A virtual manual transmission system for an electric vehicle for simulating the behavior of a vehicle having a manual transmission by controlling a motor while protecting an electric storage device. A controller is configured to: change torque of the motor when a virtual manual shifting is executed by operating a clutch device, an accelerator device, and a shifting device; and reduce a regulation on a change rate of the torque of the motor or an input/output power to/from the electric storage device.

A method for estimating the usable charge capacity of an electrical energy store includes determining a nominal initial charge capacity and a maximum initial estimation error of a new or slightly degraded energy store; determining one or more nominal charge capacities and one or more maximum estimation errors for the degraded energy store; interpolating a graph for the nominal charge capacity and for the maximum lower and upper estimation errors between the interpolation points of the nominal charge capacity and the interpolation points of the maximum estimation errors; and estimating a nominal charge state, a current usable charge capacity with a lower estimation reserve, and a current usable charge capacity with an upper estimation reserve for a current degradation state of the electrical energy store.

A simulating method for an electric vehicle (EV) includes creating a simulation model associating a plurality of target behaviors of a target vehicle with the EV, obtaining a plurality of vehicle parameters of the EV to generate a set of EV control parameters, obtaining a plurality of configuration parameters of the target vehicle, based on the set of EV control parameters and the plurality of configuration parameters of the target vehicle, using the simulation model to provide a set of simulated target-vehicle controls, where the simulation model is a neural network trained to reflect a relationship between the set of EV control parameters and the set of simulated target-vehicle controls, and outputting the set of simulated target-vehicle controls to the EV, such that the EV is controlled to achieve the plurality of target behaviors of the target vehicle based on the set of simulated target-vehicle controls.

A system in a vehicle includes a current regulator to obtain current commands from a controller based on a torque input and provide voltage commands and an inverter to use the voltage commands from the current regulator and direct current (DC) supplied by a battery to provide alternating current (AC). The system also includes an electric traction motor to provide drive power to a transmission of the vehicle based on injection of the AC from the inverter. The current regulator adjusts parameters of a transfer function implemented by the current regulator, based on feedback of an input to and an output from the electric traction motor to achieve the AC corresponding with the torque input in no more than two control cycles.

The disclosure relates to a method for actuating an electric drive of a trailer vehicle with a towing vehicle, including the steps: determining a current slip of at least one driven wheel of a towing vehicle pulling the trailer vehicle, determining an expected slip for the driven wheel of the towing vehicle, determining an acceleration demand depending on the determined current slip and the determined expected slip and actuating the electric drive depending on the acceleration demand. The disclosure also relates to a control unit for executing the method, a towing vehicle, a trailer vehicle and a vehicle combination.

The present application discloses a motor controller, a motor control method and a computer program product for vehicle assist control. An assist torque command for a motor device to perform vehicle assist control is generated according to an execution command of a vehicle assist determination unit and a rotor position signal and a rotor speed signal of a motor device. An original position signal of the motor device and the rotor position signal are calculated, and a position ratio calculation is performed to generate a front-order torque command. A torque damping command is generated according to the speed ratio calculation based on the rotor speed signal, and is calculated with the front-order torque command to generate an assist torque command. Thus, position information of the rotor of the motor device can be directly used in the calculation and speed information is at the same time used for an assist calculation, thereby preventing an error and solving the issue of sliding during parking.

In one aspect, computer-implemented method may include receiving, from a cloud-based computing system, one or more machine learning model parameters that are configured to enable predicting a remaining useful life of each cell of a battery pack of a vehicle. The method may include loading, into memory of a processing device at the vehicle, the one or more machine learning model parameters, receiving data comprising one or more measurements and one or more user battery usage profiles, and based on the data, executing a trained machine learning model with the one or more parameters to input the data and to output the remaining useful life of each cell of the battery pack.

Systems and methods are provided herein for charging an electric vehicle based on the inferred dwell time of a user of the electric vehicle. This may be accomplished by an electric vehicle charging station (EVCS) receiving a request from a user to charge an electric vehicle. The EVCS can use the request to identify a profile associated with the user, wherein the profile comprises user information related to the user. The EVCS then estimates a dwell time for the user using the user information. The EVCS can use the dwell time to estimate a total charge time for the electric vehicle. The EVCS then charges the electric vehicle based on the estimated charge time.

The present disclosure relates to systems, devices, and methods for analyzing health of vehicle batteries. Vehicle batteries tend to degrade over time. The described systems, devices, and methods quantify this degradation (or quantify remaining health of the battery) by comparing average energy used to charge or discharge the battery by a charge level unit to a nominal quantity of energy used to charge or discharge a battery in optimal health by a charge level unit. Charge data for previous charge events of the vehicle battery can be used in the calculation, and can be filtered by identifying qualified charge events based on at least one of a number of metrics. Usage data for previous usage events of the vehicle battery can be used in the calculation, and can be filtered by identifying qualified usage events or subgroups of usage event based on at least one of a number of metrics.

A battery management system (BMS) for a vehicle includes a module for estimating the state of a rechargeable battery, such as its state of charge, in real time. The module includes a learning model for predicting the state of a battery based on the vehicle's usage and related factors unique to the vehicle, in addition to a sensed voltage, current and temperature of a battery.