Optimization Approach for Long-Term Planning of Charging Infrastructure for Fixed-Route Transportation Systems
As the electrification of the transportation sector advances, fleet operators have to rethink their approach regarding fleet management against the background of limiting factors, such as a reduced range or extended recharging times. Charging infrastructure plays a critical role, and it is worthwhil...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-12-01
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| Series: | World Electric Vehicle Journal |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2032-6653/12/4/258 |
| _version_ | 1797499766585688064 |
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| author | Benjamin Daniel Blat Belmonte Stephan Rinderknecht |
| author_facet | Benjamin Daniel Blat Belmonte Stephan Rinderknecht |
| author_sort | Benjamin Daniel Blat Belmonte |
| collection | DOAJ |
| description | As the electrification of the transportation sector advances, fleet operators have to rethink their approach regarding fleet management against the background of limiting factors, such as a reduced range or extended recharging times. Charging infrastructure plays a critical role, and it is worthwhile to consider its planning as an integral part for the long-term operation of an electric vehicle fleet. In the category of fixed route transportation systems, the predictable character of the routes can be exploited when planning charging infrastructure. After a prior categorization of stakeholders and their respective optimization objectives in the sector coupling domain, a cost optimization framework for fixed route transportation systems is presented as the main contribution of this work. We confirm previous literature in that there is no one-fits-all optimization method for this kind of problem. The method is tested on seven scenarios for the public transport operator of Darmstadt, Germany. The core optimization is formulated as a mixed integer linear programming (MILP) problem. All scenarios are terminated by the criterion of a maximum solving time of 48 h and provide feasible solutions with a relative MIP-gap between 7 and 24%. |
| first_indexed | 2024-03-10T03:52:08Z |
| format | Article |
| id | doaj.art-59a3a906506543368bba158df7a0bc90 |
| institution | Directory Open Access Journal |
| issn | 2032-6653 |
| language | English |
| last_indexed | 2024-03-10T03:52:08Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | World Electric Vehicle Journal |
| spelling | doaj.art-59a3a906506543368bba158df7a0bc902023-11-23T11:03:53ZengMDPI AGWorld Electric Vehicle Journal2032-66532021-12-0112425810.3390/wevj12040258Optimization Approach for Long-Term Planning of Charging Infrastructure for Fixed-Route Transportation SystemsBenjamin Daniel Blat Belmonte0Stephan Rinderknecht1Department of Mechanical Engineering, Institute for Mechatronic Systems, Technical University of Darmstadt, 64287 Darmstadt, GermanyDepartment of Mechanical Engineering, Institute for Mechatronic Systems, Technical University of Darmstadt, 64287 Darmstadt, GermanyAs the electrification of the transportation sector advances, fleet operators have to rethink their approach regarding fleet management against the background of limiting factors, such as a reduced range or extended recharging times. Charging infrastructure plays a critical role, and it is worthwhile to consider its planning as an integral part for the long-term operation of an electric vehicle fleet. In the category of fixed route transportation systems, the predictable character of the routes can be exploited when planning charging infrastructure. After a prior categorization of stakeholders and their respective optimization objectives in the sector coupling domain, a cost optimization framework for fixed route transportation systems is presented as the main contribution of this work. We confirm previous literature in that there is no one-fits-all optimization method for this kind of problem. The method is tested on seven scenarios for the public transport operator of Darmstadt, Germany. The core optimization is formulated as a mixed integer linear programming (MILP) problem. All scenarios are terminated by the criterion of a maximum solving time of 48 h and provide feasible solutions with a relative MIP-gap between 7 and 24%.https://www.mdpi.com/2032-6653/12/4/258cost optimizationcharging infrastructurefixed-route transportation systemselectric vehicle fleetsoperations researchelectromobility |
| spellingShingle | Benjamin Daniel Blat Belmonte Stephan Rinderknecht Optimization Approach for Long-Term Planning of Charging Infrastructure for Fixed-Route Transportation Systems World Electric Vehicle Journal cost optimization charging infrastructure fixed-route transportation systems electric vehicle fleets operations research electromobility |
| title | Optimization Approach for Long-Term Planning of Charging Infrastructure for Fixed-Route Transportation Systems |
| title_full | Optimization Approach for Long-Term Planning of Charging Infrastructure for Fixed-Route Transportation Systems |
| title_fullStr | Optimization Approach for Long-Term Planning of Charging Infrastructure for Fixed-Route Transportation Systems |
| title_full_unstemmed | Optimization Approach for Long-Term Planning of Charging Infrastructure for Fixed-Route Transportation Systems |
| title_short | Optimization Approach for Long-Term Planning of Charging Infrastructure for Fixed-Route Transportation Systems |
| title_sort | optimization approach for long term planning of charging infrastructure for fixed route transportation systems |
| topic | cost optimization charging infrastructure fixed-route transportation systems electric vehicle fleets operations research electromobility |
| url | https://www.mdpi.com/2032-6653/12/4/258 |
| work_keys_str_mv | AT benjamindanielblatbelmonte optimizationapproachforlongtermplanningofcharginginfrastructureforfixedroutetransportationsystems AT stephanrinderknecht optimizationapproachforlongtermplanningofcharginginfrastructureforfixedroutetransportationsystems |