KR100916510B1 - Method and system for joint battery state and parameter estimation - 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 internal increased state prediction; Correcting the internal increased state prediction and the uncertainty prediction; And performing the internal increased state prediction, performing the uncertainty prediction, and correcting to calculate an ongoing estimate for the increased state and an ongoing uncertainty for the increased state estimate. Applying an algorithm.

Battery chemical cell, elctrochemical cell, state of charge, SOC, state, parameter, internal augmented state

Description

Joint battery state and parameter estimation {Method and system for joint battery state and parameter estimation}

The present invention provides an apparatus and method for estimating the state and parameters of a battery pack system using digital filtering techniques, in particular joint kalman filtering and joint extended kalman filtering. It is about.

In the field of rechargeable battery pack technology, it is not directly measurable, but it is required in some applications to be able to estimate the quantities that can represent the current battery pack status. Some of these quantitative factors can change rapidly, such as the state of charge (SOC) of the pack, and can even turn the entire area range in minutes. Others can change very slowly, such as cell capacity, and can vary in as little as 20% over 10 years of normal use. Quantitative elements that tend to change rapidly constitute the state of the system, and quantities that tend to change slowly constitute the time varying parameter of the system.

Battery system applications, in particular, do not adversely affect battery life, such as, for example, hybrid electric vehicles (HEVs), battery electric vehicles (BEVs), laptop computer batteries, portable equipment battery packs, and the like. For those that need to be operated for as long as possible, if possible, it is desirable to use information about rapidly changing parameters (e.g., SOCs) to estimate how much battery energy can currently operate. . In addition, the slow varying parameters (to help maintain correct prior calculations and determine the pack's state of health (SOH) throughout the pack's lifetime while extending useful service time) For example, it is desirable to check the information on the total dose).

There are a variety of existing methods of cell state prediction that are generally involved in predicting three quantities of SOC (power quantitative factor), power-fade, and capacity-fade. For example, although other methods may also be used, power decay can be calculated if the current and initial pack electrical resistances are known, and capacity decay can be calculated if the current and initial pack total capacity is known. Power and capacity decay is often expressed in terms of state of health (SOH). Other information can be inferred using the values of these variables, such as the maximum power available from the pack at any given time. Additional state members and parameters may also be needed for a particular application, and individual algorithms are typically required to find each one.

SOC, typically expressed in percent, represents a fraction of the currently available cell capacity. Many other approaches to estimating SOC are employed: discharge testing, amp-hour counting (coulomb counting), open-circuit voltage measurements, linear and nonlinear circuit modeling, impedance spectroscopy, internal resistance There are several forms of measurement, coup de fouet and Kalman filters. The discharge test must completely discharge the cell to determine the SOC. This test interrupts system functionality while the test is in progress and in many cases is not useful and can be excessively time consuming. Ampere-hour counting is a kind of open-loop methodology, and due to accumulated measurement errors, its accuracy decreases over time. The measurement of electrolyte is feasible in the case of vented lead-acid batteries and therefore has limited utility. Open-circuit voltage measurements can only be performed after extended cell inactivity periods, can be performed for cells with negligible hysteresis effects, and do not operate in a dynamic state. Linear and nonlinear circuit modeling methods do not yield SOC directly. SOC should be inferred from the calculated values. Impedance spectroscopy requires measurements that are not always useful for typical applications. Internal resistance measurements are very sensitive to measurement errors and require measurements that are not useful for typical applications. The coup de fouet only works on lead acid batteries. Kalman filtering, which does not use SOC as a filter state, does not directly yield an error bound to the estimate. In another method disclosed in US Pat. No. 6,534,954, a filter, preferably a Kalman filter, is used to estimate the SOC by employing known mathematical models for the measurement of cell dynamics and cell voltage, current and temperature. This method directly estimates the state values. However, parameter values are not dealt with.

Knowledge of SOH as well as SOC is required. Power fades in this area are related to the increase in cell electrical resistance with cell lifetime. This increasing resistance causes the power supplied by the cell to drop. Capacity fade is related to the phenomenon that the total capacity of a cell decreases with the life of the cell. Both the resistance and the capacity of the cell are time varying parameters. The prior art uses the following other approaches to the estimation of SOH: discharge tests, chemistry-dependent methods, ohmic tests and partial discharges. The discharge test completely discharges a fully charged cell to determine the total capacity of the cell. This test disrupts system functionality and wastes cell energy. Chemically dependent methods include measuring the level of plate corrosion, electrolyte density. Coup de fouet is used in lead acid batteries. Ohm tests include resistance, conductance, and impedance tests, which are optionally associated with fuzzy-logic algorithms and / or neural networks. These methods require measurements of invasiveness. Partial discharge and other methods compare a good cell or a model of a good cell with the cell under test.

