A method for planning a power exchange between a charging infrastructure and an electricity supply grid. The infrastructure has a plurality of terminals for connecting and charging electric vehicles such that the electric vehicles can exchange power with the grid via the terminals. Each electric vehicle has an electrical storage unit with a variable individual state of charge for drawing and outputting power, and all of the storage units connected to the infrastructure form an overall storage unit of the infrastructure, which overall storage unit is characterized by a total storage capacity and a total state of charge that are variable. The prediction of arrival times of the vehicles at the terminals thereof is created, and a total state of charge prediction is created for a prediction period depending on the prediction of the arrival times, wherein the total state of charge prediction is created as a time profile.

Provided is a battery residual value determination system capable of easily and accurately determining the residual value of a battery. The battery residual value determination system includes: a battery information reception unit that receives information on the voltage, the current, the temperature, and the period of time elapsed from the time of manufacture of a battery; a first residual value determination unit that determines a first residual value indicating the SOH of the battery; an attenuation function determination unit that determines an attenuation function which indicates a time-dependent change of the SOH specific to the battery and is used for correcting the first residual value of the battery; and a second residual value output unit that determines and outputs a second residual value obtained by correcting the first residual value by using the attenuation function according to the period of time elapsed from the time of manufacture of the battery.

A control device and method of an electric vehicle for realizing virtual drive system sensibility is disclosed. A main objective is to provide the control device and method of the electric vehicle capable of realizing and providing differentiated driving sensibility that may be felt in other drive systems such as a drive system of an internal combustion engine vehicle. The control method includes determining a basic torque command, for controlling operation of a driving motor, from vehicle driving information collected by a controller while driving a vehicle; determining a virtual drive system torque command, which is a corrected torque command for realizing virtual drive system sensibility, from a determined basic torque command by using a virtual drive system model preset in the controller, and controlling torque of the driving motor by the controller according to a determined virtual drive system torque command.

A controller and a method for controlling motion of a vehicle is provided. The method comprises acquiring motion information including a current state of the vehicle and a desired state of the vehicle, determining a combination of a steering angle of the wheels and motor forces for moving the vehicle from the current state into the desired state by using a first model of the motion of the vehicle and a second model of the motion of the chassis of the vehicle, determining a cost function of the motion of the vehicle, optimizing the cost function of the motion of the vehicle to compute a command signal for controlling the steering wheel and the plurality of electric motors, and controlling the steering angle of the wheels and the motor forces based on the command signal.

The invention relates to an optimal allocation method for stored energy coordinating electric vehicles (EVs) to participate in auxiliary service market (ASM), including the following steps: 1. Predict the reported capacity of daily 96 points for EVs to participate in the ASM by least square support vector machine (LSSVM). 2. Fit the daily total load distribution of EVs. 3. Determine the error distribution between the reported capacity and the actual response capacity, and simulate the total daily load capacity of EVs in the future with Monte Carlo method. 4. Calculate the energy storage capacity required by EVs daily participating in ASM. 5. Build the objective function to minimize the scheduling risk of auxiliary service. 6. Solve the energy storage model in step 5 with particle swarm optimization (PSO), and output the configuration results of optimal energy storage capacity and energy storage power. The invention can improve the adjustable capacity of EVs participating in ASM.

A method of controlling anti jerk of a vehicle, includes determining a correction factor based on a wheel slip amount of the vehicle, determining a corrected model speed from a predetermined model speed for a motor of the vehicle based on the correction factor and a motor speed of the vehicle from which a vibration component is removed, determining a vibration component based on the motor speed and the corrected model speed and generating an anti jerk torque based on the determined vibration component, and generating a final output torque of the motor based on a driver demand torque of the vehicle and the generated anti jerk torque.

A motor drive device (200) includes: a power conversion circuit (204) that drives an AC motor; and a controller (203) that controls the power conversion circuit. The controller includes: a command current calculation unit (206) that generates a command current according to command torque for the AC motor; a current control unit (208) that performs feedback control for adjusting a current applied to the AC motor to the command current; and a control gain setting unit (207) that calculates a control gain used for the feedback control based on the command torque and sets the calculated control gain in the current control unit. The control gain setting unit performs control such that a time from a decrease of an absolute value of the command torque to switching of the control gain is longer than a time from an increase of the absolute value of the command torque to switching of the control gain. As a result, deterioration of control stability of motor torque during a transient response is avoided.

A battery system includes a rechargeable energy storage system and a battery controller. The rechargeable energy storage system has a rapid charging mode and a discharging mode. The battery controller is electrically coupled to the rechargeable energy storage system and is configured to store multiple charging tables that contain multiple charge current limit entries, where each charging table corresponds to a unique one of multiple initial state-of-charge values, determine a starting state-of-charge value of the rechargeable energy storage system in response to entering the rapid charging mode, select up to two charging tables in response to the starting state-of-charge value of the rechargeable energy storage system being adjacent to up to two of the initial state-of-charge values, and control a charging current provided to the rechargeable energy storage system based on the charge current limit entries in the up to two charging tables as selected.

Embodiments described herein relate to the field of transport, particularly motor vehicles. A motor with predictive adjustment is described, as well as a motor controller of a vehicle, which is capable of automatically adjusting a physical parameter of a motor, such as the width of the air gap of an electric motor. A motor of a vehicle can include at least one physical parameter capable of being adjusted according to characteristic data predicted from the current path of the vehicle based on data provided by at least one vehicle motor sensor. Thus, the motor can be automatically adjusted according to characteristic data predicted from the current path based on the data of a motor sensor for optimizing the use of the motor, with respect to a parameter such as power consumption, transmission efficiency, or rotor warming, regardless of the route.

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.