EP4252018A1 - Procédé de détermination d'un état d'un accumulateur d'énergie - Google Patents

Procédé de détermination d'un état d'un accumulateur d'énergie

Info

Publication number
EP4252018A1
EP4252018A1 EP21824300.4A EP21824300A EP4252018A1 EP 4252018 A1 EP4252018 A1 EP 4252018A1 EP 21824300 A EP21824300 A EP 21824300A EP 4252018 A1 EP4252018 A1 EP 4252018A1
Authority
EP
European Patent Office
Prior art keywords
model
kalman filter
energy store
current
parameters
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
EP21824300.4A
Other languages
German (de)
English (en)
Inventor
Devin Atukalp
Kilian Kink
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Twaice Technologies GmbH
Original Assignee
Twaice Technologies GmbH
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Twaice Technologies GmbH filed Critical Twaice Technologies GmbH
Publication of EP4252018A1 publication Critical patent/EP4252018A1/fr
Pending legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. SoC
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. SoC
    • G01R31/3842—Arrangements for monitoring battery or accumulator variables, e.g. SoC combining voltage and current measurements

Definitions

  • the present invention relates to a Kalman filter method for determining a state of an energy store, in particular a state of charge of an energy store; a corresponding device, a computer program and an electronically readable data carrier are also provided.
  • KF Kalman filters
  • a state of the energy store is determined iteratively in successive iterations of a Kalman filter method based on a large number of measured values of current and voltage of a charging or discharging process of the energy store.
  • an error term of the Kalman filter method is updated in each case in successive iterations of the Kalman filter method.
  • An error term can be a covariance matrix, for example.
  • the error term of the Kalman filtering method may be determined using at least one absolute value of at least one model parameter of the Kalman filtering method and/or at least one absolute value of at least one measurand of current and voltage.
  • At least one uncertainty of a parameter of the model can be determined using an absolute value of at least one parameter of the model and/or at least one measurand of current and voltage.
  • a Kalman filtering method error term may be determined using uncertainties of values of at least one parameter of a Kalman filtering method model.
  • An error term of the Kalman filter method can be determined using an uncertainty of current and/or voltage of the charging or discharging process of the energy storage device.
  • An uncertainty can be an error, hence an error/uncertainty described by a probability distribution, for example a variance.
  • An error term of the Kalman filter method can be determined using an uncertainty or uncertainties of the current and/or voltage of the charging or discharging process of the energy store.
  • the model can be a process model that determines a change in current and voltage of the energy storage device as a function of the iterations based on an equivalent circuit model.
  • the at least one parameter may be selected from a set including: quantization of time steps associated with the iterations of the Kalman filtering method; Time constant of an oscillating circuit
  • the error term of the filter method can thus be determined using, for example, two, three or more uncertainties of model parameters determined in one iteration, and/or uncertainties of the measured variables current/voltage.
  • the error term of the process model can be a covariance matrix that describes a cross-dependence of the uncertainties of process parameters of a large number of parameters of a model of the Kalman filter method. Therefore, an error term (or in other words term, or part of the equation) associated with the process model (process equation), in particular an additive term, can describe or contain the process noise (also system noise), in other words modeling errors or modeling inaccuracies Measurement model (measurement equation) associated error term describe or contain the measurement noise, i.e. measurement errors or measurement inaccuracies.
  • the uncertainties of the process parameters of the multiplicity of process parameters can be modeled by probability distributions which are selected from: Gaussian normal distribution, uniform distribution, Weibull distribution.
  • a device for determining a state of an energy store comprises a computing unit, a memory unit, an interface unit, the memory unit storing instructions that can be executed by the computing unit, the device being embodied in which Execution of the commands in the computing unit to iteratively determine a state of an energy store in successive iterations of a Kalman filter method based on a large number of measured values of current and voltage of a charging or discharging process of the energy store.
  • an error term of the Kalman filter method is updated in each case in successive iterations of the Kalman filter method. In some examples, 2, 3, more, or all of the error terms of the Kalman filtering method can be updated.
  • the disclosed techniques enable a more accurate determination of the state of charge of the energy storage device because error parameters are no longer abstract and predetermined values, but are determined dynamically for each iteration directly based on the uncertainties of the individual model parameters and the sensor noise. Tuning error parameters is easier by only defining the uncertainty of model parameters and sensors. The uncertainty of the model parameters is adjusted according to the needs of the application: robustness vs. convergence. Uncertainties of model parameters are (usually) transferable to different battery types (eg ⁇ 20% for Ro independent of the battery). The sensor noise, or sensor inaccuracy, is known from sensor specifications or benchmarking experiments.
  • the error parameters are dynamic. For example, the dynamics of the input current has an impact on the error parameters.
