A system is provided for performing an automated electrification operation for an electric vehicle (102) using a processor (122). An electrification controller (126) communicates with a model generation unit (128). The model generation unit (128) generates a model representative of a power consumption trend of the electric vehicle (102). The electrification controller (126) sets a target power margin for the electric vehicle (102) based on the model such that the target power margin is close to a minimum state-of-charge (SOC) threshold of an energy storage supply (124) of the electric vehicle (102). The target power margin represents a difference between the minimum SOC threshold and an ending power level of the energy storage supply (124) after completion of a mission associated with the electric vehicle (102). The processor (122) performs the automated electrification operation for the electric vehicle (102) based on the target power margin.

A charge-discharge control system includes an electronic control unit. The electronic control unit is configured to: calculate an excess or shortage of a state of charge of the battery to a target state of charge as a state-of-charge difference; increase an output voltage of an electric power supply device when the shortage of the state of charge is larger than a first prescribed value; decrease the output voltage of the electric power supply device when the excess of the state of charge is larger than a second prescribed value; calculate an amount of change in the output voltage of the electric power supply device per unit time such that the amount of change in the output voltage of the electric power supply device per unit time is smaller when the calculated full charge capacity is low than when the calculated full charge capacity is high.

A vehicle power control system using big data, may include a big-data server configured to receive driving-related data of a vehicle, generated by the vehicle, to generate a factor related to an acceleration pattern of the vehicle by processing the received driving-related data, and to store the generated factor, and a controller installed in the vehicle and configured to, when the vehicle is requested to be accelerated or propelled, change output power of a battery with reference to pre-stored available power of the battery and the factor stored in the big-data server.

Methods and systems of power management in a hybrid vehicle are disclosed. A control system of the hybrid vehicle obtains battery temperature and catalyst temperature. The control system determines (a) whether the battery temperature is within an optimal battery temperature range and (b) whether the catalyst temperature is within an optimal catalyst temperature range. The control system determines a power split ratio (PSR) based on the determination of (a) and (b). The control system controls the engine and the motor-generator based on the determined PSR.

A control method for an electrically driven road vehicle comprising the steps of: providing an electric powertrain system; determining a plurality of first virtual gears for a boost configuration and/or a plurality of second virtual gears for a release configuration; detecting, following actuation of an interface system by the driver, while driving, a first selection for one of the first virtual gears and/or a second selection for one of the second virtual gears; and delivering, in the boost configuration, a drive torque to the at least two wheels as a function of the first selection; or delivering, in the release configuration, an anti-motive torque to the at least two wheels according to the second selection.

A hybrid vehicle control method for a hybrid vehicle is provided for a drive system including an internal combustion engine, a generator that is driven by the internal combustion engine, and a battery that is charged with electric power generated by the generator. A target power generated by the generator is set and the target engine output is calculated for the internal combustion engine according to the target generated power. The air density in the environment in which the vehicle travels is detected. The target engine output is corrected based on the detected air density with respect to the decrease in air density, and the generated power of the generator is made to follow the target generated power. The execution of air density correction is permitted or stopped depending on an operating state of the drive system.

Embodiments described herein improve fuel economy by controlling a vehicle powertrain based on a predicted vehicle velocity. The vehicle velocity is predicted based on vehicle-to-vehicle data when a prediction horizon is a longer prediction horizon and the vehicle velocity is predicted based on historical drive cycle data when the prediction horizon is a shorter prediction horizon. A time duration of the shorter prediction horizon is shorter than the time duration of the longer prediction horizon. A plurality of drive cycles are established for both the longer and the shorter prediction horizons using a neural network. A shorter prediction horizon drive cycle uses nonlinear autoregressive exogenous model neural networks and the longer prediction horizon drive cycle uses two layer feedforward neural networks. The predicted vehicle velocity is determined from a similar drive cycle of the plurality of drive cycles of either the shorter and/or the longer prediction horizon drive cycles.

A hybrid electric vehicle comprises a set of wheels, a mechanical transmission adapted to provide torque to the set of wheels, an electric motor coupled to and providing motive force to the mechanical transmission, battery storage coupled to and supplying electrical power to the electric motor, and a gasoline compression ignition (GCI) engine coupled to at least one of the mechanical transmission and the electric motor so as to provide mechanical energy to the mechanical transmission and energy for operating the electric motor.

Personal mobility vehicles, their various components, methods and systems for controlling, using, tracking, and/or interacting with personal mobility vehicles, and methods and systems for integrating personal mobility vehicles within dynamic transportation networks so that a personal mobility vehicle (PMV) can be used in combination with a vehicle of a transportation provider to efficiently complete a transportation request are discussed. For example, a PMV may be used in combination with a lane-constrained vehicle to improve travel time between two locations in situations where the time it may take for a lane-constrained vehicle to reach the starting location may be affected by traffic congestion at the starting location. The PMV may transport a transportation requestor from a starting location to an intermediate location away from the traffic congestion to then transfer to a lane-constrained vehicle for the remainder of the trip.

In a driven state phase, target input torque is limited to a first limit value, whereas in a backlash-elimination state phase, the target input torque is limited to a second limit value and target engine torque and torque of a motor-generator are controlled according to the target input torque. In the backlash-elimination state phase, the target input torque is limited to the second limit value that is suitable for mitigating rattling shock, so that rattling shock can be appropriately mitigated. Meanwhile, in the driven state phase, the target input torque is limited to the relatively high first limit value, so that an MG rotation speed can be quickly increased to eliminate a rotational difference, which enhances the responsiveness of driving power up to when required driving power is obtained after elimination of the backlash.