EP2710391A2 - Procédé et dispositif permettant de déterminer un paramètre d'état d'une batterie - Google Patents

Procédé et dispositif permettant de déterminer un paramètre d'état d'une batterie

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Publication number
EP2710391A2
EP2710391A2 EP12721470.8A EP12721470A EP2710391A2 EP 2710391 A2 EP2710391 A2 EP 2710391A2 EP 12721470 A EP12721470 A EP 12721470A EP 2710391 A2 EP2710391 A2 EP 2710391A2
Authority
EP
European Patent Office
Prior art keywords
samples
battery
current
processor
weighting factors
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.)
Withdrawn
Application number
EP12721470.8A
Other languages
German (de)
English (en)
Inventor
Simon SCHWUNK
Stefan MATTING
Matthias Vetter
Nils ARMBRUSTER
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.)
Fraunhofer Gesellschaft zur Forderung der Angewandten Forschung eV
Original Assignee
Fraunhofer Gesellschaft zur Forderung der Angewandten Forschung eV
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Filing date
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Priority to EP12721470.8A priority Critical patent/EP2710391A2/fr
Publication of EP2710391A2 publication Critical patent/EP2710391A2/fr
Withdrawn legal-status Critical Current

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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
    • G01R31/3842—Arrangements for monitoring battery or accumulator variables, e.g. SoC combining voltage and current measurements

Definitions

  • Embodiments according to the invention relate to the state determination of batteries and in particular to an apparatus and a method for determining a state parameter of a battery.
  • Rule-based systems are difficult to develop because creating the rules is very time-consuming and often requires a wealth of experience.
  • the object of the present invention is to provide a concept for determining a state parameter of a battery which enables the state parameter with high reliability even for batteries having a flat open circuit voltage characteristic and / or hysteresis of the open circuit voltage characteristic and / or high gena to determine. This object is achieved by a device according to claim 1 or a method according to claim 17.
  • An embodiment according to the invention provides an apparatus for determining a state parameter of a battery having a voltage detector and a processor.
  • the voltage detector is designed to measure a terminal voltage of the battery.
  • the processor is configured to calculate a plurality of current samples based on a plurality of known samples of a previous probability distribution of a state indicator of the battery and a plurality of error probability samples of an error probability distribution. Further, the processor is configured to determine a plurality of weighting factors for the plurality of current samples based on the plurality of current samples and the measured clamping voltage. Furthermore, the processor is configured to calculate the state parameter of the battery based on the plurality of current samples and the plurality of weighting factors.
  • Embodiments according to the invention are based on the core idea of using the principle of a particle filter (eg based on the probability theorem of Bayes) for the calculation of a state parameter of a battery. This is done by calculating current samples taking into account an earlier probability distribution of a state indicator of the battery and an error probability distribution and calculating weighting factors to the current samples taking into account a measured clamping voltage. Any distributions for the error can be taken into account, whereby As well as a hysteresis behavior in the filter can be mapped. Therefore, by using the described concept, the state parameter of the battery can also be determined with high reliability and / or high accuracy even in batteries with a flat open circuit voltage characteristic and / or a hysteresis of the open circuit voltage characteristic.
  • the processor determines a plurality of unweighted current samples based on the plurality of current samples and the plurality of weighting factors, such that the plurality of unweighted present samples correspond to a probability distribution represented by the weighting factor-weighted present samples are distributed.
  • the plurality of unweighted present samples may then be used for a later recalculation of the condition parameter of the battery as a plurality of known samples.
  • the condition parameter of the battery can be e.g. be determined at regular intervals in order to be able to continuously check the status parameter.
  • Some embodiments according to the invention include a current detector configured to measure an amount of charge that has flowed into or out of the battery during a time interval. The measured charge amount can then be considered by the processor in calculating the plurality of current samples. Thereby, the reliability and / or the accuracy of the determination of the state parameter of the battery can be increased.
  • a current detector configured to measure an amount of charge that has flowed into or out of the battery during a time interval. The measured charge amount can then be considered by the processor in calculating the plurality of current samples. Thereby, the reliability and / or the accuracy of the determination of the state parameter of the battery can be increased.
