An electric vehicle charging station management method using a blockchain is provided, including the following steps: obtaining a maximum charging and a discharging electric power of each electric vehicle in each to-be-planned pane; obtaining a charging and discharging electric power of each electric vehicle in each to-be-planned pane according to electric vehicle information corresponding to the electric vehicle, at least one purchase price, at least one winning bid price, and at least one maximum charging and discharging electric power; determining whether at least one overloaded pane is provided according to a total consumed electric power of a charging station in each time pane; and adjusting the purchase price of each overloaded pane when it is determined that at least one overloaded pane is provided and re-planning the charging and discharging electric power of the electric vehicle in each to-be-planned pane until it is determined that no overloaded pane is provided.

Demand associated with one or more vehicle systems can be predicted for a vehicle traversing a planned travel path. Based at least in part on the predicted demand, a control strategy can be determined for controlling operation of the one or more vehicle systems to optimize for efficiency, cabin temperature, component temperature, passenger comfort, etc. The one or more vehicle systems can be controlled, based at least in part on the control strategy, at least one of before the vehicle traverses the travel path, or as the vehicle traverses the travel path.

An approach is provided for estimating the optimal number and mixture of types of electric vehicle (EV) charging stations (EVCS) at one or more points of interest (POIs). A method includes generating, based on an EV adoption model, an EV adoption prediction. The method includes generating, based on a mobility simulation model, a driver-type prediction that predicts percentages of EV drivers qualifying for various EV driver types. The method includes generating, based on the EV adoption prediction, the driver-type prediction, and a visitation model, a visitation prediction that predicts how many EV drivers of each type of EV driver will visit the POI. The method includes determining and displaying, based on how many EV drivers of each type of EV driver is predicted to visit the POI, for each type of EV charging station of a plurality of types of EV charging stations, an optimal number of EVCS to install.

Aspects relate to methods and systems for optimizing battery recharge management for use with an electric vertical take-off and landing aircraft. An exemplary system includes an electric vertical take-off and landing (eVTOL) aircraft comprising at least battery mechanically coupled to the eVTOL aircraft and configured to power at least an aircraft component of the eVTOL aircraft, wherein the at least a battery comprises a plurality of battery cells, and at least a sensor, configured to measure battery data associated with the at least a battery, and a server remote from the eVTOL and in communication with the at least a sensor, wherein the server is configured to receive the battery data from the at least a sensor, receive mission data associated with a planned flight mission of the eVTOL aircraft, and generate a recharge time as a function of the battery data and the mission data.

This application provides a charging time estimation method and apparatus, and a storage medium. The method includes: obtaining a current temperature of a to-be-charged device and a current state of charge (SOC) of the to-be-charged device in a calculation period, and obtaining a required current of the to-be-charged device based on the current temperature and the current state of charge; and determining a charging current of the to-be-charged device; and obtaining a charging time. The method can be applied to a thermal management system of an electric vehicle or an optimization model of an off-line thermal management strategy. In this method, energy consumption of the thermal management system is estimated in a charging process, so as to resolve a problem that the energy consumption of the thermal management system is not considered in the conventional charging time estimation method, to make the estimated charging time more accurate.

A method for operating an electric charging device for an electrically drivable motor vehicle. The electric charging device has a ground unit for positioning in the ground and for generating an alternating magnetic field for an electric charging operation. The method includes sensing a motor vehicle within a predefined perimeter surrounding the ground unit; sensing a driving parameter value of at least one driving parameter of the sensed motor vehicle at at least one respective predefinable point in time; storing the at least one driving parameter value if an electric charging operation is carried out for the detected motor vehicle; determining a driving recommendation on the basis of the at least one sensed and stored driving parameter value; and transmitting the driving recommendation if another motor vehicle is sensed within the predefined perimeter surrounding the ground unit.

A device supports a refueling process of a vehicle having a fuel cell, wherein the vehicle is configured to carry out a switch-off process of the fuel cell prior to the refueling process. The device includes a control unit that determines that a refueling process shall be carried out. The control unit also, in response to the determination, performs one or more measures that reduce a delay time period for starting the refueling process caused by the switch-off process of the fuel cell.

A highly safe power storage system is provided. If n (n is an integer over or equal to three) secondary batteries are used in a vehicle such as an electric vehicle, a circuit configuration is used with which the condition of each secondary battery is monitored using an anomaly detection unit; and if an anomaly such as a micro-short circuit is detected, only the detected anomalous secondary battery is electrically separated from the charging system or the discharging system. At least one microcomputer monitors anomalies in n secondary batteries consecutively, selects the anomalous secondary battery or the detected secondary battery which causes an anomaly, and gives an instruction to bypass the secondary battery with each switch.

A computer-implemented method is introduced for predicting a residual service life of vehicle batteries of a fleet of electric vehicles. In the method, parameters of the vehicle batteries are measured during the operation of the electric vehicles and transmitted to a server; a conditional probability is determined that the residual service life of a specific vehicle battery undershoots a predefined limit value at a point in time lying in the past; and the residual service life of vehicle batteries of the fleet is predicted as a function of the conditional probability.

A method for performance analysis and use management of a battery module is disclosed, wherein the battery module includes a multitude of interconnected battery cells and a battery management system with a plurality of dedicated analysis/control units (ACUs) that analyze performance of the battery module, the ACUs being assigned to individual battery cells and/or battery blocks of battery module. The method includes measuring current and voltage of one or more of an individual battery cell and a battery block; calculating a charge removal from the one or more of the individual battery cell and the battery block; calculating a loading charge of the one or more of the individual battery cell and the battery block; determining the remaining charge of the one or more of the individual battery cell and the battery block; and failure monitoring of the one or more of the individual battery cell and the battery block.