Systems, methods, and at least one computer-readable medium are described. The system comprises at least one processor and at least one computer-readable storage medium having encoded thereon instructions that, when executed, program the at least one processor to for each candidate model of a plurality of candidate models, determine a reward for using the candidate model in a context, wherein the context comprises a value of a feature selected from a group consisting of, a feature relating to an environment in which an energy application is operating, a feature relating to the energy application, and a feature relating to one or more energy storage devices associated with the energy application. The at least one processor being further programmed to select a model from the plurality of candidate models, based at least in part on the respective rewards for using the candidate models in the context.

A vehicle includes an electric machine and a controller. The electric machine is configured to draw energy from a battery to propel the vehicle and to recharge the battery during regenerative braking. The controller is programmed to, in response to identifying a regenerative braking opportunity along an upcoming road segment based on a classification of driver behavior and a classification of the upcoming road segment, operate the electric machine to recharge the battery along the upcoming road segment.

A charging system for electric vehicles that includes a plurality of charging stations each adapted to exchange electrical power with at least one electric vehicle. At least one charging profile determination module adapted to: obtain a charging station identifier associated with a first charging station of the plurality of charging stations and a user identifier associated with a user of an electric vehicle to be charged at the first charging station during a charging process. Determining based on the obtained user identifier and the obtained charging station identifier, at least one historical charging process data set relating to the user identifier and the charging station identifier. Estimating at least one charging parameter for the charging process based on the at least one historical charging process data set, and determining the charging curve profile for the charging process based on at least the at least one estimated charging parameter.

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.

The disclosure relates to a method for operating a movement apparatus having a first assembly and a second assembly. The first assembly includes a base and several permanent-magnet arrangements that are connected to the base via actuators such that they move as a whole relative to the base in at least one degree of freedom by the assigned actuator, the second assembly including a base and a permanent-magnet arrangement arranged firmly relative to the base. Position controllers are provided, each with a controlled variable and with a correcting variable. The controlled variable is one of six possible degrees of freedom with regard to a relative position between the first and second assembly. The correcting variable represents a force or a torque that has been assigned to the degree of freedom. Desired positions of the actuators are computed from the correcting variables and the actuators are set accordingly.

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 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 control method of a secondary battery in which malfunction is less likely to occur and abnormality detection can be performed with high accuracy is provided. A charge state estimation device of a secondary battery including a device which generates electromagnetic noise, a first detection means which measures a voltage value of a secondary battery electrically connected to the device, a second detection means which measures a current value of the secondary battery electrically connected to the device, a correction means which extracts a causal relationship between electromagnetic noise and a driving pattern from data including multiple electromagnetic noise obtained using the first detection means or the second detection means, and an arithmetic means which calculates a charge rate using a regression model based on data after data correction.

A motor control device includes an acquisition unit and a torque control unit that controls, by selectively using one of a first map and a second map, a motor torque defined in correspondence with a requested torque and an engine rotation speed in each of the first map and the second map. The torque control unit controls the motor torque using the first map when the battery temperature is less than a switch temperature lower than a limit start temperature at which the motor torque is limited, and controls the motor torque using the second map when the battery temperature is greater than or equal to the switch temperature and less than the limit start temperature. The second map includes a larger assist region than the first map. The assist region of the second map defines a smaller maximum torque than the assist region of the first map.

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.