  • the error parameters of system states (SOC, U RC , ...) depend on the input I. Z soc is a function of I(k), so higher I(k) changes SOC(k) more, increasing the error parameter of SOC improves accuracy.
  • the device, energy storage, or energy system may include a processor, memory, and an interface, where the memory includes instructions that, when executed by the processor, cause the processor to perform the steps of any method according to the present disclosure.
  • a computer program includes instructions that, when executed by a processor, cause the program to perform the steps of any method according to the present disclosure.
  • An electronically readable medium includes instructions that, when executed by a processor, cause it to perform the steps of any method according to the present disclosure.
  • the data and commands for executing the method according to the invention and/or the measurement data can be stored in a distributed database, in particular a cloud.
  • FIG. 1 schematically shows an SOC determination accuracy based on a known Kalman filter as a function of temperature and service life.
  • FIG. 2 schematically shows an equivalent circuit model in the form of an RC model with i RC circuits according to exemplary embodiments of the invention.
  • FIG. 3 shows a flow chart with steps of a Kalman filter for determining a state of charge of an energy store, according to exemplary embodiments of the invention.
  • FIG. 4 schematically shows a device which is configured to determine a state of charge of an energy store using a method according to the invention.
  • the terms calculate, determine, generate, configure, filter and the like preferably refer to actions and/or processes and/or processing steps that modify and/or generate data and/or the data converted into other data, the data being represented or being present in particular as physical quantities, for example as electrical impulses.
  • the terms computer, control device or device should be interpreted as broadly as possible in order to cover in particular all electronic devices with data processing properties.
  • Computers can thus be, for example, personal computers, servers, programmable logic controllers (PLCs), handheld computer systems, pocket PC devices, IoT devices, mobile radio devices and other communication devices, cloud applications, processors and other electronic devices for data processing, ie the computer-aided data processing.
  • KF Kalman filters
  • FIG. 1 schematically shows an SOC estimation accuracy based on a conventional Kalman filter as a function of temperature and service life.
  • the nonlinear state space model is given where the error terms w k and v k are independent Gaussian error terms with covariance matrices and , respectively.
  • the error terms capture modeling errors and sensor noise. This means that a process model with a corresponding error term and a measurement model with a corresponding error term are taken into account.
  • Constant error parameters when properly tuned, represent a "compromise" for the conditions to which they were tuned (e.g. SOC range, temperature range, input current dynamics, etc.).
  • constant error parameters tuned for the best SoC estimation accuracy for electronic vehicle (EV) conditions are not necessarily the best error parameters for power tool conditions.
  • EV electronic vehicle
  • process error parameters can be broken down to the sources of uncertainty.
  • the present disclosure addresses the challenge of ensuring higher SOC estimation accuracy over battery lifetime by providing a method for tuning a battery-backed SOC Kalman filter.
  • the concept is explained using an example of a SOC-KF with an equivalent circuit model for the battery.
  • the equivalent circuit model is designed as an RC model.
  • the principle can be applied to any other (battery) model.
  • FIG. 2 schematically shows an equivalent circuit model in the form of an RC model with i RC circuits according to exemplary embodiments of the invention.
  • FIG. 3 shows a flowchart with steps for determining a state of charge of an energy store, according to exemplary embodiments of the invention.
  • step T10 The method begins in step T10.
  • step T20 random variables and constants are defined.
  • This step defines which arguments of the process and measurement equations contain uncertainty. This is how the sources of uncertainty are broken down.
  • the input (current, voltage) and the model parameters contain a certain degree of uncertainty (random variables), for example described by variances.
  • the step time has an uncertainty.
  • the random variables are normally distributed.
  • the expected value of the random variable is the mean and the uncertainty is quantified using the variance.
  • each random variable is assigned a variance.
  • the uncertainty can also be represented by various other distributions (normal, uniform, Weibull, etc). It is to be understood that one or more parameters, at least one parameter, can have an uncertainty which, according to the invention, can be used in a dynamic update of an error term (covariance matrix) in the model.
  • the model parameters can also have uncertainties, as described below.
  • the time steps dt can take place at varying time intervals.
  • the variance of the time step also called jitter
  • var(dt) can be described with var(dt).
  • R0 with var (RO) captures the uncertainty of the ohmic resistance.
  • ⁇ i with var( ⁇ i) captures the uncertainty of the time constant of the i-th RC circuit.
  • Ri with var (Ri) captures the uncertainty of the resistances of the i-th RC circuit.
  • CN with var (CN) captures the uncertainty of (battery) charge capacity.
  • step T30 uncertainties are introduced into process/measurement equations.
  • ⁇ u (k) var(U 0CV + R 0 I) with var(U 0CV ) , var(R 0 ) , var(I)
  • step T40 process errors are determined.
