ELECTRIC VEHICLE CHARGING OPTIMIZATION BASED ON PREDICTIVE ANALYTICS UTILIZING MACHINE LEARNING

A method for managing charging resources of a charging system for plug-in electric vehicles (PEVs), the charging system including a central recording center including a tracking database. The method including initiating a charging session to a first PEV based on detecting that the first PEV has been...

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Bibliographische Detailangaben
Hauptverfasser:	Shedu, Emmanuel, Nair, Rishabh Anup, Madiraju, Saisujit, Mirashi, Niranjan Abhijeet, Barber, Ronald J, DeLuca, Chad Eric, Uba, Uche, Loya, Francisco
Format:	Patent
Sprache:	eng
Schlagworte:	CALCULATING CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTINGELECTRIC POWER COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING CONVERSION OR DISTRIBUTION OF ELECTRIC POWER COUNTING ELECTRIC EQUIPMENT OR PROPULSION OF ELECTRICALLY-PROPELLEDVEHICLES ELECTRICITY ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES, IN GENERAL GENERATION MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES PERFORMING OPERATIONS PHYSICS SIGNALLING SYSTEMS FOR STORING ELECTRIC ENERGY TRAFFIC CONTROL SYSTEMS TRANSPORTING VEHICLES IN GENERAL
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Beschreibung
Zusammenfassung:	A method for managing charging resources of a charging system for plug-in electric vehicles (PEVs), the charging system including a central recording center including a tracking database. The method including initiating a charging session to a first PEV based on detecting that the first PEV has been plugged into a charging station. The first PEV is associated with the charging session in the tracking database. The first PEV is associated, in the tracking database, to a first user and a first PEV profile. The first PEV is charged in accordance with information from the first PEV profile. Charging session data is monitored and stored in the tracking database during the charging session. A machine learning model is generated based on collective charging data. Charge completion time of the first PEV is predicted based on the machine learning model.