Optimal Control Strategy for Coordinated Charging of Electric Bus Fleet Based on Traffic-Electrical Coupling Mathematical Model Using Mixed Integer Programming Algorithm

Optimal Control Strategy for Coordinated Charging of Electric Bus Fleet Based on Traffic-Electrical Coupling Mathematical Model Using Mixed Integer Programming Algorithm | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 4 August 2025 V3 Latest version Share on Optimal Control Strategy for Coordinated Charging of Electric Bus Fleet Based on Traffic-Electrical Coupling Mathematical Model Using Mixed Integer Programming Algorithm Author : Yuanzhe Chen 0009-0000-1129-214X [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.175382399.91796085/v3 326 views 102 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Electric vehicles (EVs) play a crucial role in addressing climate change and achieving sustainable development by utilizing clean renewable energy, significantly reducing carbon emissions in the transportation sector. However, unregulated charging of large-scale EV fleet can drastically increase grid loads, especially during peak hours, jeopardizing grid safety, stability and economy. This paper focuses on optimizing coordinated charging management strategies for electric buses. It reasonably models daytime charging loads of electric bus using Monte Carlo simulation and mathematical statistics and constructs a multi-objective optimization function with mixed-integer programming algorithms. The objectives include reducing grid peak-valley differences and volatility and lowering total charging costs of electric bus fleet. Constraints are established considering grid safe operation limits, battery State of Charge (SOC), charging pile capacity, and charging power. Specific attention is given to formulating coordinated charging management strategies for electric buses using charging modes, with simulation cases verifying the technical feasibility of the proposed optimization algorithm. Results show reduced grid peak-valley differences and total charging costs, achieving expected optimization goals. Supplementary Material File (optimal control strategy for coordinated charging of electric bus fleet based on traffic-electrical coupling mathematical model using mixed integer programming algorithm_yuanzhe chen_unsw.pdf) Download .46 KB Information & Authors Information Version history V1 Version 1 29 July 2025 V2 Version 2 01 August 2025 V3 Version 3 04 August 2025 Copyright This work is licensed under a Creative Commons Attribution 4.0 International License Keywords charging load modeling coordinated charging management strategy electric bus mixed-integer programming algorithm monte carlo simulation Authors Affiliations Yuanzhe Chen 0009-0000-1129-214X [email protected] UNSW Sydney View all articles by this author Metrics & Citations Metrics Article Usage 326 views 102 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Yuanzhe Chen. Optimal Control Strategy for Coordinated Charging of Electric Bus Fleet Based on Traffic-Electrical Coupling Mathematical Model Using Mixed Integer Programming Algorithm. Authorea . 04 August 2025. DOI: https://doi.org/10.22541/au.175382399.91796085/v3 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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