  • FIG. 2 is a block diagram of an apparatus for determining a state parameter of a battery
  • Fig. 3 is a schematic representation of samples and weighting factors
  • 4 shows a schematic illustration of a determination of a state parameter of a battery
  • 5 shows a schematic representation of a determination of a state parameter of a battery
  • Fig. 6 is an impedance model of a battery
  • Fig. 7 is a diagram of a possible density function for the probability based on samples (sampling).
  • Fig. 8 is a schematic illustration of a low variance sampling method
  • Fig. 9 is a diagram of the open-circuit voltage characteristic of a
  • Lithium iron phosphate battery with hysteresis due to charging and discharging Lithium iron phosphate battery with hysteresis due to charging and discharging
  • FIG. 10 is a flowchart of a method for determining a state parameter of a battery.
  • the device 100 comprises a voltage detector 110, which is connected to a processor 120.
  • the voltage detector 1 10 measures a terminal voltage U mess of the battery.
  • the processor 120 calculates a plurality of current samples based on a plurality of known samples 102 of a previous probability distribution of a state indicator of the battery and a plurality of error probability samples of an error probability distribution.
  • the processor 120 determines a plurality of weighting factors for the plurality of current samples based on the plurality of current samples and the measured clamping voltage U meSs .
  • the processor 120 calculates the state parameter 122 of the battery based on the plurality of current samples and the plurality of weighting factors.
  • the state parameter of the battery can be determined very reliable and / or with high accuracy.
  • the computed present samples may then be the basis for the known samples 102 for recalculation of the state parameter of the battery at a later time.
  • the accuracy can be further increased and / or continuous monitoring of the state parameter can be enabled.
  • the state parameter 122 of the battery may be, for example, the state of charge (eg, percent of the maximum amount of charge), an age of the battery (eg, the ratio C / C "), an internal resistance of the battery, or an internal operating temperature of the battery.
  • the battery (battery type to which the described concept may be applied) is, for example, a lead battery, nickel-based systems, sodium-based batteries, lithium to ion batteries of any kind (such as LiCo0 2, LiNi0 2, LiMn 2 0 4, LiFeP0 4, LiMnP0 4 , LiNiP0 4 and combinations of these on the cathode side, carbon and silicon based electrodes, titanates on the anode side, any materials as electrolytes), redox flow batteries, lithium-sulfur batteries, lithium metal batteries, lithium-air batteries, Zinc-air batteries or similar batteries.
  • the battery is thus mainly batteries, but it can also be a disposable battery.
  • the samples represent individual points of a probability distribution and are distributed according to the represented probability distribution over the corresponding value range (eg from 0 to 1).
  • the plurality of known samples 102 represent an earlier probability distribution of a state indicator.
  • the status indicator corresponds to the state parameter of the battery or depends deterministically on the state parameter of the battery.
  • An earlier probability distribution is a probability distribution from an earlier (previous) calculation of the state parameter 122 of the battery or an initial probability distribution (initialization) of the state indicator.
  • the former probability distribution of the state indicator for example, represents an earlier probability distribution of the state parameter of the battery, or an earlier probability distribution of the accessory parameter of the battery is deterministically computable based on the earlier probability distribution of the state indicator.
  • the state indicator may represent a state of charge of the battery in a value range of 0 to 1, while the state parameter of the battery represents a state of charge of the battery in percent where 100% corresponds to a fully charged battery.
  • the state parameter 122 of the battery can also depend on the state indicator in a more complex manner than only by mapping to different value ranges.
  • the plurality of error probability samples represent an error probability distribution.
  • the error probability distribution may be represented by a set of samples from which the error probability samples are selected or the error probability distribution may be represented by a function with which the error probability samples may be calculated.
  • the error probability distribution depends, for example, on the battery type. With the described concept, any error probability distributions can be taken into account, which is not possible with known approaches. As a result, the described concept can be used, for example, with batteries with a flat open-circuit voltage characteristic and / or hysteresis behavior with high reliability and accuracy.
  • the current samples are calculated based on the known samples and the error probability samples and represent, for example, a transition of the state of the battery at an earlier time (defined by the known samples) to a state of the battery (which may also correspond to the previous state of the battery if the state has not changed) at the present time.
