Abstract: A system for optimizing ownership cost of a fleet having electric vehicles includes a command unit adapted to selectively execute a simulation module, a sampling module and an optimization module. The command unit is configured to construct a multi-agent model based at least partially on historical fleet trip data and mobility pattern data of the fleet. Route data for a set of fleet tasks is obtained, including charging infrastructure data. The command unit is configured to simulate different configurations of the electric vehicles carrying out the set of fleet tasks over a predefined period, via the simulation module, based in part on the multi-agent model and the route data. The command unit is configured to determine an optimal configuration from the different configurations of the electric vehicles, via the optimization module. The optimal configuration minimizes investment and operational costs of the fleet.

Abstract: A computer-implemented method for scheduling pre-departure charging for electric vehicles includes predicting a user-departure time based on a first machine learning prediction model. The method further includes determining a cabin temperature to be set for the user at the user-departure time based on a second machine learning prediction model. The method further includes determining a battery-temperature to be set at the user-departure time based on a third machine learning prediction model. The method further includes determining a present charge level of a battery of the electric vehicle. The method further includes computing a charging start-time to start charging the battery based on one or more attributes of a charging station to which the electric vehicle is coupled, and based on the user-departure time, the cabin temperature, and the battery-temperature. The method further includes initiating charging the battery at the charging start-time.

Abstract: A system for managing a fleet of electric vehicles and respective fleet drivers includes a command unit having a processor and tangible, non-transitory memory on which instructions are recorded. The command unit is adapted to obtain input variables, including respective fleet tasks and their priority status. Route data for the respective fleet tasks is obtained. The command unit is adapted to obtain an objective function defined by a plurality of influence factors having respective weights. The command unit is adapted to obtain optimal charging schedules respectively for the electric vehicles and match the respective fleet tasks to the electric vehicles and the respective fleet drivers, based in part on the objective function, input variables and the route data. The influence factors may include energy cost optimization, timeliness of task completion and minimizing range anxiety. In some embodiments, the respective weights of the influence factors are designated by a fleet manager.

Abstract: An operating system for a vehicle having an electric vehicle (EV) drivetrain and a plurality of electrically-powered accessories is described. A controller determines, via a navigation system, a target off-road trail segment, and characterizes the subject vehicle, ambient conditions, and the target off-road trail segment to determine an estimated consumption of electric energy for the vehicle to operate over the target off-road trail segment. The EV drivetrain and the electrically-powered accessories are controlled during operation of the vehicle on the off-road trail segment based upon the estimated consumption of electric energy for the subject vehicle. This is done to minimize a likelihood of a low SOC event for the DC power source for the trail segment and to avoid a low battery state at a location that is distal from a charging station.

Abstract: Presented are intelligent vehicle systems for off-road driving incident prediction and assistance, methods for making/operating such systems, and vehicles networking with such systems. A method for operating a motor vehicle includes a system controller receiving geolocation data indicating the vehicle is in or entering off-road terrain. Responsive to the vehicle geolocation data, the controller receives, from vehicle-mounted cameras, camera-generated images each containing the vehicle's drive wheel(s) and/or the off-road terrain's surface. The controller receives, from a controller area network bus, vehicle operating characteristics data and vehicle dynamics data for the motor vehicle. The camera data, vehicle operating characteristics data, and vehicle dynamics data is processed via a convolutional neural network backbone to predict occurrence of a driving incident on the off-road terrain within a prediction time horizon.

Abstract: An operating system for a vehicle having an electric vehicle (EV) drivetrain and a plurality of electrically-powered accessories is described. A controller determines, via a navigation system, a target off-road trail segment, and characterizes the subject vehicle, ambient conditions, and the target off-road trail segment to determine an estimated consumption of electric energy for the vehicle to operate over the target off-road trail segment. The EV drivetrain and the electrically-powered accessories are controlled during operation of the vehicle on the off-road trail segment based upon the estimated consumption of electric energy for the subject vehicle. This is done to minimize a likelihood of a low SOC event for the DC power source for the trail segment and to avoid a low battery state at a location that is distal from a charging station.

Abstract: A method for controlling operation of an intelligent vehicle navigation (IVN) system includes a system controller receiving path plan data for a desired route of a vehicle and, using this path plan data, identifying a low/no-connectivity (LNC) area with limited/no wireless service within the desired route. The IVN system retrieves historical trip data containing time durations to cross the LNC area for a statistically significant number of prior trips across the LNC area; the IVN system constructs a probability distribution of these trip durations. The IVN system tracks a time lapse during which no wireless signal is received from the vehicle after output of the vehicle's last wireless signal before entering the LNC area. A driving incident is predicted responsive to the no-signal time lapse exceeding a predefined threshold within the probability distribution. The system controller responds to the predicted driving incident by transmitting an alert to a third-party entity.

Abstract: A system includes a memory device, and one or more processors for detection, characterization, and presentation of charging stations for electric vehicles. The processors determine that a charging station is an undocumented charging station; a documented charging station is one that is part of a dataset of known charging stations. A confidence score is computed to indicate whether the charging station is a public charging station. In response to the confidence score being greater than a first predetermined threshold, the undocumented charging station is documented as a public charging station. In response to the confidence score being lesser than the first predetermined threshold and greater than a second predetermined threshold, the undocumented charging station is added to a list of charging stations to investigate. In response to the confidence score being lesser than the second predetermined threshold, the undocumented charging station is documented as a private charging station.

Abstract: A computer-implemented method for scheduling pre-departure charging for electric vehicles includes predicting a user-departure time based on a first machine learning prediction model. The method further includes determining a cabin temperature to be set for the user at the user-departure time based on a second machine learning prediction model. The method further includes determining a battery-temperature to be set at the user-departure time based on a third machine learning prediction model. The method further includes determining a present charge level of a battery of the electric vehicle. The method further includes computing a charging start-time to start charging the battery based on one or more attributes of a charging station to which the electric vehicle is coupled, and based on the user-departure time, the cabin temperature, and the battery-temperature. The method further includes initiating charging the battery at the charging start-time.

Abstract: Battery thermal preconditioning includes scheduling thermal preconditioning in accordance with user presets, preferences and battery and/or vehicle conditions and profiles. Thermal preconditioning in advance of charging events may optimize charge time, battery health and range. Manual and predictive intelligence methods may be employed to attain and maintain a predetermined range of battery pack temperatures.