Traffic light assistant system for optimized energy consumption in an electric vehicle

Kural, Emre; Jones, Stephen; Parrilla, Alejandro Ferreira; Grauers, Anders

doi:10.1109/iccve.2014.7297619

Cited by 21 publications

(20 citation statements)

References 4 publications

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“…Optimal control theory is proposed to solve the problem by searching the best solution from all alternative control selections. 23,24 Preview follower theory is proposed by the Chinese Academy of Engineering GuoKonghui in 1982. 25 The theory describes the characteristics of system based on future input information.…”

Section: Battery System Modelingmentioning

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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Section: Battery System Modelingmentioning

confidence: 99%

Electric vehicle energy predictive optimal control by V2I communication

Zhu

Zheng

et al. 2019

Advances in Mechanical Engineering

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“…Moreover, the human driver is motivated to apply eco-driving techniques or directly drive in semiautonomous longitudinal driving mode based on advanced driving assistant systems (e.g. Traffic Light Assistant, Coasting Assistant) [2,3]. In any case, the interaction with the driver through advanced Human Machine Interface is one of the key parts of the development of these advanced systems to optimally determine the quality and quantity of the information flow.…”

Section: Introductionmentioning

confidence: 99%

“…To do so, two 4WD Battery Electric Vehicles prototypes each with two 50 kW electric machines coupled to front and rear axle respectively, were built and equipped with multitude of sensors and communication units [4]. This large variety of data and data sources creates an electronic horizon that can be used to further optimize OpEneR developed functions such as Traffic Light Assistant, Coasting Assistant or Adaptive ACC [2,3]. Developed control strategies were then tested on real world driving conditions, with highly satisfying results [4].…”

Section: Introductionmentioning

confidence: 99%

Advanced co-simulation HMI environment for fully Electric Vehicles

Sixto

López

Sánchez

et al. 2014

2014 IEEE International Electric Vehicle Conference (IEVC)

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Today's Electric Vehicles (EVs) have limited driving ranges. Therefore, great efforts are being made in this direction, e.g. higher capacity batteries, powertrain efficiency, etc. This paper aims at gathering the information concerning the process of HMI concept definition, development and integration in an advanced co-simulation environment as a support tool for specific function development process in the OpEneR project. In order to do so, specific interfaces to the complex co-simulation tool chain employed in the project were defined and demonstrated in this paper. Additionally, a mock-up demonstrator was built which allowed for testing of specific functions as well as of HMI software.

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“…The need for increasing energy efficiency and the advances in driver assistance systems brought eco-cruise control strategies to life [13], [14], [15], [16]. In urban areas interaction with traffic control devices dictate energy savings [17]. Speed advisory systems incorporating V2I communication with traffic lights can result in significant energy reduction [18].…”

Section: Introductionmentioning

confidence: 99%

Energy-aware predictive control for electrified bus networks

2019

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For an urban bus network to operate efficiently, conflicting objectives have to be considered: providing sufficient service quality while keeping energy consumption low. The paper focuses on energy efficient operation of bus lines, where bus stops are densely placed, and buses operate frequently with possibility of bunching. The proposed decentralized, bus fleet control solution aims to combine four conflicting goals incorporated into a multi-objective, nonlinear cost function. The multi-objective optimization is solved under a receding horizon model predictive framework. The four conflicting objectives are as follows. One is ensuring periodicity of headways by watching leading and following vehicles i.e. eliminating bus bunching. Equal headways are only a necessary condition for keeping a static, predefined, periodic timetable. The second objective is timetable tracking. A conflicting objective to the former is minimizing passenger waiting time. When more than the expected passengers are waiting for the bus it is desirable to haste the vehicle in order to prevent bunching. The final objective is energy efficiency. To this end, an energy consumption model is formulated considering battery electric vehicles with recuperation during braking. Different weighting strategies are compared and evaluated through realistic scenarios, realized in a validated microscopic traffic simulation environment. Simulation results suggest 3 − 8% network level energy saving compared to bus holding control while maintaining punctuality and periodicity of buses. Formulating the multi-objective cost functionBuses operate on a route based on their timetable. When the schedule is tight, their trajectory shall be carefully planned while considering the schedule, passenger demand, and energy efficiency.The proposed bus velocity control algorithm (referred hereafter as control algorithm) creates an optimal trajectory within a predefined prediction horizon, considering the schedule, the number of passengers waiting and the location of other buses. The trade-off in the optimization is the total energy consumed, which is modeled as a physical energy consumption model. During operation, due to irregular dwell times and traffic disturbances, they tend to get out of sync with the schedule and start bunching. The goal of the control algorithm is to ensure timetable and

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Traffic light assistant system for optimized energy consumption in an electric vehicle

Cited by 21 publications

References 4 publications

Electric vehicle energy predictive optimal control by V2I communication

Electric vehicle energy predictive optimal control by V2I communication

Advanced co-simulation HMI environment for fully Electric Vehicles

Energy-aware predictive control for electrified bus networks

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