Provided is an electric storage device including: an electric storage unit; a temperature measurement unit that detects the temperature of the electric storage unit; a current measurement unit that measures the charge/discharge current of the electric storage unit; and a safety evaluation unit that calculates the safety evaluation value of the electric storage unit, where the safety evaluation unit determines a temperature range to which the temperature detected by the temperature measurement unit belongs among multiple temperature ranges, and calculates a safety evaluation value, based on the temperature range as a result of the determination and an accumulated value of a value related to the charge/discharge time of the electric storage unit.

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 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 method for detecting a lithium plating state of a battery includes regularly collecting a voltage of a battery after the end of charging at a preset time interval after the battery is in an idle state, and associatively storing the collected voltage and a collection time as voltage data; constructing a time-differential voltage curve in a voltage-time coordinate system according to the voltage data; detecting whether a characteristic peak voltage exists in the time-differential voltage curve; and sending a warning message when detecting that the characteristic peak voltage exists in the time-differential voltage curve.

A battery management system includes a low-power chip that allows battery management system, or components thereof, to alternate between operational states. Operational states may include a first state, a second state, a third state, and the like. A method of operation by a battery management system for an electric aircraft is also provided.

A battery management system includes a low-power chip that allows battery management system, or components thereof, to alternate between operational states. Operational states may include a first state, a second state, a third state, and the like. A method of operation by a battery management system for an electric aircraft is also provided.

A method for abnormality detection in an energy unit includes passively detecting an abnormality in an energy unit by detecting electromagnetic radiation generated by the abnormality, the energy unit comprising at least one of an electrical energy unit and an electrochemical energy unit. A method for detecting an abnormality in an energy unit includes (a) applying a signal to the energy unit, (b) performing a plurality of measurements, at a respective plurality of different locations within the energy unit, of a response of the energy unit to the signal, and (c) processing the plurality of measurements to identify the abnormality.

A method for abnormality detection in an energy unit includes passively detecting an abnormality in an energy unit by detecting electromagnetic radiation generated by the abnormality, the energy unit comprising at least one of an electrical energy unit and an electrochemical energy unit. A method for detecting an abnormality in an energy unit includes (a) applying a signal to the energy unit, (b) performing a plurality of measurements, at a respective plurality of different locations within the energy unit, of a response of the energy unit to the signal, and (c) processing the plurality of measurements to identify the abnormality.

A deterioration estimation apparatus includes a storage processing unit and a calculation unit. The storage processing unit acquires a plurality of models from a model generation apparatus, and stores the models in a model storage unit. A plurality of models are generated by performing machine-learning on training data, the training data using, as input values, measurement data for training indicating a result of measuring a state of a storage battery when the number of charge and discharge times is α.sub.i to α.sub.j (where j≥i), and using, as a target value, SOH indicating a deterioration state of the storage battery when the number of charge and discharge times is β (where β>α.sub.j). The calculation unit uses a plurality of models stored in a model storage unit to calculate an estimation result of transition of SOH of a storage battery managed by the deterioration estimation apparatus.

A deterioration estimation apparatus includes a storage processing unit and a calculation unit. The storage processing unit acquires a plurality of models from a model generation apparatus, and stores the models in a model storage unit. A plurality of models are generated by performing machine-learning on training data, the training data using, as input values, measurement data for training indicating a result of measuring a state of a storage battery when the number of charge and discharge times is α.sub.i to α.sub.j (where j≥i), and using, as a target value, SOH indicating a deterioration state of the storage battery when the number of charge and discharge times is β (where β>α.sub.j). The calculation unit uses a plurality of models stored in a model storage unit to calculate an estimation result of transition of SOH of a storage battery managed by the deterioration estimation apparatus.