A battery management system includes a control device and a storage. The storage stores at least one trained neural network. The trained neural network includes an input layer that accepts input data that represents a numeric value for each pixel in an image where a prescribed CCV waveform (a CCV charging waveform or a CCV discharging waveform) of a secondary battery is drawn in a region constituted of a predetermined number of pixels, and when input data is input to the input layer, the trained neural network outputs a full charge capacity of the secondary battery. The control device estimates the full charge capacity of a target battery by inputting input data obtained for the target battery into the input layer of the trained neural network.

A method for estimating a present energy consumption of an electrically propelled vehicle powered by a propulsion battery. The method includes obtaining previous energy consumption values for a set of previous time instants, and a present drive pattern parameter value; estimating a present energy consumption based on a weighted moving average model fed with the energy consumption values, wherein, the weighted moving average model includes a modelled gain factor for each of at least a portion of the previous energy consumption values, where the modelled gain factors are modelled as a function of the drive pattern parameter.

A vehicle includes drive wheels, an energy source having an available energy, a torque-generating device powered by the energy source to provide an input torque, a transmission configured to receive the input torque and deliver an output torque to the set of drive wheels, and a controller. The controller, as part of a programmed method, predicts consumption of the available energy along a predetermined travel route using onboard data, offboard data, and a first logic block, and also corrects the predicted energy consumption using the onboard data, offboard data, and an error correction loop between a second logic block and the first logic block. The controller also executes a control action with respect to the vehicle using the corrected energy consumption, including changing a logic state of the vehicle.

The autonomous electric tractor is completely operated by battery and remote programming using artificial intelligence and soil sensors. The electric track has an in-track electric motor to drive a rear drivetrain sprocket to provide movement to the autonomous electric tractor. The electric tracks are slip proof and tilt proof. The mudguards protect them heavy soil contamination. The autonomous electric tractor is controlled by a cloud based remote tractor management system. Apart from 3D cameras and sensors, global positioning system and client need based data is used for utilizing the tractor for farming operations.

A vehicle includes drive wheels, an energy source having an available energy, a torque-generating device powered by the energy source to provide an input torque, a transmission configured to receive the input torque and deliver an output torque to the set of drive wheels, and a controller. The controller, as part of a programmed method, predicts consumption of the available energy along a predetermined travel route using onboard data, offboard data, and a first logic block, and also corrects the predicted energy consumption using the onboard data, offboard data, and an error correction loop between a second logic block and the first logic block. The controller also executes a control action with respect to the vehicle using the corrected energy consumption, including changing a logic state of the vehicle.

A charging system, a management terminal, a vehicle, a charging method, and a non-transitory computer-readable recording medium capable of completing charging of a plurality of vehicles by scheduled departure times of the plurality of vehicles are provided. The charging system includes an obtaining section that obtains a scheduled departure time and a target level of charge of each of a plurality of vehicles, a setting section that sets, based on the scheduled departure times and the target levels of charge, a charging plan including a period where a battery of each of the plurality of vehicles is charged, and a control section that controls the charging of the battery of each of the plurality of vehicles in accordance with the charging plan.

A series-hybrid powertrain includes a range extender, a battery, an electric motor and a controller. The range extender may include an internal combustion engine and a generator. The powertrain has three modes of operation. In electric only mode, the battery powers the motor and the range extender is not allowed to operate. In forced charge mode, the range extender attempts to power the drive motor and also re-charge the battery if the battery is discharged to, or is found below, a specified charge. In hybrid mode, the range extender is operated by a controller that determines the output of the range extender in hybrid mode considering inputs that relate to the state of charge of the battery and the power consumption of the electric motor. Optionally, the controller uses fuzzy logic to determine the output of the range extender. The powertrain may be used in an industrial vehicle.

Disclosed are a vehicle control unit (VCU) and an operation method thereof that calculate a speed variation of a vehicle based on input information, predict an average speed of the vehicle based on the calculated speed variation, generate a first speed profile based on the predicted average speed, and generate a second speed profile by applying speed noise information to the first speed profile.

Systems, methods, and at least one computer-readable medium for selecting a velocity profile for an electric vehicle. In some embodiments, a first parameter value may be determined for a road segment in a selection horizon, and a second parameter value may be determined for an energy storage device of the electric vehicle. The first and second parameter values may be used to predict a plurality of velocity profiles over the selection horizon, wherein each velocity profile is predicted based on a corresponding value of a variable relating to a driving style of a driver of the electric vehicle. An energy consumption cost and a travel time cost may be computed for each velocity profile. A velocity profile may be selected from the plurality of velocity profiles, based on the respective energy consumption costs and the respective travel time costs.

The present disclosure relates generally to flight control of electric aircraft and other powered aerial vehicles. In one embodiment, a method is disclosed, comprising: receiving a descent rate command from a pilot input device, determining a proximity of each propeller of at least two propellers to a vortex ring state; and controlling the aircraft's descent rate to be less than the commanded descent rate when at least one of the at least two propellers is within a first threshold proximity to the vortex ring state.