Charging Station Layout Tool
Welcome to our intelligent optimization tool! Our website is designed for charging station operators, aiming to help you quickly plan charging station layouts using optimal algorithms and a streamlined interface to increase your revenue and save your time.
Finding the optimal locations for charging stations in cities is no easy task, especially under various complex real-world conditions.
Layout Challenges
Charging station layout needs to consider multiple factors such as pedestrian flow, traffic flow, power grid capacity, and construction costs. Manual planning is inefficient and difficult to optimize.
Data-Driven
Our tool adopts a data-driven approach, combining real city data with advanced algorithms to provide intelligent layout solutions.
How Our Web Tool Works
Fast and Accurate
Supports input of your budget, planned number of charging stations, optimization methods and precision. Click the question mark for quick information.
Multiple Parameters
Various optimization parameters can be set, including user coverage rate, construction costs, and operational revenue, to achieve personalized layout plans.
Result Export
Optimization results can be directly downloaded or visualized on maps for easy analysis and decision implementation.
Our layout tool offers multiple advantages.
Intelligent Algorithm
Using advanced machine learning and operations research algorithms to find optimal solutions while considering numerous constraints.
Simple Interface
The tool interface is designed to be intuitive and simple, allowing users without professional background to operate easily and get quick results.
Economic Benefits
Optimized layout plans can significantly improve charging station utilization and return on investment, creating greater value for operators.
Powerful and intuitive map display functionality.
Information Overview
Provides a comprehensive overview of all charging station locations and financial information on the map, making it clear and informative at a glance.
Detail View
Click to view detailed information, including capacity, return on investment, and user coverage for each location.
Interactive Operations
Supports various interactive operations such as map zooming, dragging, and filtering, allowing users to analyze layout plans from different perspectives.
Number of Candidate Charging Stations
Optimization Target Methods
Minimum Planning Efficiency Improvement (%)
Tool Release Year
Hu Jianping
Chief Executive Officer of Haylion TechnologiesFounder of eMaaS, founder and chairman of Haylion Technologies, chief expert of the Urban Passenger Transport Expert Committee of China Road Transport Association, Ph.D. in Road and Traffic Engineering from Tongji University. Previously served as general manager and chairman of Shenzhen Bus Group Co., Ltd., executive deputy general manager of Shenzhen Metro Group Co., Ltd., assistant to the director and head of personnel department of Shenzhen Government Transport Bureau, vice chairman of China Road Transport Association, and senior vice president of Microvast Power Solutions.
Qiu Jiandong
Senior Engineer of SUTPCDirector of Transportation Information and Model Institute, high-level leading talent in Shenzhen. 17 years of experience in traffic modeling and big data. Led over 30 urban modeling and big data platform projects and 10 national and provincial research projects. Authorized 30 patents, participated in compiling 5 monographs and industry standards, published 35 papers, and received 24 national and provincial awards.
Ke Jintao
Assistant Professor of HKUServes as youth editor of TRC and editor of TRE, leading international journals in transportation. Led multiple research projects, including National Natural Science Foundation, Hong Kong Transport Department Smart Transport Fund, Hong Kong Research Grants Council Fund, and Hong Kong Environmental Protection Department Green Fund, with total funding exceeding HKD 10 million. Published over 50 SCI/SSCI papers with more than 4,900 citations, recognized as a World Top 2% Scholar in Smart Transportation by Stanford in 2023.