In various embodiments, methods, systems, and vehicle apparatuses are provided. A method for implementing torque control using a Neural Network (NN) for a torque prediction model to receive a set of measured vehicle operating inputs associated with torque prediction; substituting a set of multiple independent variables into the torque prediction model so that the NN is then taking the form of a simplified pseudo-NN that contains a reduced variable set of one independent variable; processing, the set of measured vehicle operating inputs by the pseudo-NN based on the NN prediction model by using only one independent variable in a pseudo-NN's simplified mathematical expression; and solving for at least one root of the pseudo-NN's simplified mathematical expression by obtaining a root value without having to rely on an inversion operation of a mathematical expression that consists of an entire set of independent variables.

A method for operating an assistance system for a motor vehicle involves providing a configuration set personalized for a user of the assistance system in an electronic computing device of the assistance system for at least one functional unit of the motor vehicle. The personalized configuration set is set by the electronic computing device depending on a triggering criterion. The user is identified as the trigger criterion by a first sensor device and/or a predetermined piece of information relating to the motor vehicle is detected as the trigger criterion by a second sensor device.

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.

A powertrain system may determine a power distribution for one or more power sources of a vehicle. The powertrain system may be coupled to a perception system that may provide perception data indicating a scenario, situation, or environment that has been encountered by the vehicle. The powertrain system may include machine learning model that may generate the power distribution based on one or more of the perception data and a power request.

In various embodiments, methods, systems, and vehicle apparatuses are provided. A method for implementing torque control using a Neural Network (NN) for a torque prediction model to receive a set of measured vehicle operating inputs associated with torque prediction; substituting a set of multiple independent variables into the torque prediction model so that the NN is then taking the form of a simplified pseudo-NN that contains a reduced variable set of one independent variable; processing, the set of measured vehicle operating inputs by the pseudo-NN based on the NN prediction model by using only one independent variable in a pseudo-NN's simplified mathematical expression; and solving for at least one root of the pseudo-NN's simplified mathematical expression by obtaining a root value without having to rely on an inversion operation of a mathematical expression that consists of an entire set of independent variables.

An extended-range fuel cell electric vehicle power device includes a driving motor, a bidirectional converter, a chopper, a power cell, a fuel cell, a high-pressure hydrogen storage tank, an electric control valve, a controller, an accelerator pedal and a brake pedal. An output of the driving motor is connected to a transmission shaft of an electric vehicle through a speed change gearbox, and an input of the driving motor is connected to an alternating current output end of the bidirectional converter; a direct current input end of the bidirectional converter is connected in parallel to an output of the power cell and an output of the chopper, and an input of the chopper is connected to a power source output of the fuel cell.

Fuzzy-logic based traction control for electric vehicles is provided. The system detects a wheel slip ratio for each wheel. The system receives an input torque command. The system determines a slip error for each wheel based on the wheel slip ratio for each wheel and a target wheel slip ratio. The system, using the fuzzy-logic based control selection technique, selects a traction control technique from one of a least-quadratic-regulator, a sliding mode controller, a loop-shaping based controller, or a model predictive controller. The system generates a compensation torque value for each wheel. The system generates the compensation torque value based on the traction control technique selected via the fuzzy-logic based control selection technique and the slip error for each wheel. The system transmits commands to actuate drive units of the vehicles based on the compensation torque value.

A battery conditioning system and method configured to shorten charging time and block energy consumption due to unnecessary conditioning by performing battery pre-conditioning in a timely manner through collecting customer charging tendency data, etc., using big data and generating a charging scenario by combining charge-inducing factors to perform pre-conditioning.

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 system and method for setting a regenerative braking value are disclosed. The disclosed system may include: a first measurement unit configured to measure the displacement information of a brake pedal of an electric vehicle; a second measurement unit configured to measure the speed of the electric vehicle; a third measurement unit configured to measure the distance from the electric vehicle to an object in front of the electric vehicle; a first computation unit configured to compute the brake force required for a deceleration of the electric vehicle by inputting the displacement information and the speed into a first fuzzy logic algorithm; and a second computation unit configured to compute the regenerative braking value by applying the brake force and the distance to the object to a second fuzzy logic algorithm.