  • E(A) or E(B) Expected value of A or B which in this case can be absolute values of model parameters of the Kalman filter method and/or measured variables of current and/or voltage; var(A) or var(B) variance of A or B, which in this case can be the uncertainty of a model parameter of the Kalman filter method and/or the uncertainty of the measured quantities of current and/or voltage; and covar (A,B) Covariance of A and B, which in this case can be the covariance of the uncertainties of a model parameter of the Kalman filter method, and/or the covariance of the uncertainty of the measured quantities of current and/or voltage.
  • the resulting covariance matrix can be calculated using at least one absolute value of at least one model parameter of the Kalman filter method, and/or at least one uncertainty of a model parameter of the Kalman filter method, and/or at least one measured variable of current and/or voltage, and /or an uncertainty in the measured quantities of current and/or voltage.
  • one or more model parameter uncertainties may be assumed to be zero.
  • one or more of the above quantities can be determined by a simulation.
  • functions can be linearized to approximate a resulting variance.
  • a delta procedure uses second-order Taylor expansions to approximate the variance of a function of one or more random variables: see Taylor expansions for the moments of functions of random variables. For example, the approximate variance of a function of one variable is given by
  • a distribution with a finite number of particles can be approximated with uncented transform methods. This approach has the advantage of reducing the computational effort by avoiding a large number of particles.
  • step T50 the noise covariance matrices are updated.
  • the resulting measurement-noise covariance matrix is:
  • step T60 the process and measurement covariances are a function of step time (step time variance), model parameters, model parameter variance, input, and input variance.
  • the covariance matrix can be understood as the error term of the Kalman filter method, and the variances of the model parameters and/or the variances of the measured variables current or voltage can be understood as uncertainties in the corresponding arguments.
  • the uncertainties of the Kalman filter can be dynamically re-determined in each step, for example based on corresponding values of the relevant parameters in previous iterations and/or the current iteration of the filter method.
  • error terms of the Kalman filter method can thus be understood as one or more of:
  • the SOC-KF tuning is more robust to different operating conditions by relating the parameters of the process errors to the model parameter uncertainty (e.g. modeling errors, change of parameters due to aging) and to the input uncertainty (e.g. sensor noise).
  • model parameter uncertainty e.g. modeling errors, change of parameters due to aging
  • input uncertainty e.g. sensor noise
  • FIG. 4 schematically shows a device which is configured to determine a state of charge of an energy store using a method according to the invention.
  • the device 10 comprises an interface 20 for sending/receiving data, a memory 30, and a processor 40, the memory 30 comprising instructions which, when executed by the processor 40, cause the latter perform steps of any Kalman filtering method according to the present disclosure.
  • a processor can be understood to mean, for example, a machine or an electronic circuit.
  • a processor can in particular be a central processing unit (CPU), a microprocessor or a microcontroller, for example an application-specific integrated circuit or a digital signal processor, possibly in combination with a memory unit for storing program instructions, etc .
  • a processor can, for example, also be an IC (integrated circuit), in particular an FPGA (field programmable gate array) or an ASIC (application-specific integrated circuit), or a DSP (Digital Signal Processor) or a GPU (Graphic Processing Unit).
  • a processor can also be understood to mean a virtualized processor, a virtual machine or a soft CPU.
  • a memory, a memory unit or memory module and the like can be understood in connection with the invention as, for example, a volatile memory in the form of random access memory (RAM) or a permanent memory such as a hard disk or a data carrier.
  • RAM random access memory
  • permanent memory such as a hard disk or a data carrier.
  • examples of the present disclosure contemplate a variety of circuits, data storage, interfaces or electrical processing devices such as processors. All references to these units and other electrical devices and the functions they provide are not limited to what is illustrated and described. While specific labels may be associated with the various circuits or other electrical devices disclosed, these labels are not intended to limit the functionality of the circuits and other electrical devices. These circuits and other electrical devices may be combined and/or separated depending on the type of electrical implementation desired. It is to be understood that any circuit or other electrical device disclosed may include any number of microcontrollers,
  • GPU Graphics processing units
  • memory devices e.g. FLASH, random access memory (RAM), read only memory (ROM), electrically programmable read only memory (EPROM), electrically erasable programmable read only memory (EEPROM), or any other suitable embodiment thereof may include, as well as software that cooperate with each other to perform the method steps disclosed herein.
  • each of the electrical devices may be configured to execute program code embodied in an electronically readable medium and configured to perform any number of steps according to the methods of the present disclosure.
  • the techniques disclosed relate to storage devices for electrical energy, or in other words energy storage devices chargeable by electrical energy (electricity), electrical energy storage devices, batteries or rechargeable batteries, in particular lithium-ion batteries.
  • electrical energy electrical energy
