Abstract: Methods and systems are provided for transmitting a charging recommendation from a cloud-based server to an electric vehicle (EV), where the charging recommendation includes preferred options for charging stations and/or charging times. In one example, a method comprises receiving, at a cloud-based charging recommendation system of an EV charging system, a set of battery charging parameters from the EV via a secure anonymous connection; determining a set of candidate charging stations for the EV, based on the received battery charging parameters; estimating sustainability, cost, and preference metrics for each candidate charging station; ranking the future charge events based on the sustainability, cost, and preference metrics; selecting one or more charge options for the EV from the ranked future charge events; generating the charging recommendation with the one or more charge options; and transmitting the charging recommendation to the EV via the secure anonymous connection.

Abstract: This disclosure describes an environmentally sustainable peer-to-peer electricity source network. An example method may include receiving, by a processor, a request to perform a charging of a first device. An example method may include determining, by the processor and using a peer-to-peer network including one or more charging sources, a first charging source to provide charge to the first device, wherein determining the first charging source is based on an amount of emissions that would result from charging using the first charging source. An example method may include determining, by the processor, a first time and a first location at which to perform charging of the first device using the first charging source. An example method may include determining, by the processor, that the charging of the first device has been performed.

Abstract: The disclosure generally pertains to systems and methods for assigning travel routes to vehicles. An example method may involve evaluating a first vehicle for deploying on a first travel route and a second vehicle for a second travel route. Evaluating the first vehicle may include determining a first probability that the first vehicle will need a first energy replenishment operation during deployment on the first travel route, and also determining a first deployment cost for the first vehicle. The first deployment cost can include a first energy replenishment cost based on the first probability. Evaluating the second vehicle may include determining a second deployment cost for the second vehicle, the second deployment cost including a second energy replenishment cost. The first vehicle is assigned to the first travel route and the second vehicle to the second travel route if the first deployment cost is less than the second deployment cost.

Abstract: A computer-implemented method for generating recommendations for vehicle powertrain changes is described herein. Disclosed systems and methods include receiving operational data associated with one or more vehicles in a fleet of networked vehicles, and receiving maintenance data associated with the vehicles. The maintenance data can include a vehicle parts history associated with one or more fleet vehicle parts. A machine learning analytical model is described that determines, based in part on the operational data and the maintenance data, a total cost of ownership for the vehicles, and generates, based at least in part on the total cost of ownership for the vehicles, a vehicle recommendation indicative of a powertrain changes for the vehicles in the fleet. Aspects of the present disclosure may provide automated sources of crowdsourced vehicle fleet information with which vehicle fleet managers can make actionable decisions for fleet powertrain upgrades.