  • the number of samples may vary depending on the application. By a high number of samples, a probability distribution with high accuracy can be mapped and thereby a high accuracy of the determination of the state parameter of the battery can be achieved and / or the stability of the algorithm can be improved. On the other hand, the calculation time can be shortened significantly with a smaller number of samples.
  • the weighting factors represent a measure of the probability that a value of a current sample will be obtained taking into account the measured clamping voltage UMess. In doing so, a weighting factor is determined for each current sample of the plurality of current samples.
  • the state parameter 122 of the battery can then be calculated. This can be done, for example, by calculating an average, a center of gravity, a clustering or a maintenance values of the current samples taking into account the weighting factors or samples based on the current and weighting factors (eg unweighted, current samples).
  • the determination of the state parameter 122 of the battery may be repeated at random, predefined, or periodic intervals, for example, to allow repeated indication of the battery's state parameter 122 (eg, charge status indicator of a laptop or cell phone).
  • a plurality of new samples can be generated from the current samples and the weighting factors, which can be used in the next iteration of the state parameter determination as a plurality of known samples.
  • the processor 120 may determine a plurality of unweighted current samples based on the plurality of current samples and the plurality of weighting factors such that the plurality of unweighted present samples are distributed according to a probability distribution represented by the weighting factors-weighted present samples , These unweighted, current samples may then represent the plurality of known samples 102 in the next iteration of state parameter determination.
  • the determination of the plurality of unweighted present samples may be made, for example, based on a low-variance importance sampling method of the weighting factor-weighted present samples.
  • the state parameter 122 of the battery may then also be calculated based on the plurality of unweighted present samples (based on the plurality of current samples and the plurality of weighting factors).
  • the voltage detector 110 can again measure the terminal voltage U meSs of the battery. This can then be used to recalculate weighting factors.
  • the voltage detector 110 may again measure the clamp voltage U mess after a predefined (or random) time interval, and the processor 120 may determine the plurality of unweighted samples determined in a previous time interval as a plurality of known samples 102 of the probability distribution of the state indicator Use the battery to recalculate a plurality of current samples and again determine a plurality of weighting factors for the plurality of current samples based on the plurality of current samples and the re-measured clamp voltage U mess .
  • the processor 120 may first calculate a plurality of unnormalized weighting factors and a sum of the plurality of unnormalized weighting factors, and thereafter divide each unnormalized weighting factor of the plurality of unnormalized weighting factors by the calculated sum to obtain the plurality of (normalized) weighting factors ) To obtain weighting factors. This can ensure that the sum over the plurality of normalized weighting factors 1 results.
  • a weighting factor for a current sample may be calculated based on a comparison of the measured clamp voltage U mess and a modeled clamp voltage.
  • the modeled clamping voltage can be based on the current sample and calculated, for example, based on an impedance model of the battery.
  • the reliability and / or accuracy of the state parameter determination of the battery is additionally increased by means of a current measurement.
  • 2 shows a block diagram of a device 200 for determining a state parameter 122 of a battery.
  • the device 200 corresponds to the device shown in FIG. 1, but additionally includes a current detector 230 connected to the processor 120.
  • the current detector 230 determines an amount of charge I that has flowed into or out of the battery during a time interval.
  • the current detector 230 may measure the current in or out of the battery and summate over the time interval or measure the charge current and multiply it over time to obtain the charge amount I.
  • current detector 230 may measure charging current I and multiplied by the duration of the time interval, for example, later by processor 120.
  • Processor 120 then calculates the plurality of current samples based on the plurality of known samples 102, the plurality of error probability samples (calculated, for example, by means of a distribution function) and the measured charge amount I.
  • the charge amount I which has flowed into or out of the battery since the last determination of the state parameter 122 of the battery can be taken into account for calculate a value of a known sample taking into account the error probability sample the current sample.
  • Some embodiments according to the invention relate to determining a plurality of different state parameters of a battery. For example, the state of charge and battery capacity or any other combination or number of different state parameters of a battery may be calculated.
  • the state parameters can be calculated independently or one state parameter can depend on another state parameter.
  • the processor 120 may have another plurality of current samples (for determining another or second state parameter of the battery) based on another plurality of known samples of another prior probability distribution of another state indicator (corresponding to the other or second state parameter) of the battery calculate another plurality of error probability samples of an error probability distribution (which may correspond to the error probability distribution for the determination of the first state parameter or may be another error probability distribution).