There is a need for a method of estimating cell state and parameters simultaneously. Moreover, methods that do not disrupt system function, do not waste energy, enable general applications (e.g., other types of cell electrochemistry, other applications), methods that do not require invasive measurements and fairly harsh approaches. There is a need for. There is a need for a method and apparatus for automatically estimating time varying parameters, such as cell resistance and capacitance, for example. There is a need for a method that can operate on other configurations of parallel and / or series cells in a battery pack.

A method according to one aspect of estimating an augmented state of an electrochemical cell, includes internal augmented states prediction comprising at least one internal state value and at least one internal parameter value of the cell. Performing; 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, performing the uncertainty prediction, and correcting to calculate an ongoing estimate for the increased state and an ongoing uncertainty for the increased state estimate. Applying an algorithm.

An apparatus according to another aspect of an embodiment configured to estimate a present augmented state of a cell pack system includes: a component configured to perform internal increased state prediction of a cell; A component configured to perform an uncertainty prediction of the internal increased state prediction; A component configured to correct the internal increased state prediction and the uncertainty prediction; And a component configured to perform an internal increased state prediction to calculate an ongoing estimate for the increased state and an ongoing uncertainty for the increased state estimate, a component configured to perform an uncertainty prediction, and a component configured to correct A component configured to apply an algorithm to repeat the steps taken by it.

In addition, a system for estimating a current increased state of an electrochemical cell disclosed herein as an embodiment performs an internal increased state prediction including at least one internal state value and at least one internal parameter value of the cell. Means for doing so; Means for performing an uncertainty prediction of the internal increased state prediction; Means for correcting the internal increased state prediction and the uncertainty prediction; And means for applying an algorithm that repeats the internal increased state prediction, the uncertainty prediction, and the correction to calculate an ongoing estimate for the increased state and an ongoing uncertainty for the increased state estimate. .

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

In addition, another embodiment of a computer data signal implemented as a computer readable medium is disclosed. The computer data signal includes code configured for a computer to perform the method of estimating a current increased state of an electrochemical cell.

Various features, aspects, and advantages of the invention will be more clearly understood from the detailed description of the invention and the appended claims and drawings. In the drawings, like elements are denoted by like reference numerals.

1 is a block diagram illustrating an example of a system for estimating states and parameters in accordance with a preferred embodiment of the present invention;

2 is a block diagram illustrating a method of joint filtering according to an exemplary embodiment of the present invention.

Various embodiments methods, systems, and apparatus are disclosed for estimating the state and parameters of an electrochemical cell using joint filtering. 1 and 2, in the following description numerous specific details are set forth in order to provide a more complete understanding of the present invention. Preferred embodiments are described with reference to battery cells, but include batteries, battery packs, ultracapacitors, capacitor banks, fuel cells, electrolyte cells, etc., corresponding to the cells, and combinations of at least one of the foregoing. It should be understood that various electrochemical cells can be employed, including. In addition, a battery or battery pack may include a plurality of cells, and it is to be understood that one embodiment disclosed may be applied to one or more cells.

One or more embodiments of the present invention use joint filtering to estimate cell state and parameters. One or more embodiments of the invention use joint Kalman filtering to estimate cell state and parameter values. One embodiment of the present invention estimates cell state and parameter values using joint extension Kalman filtering. One embodiment simultaneously estimates SOC, power decay, and / or capacity decay, and another embodiment estimates additional cell state values and / or additional time varying parameter values. The term filtering is employed to describe and represent exemplary embodiments, and in terms of terminology includes, without limitation, Kalman filtering and / or extended Kalman filtering, including recursive prediction and correction commonly indicated by filtering. It is to be understood that it is intended to include a methodology of correction.

