Energy-efficient routing strategies based on real-time data of a local traffic management center

Liebscher, Anja; Krumnow, Mario; Krimmling, Jürgen; Hanisch, Falk; Bäker, Bernard

doi:10.1109/mtits.2015.7223239

Cited by 5 publications

(3 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To decrease computational times associated with shortest path algorithms that can handle negative link costs such as Floyd-Warshall, the authors propose the shifting technique described by Johnson in [34]. An energy-efficient routing model for EVs based on real-time data of a local traffic center has been presented in [35]. The authors proposed an altered Bellman-Ford algorithm that can deal with time-variant, negative links and dimensionless nodes are expanded to areas of intersections to be able to implement the dynamic traffic data.…”

Section: B Energy-efficient Routingmentioning

confidence: 99%

A Model for Range Estimation and Energy-Efficient Routing of Electric Vehicles in Real-World Conditions

Cauwer

Verbeke

Mierlo

et al. 2020

IEEE Trans. Intell. Transport. Syst.

View full text Add to dashboard Cite

This paper presents an integrated model for energy consumption and range estimation, capable of energy-efficient routing. This integrated model predicts the energy consumption on all road segments in the road network and applies shortest path algorithms to calculate energy-efficient routes. A temperature-dependent model of the battery internal resistance based on real-world data translates the energy consumption prediction into driving range. The graph representation of the road network is transformed from a node-based graph to an edge-based graph to allow cost allocation of one edge based on the characteristics of the preceding edge. The integrated model is used to perform an analysis of energy-efficient routes for a real-life scenario. The analysis showed that the energy-optimal route is different from the time-and distance-optimal route with energy-efficiency gains up to 37% for the chosen scenario. The energy-efficient route tends to lean toward a distance-optimal route in the case of low auxiliary consumption and to lean toward a time-optimal route in the case of high auxiliary consumption. The energy-efficient route generation is tested in real life for one case of the chosen scenario. The measured results showed a good match with the predicted values for the energy consumption with a 9% mean relative error and preserved the ranking of all routes in terms of the travel distance, travel time, and energy consumption. The proposed integrated model is a functional model for energy consumption in real-life that differentiates many energy consumption influencing factors and produces energy-efficient routes with good accuracy.

show abstract

Section: B Energy-efficient Routingmentioning

confidence: 99%

A Model for Range Estimation and Energy-Efficient Routing of Electric Vehicles in Real-World Conditions

Cauwer

Verbeke

Mierlo

et al. 2020

IEEE Trans. Intell. Transport. Syst.

View full text Add to dashboard Cite

show abstract

“…The work in [39] employs Dijkstra's algorithm to calculate fuel-efficient paths in terms of the physical length and the estimated fuel consumption on each road segment which is estimated from the average travelling speed. Similarly, an energy-efficient routing algorithm for electric cars is presented in [32], where a cost function is defined for each edge in the network in terms of road slope, vehicle speed, vehicle acceleration, journey time and road surface conditions; since edge weights may evaluate negative values which cannot be handled by Dijkstra's or A* algorithms, the Bellman-Ford-algorithm is employed to compute the most energy-efficient paths. A fuel-efficient driving strategy for high-way vehicles is proposed in [29], using the Max-Min Ant system [36] to optimize the vehicle speed and acceleration commands.…”

Section: Energy-efficient Traffic Managementmentioning

confidence: 99%

Energy-Aware Navigation in Large-Scale Evacuation Using G-Networks

Bi¹,

Abdelrahman²

2016

Prob. Eng. Inf. Sci.

View full text Add to dashboard Cite

Previous studies on emergency management of large-scale urban networks have commonly concentrated on system development to off-load intensive computations to remote cloud servers or improving communication quality during a disaster and ignored the effect of energy consumption of vehicles, which can play a vital role in large-scale evacuation owing to the disruptions in energy supply. Hence, in this paper we propose a cloud-enabled navigation system to direct vehicles to safe areas in the aftermath of a disaster in an energy and time efficient fashion. A G-network model is employed to mimic the behaviors and interactions between individual vehicles and the navigation system, and analyze the effect of re-routing decisions toward the vehicles. A gradient descent optimization algorithm is used to gradually reduce the evacuation time and fuel consumption of vehicles by optimizing the probabilistic choices of linked road segments at each intersection. The re-routing decisions arrive at the intersections periodically and will expire after a short period. When a vehicle reaches an intersection, if the latest re-routing decision has not expired, the vehicle will follow this advice, otherwise, the vehicle will stick to the shortest path to its destination. The experimental results indicate that the proposed algorithm can reduce the evacuation time and the overall fuel utilization especially when the number of evacuated vehicles is large.

show abstract

“…Investigations on such models have been conducted considering various factors. Most approaches take inputs as road gradient and route length [6][7][8][9][10][11][12] in energy estimation, and other factors like vehicle specifications and driving behavior are also considered [6,10,11]. With input factors determined, a model can be established via recording all the considered factors in a testing driving trip, followed by training and generating the model parameters based on the recorded data [12,13].…”

Section: Introductionmentioning

confidence: 99%

An Energy Consumption Model for Electrical Vehicle Networks via Extended Federated-learning

Zhang

2021

Preprint

View full text Add to dashboard Cite

Electrical vehicle (EV) raises to promote an eco-sustainable society. Nevertheless, the "range anxiety" of EV hinders its wider acceptance among customers. This paper proposes a novel solution to range anxiety based on a federated-learning model, which is capable of estimating battery consumption and providing energy-efficient route planning for vehicle networks. Specifically, the new approach extends the federated-learning structure with two components: anomaly detection and sharing policy. The first component identifies preventing factors in model learning, while the second component offers guidelines for information sharing amongst vehicle networks when the sharing is necessary to preserve learning efficiency. The two components collaborate to enhance learning robustness against data heterogeneities in networks. Numerical experiments are conducted, and the results show that compared with considered solutions, the proposed approach could provide higher accuracy of battery-consumption estimation for vehicles under heterogeneous data distributions, without increasing the time complexity or transmitting raw data among vehicle networks.

show abstract

Energy-efficient routing strategies based on real-time data of a local traffic management center

Cited by 5 publications

References 3 publications

A Model for Range Estimation and Energy-Efficient Routing of Electric Vehicles in Real-World Conditions

A Model for Range Estimation and Energy-Efficient Routing of Electric Vehicles in Real-World Conditions

Energy-Aware Navigation in Large-Scale Evacuation Using G-Networks

An Energy Consumption Model for Electrical Vehicle Networks via Extended Federated-learning

Contact Info

Product

Resources

About