  • batteries electrical energy storage devices
  • rechargeable batteries in particular lithium-ion batteries.
  • the described Kalman filtering methods can be applied to any general technical system and to Determining any system state can be applied.
  • One or more filtering methods can be applied at cell, module, pack and system level.
  • the disclosed techniques can relate to determining system states of energy stores or energy storage systems, for example vehicles, aircraft, mobile or hand-held electrical devices, in particular electrically powered cars, but also, for example, mobile electrical communication devices.
  • System states may include, for example, a state of charge (SOC), a state of health or state of health (SOH), i.e., a current capacity, or the like.
  • the estimation may include estimating the state of charge with dynamic error limits, or estimating the available discharge/charge power of the energy storage device, or tracking changing energy storage parameters, such as a maximum available capacity and thus a quantitative estimation of the Aging condition of an energy storage device.
  • the techniques can be applied during a charging or discharging process of an energy store.
  • the device may be configured to receive and/or store measurements of a charge/discharge process.
  • the measured values of a charging/discharging process can be stored in a memory or read from a memory, for example in a cloud, in a distributed database, in a central memory of an energy system, or locally in a memory of the energy store, or locally in a memory the device.
  • the measured values can be recorded by one or more sensors, which can be arranged on the energy store itself, or a charging device of the energy store, or a consumer of the energy store, or can generally be connected between the energy store and energy consumer.
  • the readings can in particular include a time series of consecutive measured values of the current, the voltage, or a temperature during the charging/discharging process.
  • the device can also be further configured, for example, to carry out the charging/discharging process on an energy store.
  • the device can be configured to include, measure and/or store a current, a voltage, and a temperature of the energy store over time, i.e. as a measurement curve, or the progression of measurement points over time during a charging/discharging process.
  • the device can be configured to determine a charging/discharging process of the energy store.
  • the device can also be configured to determine a beginning and/or an end of a charging/discharging process, in other words to determine the performance of a charging/discharging process in order to determine the measurement data based thereon.
  • computer-aided or computer-implemented can be understood, for example, as an implementation of the method in which, in particular, a processor executes at least one method step of the method.
  • data and/or measurement data and/or parameters can include, for example, (computer-aided) storage of corresponding information or a corresponding datum in a data structure/data set (which, for example, is in turn stored in a storage unit is) to be understood.
  • Provision, in particular with regard to data and/or information, can be understood in connection with the invention as computer-aided provision, for example.
  • the provision takes place, for example, via an interface (eg a database interface, a network interface, an interface to a storage unit).
  • This interface can be used, for example, when providing corresponding data and/or information is transmitted and/or sent and/or retrieved and/or received.
  • Measured values of a charging process or a discharging process of an energy storage device can characterize the charging/discharging process in which electrical energy is supplied to/removed from the energy storage device.
  • readings may include one or more regularly sampled current or voltage values from a current or voltage waveform measured by one or more sensors.
  • the current or the voltage in other words a time profile of these measured variables, can be measured and recorded/stored by one or more sensors on the energy store or a charging energy source or consumer.
  • the device may be configured to provide an energy storage status, i.e. a system status of the energy storage, such as a current state of charge or a current energy storage capacity.
  • a system status of the energy storage such as a current state of charge or a current energy storage capacity.
  • the current state of charge can be output, for example, as a percentage of its maximum value or as an absolute value.
  • the techniques disclosed may relate to Kalman filtering methods. Therefore, the techniques can be methods for iterative estimation of (parameters for describing) system states, in particular on the basis of erroneous measurements.
  • Various examples may relate to a Kalman filter for linear and/or non-linear stochastic systems.
  • Various examples may relate to any type of Kalman filter, such as extended Kalman filter (EKF), unscented Kalman filter (UKF), sigma-point Kalman filter, fading Kalman filter (FKF), strong tracking cubature extended Kalman filter (STCEKF), multirate strong tracking extended Kalman filter (MRSTEKF), lazy extended Kalman filter (LEKF), or any other type of Kalman filter.
  • Various examples relate to an adaptive Kalman filter, with a covariance matrix of the Kalman filter, in other words an error term of the Kalman filter method, being determined or updated in each of at least two consecutive iterations.
  • Various examples relate to determining a system state using multidimensional probability distributions, for example probability distributions of possible errors around each estimated value, and/or probability distributions of possible errors in measured values, and/or probability distributions of possible errors in model parameters and/or model variables, and thus also correlations between the estimation errors of different variables.