  • the processor 120 may determine another plurality of weighting factors for the other plurality of current samples based on the other plurality of current samples and the calculated state parameter of the battery. As a result, the dependence of the other or second state parameter (eg battery capacity) on the first or already calculated state parameter (eg state of charge) of the battery can be taken into account. Thereafter, the processor 120 may calculate the other (or second) state parameter of the battery based on the other plurality of current samples and the other plurality of weighting factors.
  • the other or second state parameter eg battery capacity
  • the processor 120 may calculate the other (or second) state parameter of the battery based on the other plurality of current samples and the other plurality of weighting factors.
  • the family of these algorithms is based on the probability theorem of Bayes, where u denotes the inputs (mostly also measured values), x the states and z the measured values:
  • the functions of the probabilities P are mapped over samples. By means of this it is possible to map arbitrary probabilities with a sufficient number of samples, and thus also to map ambiguities of a hysteresis sufficiently well stochastically.
  • the function P * (x t . ⁇ ) Shows the probability that the battery is in a certain state of charge after the previous time step.
  • x ( _, w, ",) represents the probability that the state of charge of a battery changes from one instant to the other at given inputs - eg the current - in another state of charge Integrally over these two probabilities, a first estimate of the probability of the state of charge is given.
  • the function ⁇ (zx,) indicates the probability that for a given
  • Measured value z - here the terminal voltage or the calculated open circuit voltage of a battery - a certain state of charge is given.
  • the multiplication of the previously calculated integral with the just mentioned probability function results in a probability for the state of charge taking into account all available measured values and inputs.
  • An advantage of the described concept is the assumption of arbitrary distributions for the error. This allows the error to be matched with any distributions, e.g. caused by the hysteresis behavior, also be imaged in the filter. It is also possible to dynamically adjust the errors according to the history of the system.
  • a device for management and monitoring of batteries based on a microcontroller measures e.g. Current, voltage and optionally temperature of one or more batteries.
  • the measured values can also be measured via external devices (current detector, voltage detector, temperature sensor) and transmitted to the microcontroller via bus systems.
  • microcontroller (processor) then runs the algorithm, which was previously described and determines the state of charge of the battery from the measured values.
  • the particulate filter is e.g. an implementation of the recursive Bayesian filter using a Monte Carlo method.
  • the prior distribution is approximated by a fixed number of samples in the state space. The more samples fall into a certain area in the state space, the higher its probability.
  • the algorithm can be divided into three steps.
  • the first step corresponds to the integral p ⁇ x, in the filter equation.
  • a new sample (current sample) is drawn from the state transition distribution.
  • » (*, _,) Represents the estimate for the state of charge in the previous section.
  • P (x,) represents the probability of transferring the state of charge to a new state of charge.
  • this transition is to be ensured by first Calculates what charge has flowed into the battery in the last time step. The measurement uncertainty (error probabil- ity sample) and its distribution are then used together with the previously determined value to determine the probability distribution of the estimate.
  • the second step corresponds to the part (Z,
  • Level drawn sample is weighted with its probability (likelihood). Subsequently, all weights (unnormalized weighting factors) are normalized so that they add up to the value 1 in the sum.
  • the basis for calculating the probability is, for example, an impedance model. From this, one or more possible solutions can be determined, which correlate the measured value (clamping voltage) with the battery state (state parameter). Shown is such an impedance model and the possible probability distribution determined therefrom in FIGS. 6 and 7.
  • FIG. 6 shows a possible impedance model for a battery.
  • the open circuit voltage U 0 open circuit voltage
  • SOC state of charge
  • the internal resistance Rj is defined as a function of the battery temperature TAMB, the current I ⁇ at and the state of charge SOC.
  • the current I Ba denotes t ⁇ 0 for discharging the battery and Iatat> 0 for charging the battery.
  • the internal resistance of the Rj is once an internal discharge resistor RDIS and once an internal charging resistance RCHA-
  • FIG. 7 shows an example of a possible probability distribution of the state indicator according to which the plurality of known line samples are distributed.
  • importance sampling produces an unweighted set of N samples from the weighted N samples.
  • the unweighted quantity is generated by random sampling of the weighted quantity.
