A Backpressure Framework Applied to Road Traffic Routing for Electric Vehicles

Abstract

Electric vehicles (EVs) emerged in the transport domain, due to their energy efficiency and clean energy that they utilise. The electric vehicle routing problem is essentially a problem of selecting a set of minimum cost routes, while the demand of the customers is achieved. Route cost metrics include energy consumption and driving time. In this work, we model the electric vehicle routing problem using a wireless network methodology, namely the backpressure framework. The penalty imposed to every route includes the driving time of each road. We derive a weight as a function of the road queue backpressure and the driving time of a car. The next route for our EV is the one that has the highest weight. It turns out that this methodology leads to faster routes in that there are often roads with accidents or traffic jams, even though they are in the shortest path of the route to the destination. We present results via simulations, which verify the fact that backpressure is an efficient algorithm to be applied to electric vehicle routing. 235 Spyrou E. and Mitrakos D. A Backpressure Framework Applied to Road Traffic Routing for Electric Vehicles.

Distributed Backpressure Routing and Byzantine Generals Fault Detection for Electric Vehicles

“…We formulate the road network as a stochastic optimization problem in which routing decisions minimize function f and keeping strongly stable queues simultaneously. That is: minimise: f ( x) subject to: Strongly Stable (8) Assuming that f ( x) is a cost metric of the routing process, we can derive the solution of equation (8) using the Utility Optimal Lyapunov Networking framework [36,37]. This can show that we may have the result of routing decisions resulting in a backpressure routing policy.…”

“…where ∆Q i, j = Q i − Q j is the queue backpressure and Q i , Q j are the backlogs of junctions i, j respectively, R is the road car driving rate and θ i, j a road usage penalty that depends upon the particulars of the utility and penalty functions of (8). The parameter τ is a constant trades system queue occupancy for minimizing the penalty.…”

“…The main challenge we are facing is to identify the most suitable penalty function f ( x) in equation (8), in order to provide efficient performance when we use it in a real work road traffic network.…”

“…We address the EVRP, where the cars communicate in a wireless manner with other cars and traffic lights to decide on the next routing option that is decided in a distributed manner, as it is described in [8]. However, there might be faults in the car-to-car communication or in the car-to-traffic light communication.…”

“…We attempt to complete the Backpressure framework proposed in [8], in order to ensure a robust operating of the routing protocol. We employ a very well-known algorithm, the Byzantine Generals algorithm, to provide the system with a distributed way to detect faults in communications.…”