Model predictive control for hybrid vehicle ecological driving using traffic signal and road slope information

Yu, Kaijiang; Yang, Junqi; Yamaguchi, Daisuke

doi:10.1007/s11768-015-4058-x

Cited by 57 publications

(26 citation statements)

References 11 publications

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“…At every time step, the MPC computes an optimal control trajectory over a finite prediction horizon (t p ), and this process with feedback of current vehicle information is repeated as the prediction horizon recedes. If the control inputs are defined by u := T m and W := F b /m, the MPC problem is formulated using (1)(2)(3)(4)(5)(6)(7)(8):…”

Section: Model Predictive Control Problem Formulationmentioning

confidence: 99%

“…In these energy-efficient ACC approaches, the conflicting objectives of speed/distance tracking and energy efficiency are merged in a single cost function to be minimized in model predictive control (MPC) framework. MPC relies on a prediction horizon over which an linear/nonlinear optimization is performed [2], [3]. Nonlinear optimization methods outperform linear ones, but they increase computational time [4], [5].…”

Section: Introductionmentioning

confidence: 99%

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Safe- and Eco-Driving Control for Connected and Automated Electric Vehicles Using Analytical State-Constrained Optimal Solution

Han

Sciarretta

Ojeda

et al. 2018

IEEE Trans. Intell. Veh.

119

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Safe-and eco-driving control for connected and automated electric vehicles using analytical state-constrained optimal solution.Abstract-Speed advisory systems have been proposed for connected vehicles in order to minimize energy consumption over a planned route. However, for their practical diffusion, these systems must adequately take into account the presence of preceding vehicles. In this paper, a safe-and eco-driving control system is proposed for connected and automated vehicles to accelerate or decelerate optimally while guaranteeing vehicle safety constraints. We define minimum inter-vehicle distance and maximum road speed limit as state constraints, and formulate an optimal control problem minimizing the energy consumption. Then, an analytical state-constrained solution is derived for realtime use. A feasible range of terminal conditions is established, and such conditions are adjusted to guarantee the existence of the analytical solution. The proposed system is evaluated through simulation for various driving scenarios of the preceding vehicle. Results show that it can significantly reduce energy consumption and also avoid collision without increasing trip time. Moreover, the proposed system can serve as an energy-efficient advanced cruise control by setting a short prediction horizon.

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Section: Model Predictive Control Problem Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Safe- and Eco-Driving Control for Connected and Automated Electric Vehicles Using Analytical State-Constrained Optimal Solution

Han

Sciarretta

Ojeda

et al. 2018

IEEE Trans. Intell. Veh.

119

View full text Add to dashboard Cite

show abstract

“…The controller optimizes vehicle speed and power-split together. Besides, some authors have taken into account the road slope information, which optimized not only velocity, but also the battery state of charge (SOC) [15,16].…”

Section: Introductionmentioning

confidence: 99%

Research on Speed Optimization Strategy of Hybrid Electric Vehicle Queue Based on Particle Swarm Optimization

Wang

Shi

et al. 2018

Mathematical Problems in Engineering

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Traffic lights intersections are common in cities and have an impact on the energy consumption of vehicles, so it is significant to optimize the velocities of vehicles in urban road conditions. The novel speed optimization strategy for hybrid electric vehicle (HEV) queue that helps reduce fuel consumption and improve traffic efficiency is presented in this paper, where real-world traffic signal information is used to construct the research scenario. The initial values of the target velocities are obtained based on the signal phase and timing (SPAT). Then the particle swarm optimization (PSO) algorithm is used to solve the nonlinear constrained problem and obtain the optimal target velocities based on vehicle to vehicle communication (V2V) and vehicle to infrastructure communication (V2I). The lower controller, which applies rule based control strategy, is designed to split the power of the engine and two electric motors in a power split HEV, which is quite promising because of its advantages in fuel economy. Simulation results demonstrate the superior performance of the proposed strategy in reducing fuel consumption of the HEV queue and improving traffic smoothness.

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“…Yu et al [26], [27] used MPC to control inter-vehicle distances, and minimize fuel efficiency. They also used road slope to optimize the recuperation of free braking energy.…”

Section: Introductionmentioning

confidence: 99%

Ecological Adaptive Cruise Controller for Plug-In Hybrid Electric Vehicles Using Nonlinear Model Predictive Control

Vajedi

Azad

2016

IEEE Trans. Intell. Transport. Syst.

170

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Plug-in hybrid electric vehicles (PHEVs) are promising options for future transportation. Having two sources of energy enables them to offer better fuel economy and fewer emissions. Significant research has been done to take advantage of future route information to enhance vehicle performance. In this paper, an ecological adaptive cruise controller (Eco-ACC) is used to improve both fuel economy and safety of the Toyota Prius Plug-in Hybrid. Recently, an emerging trend in the research has been to improve the adaptive cruise controller. However, the majority of research to date has focused on driving safety, and only rare reports in the literature substantiate the applicability of such systems for PHEVs. Here, we demonstrate that using an Eco-ACC system can simultaneously improve total energy costs and vehicle safety. The developed controller is equipped with an onboard sensor that captures upcoming trip data to optimally adjust the speed of PHEVs. The nonlinear model predictive control technique (NMPC) is used to optimally control vehicle speed. To prepare the NMPC controller for real-time applications, a fast and efficient control-oriented model is developed. The authenticity of the model is validated using a high-fidelity Autonomie-based model. To evaluate the designed controller, the global optimum solution for cruise control problem is found using Pontryagin's minimum principle (PMP). To explore the efficacy of the controller, PID and linear MPC controllers are also applied to the same problem. Simulations are conducted for different driving scenarios such as driving over a hill and car following. These simulations demonstrate that NMPC improves the total energy cost up to 19%. Index Terms-Ecological adaptive cruise controller, intelligent transportation systems, advanced driving assistant systems, nonlinear model predictive controller. ManuscriptMahyar Vajedi received the B.Sc. and M.Sc. degrees in mechanical engineering from Sharif University of Technology, Tehran, Iran. He is currently working toward the Ph.D. degree with the Department of Systems Design Engineering, University of Waterloo. He is the author of several published papers. His research interests include intelligent vehicle control systems, such as eco-cruise controllers, adaptive cruise controllers, and intelligent energy management systems for electric propulsion vehicles. Nasser L. Azad is currently an Assistant Professor with the Department of Systems Design Engineering, University of Waterloo. His research program operates at the intersection of advanced vehicle electrification and vehicle communication technologies, where he specializes in the development of intelligent automotive controllers that can leverage information provided by emerging ITS, GPS, GIS, and advanced wireless technologies to optimize key performance characteristics, such as fuel economy and emission reduction, in real time. The multidisciplinary nature of his research has allowed him to establish strong collaborative networks with researchers from universities in Europe...

show abstract

Model predictive control for hybrid vehicle ecological driving using traffic signal and road slope information

Cited by 57 publications

References 11 publications

Safe- and Eco-Driving Control for Connected and Automated Electric Vehicles Using Analytical State-Constrained Optimal Solution

Safe- and Eco-Driving Control for Connected and Automated Electric Vehicles Using Analytical State-Constrained Optimal Solution

Research on Speed Optimization Strategy of Hybrid Electric Vehicle Queue Based on Particle Swarm Optimization

Ecological Adaptive Cruise Controller for Plug-In Hybrid Electric Vehicles Using Nonlinear Model Predictive Control

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