JP2008522153A - Joint battery condition and parameter estimation system and method - Google Patents

Abstract

  A method and apparatus for predicting an increased state of an electrochemical cell, comprising performing an internal increased state prediction comprising at least one internal state value and at least one internal parameter value of the cell; Performing an uncertainty prediction of the increased state prediction, correcting the internal increased state prediction and the uncertainty prediction, an ongoing estimate of the increased state and the increased state Applying an algorithm that repeats the internal increased state prediction, the uncertainty prediction step, and the correction step so as to calculate an ongoing uncertainty for the estimation; including.

Description

  The present invention is for the estimation of battery pack system state and parameters using digital filtering techniques, in particular joint Kalman filtering and joint extended Kalman filtering. The present invention relates to the apparatus and method.

  In the field of rechargeable battery pack technology, although direct measurement is not possible, it is required in some applications that a quantitative factor that can represent the current state of the battery pack can be estimated. Some of these quantitative factors can be rapidly changed, such as the state of charge (SOC) of the pack, and can cross the entire area range within minutes. Others vary very slowly, such as cell capacity, and can vary as little as 20% over 10 years of normal use. Quantitative elements that tend to change rapidly constitute the state of the system, and quantitative elements that tend to change slowly constitute the time varying parameters of the system.

  In the field of battery systems, in particular, for example, hybrid electric vehicles (HEV), battery electric vehicles (BEV), laptop batteries, mobile battery packs, etc. Information on rapidly changing parameters (eg, SOC) can be used to estimate how much battery energy is currently operational, such as those that need to operate actively for as long as possible without adversely affecting their lifetime. It is desirable to use In addition, to extend the useful service time while maintaining the accuracy of prior calculations for the lifetime of the pack and to help determine the health of the pack (SOH) It is desirable to check information for slowly changing parameters (eg, total volume).

  Various methods of cell state prediction are known, which generally relate to the prediction of three quantitative factors: SOC (rapidly changing quantitative factor), power-fade, and capacity-fade. Yes. For example, other methods can be used, but power reduction can be calculated if the current and initial pack electrical resistance is known, and capacity reduction can be calculated if the current and initial total pack capacity is known. it can. The decline in power and capacity is usually shown as a representation of the state of health (SOH). Other information can be inferred using values of variables such as the maximum power available from the pack at a given time. Additional state elements and parameters may also be required for a particular application, and individual algorithms are typically sought to look for each.

  The SOC, usually expressed as a percentage, represents a portion of the currently operable cell capacity. Many other approaches have been taken to estimate SOC, including discharge testing, ampere-hour counting (coulomb counting), open-circuit voltage measurement, linear and non-linear circuit modeling, impedance spectroscopy (impedance spectroscopy) , Internal resistance measurement, coup de fuet, and some forms of Kalman filter. In the discharge test, the cell must be completely discharged to determine the SOC. This test disrupts system functionality while the test is being performed and is not useful in most cases and can be excessively time consuming. Ampere hour counting is a kind of open-loop methodology, the accuracy of which decreases over time due to accumulated measurement errors. Electrolyte measurements can be performed in the case of vented lead-acid batteries and thus have limited utility. Open circuit voltage measurements can only be made after an extended cell inactivity period, can be performed on cells with negligible hysteresis effects, and do not operate in a dynamic state. Linear and nonlinear circuit modeling methods do not directly calculate the SOC. The SOC must be inferred from the calculated value. Impedance spectroscopy requires measurements that are not always useful for general applications. Internal resistance measurements are very sensitive to measurement errors and require measurements that are not useful for general applications. The cup de fuet operates only on lead acid batteries. A form of Kalman filtering that does not use SOC as a filter state does not directly calculate an error range for estimation. In another method disclosed in US Pat. No. 6,534,954, the filter, preferably the Kalman filter, employs known dynamical models for cell dynamics and cell voltage, current and temperature measurements. This is used to estimate the SOC. This method directly estimates the state value. However, parameter values are not handled.

  Knowledge about SOH as well as SOC is required. In the art, power decay is related to a phenomenon in which cell electrical resistance increases with cell life. Increasing resistance reduces the power sourced / sinked by the cell. Capacity decline relates to a phenomenon in which the overall capacity of a cell is reduced according to the cell life. Both cell resistance and capacitance are time-varying parameters. The prior art uses the following other approaches to SOH estimation: discharge testing, chemistry-dependent methods, ohmic tests, and partial discharge. The discharge test completely discharges a fully charged cell to determine the overall capacity of the cell. This test disrupts system function and wastes cell energy. The chemical dependent method involves measuring the level of corrosion, electrolyte density of the plate. Coup de fuet is used for lead acid batteries. Ohmic tests include resistance, conductance, and impedance tests, but are sometimes coordinated with fuzzy-logic algorithms and / or neural networks. Such a method requires invasive measurements. Partial discharge and other methods compare the cell under test with a good quality cell or model of a good quality cell.

