Smart City Gnosys
Smart city article details
| Title | A Decision Support Tool For Optimal Charging Scheduling For Individual Electric Vehicle Users |
|---|---|
| ID_Doc | 1305 |
| Authors | Kim I.-C.; Sivarkumar A.; Daina N. |
| Year | 2020 |
| Published | 4th International Conference on Smart Grid and Smart Cities, ICSGSC 2020 |
| DOI | http://dx.doi.org/10.1109/ICSGSC50906.2020.9248553 |
| Abstract | The increase in uptake of electric vehicles (EV) is accompanied by an increasingly complex charging service ecosystem characterised by variability in charging tariffs, charging powers, and availability of charging infrastructure in space and time. This increases the necessity of decision support tools (DSTs) that can minimise operating costs by utilizing best charging opportunities, and ensuring all mobility needs are met. We formulate and solve an optimization problem resulting in optimal charging amounts at each stay location based on the users' intended travel itinerary to informtheir decision making. We apply this to a representative sample of London drivers' full day travel itineraries. We analyse the resulting distribution of cost reduction compared to dumb charging. We analyse factors that affect the cost reductions, such as driving mileage, which shows the types of users for whom such tools can be most beneficial, and thus appropriately prioritise its adoption. The preliminary results gave average daily benefits of £0.382 and cost reduction of 43.5% for each user, with the average price of energy being 3 times cheaper in £/kWh. The tool can easedecision making for EV users effectively, by navigating for them the large amount of information. The results showed that even relatively short, uncomplicated travel itineraries could benefit from the DST, with even more benefits for more complicated itineraries. Thus, this optimization could be suitable and beneficial for the majority of BEV users. © 2020 IEEE. |
| Author Keywords | charging scheduling; decision support tool; electric vehicles; optimization; travel itinerary |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
40337  | 0.872 | Alanazi F.; Alshammari T.O.; Azam A. | Optimal Charging Station Placement And Scheduling For Electric Vehicles In Smart Cities | Sustainability (Switzerland), 15, 22 (2023) |
| 42118 | 0.866 | Goyal S.; Rawat N.; Charu | Placement Of Charging Stations And Technology In Electric Vehicles: A Review | Proceedings - 2024 5th International Conference on Image Processing and Capsule Networks, ICIPCN 2024 (2024) |
| 49243 | 0.864 | Wu O.Q.; Yücel Ş.; Zhou Y. | Smart Charging Of Electric Vehicles: An Innovative Business Model For Utility Firms | Manufacturing and Service Operations Management, 24, 5 (2022) |
| 38094 | 0.862 | Pok P.J.; Yeo H.; Woo S. | Multi-Agent Game-Theoretic Modelling Of Electric Vehicle Charging Behavior And Pricing Optimization In Dynamic Ecosystems | Procedia Computer Science, 257 (2025) |
| 35913 | 0.858 | Shahriar S.; Al-Ali A.R.; Osman A.H.; Dhou S.; Nijim M. | Machine Learning Approaches For Ev Charging Behavior: A Review | IEEE Access, 8 (2020) |
| 22543 | 0.857 | Gurjar P.K.; Barala C.P.; Mathuria P.; Bhakar R. | Electric Vehicles Navigation Planning And Operation For Smart City: A Review | 2022 1st International Conference on Sustainable Technology for Power and Energy Systems, STPES 2022 (2022) |
| 23303 | 0.857 | Guo X.; Pan K.; Xu Y.; Cao S. | Energy Management And Multi-Objective Decision-Making Systems For Smart Charging Of Zero-Emission Electric Vehicles With Different Charging Speeds | Lecture Notes in Electrical Engineering, 1425 LNEE (2025) |
| 40338 | 0.857 | Kandar S.; Verma P.; Nath S. | Optimal Charging Station Selection For Electric Vehicle In A Smart City In Real-Time Traffic Environment With Minimum Total Trip Time | Modern Computing Technologies for EV Efficiency and Sustainable Energy Integration (2025) |
| 40684 | 0.855 | Vijay Kumar M.; Gondesi J.R.; Krishna G.S.; Kumar I.A. | Optimization Of Users Ev Charging Data Using Convolutional Neural Network | Lecture Notes in Networks and Systems, 731 LNNS (2024) |
| 40628 | 0.855 | Fernandez V.; Pérez V. | Optimization Of Electric Vehicle Charging Control In A Demand-Side Management Context: A Model Predictive Control Approach | Applied Sciences (Switzerland), 14, 19 (2024) |