1 illustrates components of a parameter prediction system 10 according to an embodiment of the present invention. An electrochemical cell pack 20 (eg a battery) comprising a plurality of cells 22 is connected to a load circuit 30. For example, the load circuit 30 may be a motor of an electric vehicle (EV) or a heterogeneous vehicle (HEV). Apparatus for measuring the characteristics and properties of the various cells is provided at 40. The measuring device 40 can include without limitation a device for measuring cell terminal voltage, such as a voltage sensor 42 (eg, a voltmeter, etc.). On the other hand, the measurement of the cell current is made by the current sensing device 44 (for example, ammeter, etc.). Optionally, the temperature of the cell is measured by a temperature sensor 46 (eg a thermometer, etc.). Additional cell characteristics such as internal pressure or impedance may be measured using, for example, a pressure sensor and / or impedance sense 48 and may also be employed depending on the selected form of the cells 22 of the cell pack 20. have. Various sensors may be employed to obtain the properties and properties of the cell (s) 22. Voltage, current, and optional temperature and cell characteristic measurements are made by an arithmetic circuit 50 (eg, processor, computer), which estimates the parameters of the cell (s) 22. The system may further include a storage medium 52, which includes a computer usable storage medium known to those of ordinary skill in the art. By employing a variety of means including, without limitation, the radio signal 54, the storage medium is in a state capable of communicating with the repair circuit 50. It should be understood that any means for measurement from the internal chemical constituents of cell 22 may be used in the present invention, although it is not necessary. In addition, it should be understood that all measurements may be non-invasive. That is, signals that may interfere with the normal operation of the load circuit 30 should not be injected into any system.

As a result of performing the specified functions and desired processing and operations (e.g., modeling, estimation of the parameters defined herein, etc.), the repair circuit 50 may include a processor, gate array, custom logic, memory, storage media, It may include without limitation including a combination of a register, a timing, an interrupt (s), a communication interface, and an input / output signal interface and at least one of the above. The repair circuit 50 may also include input and input signal filtering, etc. to perform acquisition or conversion of signals and inputs from the communication interface and accurate sampling. Additional features and processors of the repair circuit 50 are discussed in detail later.

One or more embodiments of the invention may be implemented by newly updated firmware and software that is executed in repair circuitry 50 and / or other processing control means. Software functions include, without limitation, firmware, and may be implemented in hardware, software, or a combination thereof. Thus, a distinct advantage of the present invention is that charging and control of the electrochemical cell can be implemented using current and / or new processing systems.

In one preferred embodiment, the repair circuit 50 uses a mathematical model of the cell 22 that includes indicia of the dynamic system state as the model state. In one embodiment of the invention, a discrete-time model is used. An example model (possibly nonlinear) in discrete-time state-space has the form

In Equation 1, x _k is a system state, θ _k is a set of time-variable model parameters, u _k is an input from the outside, y _k is a system output, and w _k and v _k are noise inputs. All quantities can be scalar or vector. f (·, ·, ·) and g (·, ·, ·) are functions defined by the model being used. The non-time-varying numerical values required by the model can be embedded within f (·, ·, ·) and g (·, ···) and not included in θ _k .

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

Mathematical models of parameter dynamics are also utilized. An exemplary model has the following form.

The equation indicates that the parameters are essentially constant, but may change slowly over time in the example modeled by the fictitious noise process, denoted by r _k .

Also referring to FIG. 2, the kinetics of the state and the kinematics of the parameters within the joint filter, generally indicated at 100, combine to form an increased system. The example model has the following form:

It should be appreciated that for the sake of simplicity, the vector containing the current state and the current parameter will sometimes be represented by χ _k .

을 사실의 증가된 상태(the true augmented state)의 최상의 추측값(the best guess)으로 세팅하고 최상위 부분(top portion)을 E[x₀]로, 최하위 부분(bottom portion)을 E[θ₀]로 세팅함으로써 개시된다. 상기 추정-오차 공분산 매트릭스(estimation-error covariance matrices)

또한 개시된다. The process of joint filtering is applied to an increased model of system state dynamics and parameter dynamics defined in one embodiment. Once again, the joint Kalman filter 100 or the joint expansion Kalman filter 100 may optionally be employed. Table 1 is an example implementation of a system and methodology that utilizes joint extension Kalman filtering. The process includes an increased state estimate

Is set to the best guess of the true augmented state, the top portion is set to E [x ₀ ], and the bottom portion is set to E [θ ₀ ]. It is started by setting to. Estimation-error covariance matrices

Also disclosed.