  What is needed is a method for simultaneously estimating cell state and parameters. In addition, tests that do not waste energy without interrupting system functions, methods that allow general applications (eg, other types of cell electrochemistry, other applications), invasive measurements, and very tight access An unsought method is needed. For example, what is needed is a method and apparatus for automatically estimating time-varying parameters such as cell resistance and capacitance. Furthermore, there is a need for a method that works for other configurations of parallel and / or series cells in a battery pack.

  A method for estimating an increased state of an electrochemical cell according to one aspect of the present invention includes an internal augmented states including at least one internal state value and at least one internal parameter value of the cell. performing a prediction), performing an uncertainty prediction of the internal increased state prediction, correcting the internal increased state prediction and the uncertainty prediction, and Performing the internal increased state prediction to calculate an ongoing estimate for the increased state and an ongoing uncertainty for the increased state estimate; and performing the uncertainty prediction And applying an algorithm that repeats the correcting step.

  An apparatus configured to estimate a current increased state of a cell pack system according to another aspect of the invention includes an element configured to perform an increased state prediction of a cell interior, and the internal An element configured to perform an uncertainty prediction of the increased state prediction, an element configured to correct the internal increased state prediction and the uncertainty prediction, and the increased An element configured to perform an internal increased state prediction to calculate an ongoing estimate for the state and an ongoing uncertainty for the increased state estimate, and to perform the uncertainty prediction And an element configured to apply an algorithm that repeats the steps taken by the element configured and the element configured to correct.

  Also, a system for estimating the current increased state of an electrochemical cell disclosed herein as an embodiment includes an internal increase including at least one internal state value and at least one internal parameter value of the cell. Means for performing an improved state prediction, means for performing an uncertainty prediction of the internal increased state prediction, and correcting the internal increased state prediction and the uncertainty prediction Means for calculating the internal increased state prediction, the uncertainty prediction, and the internal state prediction so as to calculate an ongoing estimate for the increased state and an ongoing uncertainty for the increased state estimate, and Means for applying an algorithm for repeating the correction.

  Further, a storage medium encoded with machine readable computer program code including instructions for causing a computer to perform the above method for estimating the current increased state of an electrochemical cell is another embodiment. As disclosed.

  Yet another embodiment for a computer data signal embodied in a computer readable medium is disclosed. The computer data signal includes code configured for the computer to perform the above method of estimating the current increased state of the electrochemical cell.

  The various features, aspects, and advantages of the present invention can be more clearly understood through the detailed description of the invention and the appended claims and drawings. In the drawings, the same components are denoted by the same reference numerals.

  Various example methods, systems, and apparatus for estimating electrochemical cell states and parameters using joint filtering are disclosed. In the following description with reference to FIGS. 1 and 2, many specific details are presented in order to provide a more thorough understanding of the present invention. Although the preferred embodiment is described with reference to a battery cell, a variety of electrochemicals are included, including batteries that hit the cell, battery packs, ultracapacitors, capacitor banks, fuel cells, electrolyte cells, and combinations including at least one of the foregoing. It must be understood that the cell can be employed. It should also be understood that a battery or battery pack can include a plurality of cells, and one disclosed embodiment can be applied to one or more cells.

  One or more embodiments of the present invention use joint filtering to estimate cell states and parameters. One or more embodiments of the present invention use joint Kalman filtering to estimate cell states and parameter values. One embodiment of the present invention uses joint extended Kalman filtering to estimate cell states and parameter values. One embodiment simultaneously estimates SOC, power decline and / or capacity decline, and other embodiments estimate additional cell state values and / or additional time-varying parameter values. The term filtering is employed to describe an exemplary embodiment and includes, without limitation, terminologically limiting Kalman filtering and / or extended Kalman filtering, and recursive prediction generally indicated as filtering. ) And correction methodologies should be understood to be included.

  FIG. 1 shows components of a parameter prediction system 10 according to an embodiment of the present invention. An electrochemical cell pack 20 (eg, a battery) including a plurality of cells 22 is connected to a load circuit 30. For example, the load circuit 30 may be an electric vehicle (EV) or a hybrid electric vehicle (HEV) motor. An apparatus for measuring various cell characteristics and attributes is provided as reference 40. The measuring device 40 includes without limitation a device for measuring a cell terminal voltage, such as a voltage sensor 42 (for example, a voltmeter). On the other hand, the cell current is measured by a current sensing device 44 (for example, an ammeter). Optionally, the cell temperature is measured by a temperature sensor 46 (eg, a thermometer). Additional cell characteristics, such as internal pressure or impedance, can be measured using, for example, a pressure sensor and / or impedance sensor 48 and may be employed depending on the selected type of cell 22 of the cell pack 20. obtain. Various sensors necessary for determining the characteristics and attributes of the cell 22 may be employed. Measurements of voltage, current, and selective temperature and cell characteristics are performed by an arithmetic circuit 50 (eg, processor, computer), which estimates the parameters of the cell 22. The system may additionally include a storage medium 52, which includes computer usable storage media known to those skilled in the art. Various means including without limitation the radio signal 54 are adopted, and the storage medium is in a state where it can communicate with the arithmetic circuit 50. It should be understood that any means for measurement from the internal chemical components of the cell 22 can be used in the present invention, but is not necessary. Also, it should be understood that all measurements can be non-invasive. That is, no signal that can interfere with the normal operation of the load circuit 30 should enter the system.

