Artificial intelligence-based system for integrated optimization of autonomous electric vehicle fleets across transportation and electricity networks

A computer-implemented system for integrated management of electric vehicles, comprising:

• a computing device comprising at least a memory and a processor;
• a plurality of programming instructions stored in the memory and operable on the processor, wherein the plurality of programming instructions, when operating on the processor, cause the computing device to:
• acquire data related to transportation networks and electricity networks, wherein the acquired data comprises energy mix temporal forecasts, earth observation measurements of atmospheric emissions, and emission-based route penalties;
• receive scheduling information for a plurality of electric vehicles, wherein the scheduling information designates time periods according to different availability categories;
• allocate the plurality of electric vehicles between transportation services and electricity grid services based on the scheduling information and the acquired data, wherein allocating comprises:
• implementing a hierarchical optimization approach that operates across multiple time horizons comprising at least a long-term planning layer, a day-ahead scheduling layer, and a real-time dispatch layer, wherein each layer operates at different temporal granularities and provides inputs to subsequent layers;
• and
• applying genetic algorithm optimization process comprising:
• encoding vehicle schedules as chromosomes structured as three-dimensional matrices with dimensions corresponding to vehicles, time slots, and assignment types, the chromosomes representing complete allocation patterns across the plurality of electric vehicles and time periods;
• evaluating fitness of each chromosome based on weighted objective functions for revenue generation, energy costs, emissions reduction, and battery degradation;
• and
• performing genetic operations comprising tournament selection, specialized crossover operators, and mutation operators tailored for dual-flexibility coordination;
• implement the optimization process to determine vehicle allocations that balance multiple objectives;
• for certain time periods, provide alternative transportation options while allocating associated vehicles to other services;
• present allocation decisions to users;
• manage financial transactions associated with the vehicle services;
• and
• transmit allocation instructions to at least one of an onboard controller of each of the plurality of electric vehicles or a charging infrastructure controller to control vehicle charging and discharging operations based on the determined allocations.