Battery Sharing: A Feasibility Analysis through Simulation
Abstract
:1. Introduction
- The service life of batteries is decoupled from the service life of vehicles. In this way, the use of batteries might significantly increase.
- Batteries would be managed by a few easy-to-control companies, thus reducing the risk of illegal disposal.
- The need to redistribute batteries may give rise to many new job positions.
2. Related Work
3. Simulation Components and Architecture
3.1. Batteries
3.2. Vehicles
3.3. Distributors
3.4. Stations
3.5. Road Network
3.6. Runner
Algorithm 1 Main simulation process |
Instantiate N vehicle trip processes while Simulation is not concluded do Get the concluded vehicle trip processes for each process concluded do Instantiate a new vehicle trip process end for end while |
4. Incorporated Algorithms
4.1. Driver’s Path Definition
- 1.
- 2.
- If a path is not possible, we have a graph error and the trip is considered concluded as well as excluded from the final statistics.
- 3.
- If a path can be covered without stopping at the charging stations, the trip is simulated and the process concluded.
- 4.
- If b cannot be reached without stops, the algorithm iterates the nodes from a to b looking for a charging station s.
- 5.
- If s exists and is on the original path, we simulate a trip from a to s, set , and go to step 1. Otherwise, if there are no stations on the path or the station s cannot be reached with residual battery, the algorithm moves to the next step.
- 6.
- For each node i from the last reachable node to a, consider i as the root and start a breadth-first search method [39], looking for a station outside the original path. If a station is found, then simulate a trip to s, set , and go to step 1.
4.2. Battery Redistribution
5. Validation and Results
- The simulation handles three types of batteries—small, medium, and large—with respective capacities 10, 15, and 20 kWh. For experimental purposes, we have considered that the batteries do not reduce their performance with use. Thus, the simulator assumes that the capacity of the batteries does not decrease with the charge cycles.
- In each road network, exactly of nodes are characterized by the presence of a charging station. This results in a different number of charging stations for each network: 4 charging stations in the test network, 69 in Sassari’s network, 66 in Modena’s network, and 196 in Barcelona’s network.
- The simulation handles three types of vehicles. The first two types are more frequent and powered by two batteries each, while the latter is less frequent and powered by three batteries.
- All vehicles have the same consumption rate, kWh/km, and their consumption is equally affected by the road slope.
- The simulation handles two types of equally spread charging station, small and large. The first one can process only one vehicle at a time and disburses a power of 10 kWh. The second one can process two vehicles together and disburses a power of 12 kWh. The charging time can be easily estimated using the following equation:
- The number of traveling vehicles is constant, exactly 1100 vehicles, during the entire duration of the simulation.
- Battery swapping takes 60 s.
- There are 10 vehicles dedicated to battery redistribution, and a new redistribution is carried out every hour if the previous one is already concluded.
- The simulation runs for 8 h.
6. Conclusions and Future Work
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Scenario | Relative Travel Time [min/km] | Waiting Time [s] | Queue |
---|---|---|---|
Sharing of partially charged batteries | 1.020 | 127 | 3.247 |
Sharing of fully charged batteries | 1.091 | 3060 | 77.104 |
No sharing | 1.200 | 5803 | 103.570 |
Scenario | Relative Travel Time [min/km] | Waiting Time [s] | Queue |
---|---|---|---|
Sharing of partially charged batteries | 1.020 | 62 | 0.154 |
Sharing of fully charged batteries | 1.2 | 5517 | 30.004 |
No sharing | 1.38 | 8917 | 31.692 |
Scenario | Relative Travel Time [min/km] | Waiting Time [s] | Queue |
---|---|---|---|
Sharing of partially charged batteries | 1.020 | 62 | 0.126 |
Sharing of fully charged batteries | 1.14 | 2020 | 6.679 |
No sharing | 1.200 | 4091 | 15.641 |
Scenario | Relative Travel Time [min/km] | Waiting Time [s] | Queue |
---|---|---|---|
Sharing of partially charged batteries | 1.020 | 31 | 0.037 |
Sharing of fully charged batteries | 1.083 | 1074 | 2.012 |
No sharing | 1.267 | 2933 | 5.553 |
Scenario | Relative Travel Time [min/km] | Waiting Time [s] | Queue |
---|---|---|---|
Test network | 1.158 | 3010 | 45.210 |
Sassari | 1.28 | 2012 | 5.110 |
Modena | 1.13 | 1920 | 3.029 |
Barcelona | 1.083 | 433 | 1.512 |
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Neroni, M.; Herrera, E.M.; Juan, A.A.; Panadero, J.; Ammouriova, M. Battery Sharing: A Feasibility Analysis through Simulation. Batteries 2023, 9, 225. https://doi.org/10.3390/batteries9040225
Neroni M, Herrera EM, Juan AA, Panadero J, Ammouriova M. Battery Sharing: A Feasibility Analysis through Simulation. Batteries. 2023; 9(4):225. https://doi.org/10.3390/batteries9040225
Chicago/Turabian StyleNeroni, Mattia, Erika M. Herrera, Angel A. Juan, Javier Panadero, and Majsa Ammouriova. 2023. "Battery Sharing: A Feasibility Analysis through Simulation" Batteries 9, no. 4: 225. https://doi.org/10.3390/batteries9040225
APA StyleNeroni, M., Herrera, E. M., Juan, A. A., Panadero, J., & Ammouriova, M. (2023). Battery Sharing: A Feasibility Analysis through Simulation. Batteries, 9(4), 225. https://doi.org/10.3390/batteries9040225