  • the previous estimated values are optimally combined with the new measurements in each time step, so that remaining errors in the filter state are minimized as quickly as possible.
  • the current filter status from estimated values, error estimates and correlations forms a kind of memory for all the information obtained so far from past measured values.
  • the Kalman filter improves the previous estimates and updates the associated error estimates and correlations.
  • the techniques disclosed are based on modeling, in which an explicit distinction is made between the dynamics of the system state (process model) and the process of its measurement (measurement model).
  • the disclosed filtering methods can thus take into account dynamically changing parameters, ie system variables, and can therefore comprise at least one mathematical model to take into account dynamic relationships between the system variables.
  • the methods may include a process model and/or a measurement model.
  • the disclosed methods can determine system states in real time.
  • Model parameters can be understood to mean, for example, OCV, R, RC. Current I and voltage U cannot be understood as model parameters in examples. It is to be understood that the uncertainties of the model parameters can change from iteration to iteration of the Kalman filter method and can therefore be redetermined.
  • an uncertainty of the at least one model parameter of the Kalman filtering method can be determined using an absolute value of at least one parameter of a model and/or at least one measurand of current and voltage of the Kalman filtering method.
  • a covariance matrix in other words an error term of the Kalman filter method, can be newly determined in each of at least two consecutive iterations of the Kalman filter method using a process model and/or a measurement model.
  • an error term in the process model can describe or contain the process noise (also system noise), i.e. modeling error, correspondingly an error term in the measurement model (measurement equation) can describe or contain the measurement noise, i.e. measurement error.
  • the process model can be used to model a state of the system based on a previous state of the system.
  • the error term can therefore include a term that expresses an uncertainty in the accuracy of the process or measurement model, which is represented by process or measurement noise.
  • the error term can include a term in the process equation that accounts for process noise, i.e. inaccuracies in the modeling by the
  • the open-type techniques can include, for example, dynamically determining or updating/updating variances, in particular using variances of the process parameters determined at the time of updating, ie the parameters in the process model. Using may include, for example, determining depending on, and/or using and/or based on process parameters, and/or
  • Process parameters which are determined, for example, at least in part from previous iterations of the Kalman filter method.
  • An error term in the process model can be an additive error term.
  • Kalman filters work with Gaussian distributions. Other distributions can, for example, be mapped with a Gaussian distribution and thus deliver comparably good results in Kalman filters. It is also conceivable to use directly other probability distributions, e.g. a Weibull or a uniform distribution, with corresponding parameters or error terms that characterize or define these uncertainties.
  • a variance can generally be understood as an uncertainty of a model parameter or a measured value
  • a covariance matrix can be understood as an error term of the Kalman filter method.
  • the measurement data and/or parameters and/or the models can be stored in a cloud or in general in a network application.
  • the methods and filters can be implemented in a cloud or a distributed database.
  • a distributed database for example, a decentralized distributed database, a distributed
  • a Database system a distributed database, a peer-to-peer application, a distributed memory management system, a blockchain, a distributed ledger, a distributed storage system, a Distributed Ledger Technology (DLT) based system (DLTS), an audit-proof database system, a cloud, a cloud service, a block chain in a cloud or a peer-to-peer database.
  • a network application (or also referred to as a network application) can be a distributed database system that is implemented, for example, using a block chain or a distributed ledger.
  • a block chain or a DLTS can also be used, such as a block chain or a DLTS that is implemented using a Directed Acylic Graph (DAG), a cryptographic puzzle, a hash graph or a combination of the implementation variants mentioned.
  • DAG Directed Acylic Graph
  • cryptographic puzzle a cryptographic puzzle
  • hash graph a combination of the implementation variants mentioned.
  • a network application can also be understood, for example, as a distributed database system or a network application, of which at least some of its nodes and/or devices and/or infrastructure are realized by a cloud.
  • the corresponding components are implemented as nodes/devices in the cloud (e.g. as a virtual node in a virtual machine).
  • the device can be, for example, a charging device for charging the energy storage device using electrical energy, a control device, e.g. in an energy system for controlling a charging process of an energy storage device, or a control device integrated into an energy storage device.
  • a control device e.g. in an energy system for controlling a charging process of an energy storage device, or a control device integrated into an energy storage device.
  • Such a device can receive or send raw data, i.e. measurement data, and/or parameters of a charging/discharging process or a history of charging/discharging processes via the interface.
  • the raw data, i.e. measurement data, and/or parameters and/or a history of this data and/or parameters from past charging/discharging processes can be stored in the memory.