Table 1: Joint Expansion Kalman Filters for Status and Weight Updates

은 시간적으로 함수 F를 통하여 다음으로 진행된다. 상기 증가된 상태 백터 불확실성 또한 업데이트된다. 불확실성 추정값(uncertainty estimate)을 업데이트하는 다양한 가능성이 존재하며, 상기 테이블은 단지 일 실시예를 나타낸 다. 셀 출력값의 측정이 이루어지고, 증가된 상태 추정값 ,

에 기초한 예측된 결과값에 비교된다. 상기 차이는

의 값을 업데이트하는데 이용된다. 상기 테이블에서 나타난 스텝들은 다양한 순서에 의하여 수행될 수 있음은 자명하다. 상기 테이블은 설명의 목적을 위한 예시적인 순서들을 리스트하고 있으며, 당업자는 상기 수학식의 많은 등가적인 순서의 세트(set)를 확인할 수 있을 것이다.In this example, several steps are performed for each measurement interval. First, the increased state estimate

Proceeds temporally through function F. The increased state vector uncertainty is also updated. There are various possibilities for updating the uncertainty estimate, and the table illustrates only one embodiment. The cell output is measured, and the increased state estimate,

Is compared to the predicted results based on. The difference is

It is used to update the value of. Obviously, the steps shown in the table can be performed in various orders. The table lists example sequences for purposes of explanation, and those skilled in the art will be able to ascertain many sets of equivalent sequences of the equation.

및 증가된 상태 불확실성 추정값(augmented state uncertainty estimate)

과 함께 이전 외부 입력 u_{k -1}(예를 들어, 셀 전류 및/또는 온도를 포함할 수 있는)을 입력으로 받는다. 시간 업데이트/예측 블록(101)은 예측된 증가된 상태값

과 예측된 증가된 상태 불확실성

출력을 증가된 상태 측정 업데이트/보정 블록(102)에 제공한다. 상태 측정 업데이트/보정 블록(102)은 예측된 증가된 상태값

과 예측된 증가된 상태 불확실성

뿐만 아니라 외부 입력 u_k 및 시스템 출력 y_k을 또한 수신하는 반면, 현재 시스템에 증가된 상태 추정값

과 증가된 상태 불확실성 추정값

을 제공한다. 마이너스 표기는 벡터가 상기 필터(100)의 상기 예측 컴퍼넌트 101의 결과임을 의미하는 반면, 플러스 표기는 벡터가 상기 필터(100)의 상기 보정 컴퍼넌트 102의 결과임을 의미하는 것으로 이해되어야 한다.2, there is shown a preferred implementation of one embodiment of the present invention. One filter 100 jointly updates the state and parameter estimates. 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 the previously estimated increased state value.

And augmented state uncertainty estimate

With the previous external input u _{k -1} (which may include, for example, cell current and / or temperature). The time update / prediction block 101 is the predicted increased state value.

And predicted increased state uncertainty

The output is provided to the incremental state measurement update / calibration block 102. State measurement update / calibration block 102 is the predicted increased state value.

And predicted increased state uncertainty

In addition, it also receives external input u _k and system output y _k , while increasing the state estimate for the current system.

And increased state uncertainty estimates

To provide. A minus notation means that a vector is the result of the predictive component 101 of the filter 100, while a plus notation should be understood to mean that the vector is the result of the correction component 102 of the filter 100.

로 표시되는 분극 전압 시정수(들);

로 표시되는 분극 전압 조화 요소(들)(polarization voltage blending factor(s)); R_k로 표시되는 셀 저항(들); M_k로 표시되는 히스테리시스 조화 요소(들)(hysterisis blending factor(s));γ_k로 표시되는 히스테리시스율 상수 (들)(hysteresis rate constant(s)); 등과 전술한 적어도 하나를 포함하는 조합들을 제한하지 않고 포함한다.여기에서 제안되는 구조와 방법은 일반적이고 다른 전기화학에 적용될 수 있을지라도, 예시를 위하여 파라미터 값은 고전력 LiPB(Lithium-ion Polymer Battery)의 동역학(dynamics)을 모델링하는 이 모델 구조에 맞추어진다. Several preferred embodiments describing the present invention require mathematical models of cell state and output dynamics for specific applications. In the above example, this can be achieved by defining a specific function (function) of f (·, ···), g (·, ·, ·). One embodiment uses a cell model that includes the effects of one or more of the Open Circuit Voltage (OCV), internal resistance, voltage polarization time constant, and hysteresis level of the cell 22. . Similarly, parameter values may include efficiency factors such as coulombic efficiency _{, denoted} η _{i, k} ; Cell capacity (s) expressed as C _k ;