  • a weighted sample is drawn exactly with the probability that corresponds to its weight (weighting factor). It is therefore possible to pull the same sample several times.
  • One possible implementation of importance sampling is low-variance sampling.
  • Low-variance sampling is an importance sampling method that pulls N unweighted from a set of N weighted samples.
  • the starting value is a random number in the interval [0; 1N]. Starting from this starting value, samples are then selected in 1 / N large steps.
  • Low-variance sampling has the positive property that in the case of N equally-weighted samples, each sample is drawn exactly once. The amount of samples remains the same in this case.
  • Fig. 3 shows the various steps. First, a plurality of known samples x t '(initial distribution or samples from an earlier determination of the state parameter) are shown. Based on this, considering the error probability distribution 310, the plurality of current samples 5c, 'are calculated. For each current sample 5c / a weighting factor w ⁇ is then calculated. With the current samples 5c / and the associated weighting factors w, resampling (eg, based on a low-variance importance sampling method) may be performed to obtain a plurality of unweighted present samples x t 'that may be used for later re-determination of the condition parameter known samples can be used. In general, in FIG. 3 the index t stands for the time t (the state parameter can be determined at several successive times) and the index i for the ith samples.
  • the index t stands for the time t (the state parameter can be determined at several successive times) and the index i for the ith samples.
  • each charge state sample is moved based on the process model in the state of charge state.
  • SOC n + i is a current sample
  • SOC n is a known sample
  • I is the measured current (charging current)
  • At is the duration of the last time interval (since the last iteration).
  • a resampling step may be performed again, otherwise this step may be skipped.
  • there is no principle minimum but e.g. a limitation through measurement mimic and computing capacity.
  • Each load state sample (current sample) is assigned a weight (weighting factor) that describes how well the measurement model for that sample explains the actual measurement.
  • the measurement model describes the expected clamping voltage Umodell on the battery for a given state of charge SOC.
  • Umodell is calculated e.g. using an impedance model (s.o.) as follows:
  • Umodell U 0 (SOC) + R (T, l, SOC) l
  • U0 models the quiescent voltage and R the Irmenwiderstand and SOC the state of charge, T the battery temperature and I the charging current.
  • the weight for this state of charge is then f (U - U mod ) where f is the density function of a distribution that describes the error of the measurement model and was determined heuristically or during processing (online) during the development of the algorithm Clamping voltage and U mo deii the expected or modeled clamping voltage.
  • the N sampled samples define the new sample set (new plurality of known samples for a subsequent iteration).
  • the distribution, mean and / or variance of the samples can be determined and output. Then wait until new readings are recorded and start a new iteration.
  • any sampling method can be used to model the probability distributions.
  • Uklemmfk] UQ (SP [k]) where Uklemm [k] is the measured terminal voltage and UQ (SP [k]) is the modeled terminal voltage.
  • the f (U-Umodell) can be used in the implementation, but does not have to. In the measurement model, it can be calculated which terminal voltage causes a state of charge and the current:
  • Uklemm U0 (SOC) + Ri * I
  • the weight of the individual samples can be determined, for example, by using a probability density function with an expected value in the height of the measured value. If the value estimated in the process model meets the expected value, then it gets the highest weight. To a state of charge value may e.g. from simple average value formation of the random samples. Other variants are conceivable (focus, clustering, expected value ).
  • FIGS. 4 and 5 show further examples for calculating state parameters of a battery in the form of flowcharts for state of charge determination and capacity determination.
  • SP 0 to N samples
  • these may be assigned to time interval t-1 and the current samples then assigned to time interval t.
  • the current I can be measured by a current detector.
  • the plurality of known samples and the measured current I and a plurality of error probability samples, which are also referred to as noise the plurality of current samples can be calculated 420 using the process model.
  • SP (t) [k] is the kth current sample
  • SP (tl) [k] is the kth known sample
  • I is the measured current
  • the noise (also n [k]) is the error probability sample
  • At is the time period the time interval between time t-1 and time t
  • C the capacity of the battery
  • N the number of samples.
  • the plurality of current samples are then used 430 to determine the weighting factors based on the measurement model.