  Arithmetic circuit 50 for performing the above defined functions and desired processes and operations (eg, modeling, estimation of parameters defined herein, etc.) includes a processor, gate array, custom logic, memory, storage medium, Including, but not limited to, registers, timing, interrupts, communication interfaces, input / output signal interfaces, and combinations of at least one or more of the above. The arithmetic circuit 50 can further include input and input signal filtering, etc. to capture and convert signals and inputs from the communication interface and to perform accurate sampling. Additional features of the arithmetic circuit 50 and details of the process will be described later.

  One or more embodiments of the present invention may be implemented by newly updated firmware and software executed in the arithmetic circuit 50 and / or other process control means. The software functions include firmware without limitation and can be implemented in hardware, software, or a combination thereof. Thus, a significant advantage of the present invention is that charging and control of electrochemical cells can be implemented using current and / or new process systems.

In a preferred embodiment, the arithmetic circuit 50 uses a mathematical model of the cell 22 that includes a dynamic system state indicator as the model state. In one embodiment of the invention, a discrete time model is used. In a discrete-time state-space, an exemplary model (which may be non-linear) has the following form:

In Equation 1, _{x k} is the system state, theta _k sets of varying model parameters time, _{u k} is inputted from the outside, _{y k} is the system output, _{w k} and _{v k} is the noise input. All quantitative elements can be scalars or vectors. f (.,.,.) and g (.,.,.) are functions defined by the model used. Numerical untimely determined by the model variable (non-time-varying) is f (·, ·, ·) and g (·, ·, ·) can be contained within, but not included in the theta _k.

The system state includes a minimum amount of information along with at least the mathematical model of the cell 22 and the current input needed to predict the current output. In the case of the cell 22, the state includes, for example, the SOC, the level of polarization voltage with respect to other time constants, and the hysteresis level. The input u _k from outside the system includes the minimum value of the current cell current i _k and can optionally include the cell temperature (if the temperature change itself is not designed for the state). The system parameter θ _k is a value that changes slowly with time since it cannot be determined directly by knowing the system measured inputs and outputs. Such as cell capacity, resistance, polarization voltage time constant, polarization voltage blending factor, hysteresis blending factor (s), hysteresis rate constant, efficiency Includes elements such as elements without limitation. The model output y _k corresponds to the load cell voltage, which can be directly calculated from the quantitative element of the physically measurable cell or the quantitative element measured as the minimum value.

A mathematical model of dynamic parameters is also exploited. An exemplary model has the following form:

The above equation, the parameters is essentially the constant shows that slowly be changed in accordance with time, the modeled example by pseudo noise process displayed in _{r k} (fictitious noise process).

Referring also to FIG. 2, the state dynamics and parameter dynamics in a joint filter, generally designated as reference numeral 100, combine to form an increased system. An exemplary model has the following form:

In order to simplify the display, the vector including the current state and the current parameter may be denoted as χ _k .

を真の増加された状態の最上の推測値にセットし、最上位部分をＥ［ｘ_０］に、最下位部分をＥ［θ_０］にセットすることで開始される。推定−誤差共分散マトリクス（ｅｓｔｉｍａｔｉｏｎ−ｅｒｒｏｒ ｃｏｖａｒｉａｎｃｅ ｍａｔｒｉｃｅｓ）

も開始される。

The joint filtering process is applied to the increased model of dynamic system states and dynamic parameters defined in one embodiment. Again, the joint Kalman filter 100 or the joint extended Kalman filter 100 can be selectively employed. Table 1 is an exemplary implementation of a system and methodology that utilizes joint extended Kalman filtering. The process is an increased state estimate

Is set to the highest guess in the true increased state, the most significant part is set to E [x ₀ ], and the least significant part is set to E [θ ₀ ]. Estimation-error covariance matrices

Is also started.

は時間的に関数Ｆを通じて次に進む。上記増加された状態ベクトル不確実性もアップデートされる。不確実性推定値をアップデートするのには多様な可能性が存在し、前記表はその一例に過ぎない。セル出力値の測定が行われ、増加された状態推定値、

に基づいて予測された結果値と比較される。その差は、

の値のアップデートに用いられる。前記表に示した段階は多様な手順で行うことができることは明らかである。前記表は説明のために例示的な手順を記載しており、当業者は前記数式と等価的な多くの手順のセットを確認することができるであろう。 In this example, several steps are performed for each measurement interval. First, the increased state estimate

Advances through function F in time. The increased state vector uncertainty is also updated. There are various possibilities for updating the uncertainty estimate, and the table is just one example. Cell output values are measured and increased state estimates,

To the predicted result value based on The difference is

Used to update the value of. Obviously, the steps shown in the table can be performed in various procedures. The table lists exemplary procedures for purposes of explanation, and those skilled in the art will be able to ascertain a set of many procedures equivalent to the formula.