Polarization voltage time constant (s) denoted by;

Polarization voltage blending factor (s) represented by; _Represented by R _k Cell resistance (s); Hysteresis blending factor (s) denoted by M _k ; hysteresis rate constant (s) denoted by γ _k ; And combinations including at least one of the foregoing, and the like. The structures and methods proposed herein are general and applicable to other electrochemistry, but for illustrative purposes the parameter values are high power Lithium-ion Polymer Battery (LiPB). It fits into this model structure that models the dynamics of.

In one preferred embodiment, the SOC is obtained by a single state of the model. This formula for dealing with SOC is:

Where Δt is the inter-sample period (seconds), C _k is the cell capacity (ampere-seconds), z _k is the cell 22 SOC at time index k, i _k is the cell 22 The current and η _{i, k} refer to the coulombic 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 the polarization voltage time constant n _f is given,

f _{k +1} = A _f f _k + B _f i _k

는 실값 분극 전압 시정수(real-valued polarization voltage time constants)

를 가지는 대각 행렬(diagonal matrix)일 수 있다. 모든 엔트리가 1보다 작은 크기를 갖는다면 상기 시스템은 안정적이다. 벡터

는 단순히 n_f "1"로 정해질 수 있다. B_f의 엔트리는 0이 아닌 한 임계적이지 않다. A_f 매트릭스의 n_f엔트리의 값은 측정된 셀 데이터에 모델 파라미터를 가장 적합하게 하는 시스템 확인 절차(system identification procedure)의 일부분으로 채택될 수 있다. A_f와 B_f 매트릭스는 시간과 현재 배터리 팩의 작동조건에 적합한 다른 요소들에 의하여 달라질 수 있다.matrix

Is the real-valued polarization voltage time constants

It may be a diagonal matrix having. The system is stable if all entries have a size less than one. vector

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

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

_k는 시스템 확인(system identification)에 의하여 획득되는 히스테리시스율 상수이다.In Equation 5

_k is a hysteresis rate constant obtained by system identification.

In another embodiment, the overall model state values are as follows.

Other arrangements for the state values in the above equations are possible. In this example the state equations of the model are formed by the combination of all the individual equations identified above.

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

는 출력에 분극 전압 상태값들을 함께 혼합하는 분극 전압 조화 요소

의 벡터이며, R_k 은 셀저항(들)(다른 값들이 충전/방전에 사용될 수 있다)이며, M_k은 히스테리시스 조화 요소(hysteresis blending factor)이다. i_k에서 G_kf_k까지의 dc게인이 0이 되도록 G_k는 제한될 수 있다.In the above formula

The polarization voltage harmonic element mixes the polarization voltage state values together at the output.

R _k is the cell resistance (s) (other values can be used for charging / discharging), and M _k is the hysteresis blending factor. dc gain in the _k i to G _k f _k is 0 such that G _k can be restricted.

In this example, the parameter is

는 상기 상태 백터(또는 결합된 상태 백터, 예를 들어, 수학식 6)와 파라미터 백터, 예를 들어 수학식 7을 하나의 백터로 결합시 킴으로써 형성된다. 예를 들어,The increased state vector

Is formed by combining the state vector (or combined state vector, for example, Equation 6) and the parameter vector, for example, Equation 7, into one vector. E.g,

에서의 양적 요소들은 f(·,·,·)(예를 들어 수학식 (3) - (5))와 g(·,·,·)(예를 들어 수학식 7)에 대한 수학식을 연산하기 위하여 필요한 모든 세부사항을 포함한다. Other arrangements of states and parameters are possible within the increased state vector.

The quantitative elements in e compute the equations for f (·, ·, ·) (for example, equations (3) to (5)) and g (·, ·, ·) (for example, equation 7). Include all details necessary to do so.