  • the voltage detector measures the clamp voltage Umess and the processor calculates a modeled clamp voltage Ukiemm- A comparison of the modeled clamp voltage and the measured Clamping voltage then gives the weighting factors. For example, a probability density function with expected value U mess can be compared with the samples of the clamping voltage Uki emm and calculated from it with what probability the estimated value (sample of the clamping voltage) is correct.
  • Uklemm [k] U0 (samples [k]) + R * I
  • Ukiemm [k] the modeled clamp voltage
  • U 0 (SP [k]) is an open circuit voltage
  • R is the internal resistance
  • I is the measured current.
  • the weighting factors (SP weights, weights) may then be normalized 440. Based on the current samples and the calculated weighting factors, resampling 450 may be performed to obtain a plurality of unweighted, current samples. The larger the weighting factors (weights [k]), the more often, for example, an associated random sample (SP [k]) is drawn.
  • the state of charge of the battery may be determined.
  • a mean value formation of the sample quantity results in a current state of charge estimate (SOC estimate).
  • FIG. 5 shows the determination of the battery capacity as the condition parameter of the battery.
  • the battery capacity Cb a t is calculated as a function of the determination of the state of charge by the particular state of charge is taken into account in the measurement model.
  • SP (f) [k] is the kth current sample
  • SP (tl) [k] is the kth known Sample
  • the noise (also n [k]) the error probability sample. In other words, only the capacity remains the same and a slight noise is added.
  • the plurality of current samples are then used to determine 530 the weighting factors based on the measurement model.
  • the processor may use the determined state of charge SOC or a difference of the particular state of charge ⁇ SOC (with respect to an earlier state of charge).
  • a comparison of a modeled difference of the state of charge ⁇ SOC [k] and the determined state of charge ⁇ SOC then gives the weighting factors.
  • ASOC [k] is the modeled difference of the state of charge, t2-tl the duration since the previous state of charge determination, SP [k] a sample of the battery capacity and I the measured current.
  • the weighting factors may then be normalized 540. Based on the current samples and the calculated weighting factor, re-sampling 550 may be performed to obtain a plurality of unweighted, current samples. The larger the weighting factors (weights [k]), the more often, for example, an associated random sample (SP [k]) is drawn.
  • the capacity of the battery could be determined. For example, averaging of the sample quantity results in a current battery capacity (SOH estimate).
  • SOH estimate current battery capacity
  • One aim of the described concept is, for example, to be able to carry out the state of charge determination for all battery types from the readily available measured values voltage, current and temperature on a microcontroller, the temperature being merely an optional measured value.
  • One advantage of the method is that it can carry out a state of charge determination even with complex battery cell systems, such as LiFePO4 for graphite.
  • a flat open-circuit voltage characteristic paired with a hysteresis of this open-circuit voltage characteristic curve - as occurs in the aforementioned example - produce ambiguities which conventional methods do not adequately resolve or depict.
  • Fig. 9 shows an example of this Open circuit voltage characteristic of a lithium iron phosphate battery with hysteresis due to charging and discharging.
  • the concept proposed here is able to determine both the state of charge of conventional batteries such as LiCoO 2 for graphite and novel batteries such as LiFePO 4 for graphite with their ambiguities on a microcontroller.
  • the described concept can in principle be used for a large number of different state determinations. For example, an age determination, an internal resistance determination or a determination of the internal battery temperature can also be made.
  • processor 120 voltage detector 110, optional current detector 230, the optional temperature sensor, and any other optional components may be stand-alone hardware units or part of a computer, microcontroller, or digital signal processor, as well as a computer program or software product for execution on a computer Microcontroller or a digital signal processor.
  • the method 1000 includes measuring 1010 a terminal voltage of a battery and calculating 1020 a plurality of current samples based on a plurality of known samples of a previous probability distribution of a state indicator of the battery and a plurality of error probability samples of an error probability distribution. Further, the method 1000 includes determining 1030 a plurality of weighting factors for the plurality of current samples based on the plurality of current samples and the measured clamping voltage. Further, the method includes calculating 1040 the state parameter of the battery based on the plurality of current samples and the plurality of weighting factors.
  • the method 1000 may include further steps to implement one or more of the optional aspects of the proposed concept described above.
  • embodiments of the invention may be implemented in hardware or in software.