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  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Tests Of Electric Status Of Batteries (AREA)
  • Secondary Cells (AREA)

Abstract

L'invention concerne un procédé mis en œuvre par ordinateur pour déterminer un état d'un accumulateur d'énergie. Les covariances du bruit de processus ou du bruit de mesure sont mises à jour dynamiquement par itérations successives d'un procédé de filtrage de Kalman.
EP21824300.4A 2020-11-26 2021-11-26 Procédé de détermination d'un état d'un accumulateur d'énergie Pending EP4252018A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
DE102020131392.6A DE102020131392A1 (de) 2020-11-26 2020-11-26 Verfahren zum Bestimmen eines Zustandes eines Energiespeichers
PCT/EP2021/083151 WO2022112496A1 (fr) 2020-11-26 2021-11-26 Procédé de détermination d'un état d'un accumulateur d'énergie

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EP4252018A1 true EP4252018A1 (fr) 2023-10-04

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US (1) US20240118342A1 (fr)
EP (1) EP4252018A1 (fr)
DE (1) DE102020131392A1 (fr)
WO (1) WO2022112496A1 (fr)

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TAMEEMI ALI QAHTAN: "Fusion-Based Deterministic and Stochastic Parameters Estimation for a Lithium-Polymer Battery Model", IEEE ACCESS, IEEE, USA, vol. 8, 23 October 2020 (2020-10-23), pages 193005 - 193019, XP011818155, [retrieved on 20201104], DOI: 10.1109/ACCESS.2020.3033497 *

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