Artificial Intelligence-Based System for Integrated Optimization of Autonomous Electric Vehicle Fleets Across Transportation and Electricity Networks
개요
발명자
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 분류
CPC 분류
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.
원문 (중국어)
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.