The invention relates to a system for modelling at least one state of a battery, the system comprising a parametric battery model, configured to receive one or more inputs and to provide a first output based thereon, a machine learning model, which has been trained to correct an output of the parametric battery model based on battery operation comprising aging, configured to receive one or more inputs and the first output and to provide a second output based thereon, a combiner, configured to receive the first output and the second output and to provide a third output based thereon.
The present invention relates to a method for estimating a state of a battery, the method comprising the steps of: acquiring data of the battery during at least a predetermined time range, the data comprising at least a plurality of voltage measurements and a plurality of current measurements, determining a time window within the predetermined time range, the time window starting after a relaxed voltage interval and comprising a dynamic load interval, executing a subspace identification analysis within the time window resulting in the determination of one or more parameters of the battery, populating a model of the battery with the one or more parameters, estimating the state of the battery based on the model.
The invention relates to a computer-implemented method for determining a state of an energy storage device. In successive iterations of a Kalman filter procedure, the process noise or measurement noise covariances are dynamically updated.
Various examples of the disclosure pertain to determining a set of calendar aging values of a test cell of a rechargeable battery, e.g., LIBs. The set of calendar aging values of the test cell of the rechargeable battery corresponds to a set of Temperature-State of Charge (T-SOC) value pairs. The set of calendar aging values of the test cell of the rechargeable battery is determined based on a battery-generic reference model for calendar aging of a (specific or random) battery cell and on a further set of calendar aging values of the test cell of the rechargeable battery. The further set of calendar aging values is obtained/derived from measurements of the test cell of the rechargeable battery and corresponds to a further set of T-SOC value pairs.
G01R 31/367 - Software therefor, e.g. for battery testing using modelling or look-up tables
G01R 31/374 - Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC] with means for correcting the measurement for temperature or ageing
G01R 31/382 - Arrangements for monitoring battery or accumulator variables, e.g. SoC
G01R 31/392 - Determining battery ageing or deterioration, e.g. state of health
G01R 31/396 - Acquisition or processing of data for testing or for monitoring individual cells or groups of cells within a battery
5.
PROCESSING OF STATUS DATA OF A BATTERY FOR AGING ESTIMATION
A method for processing status data of a battery comprises applying an autoencoder artificial neural network to initial status data. Reconstructed status data are obtained therefrom. The method comprises carrying out an aging estimation based on the reconstructed status data in order to obtain a status indicator which is indicative of an aging status of the battery.
B60L 58/16 - Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries responding to battery ageing, e.g. to the number of charging cycles or the state of health [SoH]
B60L 58/12 - Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries responding to state of charge [SoC]
6.
DETERMINATION OF OPEN CIRCUIT VOLTAGE CURVE FOR RECHARGEABLE BATTERIES
The dependency of the open circuit voltage on the state of charge of a battery, i.e., the OCV curve is determined, e.g., to facilitate determination of the state of health or state of charge of the battery. The OCV curve is determined based on the values of multiple independent parameters, e.g., half-cell potentials derived from degradation modes such as loss of lithium inventory or loss of active material at the cathode or anode.
A method for monitoring a battery system is proposed. The method includes providing a sequence of pairs of measured current and voltage values that follow one another in time. The pairs of current and voltage values indicate the current flowing through the battery system and the voltage present at the battery system. The method also provides for an electrical equivalent model of the battery system to be provided. The equivalent electrical model has several impedances connected in series. Initial impedance parameter values are provided for the impedances of the equivalent electrical model. The method provides for adjusting a first impedance parameter of an impedance of the equivalent electrical model based on the difference between a first voltage value simulated based on the initial impedance parameter values and the first current value of the finite sequence and the first measured voltage value of the finite sequence. An optimized first impedance parameter value is thereby obtained. By minimizing the deviation of a sequence of simulated voltage values obtained on the basis of the electrical equivalent model and the first sequence of measured current values from the sequence of measured voltage values by adapting at least one further impedance parameter of the impedances of the electrical equivalent model, at least one optimized further impedance parameter value of the at least one further impedance parameter is obtained. A battery management system for carrying out the method is also proposed.
G01R 31/389 - Measuring internal impedance, internal conductance or related variables
B60L 58/16 - Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries responding to battery ageing, e.g. to the number of charging cycles or the state of health [SoH]
G01R 31/367 - Software therefor, e.g. for battery testing using modelling or look-up tables
G01R 31/3842 - Arrangements for monitoring battery or accumulator variables, e.g. SoC combining voltage and current measurements
G01R 31/392 - Determining battery ageing or deterioration, e.g. state of health
H01M 10/42 - Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
H01M 10/48 - Accumulators combined with arrangements for measuring, testing or indicating the condition of cells, e.g. the level or density of the electrolyte
8.
Characterization of Rechargeable Batteries Using Machine-Learned Algorithms
Various examples relate to techniques for carrying out a characterization of a rechargeable battery in a two-stage process. To this end, an upstream algorithm is used in order to determine one or more derived state variables of the battery. These are then used as input values for a machine-learned algorithm. An aging value of the battery is obtained therefrom.
A method for estimating an impedance of a battery. The method includes the steps of: acquiring data of the battery during at least a predetermined time range, the data including at least a plurality of voltage measurements and a plurality of current measurements; determining a time window within the predetermined time range, the time window starting after a relaxed voltage interval and including a dynamic load interval, determining an initial voltage, the initial voltage being the voltage measurement at the start of the time window, determining a plurality of dynamic voltages based on the voltage measurements in the time window and the initial voltage, executing a subspace identification analysis based on the plurality of current measurements and on the plurality of dynamic voltages, and computing the impedance from an output of the subspace identification analysis.
A state value for a rechargeable battery, for example a lithium-ion battery, is determined on the basis of several aging characteristic variables. The aging characteristic variables comprise at least one current aging value and one future aging value.
The invention relates to general technology for monitoring the state of a battery, e.g., a lithium-ion battery. A thermal simulation model is used for this purpose. Different examples relate to the parameterizing of the thermal simulation model.
A method for server-side characterization of a rechargeable battery (91-96) comprises obtaining operating values for a capacity of the battery (91-96) and an impedance (91-96) of the battery; based on the operating values: performing at least one state prediction (181-183) for the battery (91-96), each of the at least one state predictions (181-183) comprising a plurality of iterations (1099), wherein in each iteration (1099) a simulation of an electrical state of the battery (91-96) and a thermal state of the battery (91-96) is performed and an aging estimate for the capacity and the impedance is determined based on the results, wherein the aging estimate from a first iteration (1099) of the respective state prediction (181-183) is used for the simulation in a subsequent second iteration (1099) of the respective state prediction (181-183).
H02J 7/00 - Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
G01R 31/36 - Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
G01R 31/367 - Software therefor, e.g. for battery testing using modelling or look-up tables
13.
Characterisation of lithium plating in rechargeable batteries
G01R 31/378 - Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC] specially adapted for the type of battery or accumulator
G01R 31/367 - Software therefor, e.g. for battery testing using modelling or look-up tables
G01R 31/3835 - Arrangements for monitoring battery or accumulator variables, e.g. SoC involving only voltage measurements
G01R 31/392 - Determining battery ageing or deterioration, e.g. state of health
G01R 31/396 - Acquisition or processing of data for testing or for monitoring individual cells or groups of cells within a battery
H01M 10/0525 - Rocking-chair batteries, i.e. batteries with lithium insertion or intercalation in both electrodesLithium-ion batteries
H01M 10/48 - Accumulators combined with arrangements for measuring, testing or indicating the condition of cells, e.g. the level or density of the electrolyte
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