  • the implementation may be performed using a digital storage medium such as a floppy disk, a DVD, a CD, a ROM, a PROM, an EPROM, an EEPROM or FLASH memory, a hard disk, or other magnetic or optical memory.
  • a digital storage medium such as a floppy disk, a DVD, a CD, a ROM, a PROM, an EPROM, an EEPROM or FLASH memory, a hard disk, or other magnetic or optical memory.
  • the digital storage medium can be computer readable.
  • some embodiments according to the invention include a data carrier having electronically readable control signals capable of interacting with a programmable computer system to perform one of the methods described herein.
  • embodiments of the present invention may be implemented as a computer program product having a program code, wherein the program code is operable to perform one of the methods when the computer program product runs on a computer.
  • the program code can also be stored, for example, on a machine-readable carrier.
  • inventions include the computer program for performing any of the methods described herein, wherein the computer program is stored on a machine-readable medium.
  • an exemplary embodiment of the method according to the invention is thus a computer program which has program code for carrying out one of the methods described here when the computer program runs on a computer.
  • a further embodiment of the inventive method is thus a data carrier (or a digital storage medium or a computer-readable medium) on which the computer program is recorded for carrying out one of the methods described herein.
  • a further embodiment of the method according to the invention is thus a data stream or a sequence of signals, which represent the computer program for performing one of the methods described herein.
  • the data stream or the sequence of signals can be configured, for example be to be transferred via a data communication connection, for example via the Internet.
  • Another embodiment includes a processing device, such as a computer or a programmable logic device, that is configured or adapted to perform one of the methods described herein.
  • a processing device such as a computer or a programmable logic device, that is configured or adapted to perform one of the methods described herein.
  • Another embodiment includes a computer on which the computer program is installed to perform one of the methods described herein.
  • a programmable logic device eg, a field programmable gate array, an FPGA
  • a field programmable gate array may cooperate with a microprocessor to perform one of the methods described herein.
  • the methods are performed by any hardware device. This may be a universal hardware such as a computer processor (CPU) or hardware specific to the process, such as an ASIC.

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Abstract

Dispositif (100) permettant de déterminer un paramètre d'état (122) d'une batterie et comportant un détecteur de tension (110) et un processeur (120). Le détecteur de tension (110) mesure une tension de borne (Umess) de la batterie. Le processeur (120) calcule une pluralité d'échantillons instantanés sur la base d'une pluralité d'échantillons (102) connus d'une distribution de vraisemblance préalable d'un indicateur d'état de batterie et d'une pluralité d'échantillons de probabilité d'erreur d'une distribution de probabilités d'erreur. En outre, le processeur (120) détermine une pluralité de facteurs de pondération pour la pluralité d'échantillons instantanés sur la base de la pluralité d'échantillons instantanés et de la tension de borne (Umess, SOC, ΔSOC) mesurée. Le processeur (120) calcule également le paramètre d'état (122) de la batterie sur la base de la pluralité d'échantillons instantanés et de la pluralité des facteurs de pondération.
EP12721470.8A 2011-05-18 2012-05-08 Procédé et dispositif permettant de déterminer un paramètre d'état d'une batterie Withdrawn EP2710391A2 (fr)

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EP12721470.8A EP2710391A2 (fr) 2011-05-18 2012-05-08 Procédé et dispositif permettant de déterminer un paramètre d'état d'une batterie

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EP11166609 2011-05-18
DE102011079159A DE102011079159A1 (de) 2011-05-18 2011-07-14 Vorrichtung und verfahren zum bestimmen eines zustandsparameters einer batterie
PCT/EP2012/058445 WO2012156233A2 (fr) 2011-05-18 2012-05-08 Procédé et dispositif permettant de déterminer un paramètre d'état d'une batterie
EP12721470.8A EP2710391A2 (fr) 2011-05-18 2012-05-08 Procédé et dispositif permettant de déterminer un paramètre d'état d'une batterie

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EP2710391A2 true EP2710391A2 (fr) 2014-03-26

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US10054643B2 (en) 2017-01-06 2018-08-21 GM Global Technology Operations LLC Method and apparatus for isolating root cause of low state of charge of a DC power source
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WO2012156233A2 (fr) 2012-11-22
DE102011079159A1 (de) 2012-11-22

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