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 remaining capacity estimation apparatus includes a storage processing unit and a calculation unit. The storage processing unit acquires a model from a model generation apparatus and stores the model in a model storage unit. When data for updating the model are acquired from the model generation apparatus, the storage processing unit updates the model stored in the model storage unit. The calculation unit calculates a remaining capacity of a storage battery managed by the remaining capacity estimation apparatus by using the model stored in the model storage unit. At this time, data (measurement data for calculation) input to the model include a current, a voltage, and a temperature of the storage battery. When the input data when generating the model are only a current, a voltage, and a temperature, the measurement data for calculation are only a current, a voltage, and a temperature.

A remaining capacity estimation apparatus includes a storage processing unit and a calculation unit. The storage processing unit acquires a model from a model generation apparatus and stores the model in a model storage unit. When data for updating the model are acquired from the model generation apparatus, the storage processing unit updates the model stored in the model storage unit. The calculation unit calculates a remaining capacity of a storage battery managed by the remaining capacity estimation apparatus by using the model stored in the model storage unit. At this time, data (measurement data for calculation) input to the model include a current, a voltage, and a temperature of the storage battery. When the input data when generating the model are only a current, a voltage, and a temperature, the measurement data for calculation are only a current, a voltage, and a temperature.

A computer-implemented method predicts a modeled state of health of an electrical energy store having at least one electrochemical unit in a technical device. The method includes providing a data-based state of health model, based on a characteristic of at least one operating variable of the electrical energy store up to a time, to assign the electrical energy store a corresponding state of health for the time and to indicate a corresponding modeling uncertainty, and predicting the characteristic of the at least one operating variable starting from a present time into the future based on a usage pattern model that is determined by a user-specific or usage-specific usage pattern. The method further includes predicting a characteristic of the state of health based on the data-based state of health model and the predicted characteristic, generated in a model-based manner, of the at least one operating variable.

A computer-implemented method predicts a modeled state of health of an electrical energy store having at least one electrochemical unit in a technical device. The method includes providing a data-based state of health model, based on a characteristic of at least one operating variable of the electrical energy store up to a time, to assign the electrical energy store a corresponding state of health for the time and to indicate a corresponding modeling uncertainty, and predicting the characteristic of the at least one operating variable starting from a present time into the future based on a usage pattern model that is determined by a user-specific or usage-specific usage pattern. The method further includes predicting a characteristic of the state of health based on the data-based state of health model and the predicted characteristic, generated in a model-based manner, of the at least one operating variable.

In one aspect, computer-implemented method may include receiving, from a cloud-based computing system, one or more machine learning model parameters that are configured to enable predicting a remaining useful life of each cell of a battery pack of a vehicle. The method may include loading, into memory of a processing device at the vehicle, the one or more machine learning model parameters, receiving data comprising one or more measurements and one or more user battery usage profiles, and based on the data, executing a trained machine learning model with the one or more parameters to input the data and to output the remaining useful life of each cell of the battery pack.

In one aspect, computer-implemented method may include receiving, from a cloud-based computing system, one or more machine learning model parameters that are configured to enable predicting a remaining useful life of each cell of a battery pack of a vehicle. The method may include loading, into memory of a processing device at the vehicle, the one or more machine learning model parameters, receiving data comprising one or more measurements and one or more user battery usage profiles, and based on the data, executing a trained machine learning model with the one or more parameters to input the data and to output the remaining useful life of each cell of the battery pack.

A storage battery monitoring device according to an aspect of the present invention includes: a plurality of monitoring units attached to a plurality of storage batteries connected in series and/or in parallel; and a management unit capable of wirelessly communicating with the plurality of monitoring units. The management unit sequentially selects identification information about the plurality of monitoring units stored previously, and transmits a connection request message including identification information about a selected monitoring unit to the plurality of monitoring units through wireless communication in order to establish communication with the selected monitoring unit. The management unit further updates identification information about another monitoring unit to identification information about one monitoring unit through the wireless communication when the another monitoring unit is replaced with the one monitoring unit.

A portable power bank including a rechargeable battery and/or a remote server may detect loss of capacity in the power bank battery. The power bank and/or remote server determines a nominal capacity of the power bank, and an actual capacity of the power bank, the actual capacity being less than the nominal capacity. The power bank and/or remote server compares the actual capacity to the nominal capacity to determine a health value of the power bank battery. When the power bank battery health value is at or below a threshold value, the power bank and/or remote server transmits an indication of the health value.

Systems, methods, and storage media for generating a predicted discharge profile of a vehicle battery pack are disclosed. A method includes receiving, by a processing device, data pertaining to cells within a battery pack installed in each vehicle of a fleet of vehicles operating under a plurality of conditions, the data received from at least one of each vehicle in the fleet of vehicles, providing, by the processing device, the data to a machine learning server, directing, by the processing device, the machine learning server to generate a predictive model, the predictive model based on machine learning of the data, generating, by the processing device, the predicted discharge profile of the vehicle battery pack from the predictive model, and providing the discharge profile to an external device.