及び増加された状態不確実性推定値

とともに以前の外部入力ｕ_ｋ−１（例えば、セル電流及び／または温度を含むことができる）を入力される。時間アップデート／予測ブロック１０１は、予測された増加された状態値

と予測された増加された状態不確実性

の出力を増加された状態測定アップデート／補正ブロック１０２に提供する。状態測定アップデート／補正ブロック１０２は、予測された増加された状態値

と予測された増加された状態不確実性

だけでなく、外部入力ｕ_ｋ及びシステム出力ｙ_ｋをさらに受信する一方、現在システムに増加された状態推定値

と増加された状態不確実性推定値

を提供する。マイナス表記はベクトルが前記フィルター１００の前記予測要素１０１の結果であることを意味する一方、プラス表記はベクトルが前記フィルター１００の前記補正要素１０２の結果であることを意味する。 Referring now to FIG. 2, a preferred embodiment of the present invention is shown. One filter 100 updates the state and parameter estimates together. The filter has a time update or prediction 101 function and a measurement update or correction 102 function. The time update / prediction block 101 is a previously estimated increased state value.

And increased state uncertainty estimate

With a previous external input u _k-1 (which may include, for example, cell current and / or temperature). Time update / prediction block 101 is a predicted increased state value

Increased state uncertainty predicted

Is provided to the increased state measurement update / correction block 102. The state measurement update / correction block 102 calculates the predicted increased state value.

Increased state uncertainty predicted

As well as the external input u _k and the system output y _k , while the state estimate increased to the current system

And increased state uncertainty estimate

I will provide a. A minus notation means that the vector is the result of the prediction element 101 of the filter 100, while a plus notation means that the vector is the result of the correction element 102 of the filter 100.

で示される分極電圧時定数と、

で示される分極電圧調和要素（ｐｏｌａｒｉｚａｔｉｏｎ ｖｏｌｔａｇｅ ｂｌｅｎｄｉｎｇ ｆａｃｔｏｒ（ｓ））と、Ｒ_ｋで示されるセル抵抗と、Ｍ _ｋで示されるヒステリシス調和要素（ｈｙｓｔｅｒｉｓｉｓ ｂｌｅｎｄｉｎｇ ｆａｃｔｏｒ（ｓ））；γ_ｋで示されるヒステリシス率定数などと、前述した少なくとも１つを含む組合せを制限せずに含む。ここで提案される構造及び方法は、一般的であって、他の電気化学に適用されることができるが、例示のために、パラメーター値は高電力ＬｉＰＢ（Ｌｉｔｈｉｕｍ−ｉｏｎ Ｐｏｌｙｍｅｒ Ｂａｔｔｅｒｙ）の動力学（ｄｙｎａｍｉｃｓ）をモデリングするモデル構造に合わせられる。 Some preferred embodiments illustrating the present invention require a mathematical model of cell state and output dynamics for a particular application. In the above example, this is obtained by defining a specific function of f (.,.,.) And g (.,.,.). In one embodiment, a cell model is used that includes the effect of one or more of the open circuit voltage (OCV), internal resistance, voltage polarization time constant, and hysteresis level of the cell 22. Similarly, the parameter values are efficiency factors such as coulomb efficiency indicated by η _{i, k} , cell capacity indicated by C _k ,

A polarization voltage time constant represented by

A polarization voltage harmonic factor (s), a cell resistance indicated by R _k , and a hysteresis harmonic factor (hysteresis blending factor (s)) indicated by M _k ; a hysteresis indicated by γ _k A combination including a rate constant and at least one of the above is included without limitation. The structures and methods proposed here are general and can be applied to other electrochemistry, but for illustration, the parameter values are the kinetics of high power LiPB (Lithium-ion Polymer Battery). To the model structure modeling (dynamics).

In a preferred embodiment, the SOC is obtained by a single state of the model. The formula for handling the SOC is as follows.

In the above formula, Δt is the inter-sample period (seconds), C _k is the cell capacity (ampere-seconds), z _k is the SOC of the cell 22 at the time index k, i _k is the current of the cell 22, Η _{i, k} means the Coulomb efficiency of the cell 22 at the current level i _k .

In another preferred embodiment, the polarization voltage level is obtained by several filter state values. If a polarization voltage time constant n _f is provided, it is as follows.
[Equation 4]
f _{k + 1} = A _f f _k + B _f i _k

は実値分極電圧時定数（ｒｅａｌ−ｖａｌｕｅｄ ｐｏｌａｒｉｚａｔｉｏｎ ｖｏｌｔａｇｅ ｔｉｍｅ ｃｏｎｓｔａｎｔｓ）

を有する対角行列であり得る。すべてのエントリーが１より小さい値を有すると、前記システムは安定的である。ベクトル

は単にｎ_ｆ”１”に決めることができる。Ｂ_ｆのエントリーは０でない限り臨界的ではない。Ａ_ｆマトリクスのｎ_ｆエントリーの値は、測定されたセルデータにモデルパラメーターを最も適するようにするシステム同定工程の一部分として採択することができる。Ａ_ｆとＢ_ｆマトリクスは、時間及び現在バッテリーパックの作動条件に適した他の要素によって変化できる。 Matrix

Is a real-valued polarization voltage time constant.

