A method for thermal preconditioning at least one thermal buffer in a thermal system of a vehicle, the thermal system being a rechargeable energy storage system, RESS, and/or an energy transformation system comprising fuel cells. The method includes providing scheduled operational information of the thermal buffer, the scheduled operational information comprising a scheduled initialization time and scheduled operational load of the thermal buffer, determining whether the thermal buffer is in need of cooling or heating in order to reach a pre-determined temperature level, preconditioning the thermal buffer in accordance with the scheduled operational information such that the thermal buffer is thermally preconditioned by cooling or heating to the pre-determined level in accordance with the scheduled operational load at a time in accordance with the scheduled initialization time.

A method for predicting a state-of-power, SoP, of an electric energy storage system, ESS, comprising at least two battery units electrically connected in parallel. The method includes obtaining operational data from the at least two battery units of the ESS during operation of the ESS; computing the state-of-power of the ESS based on the obtained operational data and by using an algorithm based on a system-level model of the ESS, wherein the system-level model of the ESS takes into account on one hand each one of the at least two battery units of the ESS, and on the other hand at least one electrical connection between the at least two battery units, and wherein the system-level model of the ESS further takes into account a dynamic parallel load distribution between the at least two battery units.

A power calculation apparatus calculating an amount of power suppliable to a power system by an energy source connectable, at a connection point, to the power system and capable of power generation and power storing, the power calculation apparatus includes: a microprocessor and a memory connected to the microprocessor, wherein the microprocessor is configured to perform: specifying a position of the connection point of the energy source in connection with the power system; acquiring a capacity information indicating a power generation capacity or a remaining storage capacity of the energy source; and calculating an amount of power suppliable by the energy source to the power system in an area within a predetermined range including the connection point, based on the capacity information and the position of the connection point.

A battery monitoring system includes a data receiver configured to receive battery information data and vehicle information data from a data collecting device connected to a vehicle, a battery management score calculator configured to calculate, based on the battery information data and the vehicle information data, factors affecting battery degradation among a charging habit, a driving habit, and a parking habit of a user, calculate, based on the factors, a battery management score, and store the battery management score in a database, and an information transmitter configured to transmit the battery management score to a terminal.

Improvements in computer-based energy asset management technologies are provided. An energy asset management system with a data summarization mechanism can perform computations, for example relating to controlling the assets, which may include electric vehicles (EVs), with fewer computing resources. Further, the system can perform computations on large datasets where such computations would have otherwise been impractical with conventional systems due to the size of the data. A large dataset relating to the energy asset management system is reduced using the summarization mechanism, and a computation model is trained using the reduced dataset. Energy assets in the system may be controlled using the trained computational model. Assets may include EVs, and controlling the EVs may be based on generated predictions relating to charging interactions. The predictions may be based on road traffic information and/or weather related information. Further, the computational model may include an optimizer for scheduling charging interactions of EVs.

A method of controlling battery preconditioning in a vehicle includes determining a first preconditioning characteristic of a first battery of a first vehicle relative to a charging station. The method further includes transmitting the first preconditioning characteristic. The method further includes receiving, from a second vehicle, a second preconditioning characteristic of a second battery relative to the charging station. The method further includes comparing the first preconditioning characteristic and the second preconditioning characteristic to determine a preconditioning ranking for the first vehicle and the second vehicle. The method further includes determining a queue of the first and second vehicles for the charging station using the preconditioning ranking of the first and second vehicles.

The present invention provides an artificial intelligence-based system for management of electric vehicles fleet. The system receives live data and historical data feeds from charging stations, fleet telematics, meteorological services, traffic management, mobile application, fleet dashboard, renewable source of energy, battery energy storage system, and the electric utility grid. The system utilizes machine learning algorithms to predict energy usage and optimize the charging schedule of electric vehicle. The system uses real time data to generate electric vehicle trip condition training feature for predicting the remaining driving range. The system predicts the vehicle's arrival time at the charging station based on telematics data of each vehicle collected from the fleet management system.

An ECU of a vehicle monitors a charging state when charging is started. When a charging power supplied from a multi-outlet charger has changed without detecting and receiving an abnormality, the ECU causes a notification device to notify the changed charging power and a charging time based on the changed charging power. The ECU notifies a communication terminal which is owned by a user of the changed charging power and the charging time based on the changed charging power.

A computing system can receive and compile power cell data, and in certain examples, the power cell data can be distributed to a distributed ledger. The computing system can further determine approximate battery end of life (ABEL) for each power cell based on a compiled historical record of power cell data. Based on the determined ABEL, the computing system can generate ABEL reports for users, determine optimal settings for a power cell or battery-powered device, and/or transmit notifications to users, to facilitate power cell usage optimization, and/or optimal repurposing or recycling timing.

The present invention provides a management system for managing a battery mounted on a vehicle, comprising: an acquisition unit configured to acquire rank information indicating a product rank set by a user as a reuse destination of the battery; and a notification unit configured to notify the user of restriction item information indicating an item for restricting a function of the vehicle such that a deterioration state of the battery at a predetermined time satisfies a request state of the product rank, based on the rank information acquired by the acquisition unit.