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案件記録

Artificial Intelligence-Based System for Integrated Optimization of Autonomous Electric Vehicle Fleets Across Transportation and Electricity Networks

発明審査中
20請求項 · 2 独立
§ Ⅰ

案件概要

発明者

Chukwuka Gideon Monyei; Jennifer Ifechi Alphonsus; Michael Oluwadamilola Obolo; Christian Chitom Alphonsus; David Okwuchukwu Alphonsus; Peter Ayokunle Popoola; Chukwuemeka Godwin Monyei; Emmanuel Chukwunweike Monyei; Chinaza Favour Onwuka; Daniel Adeboye; Uduak Christopher Edet; Michael Olusegun Ogunlowo; Godspower Ikenna Ogbonna; Emmanuel Chibuisi Otuonye; Paul Osato Aigbekaen

IPC分類

G6Q 50/40B60L 55/G5B 13/2G6Q 10/631G6Q 20/38H2J 3/32

CPC分類

G6Q50/40B60L55/G5B13/265G6Q10/6314H2J3/322G6Q20/389

A system and method for integrated optimization of autonomous electric vehicle fleets across transportation and electricity networks which employs artificial intelligence to dynamically allocate autonomous electric vehicles between mobility services and electricity grid services based on real-time conditions. The platform acquires data including energy mix forecasts, earth observation measurements, vehicle owner schedules, and emission-based route penalties to generate coordinated allocation decisions. Vehicle owners specify availability through a scheduling interface. The system optimizes vehicle utilization through a hierarchical optimization approach implementing mobility demand-side flexibility and electricity demand-side flexibility simultaneously. Multi-objective genetic algorithm optimization balances revenue generation, energy costs, emissions reduction, and battery health. The integrated approach maximizes value creation across both transportation and energy domains, reducing urban emissions while enhancing grid stability through coordinated management of distributed energy resources in autonomous electric vehicle fleets.

原文(中国語)

A system and method for integrated optimization of autonomous electric vehicle fleets across transportation and electricity networks which employs artificial intelligence to dynamically allocate autonomous electric vehicles between mobility services and electricity grid services based on real-time conditions. The platform acquires data including energy mix forecasts, earth observation measurements, vehicle owner schedules, and emission-based route penalties to generate coordinated allocation decisions. Vehicle owners specify availability through a scheduling interface. The system optimizes vehicle utilization through a hierarchical optimization approach implementing mobility demand-side flexibility and electricity demand-side flexibility simultaneously. Multi-objective genetic algorithm optimization balances revenue generation, energy costs, emissions reduction, and battery health. The integrated approach maximizes value creation across both transportation and energy domains, reducing urban emissions while enhancing grid stability through coordinated management of distributed energy resources in autonomous electric vehicle fleets.

外部リソース