Can be a diagonal matrix. If all entries have a value less than 1, the system is stable. vector

Can simply be set to n _f "1". The entry for _Bf is not critical unless it is zero. The value of the n _f entry in the A _f matrix can be adopted as part of the system identification process that best fits the model parameters to the measured cell data. The A _f and B _f matrices can vary depending on time and other factors appropriate to the current battery pack operating conditions.

In another preferred embodiment, the hysteresis level is obtained by a single state.

In Equation 5, γ _k is a hysteresis rate constant obtained by system identification.

In another embodiment, the overall model state value is as follows.

  In the above formula, other arrangements for the state values are possible. The model equation of state is formed by the combination of all the individual equations identified above.

In this example, the output equation for combining the state values for predicting the cell voltage is as follows.

は出力に分極電圧状態値を混合する分極電圧調和要素

のベクトルであり、Ｒ_ｋはセル抵抗（異なる値が充電／放電に使用できる）であり、Ｍ_ｋはヒステリシス調和要素である。ｉ_ｋからＧ_ｋｆ_ｋまでのｄｃゲインが０になるようにＧ_ｋは制限されることがある。 In the above formula,

Is a polarization voltage harmonic element that mixes the polarization voltage state value with the output

R _k is the cell resistance (different values can be used for charging / discharging) and M _k is the hysteresis harmonic element. G _k may be limited so that the dc gain from i _k to G _k f _k becomes zero.

In this example, the parameters are as follows:

は、状態ベクトル（または結合された状態ベクトル、例えば、数式６）とパラメーターベクトル、例えば数式７とを１つのベクトルに組み合わせることで形成される。例えば、

Increased state vector

Is formed by combining a state vector (or a combined state vector, eg, Equation 6) and a parameter vector, eg, Equation 7, into one vector. For example,

における量的要素は、ｆ（・，・，・）（例えば数式３ないし数式５）とｇ（・，・，・）（例えば数式７）に関する数式を演算するために必要なすべての詳細事項を含む。 Other arrangements of states and parameters are possible within the increased state vector.

The quantitative elements in are all the details necessary to calculate the mathematical formulas for f (·, ····) (eg Equation 3 to Equation 5) and g (·, ····) (eg Equation 7). Including.

In any embodiment, the joint filter 100 can adapt the state estimates and parameter estimates to bring the input / output relationship model as close as possible to the measured input / output data. There is no guarantee that the increased state value of the model will converge to the physically increased state value. In a preferred embodiment, the cell model used for joint filtering adds to the cell model an additional cell model containing as output the increased state values that should be converged to their corrected values. Then, it can be replenished. Some embodiments take additional steps to ensure that one model state value converges to the SOC.

を用いることで近似化され得る。

The supplemented model output value is compared with the output value measured by the joint filter 100. In a preferred embodiment, the measured value for the SOC is derived by the following formula:

Can be approximated by using

を介して）及びセル化学のＯＣＶ逆関数を分かることで、この例はＳＯＣのノイズ推定値

を計算することができる。 Measurement of cell voltage and cell current under load and R _k value (from joint filter 100

And the cell chemistry OCV inverse function, this example shows the SOC noise estimate.

Can be calculated.

とともに動作する。

のノイズ（ヒステレシス効果による短期間バイアス及び分極電圧は考慮されない）は、ＳＯＣの主要推定子（ｐｒｉｍａｒｙ ｅｓｔｉｍａｔｏｒ）として使われない一方、動的環境での予想された長期間挙動は正確であり、ジョイントフィルター１００でＳＯＣ状態値の正確性が維持されることを実験を通じて分かる。 In this example, the joint filter 100 is the information measured in the measurement update on this modified model.

Works with.

Noise (which does not take into account short-term bias and polarization voltage due to hysteresis effects) is not used as the primary estimator of the SOC, while the expected long-term behavior in dynamic environments is accurate, It can be seen through experiments that the accuracy of the SOC state value is maintained in the filter 100.

Beyond industrial applicability , methods for simultaneous estimation of cell state and parameters have been described with many specific examples. One or more embodiments use a Kalman filter 100. One embodiment uses an extended Kalman filter 100. Further, some embodiments include a mechanism to enhance SOC convergence. The present invention is applicable to cell electrochemistry and a wide range of applications.

