SYSTEMS FOR AND METHODS OF LEARNING FOR ON-DEMAND OPTIMAL ELECTRIC VEHICLE (EV) FLEET OPERATION
Systems and methods for optimizing on-demand electric vehicle and mixed-fuel fleet operations use a learning-based system. In some embodiments, the method includes collecting real-time operational data, simulating service and charging processes, formulating assignment strategies, and adjusting assignments based on cost-to-go predictions in real time. The assignment strategy is updated in real time to account for parameters such as states of charge on the vehicles; changing context, such as weather or traffic; historical data; and constraints.
1 . A method of optimally assigning on-demand vehicles in a fleet of vehicles comprising:
collecting real-time data for vehicles in the fleet of vehicles;
simulating service and charging processes for the vehicles;
determining assignment strategies for the vehicles to service one or more service requests through model predictive control (MPC); and
adjusting assignments based on cost-to-go predictions from a deep reinforcement learning (DRL) model.
2 . The method of claim 1 , wherein the data comprise telematics data, operational data, context, or any combination thereof.
3 . The method of claim 1 , wherein the fleet of vehicles comprises electric vehicles.
4 . The method of claim 1 , wherein the fleet of vehicles comprises automated vehicles, human-operated vehicles, or both.
5 . The method of claim 1 , wherein the assignment strategies are determined through model predictive control.
6 . The method of claim 1 , wherein the cost-to-go predictions are determined from a deep reinforcement learning model (DRL).
7 . The method of claim 6 , wherein the DRL model evaluates an effectiveness of different vehicle assignment strategies under varying operational conditions.
8 . The method of claim 1 , wherein the assignments are based on an assignment algorithm, wherein adjusting the assignments comprises solving an optimization problem.
9 . The method of claim 8 , wherein the optimization problem has associated constraints, costs, or both.
10 . The method of claim 9 , wherein the cost-to-go predictions are based on a cost-to-go model that incorporates one or more factors comprising vehicle state of charge (SOC), traffic conditions, and predicted service demand.
11 . The method of claim 10 , wherein the constraints and cost-to-go include driver status and labor rules so that the assignment maximizes driver utilization while meeting the labor rules.
12 . The method of claim 10 , wherein the factors comprise context, the context comprising traffic conditions, weather conditions, a frequency of past service requests within a predetermined distance of a customer location, or any combination thereof.
13 . A system for optimizing on-demand fleet operations for a fleet of vehicles, the system comprising:
a learning-based predictor (LBP) configured to predict energy consumption usage for each vehicle in the fleet of vehicles for service trips;
a dispatcher coupled to the LBP, the dispatcher configured to receive service requests and assign the service requests to the vehicles to optimize parameters for servicing the service requests;
a learning-based cost-to-go (LBCG) module coupled to the LBP and the dispatcher, the LBCG module configured to maximize long-term performance of the fleet of vehicles for the service trips; and
a vehicle simulator coupled to the dispatcher and the LBCG, the vehicle simulator configured to simulate the functioning of the vehicles and transmit output and parameters to the LBCG.
14 . The system of claim 13 , further comprising a service database storing service requests, the service database coupled to the dispatcher, such that when a service is added to or deleted from the service database, the optimal assignment module is updated.
15 . The system of claim 14 , further comprising a queue database storing unassigned services, wherein entries in the queue database trigger updating the optimal assignment module.
16 . The system of claim 15 , wherein the vehicle simulator generates key performance indicators transmitted to the LBCG.
17 . The system of claim 16 , further comprising a Depot module coupled to the vehicle simulator, the Depot module configured to store states of charging queues and states of charging in a depot.
18 . The system of claim 17 , wherein the vehicle simulator comprises a finite state machine for monitoring transitions between states for the vehicles, the states comprising one or more of IDLE, DRIVE, SERVICE, CHARGE, and CHARGING QUEUE.
19 . The system of claim 13 , wherein one or more of components comprising the LBP, the dispatcher, the LBGC, and the vehicle simulator comprises a corresponding computer-readable media containing instructions for executing functionality of the one or more components and a processor for executing the functionality.
20 . The system of claim 13 , wherein the system is coupled to each of the vehicles over a cloud network.
21 . The system of claim 13 , wherein the vehicle simulator comprises an array of vehicle data structures, each vehicle data structure corresponding to a vehicle from the fleet of vehicles, each vehicle data structure comprising a state or charge of the vehicle.