Assignment Approach for Electric Vehicle Charging Using Traffic Data Collected by SUMO
Abstract
:1. Introduction
- First, we describe the architecture of an enhanced smart grid system integrating ITS-G5 technology, which provides useful real-time information and efficient communication between roadside units (RSUs) and EVs for the purpose of distributing EV charging management decisions.
- Second, an optimized approach allowed for optimal allocation of EVs by taking into account the characteristics of the EVs, charging stations, and road traffic disseminated by the RSUs and collected in our study by the SUMO simulator.
- Third, a linear mathematical program incorporated all of the constraints. This approach assigned each electric vehicle to an appropriate charging station and consumed a minimum of electric energy when it reached the designated charging station, thus maximizing the final state of charge and reducing the waiting time of EVs at the charging station.
- Finally, we performed extensive simulations to verify the effectiveness of this approach. Our evaluation was performed using the realistic traffic simulator. The proposed strategy was found to be more effective than the shorter path strategy in managing the charging of electric vehicles.
2. Related Work
2.1. Abbreviations Smart Grids Based on Traffic Management Techniques
2.2. Smart Grids Improvements Due to V2X Communications
2.3. Mobility Simulators
3. Proposed Solution
3.1. Model System
3.2. Assignment Algorithm
- The distance given by the GPS.
- The state of charge (SOC) of the remaining battery.
- The driving mode of the driver and accessories.
- The capacity and autonomy of the battery.
- The number of charging stations.
- The charging power of each station and its position.
3.2.1. Problem Formulation
- The electric vehicles are of the same brand.
- An electric vehicle can be assigned to only one station.
- The charging stations have the same characteristics (power of charge).
- The charging station has several charging points.
- The road is flat.
3.2.2. Algorithm
Algorithm 1: Assignment Algorithm |
Input: EVi[Soc0,Ci,Ai]; CSj [qij, dij, Kjamij ] |
Output: [Socf max] |
1. RSU broadcasts the EV charging service |
2. For each EV (1,….,nEV) and for each CS (1,….,mcs) |
3. Calculate SOCfij (using Equation (3)) |
4. Select CS according to occupancy (number of available outlets) |
5. If the number of outlets is less than the number of vehicles assigned to the same station, then |
6. Calculate the average of Max(Socfij) of the vehicles assigned to the same station |
7. Select the minimum of the average SOCfij |
8. Update outlet matrix after EV affectation |
9. end if |
4. Experiments
4.1. Preparation of the Simulation
4.2. Insertion of EV and CS
4.3. SUMO Connection with TraCi
5. Obtained Results: Discussion and Analysis
5.1. Illustrative Example
- n = 6 EVs.
- m = 6 CSs.
- The SOC0 (i, t) of each vehicle EVi is given.
- Number of outlets at the charging stations: n1 = 1, n2 = 1, n3 = 1, n4 = 0, n5 = 2, n6 = 2.
5.2. Evaluation and Performance
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
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Notation | Description |
---|---|
n | Number of electric vehicles (EV1, EV2, …, EVn) |
m | Number of charging stations (CS1, CS2, …, CSm) |
Alloc (i, j, t) | EVi allocation coefficient at charge station CSj at time t |
K (i, j, t) | Vehicle density instant on the route between the EVi and station CSj at time t [veh/km] |
V (i, j, t) | Vehicle speed according to traffic conditions at time t [km/h] |
vf (i, j, t) | Reference speed on the route between the EVi position and station CSj at time t [km/h]. |
kjam(i, j, t) | Traffic density, capacity supported by the road leading from the EVi position to charging station CSj at time t [veh/km]. |
q (i, j, t) | Vehicle flow [veh/h] |
d (i, j, t) | Distance between the vehicle EVi and charging station CSj at time t [km] |
C (i) | Nominal capacity of EVi’s traction battery [KWh] |
A(i) | Autonomy of the traction battery of vehicle EVi [km] |
T (i, j, t) | Time required to complete distance d (i, j, t) [h] |
SOC0(i, t) | Initial state of charge of the EVi battery at time t [%] |
SOCf (i, j, t + T) | Final state of charge of the EVi battery upon arrival at charging station CSj |
CS1 | CS2 | CS3 | CS4 | CS5 | CS6 | |
---|---|---|---|---|---|---|
EV1 SOC0 = 25% | 5.115 | 23.357 | 19.366 | 13.336 | 22.117 | 20.659 |
EV2 SOC0 = 36% | 0.218 | 34.062 | 0 | 0 | 0 | 0 |
EV3 SOC0 = 45% | 39.306 | 42.564 | 40.219 | 39.642 | 41.253 | 39.788 |
EV4 SOC0 = 21% | 10.763 | 15.938 | 12.001 | 11.236 | 13.486 | 12.001 |
EV5 SOC0 = 45% | 24.592 | 28.360 | 25.622 | 25.020 | 26.776 | 25.387 |
EV6 SOC0 = 28% | 0 | 21.840 | 0 | 0 | 6.691 | 6.784 |
CS1 | CS2 | CS3 | CS4 | CS5 | CS6 | |
---|---|---|---|---|---|---|
Number of outlets | 1 | 1 | 1 | 0 | 2 | 2 |
Optimal Assignment | EV2 | EV5 | EV4/EV1 | EV3/EV6 | ||
SOCf (%) | 34.062 | 25.622 | 13.486/22.117 | 39.788/6.784 |
CS1 | CS2 | CS3 | CS4 | CS5 | CS6 | |
---|---|---|---|---|---|---|
EV1 | 0.931 | 1.102 | 0.696 | 0.563 | 0.864 | 1.432 |
EV2 | 1.37 | 1.31 | 1.357 | 1.356 | 1.525 | 2.093 |
EV3 | 1.244 | 1.179 | 1.229 | 1.228 | 1.398 | 1.966 |
EV4 | 2.105 | 2.04 | 2.091 | 2.09 | 2.259 | 2.827 |
EV5 | 1.858 | 1.793 | 1.844 | 1.843 | 2.012 | 2.580 |
EV6 | 1.235 | 1.17 | 1.447 | 1.192 | 1.615 | 2.1831 |
Number of outlets | 1 | 1 | 1 | 0 | 2 | 2 |
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Farhani, R.; El Hillali, Y.; Rivenq, A.; Boughaleb, Y.; Hajjaji, A. Assignment Approach for Electric Vehicle Charging Using Traffic Data Collected by SUMO. World Electr. Veh. J. 2023, 14, 40. https://doi.org/10.3390/wevj14020040
Farhani R, El Hillali Y, Rivenq A, Boughaleb Y, Hajjaji A. Assignment Approach for Electric Vehicle Charging Using Traffic Data Collected by SUMO. World Electric Vehicle Journal. 2023; 14(2):40. https://doi.org/10.3390/wevj14020040
Chicago/Turabian StyleFarhani, Riham, Yassin El Hillali, Atika Rivenq, Yahia Boughaleb, and Abdelowahed Hajjaji. 2023. "Assignment Approach for Electric Vehicle Charging Using Traffic Data Collected by SUMO" World Electric Vehicle Journal 14, no. 2: 40. https://doi.org/10.3390/wevj14020040