  The disclosed method can be realized in the form of an apparatus for performing the process and a computer-executed process. The method may also be implemented in the form of computer program code including instructions executed by a medium 52 such as a floppy disk, CD-ROM, hard disk, or other computer readable storage medium. The program code is read and executed by a computer, and the computer becomes a device for executing the method. Further, the present method may be in the form of computer program code, eg, stored in a storage medium, executed by a computer, loaded into a computer, or a modulated carrier wave, electrical transmission line, cable, optical fiber or It can be realized in the form of a transmission medium such as electromagnetic transmission. When the computer program code is read and executed by a computer, the computer becomes a device that performs the method. When performed by a general-purpose microprocessor, the computer program code segments configure the microprocessor to form specific logic circuits.

  Although the invention has been described with reference to the embodiments, it will be understood that various modifications can be made by those skilled in the art, and equivalent elements may be substituted for elements of the invention without departing from the scope of the invention. It must be. Moreover, many applications may be applied to particular situations and materials related to the teachings of the invention without departing from the essential scope thereof. Accordingly, the invention is not limited to the specific embodiments disclosed as the best mode contemplated for practicing the invention, and the invention is construed to include all embodiments that fall within the scope of the appended claims. .

1 is a block diagram illustrating an example of a system for estimating states and parameters according to an embodiment of the present invention. 1 is a block diagram illustrating a joint filtering method according to an exemplary embodiment of the present invention.

Claims (51)

