Green Fixed Tour Scheduling Problem with Electric Vehicles Considering Time-Varying Traffic Congestion

2020 ◽  
Author(s):  
Siyue Zhang ◽  
Yiyong Xiao ◽  
Pei Yang
Keyword(s):  
Electric Vehicles ◽  
Traffic Congestion ◽  
Time Varying ◽  
Scheduling Problem ◽  
Tour Scheduling

scholarly journals Energy-Optimal Braking Control Using a Double-Layer Scheme for Trajectory Planning and Tracking of Connected Electric Vehicles

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Haoxuan Dong ◽  
Weichao Zhuang ◽  
Guodong Yin ◽  
Liwei Xu ◽  
Yan Wang ◽  
...  
Keyword(s):  
Double Layer ◽  
Electric Vehicles ◽  
Linear Time ◽  
Lower Layer ◽  
Dynamic Programming Algorithm ◽  
Programming Algorithm ◽  
Time Varying ◽  
Connected Vehicle ◽  
Real Time Control ◽  
Time Control

AbstractMost researches focus on the regenerative braking system design in vehicle components control and braking torque distribution, few combine the connected vehicle technologies into braking velocity planning. If the braking intention is accessed by the vehicle-to-everything communication, the electric vehicles (EVs) could plan the braking velocity for recovering more vehicle kinetic energy. Therefore, this paper presents an energy-optimal braking strategy (EOBS) to improve the energy efficiency of EVs with the consideration of shared braking intention. First, a double-layer control scheme is formulated. In the upper-layer, an energy-optimal braking problem with accessed braking intention is formulated and solved by the distance-based dynamic programming algorithm, which could derive the energy-optimal braking trajectory. In the lower-layer, the nonlinear time-varying vehicle longitudinal dynamics is transformed to the linear time-varying system, then an efficient model predictive controller is designed and solved by quadratic programming algorithm to track the original energy-optimal braking trajectory while ensuring braking comfort and safety. Several simulations are conducted by jointing MATLAB and CarSim, the results demonstrated the proposed EOBS achieves prominent regeneration energy improvement than the regular constant deceleration braking strategy. Finally, the energy-optimal braking mechanism of EVs is investigated based on the analysis of braking deceleration, battery charging power, and motor efficiency, which could be a guide to real-time control.

scholarly journals Optimal Incentives for Electric Vehicles at e-Park & Ride Hub with Renewable Energy Source

2019 ◽  
Vol 10 (4) ◽  
pp. 70 ◽  
Author(s):  
Benoît Sohet ◽  
Olivier Beaude ◽  
Yezekael Hayel ◽  
Alban Jeandin
Keyword(s):  
Energy Source ◽  
Electric Vehicles ◽  
Traffic Congestion ◽  
Theory Model ◽  
Urban Systems ◽  
Production Data ◽  
Solar Panels ◽  
Incentive Mechanisms ◽  
Electrical Grid ◽  
Optimal Incentives

As electric vehicles’ penetration increases, more impacts on urban systems are observed and related to both driving (e.g., on traffic congestion and reduced pollution) and charging (e.g., on the electrical grid). Therefore, there is a need to design coupled incentive mechanisms. To propose and numerically evaluate such incentives, a game theory model is adopted. Its originality comes from the coupling between the charging cost and the driving decisions: to drive downtown or to charge at an e-Park & Ride hub with solar panels and then take public transport, in order to reach destination. Optimal ticket fares and solar park’s size are computed using real photovoltaic production data.

scholarly journals Distribution Route Optimization for Electric Vehicles in Urban Cold Chain Logistics for Fresh Products under Time-Varying Traffic Conditions

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Zhixue Zhao ◽  
Xiamiao Li ◽  
Xiancheng Zhou
Keyword(s):  
Electric Vehicles ◽  
Ant Colony Algorithm ◽  
Time Window ◽  
Route Optimization ◽  
Cold Chain ◽  
Logistic Distribution ◽  
Time Varying ◽  
Traffic Conditions ◽  
Distribution Process ◽  
Fresh Products

