Electric vehicle charging infrastructure: Difference between revisions

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==Libraries and Tools==
 
==Libraries and Tools==
 
==Data==
 
==Data==
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* '''Open Charge Map:''' A global registry of electric vehicles charging infrastructure with API, available [https://openchargemap.org/site/develop#api here].
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==Future Directions==
 
==Future Directions==
 
==Relevant Groups and Organizations==
 
==Relevant Groups and Organizations==

Latest revision as of 14:37, 4 February 2022

๐ŸŒŽ This article is a stub, and is currently under construction. You can help by adding to it!

This page is about the applications of machine learning (ML) in the context of optimizing electric vehicle charging infrastructure. For an overview of electric vehicle charging stations or the charging networks more generally, please see the Wikipedia pages on this topic.


Deploying electric vehicles at scale requires an adequate charging infrastructure, with various planning, scheduling and management issues. ML can help for example with predicting usage of the infrastructure and load prediction.

Background Readings[edit | edit source]

Conferences, Journals, and Professional Organizations[edit | edit source]

Libraries and Tools[edit | edit source]

Data[edit | edit source]

  • Open Charge Map: A global registry of electric vehicles charging infrastructure with API, available here.

Future Directions[edit | edit source]

Relevant Groups and Organizations[edit | edit source]

References[edit | edit source]