In a method for estimating the current increased state of an electrochemical cell system,
Performing an internal increased state prediction comprising at least one internal state value and at least one internal parameter value of the cell;
Performing an uncertainty prediction of the internal increased state prediction;
Correcting the internal increased state prediction and the uncertainty prediction;
Performing the internal increased state prediction in order to calculate an ongoing estimation of the increased state and an ongoing uncertainty with respect to the increased state estimation; Applying an algorithm that repeats the steps of performing and correcting the current increased state of the electrochemical cell system. Performing the internal increased state prediction comprises:
Determining the current measurement,
Determining the voltage measurement,
The current increase of the electrochemical cell system according to claim 1, comprising using the current measurement and the voltage measurement in a mathematical model to perform the increased state prediction of the interior. How to estimate the state that was done. Performing the uncertainty prediction comprises:
3. Estimating a current increased state of an electrochemical cell system according to claim 2, comprising performing the uncertainty prediction using the current measurement and the voltage measurement in a mathematical model. how to. The correcting step includes
Calculating the gain factor;
Calculating an internal increased state prediction value corrected using the gain factor, the voltage measurement and the internal increased state prediction;
4. The current increased state of the electrochemical cell system of claim 3, comprising calculating a predicted uncertainty value corrected using the gain factor and the uncertainty prediction. How to estimate. The applying step includes:
Using the corrected internal increased state prediction value and the corrected uncertainty prediction value to obtain a prediction value for the next time step in which the algorithm is repeated, A method for estimating a current increased state of an electrochemical cell system according to claim 4. The algorithm is
6. The method for estimating the current increased state of an electrochemical cell system according to claim 5, wherein the method is at least one of a Kalman filter and an extended Kalman filter. The increased state vector is
And a voltage polarization level, a hysteresis level, a resistance, a capacity, a polarization voltage time constant, a polarization voltage harmonic element, a hysteresis harmonic element, a hysteresis rate constant, and an efficiency constant. Item 7. A method for estimating the current increased state of the electrochemical cell system according to Item 6. Performing the internal increased state prediction comprises:
Determining the temperature,
The method of claim 2, further comprising: predicting the increased state of the interior using the temperature measurement value, the current measurement value, and the voltage measurement value in a mathematical model. A method for estimating the current increased state of a cell system. Performing the uncertainty prediction comprises:
The method of claim 8, further comprising performing the uncertainty prediction using the temperature measurement value, the current measurement value, and the voltage measurement value in a mathematical model. A method of estimating the increased state. The correcting step includes
Calculating the gain factor;
Calculating an internal increased state prediction value corrected using the gain factor, the voltage measurement value, and the internal increase state prediction;
10. A current increased state of the electrochemical cell system according to claim 9, comprising calculating a predicted uncertainty value corrected using the gain factor and the uncertainty prediction. How to estimate. The applying step includes:
11. The method of claim 10, further comprising using the corrected internal state predicted value and the corrected uncertainty predicted value to obtain a predicted value for a next time step in which the algorithm is repeated. A method for estimating the current increased state of the electrochemical cell system according to claim 1. The algorithm is
The method for estimating the current increased state of an electrochemical cell system according to claim 11, wherein the method is at least one of a Kalman filter and an extended Kalman filter. The increased state vector is
And a voltage polarization level, a hysteresis level, a resistance, a capacity, a polarization voltage time constant, a polarization voltage harmonic element, a hysteresis harmonic element, a hysteresis rate constant, and an efficiency constant. Item 13. A method for estimating a current increased state of an electrochemical cell system according to Item 12. Performing the uncertainty prediction comprises:
Determining the temperature,
The method of claim 2, further comprising performing the uncertainty prediction using the temperature measurement value, the current measurement value, and the voltage measurement value in a mathematical model. A method of estimating the increased state. Performing the uncertainty prediction comprises:
Determining the current measurement,
Determining the voltage measurement,
The current increase of the electrochemical cell system according to claim 1, comprising using the current measurement and the voltage measurement in a mathematical model to perform the increased state prediction of the interior. How to estimate the state that was done. Performing the uncertainty prediction comprises:
Determining the temperature,
The method of claim 15, further comprising performing the uncertainty prediction using the temperature measurement value, the current measurement value, and the voltage measurement value in a mathematical model. A method of estimating the increased state. The correcting step includes
Calculating the gain factor;
Calculating the gain factor, the voltage measurement value, and the internal state prediction value corrected using the internal state prediction;
17. The current increased state of the electrochemical cell system of claim 16, comprising calculating a predicted uncertainty value corrected using the gain factor and the uncertainty prediction. How to estimate. The applying step includes:
18. The method of claim 17, further comprising using the corrected internal state predicted value and the corrected uncertainty predicted value to obtain a predicted value for a next time step in which the algorithm is repeated. A method for estimating the current increased state of the described electrochemical cell system. The algorithm is
The method for estimating the current increased state of an electrochemical cell system according to claim 18, wherein the method is at least one of a Kalman filter and an extended Kalman filter.   The method of estimating the current increased state of an electrochemical cell system according to claim 1, further comprising ensuring convergence of one or more increased states for each physical value. The guaranteeing step includes:
21. A method for estimating the current increased state of an electrochemical cell system according to claim 20, comprising replenishing the cell model output with the increased state where convergence is desired. The mechanism is
22. The current increased state of the electrochemical cell system of claim 21, further comprising: supplementing a measurement vector with a corresponding estimate of the increased state based on a currently measured value. How to estimate.   23. The current augmentation of the electrochemical cell system of claim 22, wherein a filter is used that adapts an increased state estimate based on the supplemented cell model output and the supplemented measurement vector. How to estimate The filter is
24. A method for estimating the current increased state of an electrochemical cell system according to claim 23, wherein the method is at least one of a Kalman filter and an extended Kalman filter. In an apparatus for estimating the current increased state of a cell pack system,
An element configured to make an increased state prediction inside the cell;
An element configured to perform an uncertainty prediction of the internal increased state prediction;
An element configured to correct the internal increased state prediction and the uncertainty prediction;
An element configured to perform the internal increased state prediction to calculate an ongoing estimate of the increased state and an ongoing uncertainty for the increased state estimate; An element configured to perform certainty prediction and an element configured to apply an algorithm that repeats the steps taken by the element configured to correct A device that estimates the current increased state. Elements configured to perform the internal state prediction are:
An element configured to determine the current measurement;
An element configured to determine a voltage measurement;
26. An element configured to perform the internal increased state prediction using the parameter estimate, the current measurement, and the voltage measurement in a mathematical model. An apparatus for estimating the current increased state of the cell pack system according to claim 1. Elements configured to perform the uncertainty prediction are:
27. The current augmentation of the cell pack system of claim 26, including elements configured to perform the uncertainty prediction using the current measurement and the voltage measurement in a mathematical model. A device that estimates the state of damage. The element configured to correct is
