.1109/chicc.2015.7260026

Cited by 4 publications

(2 citation statements)

References 5 publications

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“…A quadratic cost function is developed that considers the cruising fuel consumption model, counteracting effect of gravitational First, the route planning and road gradient angle (elevation data) on the planned route are derived simultaneously from the Google Maps API. Here, this application uses the Google Directions API [16,17] to help us the route planning. Therefore, GPS coordinates (longitude and latitude) and elevation data for all locations on the planned route are obtained as the practical up-down road surface of vehicle dynamic model.…”

Section: Methodsmentioning

confidence: 99%

Development of an Eco-Cruise Control System Based on Digital Topographical Data

Lin

Nguyen

2016

Inventions

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Rapid growth in economies and a sharp increase in the present quantity of vehicles have contributed to congested cities, traffic accidents, energy crises and air pollution, which not only cause serious economic damage but also affect human life and safety. According to the Kyoto Protocol international standards and consensus, the trend in the automotive industry is to develop more ecological vehicles in order to reduce fuel consumption and air pollution. Recently, a number of modern premium cars have been equipped with advanced driver assistance systems (ADAS), especially an adaptive cruise control (ACC) system, which was found to be one of the most widely and successful ADAS systems to date. However, the road topography on highways affects fuel consumption of vehicles. Rapid acceleration, rapid deceleration, and unreasonable gear use can all result in increasing fuel consumption. Therefore, an eco-cruise control (ECC) system on roads with up-down slopes is critical for improving vehicle fuel economy in coordination with the Global Positioning System (GPS) and Geographic Information System (GIS). This proposed approach combines the road elevation profile of digital road maps with nonlinear optimal predictive control (NOPC) algorithms to handle the vehicle velocity control; it is aimed at providing the most economical speed according to road grade. The developed algorithms not only provide ecological driving guidance for the driver, but also contribute to driving safety. Finally, extensive simulations demonstrate that the proposed controller can significantly reduce fuel consumption of a vehicle while ensuring driving comfort and safety.

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Section: Methodsmentioning

confidence: 99%

Development of an Eco-Cruise Control System Based on Digital Topographical Data

Lin

Nguyen

2016

Inventions

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“…Zheng 18 presented a bus eco-driving optimal control method to calculate the optimal driving speed by considering the platform delay. The information about traffic lights timings and passengers waiting in bus stations was obtained by the V2I communication.…”

Section: Introductionmentioning

confidence: 99%

Electric vehicle energy predictive optimal control by V2I communication

Zhu

Zheng

et al. 2019

Advances in Mechanical Engineering

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To achieve a better energy-efficient and comfortable vehicle driving, electric vehicle energy predictive optimal control was discussed in this article. It used Vehicle to Infrastructure (V2I) communication of traffic light distance and time gap to make velocity decision in order to improve whole driving performance. Based on the data of standard New European Driving Cycle, this article put forward a real-time cycle including traffic light position and time information at first. Then, a preview and optimal decision controller was built. Four evaluating indexes were set for optimal decision making to decide the optimal expected speed, according to the pass time that the forward traffic lights allowed and the distance or the forward vehicle speed and distance. After that AMESim and Simulink co-simulation platform was used to build the vehicle dynamics model and to exploit the control algorithm. The simulation results indicated that vehicle with V2I can effectively improve the operating condition of the whole vehicle and reduce the frequency of vehicle starting and stopping, which makes the vehicle driving smoother and steady. Moreover, it can reduce vehicle energy consumption and improve endurance mileage. Meanwhile, the vehicle acceleration and jerk have decreased dramatically with better comfortable performance.

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