Electric vehicles (EVs) have been widely used in urban cold chain logistic distribution and transportation of fresh products. In this paper, an electric vehicle routing problem (EVRP) model under time-varying traffic conditions is designed for planning the itinerary for fresh products in the urban cold chain. The object of the EVRP model is to minimize the total cost of logistic distribution that includes economic cost and fresh value loss cost. To reflect the real situation, the EVRP model considers several influencing factors, including time-varying road network traffic, road type, client’s time-window requirement, freshness of fresh products, and en route queuing for charging. Furthermore, to address the EVRP, an improved adaptive ant colony algorithm is designed. Simulation test results show that the proposed method can allow EVs to effectively avoid traffic congestion during the distribution process, reduce the total distribution cost, and improve the performance of the cold chain logistic distribution process for fresh products.

Equivalent Alternate Solutions for the Tour Scheduling Problem*

1991 ◽  
Vol 22 (5) ◽  
pp. 985-1007 ◽  
Author(s):  
Fred F. Easton ◽  
Donald F. Rossin
Keyword(s):  
Scheduling Problem ◽  
Tour Scheduling

scholarly journals Cooperative Multiagent System for Parking Availability Prediction Based on Time Varying Dynamic Markov Chains

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Surafel Luleseged Tilahun ◽  
Giovanna Di Marzo Serugendo
Keyword(s):  
Traffic Congestion ◽  
Shopping Mall ◽  
Time Varying ◽  
Dynamic Prediction ◽  
Long Term Changes ◽  
Parking Lot ◽  
And Performance ◽  
Prediction Mechanism ◽  
Parking Place

Traffic congestion is one of the main issues in the study of transportation planning and management. It creates different problems including environmental pollution and health problem and incurs a cost which is increasing through years. One-third of this congestion is created by cars searching for parking places. Drivers may be aware that parking places are fully occupied but will drive around hoping that a parking place may become vacant. Opportunistic services, involving learning, predicting, and exploiting Internet of Things scenarios, are able to adapt to dynamic unforeseen situations and have the potential to ease parking search issues. Hence, in this paper, a cooperative dynamic prediction mechanism between multiple agents for parking space availability in the neighborhood, integrating foreseen and unforeseen events and adapting for long-term changes, is proposed. An agent in each parking place will use a dynamic and time varying Markov chain to predict the parking availability and these agents will communicate to produce the parking availability prediction in the whole neighborhood. Furthermore, a learning approach is proposed where the system can adapt to different changes in the parking demand including long-term changes. Simulation results, using synthesized data based on an actual parking lot data from a shopping mall in Geneva, show that the proposed model is promising based on the learning accuracy with service adaptation and performance in different cases.

scholarly journals Neural-Fuzzy-Based Adaptive Sliding Mode Automatic Steering Control of Vision-based Unmanned Electric Vehicles

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Jinghua Guo ◽  
Keqiang Li ◽  
Jingjing Fan ◽  
Yugong Luo ◽  
Jingyao Wang
Keyword(s):  
Electric Vehicles ◽  
Sliding Mode ◽  
Variable Structure ◽  
Control Law ◽  
Time Varying ◽  
Steering Control ◽  
Structure Control ◽  
Adaptive Sliding Mode ◽  
Automatic Steering ◽  
Neural Fuzzy

AbstractThis paper presents a novel neural-fuzzy-based adaptive sliding mode automatic steering control strategy to improve the driving performance of vision-based unmanned electric vehicles with time-varying and uncertain parameters. Primarily, the kinematic and dynamic models which accurately express the steering behaviors of vehicles are constructed, and in which the relationship between the look-ahead time and vehicle velocity is revealed. Then, in order to overcome the external disturbances, parametric uncertainties and time-varying features of vehicles, a neural-fuzzy-based adaptive sliding mode automatic steering controller is proposed to supervise the lateral dynamic behavior of unmanned electric vehicles, which includes an equivalent control law and an adaptive variable structure control law. In this novel automatic steering control system of vehicles, a neural network system is utilized for approximating the switching control gain of variable structure control law, and a fuzzy inference system is presented to adjust the thickness of boundary layer in real-time. The stability of closed-loop neural-fuzzy-based adaptive sliding mode automatic steering control system is proven using the Lyapunov theory. Finally, the results illustrate that the presented control scheme has the excellent properties in term of error convergence and robustness.

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