An element configured to compute a gain factor,
An element configured to compute an internal increased state prediction value corrected using the gain factor, the voltage measurement, and the internal increased state prediction;
28. The element of claim 27, comprising: an element configured to calculate an uncertainty prediction value corrected using the gain factor and the uncertainty prediction. A device that estimates the increased state. The elements configured to apply are:
An element configured to use the corrected internal increased state prediction value and the corrected uncertainty prediction value to obtain a prediction value for a next time step in which the algorithm is repeated; 30. The apparatus for estimating a current increased state of a cell pack system according to claim 28, comprising: The algorithm is
30. The apparatus for estimating a current increased state of a cell pack system according to claim 29, wherein the apparatus is at least one of a Kalman filter and an extended Kalman filter. The increased state vector is
And a voltage polarization level, a hysteresis level, a resistance, a capacity, a polarization voltage time constant, a polarization voltage harmonic element, a hysteresis harmonic element, a hysteresis rate constant, and an efficiency constant. Item 31. An apparatus for estimating the current increased state of the cell pack system according to Item 30. The elements configured to perform the internal increased state prediction are:
Elements configured to determine the temperature;
27. An element configured to perform the internal increased state prediction using the temperature measurement, the current measurement, and the voltage measurement in a mathematical model. An apparatus for estimating the current increased state of the cell pack system according to claim 1. Elements configured to perform the uncertainty prediction are:
The cell pack according to claim 32, further comprising an element configured to perform the uncertainty prediction using the temperature measurement value, the current measurement value, and the voltage measurement value in a mathematical model. A device that estimates the current increased state of the system. The element configured to correct is
An element configured to compute a gain factor;
An element configured to calculate an internal increased state prediction value corrected using the gain factor, the voltage measurement value, and the internal increase state prediction;
34. An element configured to calculate an uncertainty prediction value corrected using the gain factor and the uncertainty prediction, and A device that estimates the increased state. The elements configured to apply are:
Including an element configured to use the corrected internal state prediction value and the corrected uncertainty prediction value to obtain a prediction value for a next time stage in which the algorithm is repeated. An apparatus for estimating a current increased state of a cell pack system according to claim 34. The algorithm is
36. The apparatus for estimating a current increased state of a cell pack system according to claim 35, wherein the apparatus is at least one of a Kalman filter and an extended Kalman filter. The increased state vector is
And a voltage polarization level, a hysteresis level, a resistance, a capacity, a polarization voltage time constant, a polarization voltage harmonic element, a hysteresis harmonic element, a hysteresis rate constant, and an efficiency constant. Item 37. The apparatus for estimating the current increased state of the cell pack system according to Item 36. Elements configured to perform the uncertainty prediction are:
Elements configured to determine the temperature;
27. The element of claim 26, further comprising: an element configured to perform the uncertainty prediction using the temperature measurement, the current measurement, and the voltage measurement in a mathematical model. A device that estimates the current increased state of the cell pack system. Elements configured to perform the uncertainty prediction are:
An element configured to determine the current measurement;
An element configured to determine a voltage measurement;
26. A cell pack system current element according to claim 25, comprising: an element configured to perform the uncertainty prediction using the current measurement and the voltage measurement in a mathematical model. A device that estimates the increased state. Elements configured to perform the uncertainty prediction are:
Elements configured to determine the temperature;
40. The element of claim 39, further comprising: an element configured to perform the uncertainty prediction using the temperature measurement, the current measurement, and the voltage measurement in a mathematical model. A device that estimates the current increased state of the cell pack system. The element configured to correct is
An element configured to compute a gain factor;
An element configured to calculate the gain factor, the voltage measurement value, and an internal state prediction value corrected using the internal state prediction;
41. The element of the cell pack system of claim 40, comprising: an element configured to calculate an uncertainty prediction value corrected using the gain factor and the uncertainty prediction. A device that estimates the increased state. The elements configured to apply are:
The corrected internal increased state prediction value, the corrected internal state prediction value, and the corrected uncertainty prediction to obtain a prediction value for the next time stage in which the algorithm is repeated. 42. The apparatus for estimating a current increased state of a cell pack system according to claim 41, comprising elements configured to use values. The algorithm is
43. The apparatus for estimating a current increased state of a cell pack system according to claim 42, wherein the apparatus is at least one of a Kalman filter and an extended Kalman filter.   The cell pack system according to claim 25, further comprising an element configured to ensure convergence of one or more increased states for each physical value. Device to estimate. Elements configured to ensure the convergence are:
45. The cell pack system according to claim 44, comprising elements configured to supplement the cell model output with the one or more increased states for which convergence is sought. Device to estimate. Elements configured to ensure the convergence are:
The cell of claim 45, further comprising an element configured to supplement each measurement vector with a corresponding estimate of the one or more increased states based on a currently measured value. A device that estimates the current increased state of the pack system.   The method of claim 46, further comprising an element configured to implement a filter that adapts an increased state estimate based on the supplemented cell model output and the supplemented measurement vector. A device that estimates the current increased state of the cell pack system. The filter is
48. The apparatus for estimating a current increased state of a cell pack system according to claim 47, wherein the apparatus is at least one of a Kalman filter and an extended Kalman filter. A system for estimating the current increased state of an electrochemical cell,
Means for performing an internal increased state prediction comprising at least one internal state value and at least one internal parameter value of the cell;
Means for performing an uncertainty prediction of the internal increased state prediction;
Means for correcting the internal increased state prediction and the uncertainty prediction;
Applying an algorithm that repeats the internal increased state prediction, the uncertainty prediction, and the correction to apply an ongoing estimate of the increased state and an ongoing uncertainty for the increased state estimate. A system for estimating the current increased state of the electrochemical cell, comprising: means for calculating certainty. Performing an internal increased state prediction comprising at least one internal state value and at least one internal parameter value of the electrochemical cell;
Performing an uncertainty prediction of the internal increased state prediction;
Correcting the internal increased state prediction and the uncertainty prediction;
Performing the internal increased state prediction so as to calculate an ongoing estimation of the increased state and an ongoing uncertainty with respect to the increased state estimation; A computer readable computer program comprising instructions for causing a computer to perform a method for estimating a current increased state of an electrochemical cell comprising: applying an algorithm that repeats and performing the correcting step A storage medium coded in code. Performing an internal increased state prediction comprising at least one internal state value and at least one internal parameter value of the electrochemical cell;
Performing an uncertainty prediction of the internal increased state prediction;
Correcting the internal increased state prediction and the uncertainty prediction;
Performing the internal increased state prediction so as to calculate an ongoing estimation of the increased state and an ongoing uncertainty with respect to the increased state estimation; Loading a computer comprising code configured to cause a computer to perform a method for estimating a current increased state of an electrochemical cell comprising: applying an algorithm that repeats and performing the correcting step A computer data signal implemented on a possible medium.

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