In some embodiments, the joint filter 100 may adapt 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 values of the model will converge to the physical increased state values. In a preferred embodiment, the cell model used for joint filtering can be further supplemented by adding the cell model to a secondary cell model that includes as output an increased state value that should converge to their corrected values. . Some embodiments take additional steps to ensure that one model state value converges to the SOC.

을 사용함으로써 근사화될 수 있다. The supplemented model output value is compared with the measured output value at the joint filter 100. In one preferred embodiment, the measured values for SOC are derived by the formula

Can be approximated by using

을 통하여) 및 셀 화학의 OCV 역함수를 아는 것을 통하여 이 예는 SOC의 노이즈 추정값,

을 계산할 수 있다.Measurement of cell voltage and cell current under load and R _k value (from joint filter 100

) And through knowing the OCV inverse of the cell chemistry,

Can be calculated.

와 함께 운용된다.In this example, the joint filter 100 measures the information measured in the measurement update, on this modified model.

It is operated together with.

의 노이즈(히스테레시스 효과에 의한 단기간 바이어스와 분극 전압은 고려되지 않음)는 SOC의 주요 추정자(primary estimator)로 사용되지 않는 반면, 동적 환경에서의 예상된 장기간 거동은 정확하고, 조인트 필터(100)터에서 SOC상태값의 정확성이 유지된다는 것을 실험을 통해 알 수 있다.

Noise (short-term bias and polarization voltage due to hysteresis effect is not taken into account) is not used as the primary estimator of the SOC, while the expected long-term behavior in a dynamic environment is accurate and the joint filter (100) Experiments show that the accuracy of SOC state values is maintained in the cluster.

As such, a method for simultaneous estimation of cell state and parameters has been described with many specific embodiments. One or more embodiments use Kalman filter 100. Some embodiments use extended Kalman filter 100. Moreover, some embodiments include a mechanism that enhances the convergence of the SOC. The invention can be applied to cell electrochemistry and a wide range of applications.

The disclosed method can be realized in the form of an apparatus for executing the process and a process executed on a computer. The method may also be realized in the form of computer program code containing instructions implemented by a medium 52 such as prop diskettes, CD-ROMs, hard disks or any other computer readable storage medium. The program code is loaded and executed by a computer, and the computer becomes an apparatus capable of performing the method. The method also relates to a transmission medium in the form of computer program code, for example, a carrier wave, an electric transmission line, a cable, an optical fiber, or an electromagnetic transmission, such as or stored in a storage medium, executed by or loaded into a computer. It can be realized in the form of. When computer program code is loaded and executed by a computer, the computer becomes an apparatus capable of performing the method. When executed by a general purpose microprocessor, computer program code segments configure the microprocessor to make particular logic circuits.

While the invention has been described with reference to one embodiment, it is to be understood that various modifications may be made by those skilled in the art and equivalent elements may be substituted with elements of the invention without departing from the scope of the invention. Moreover, many modifications may be made to adapt a particular situation or material to the teachings of the invention without departing from the essential scope thereof. Therefore, it is intended that the invention not be limited to the particular embodiment disclosed as the best mode contemplated for carrying out the invention, but that the invention will include all embodiments falling within the scope of the appended claims.

Claims (51)

In the method for estimating the current increased state of the 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; And An algorithm that repeats performing the internal increased state prediction, performing the uncertainty prediction, and correcting to calculate an ongoing estimate for the increased state and an ongoing uncertainty for the increased state estimate Estimating a current increased state of the electrochemical cell system, comprising applying a. The method of claim 1, wherein performing the internal increased state prediction comprises: Determining a current measurement; Determining a voltage measurement; And And performing the internal increased state prediction using the current measurement and the voltage measurement in a mathematical model. The method of claim 2, wherein performing the uncertainty prediction comprises: And performing the uncertainty prediction using the current measurement and the voltage measurement in a mathematical model. The method of claim 3, wherein the correcting step, Calculating a gain factor; Calculating a corrected internal increased state prediction using the gain factor, the voltage measurement, and the internal increased state prediction; And Computing a corrected uncertainty prediction value using the gain factor and the uncertainty prediction. The method of claim 4, wherein the applying step, Using 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. How to estimate the increased state. delete delete The method of claim 2, wherein performing the internal increased state prediction comprises: Determining a temperature; And Estimating a current increased state of the electrochemical cell system further comprising performing the internal increased state prediction using the temperature measurement, the current measurement, and the voltage measurement in a mathematical model. How to. The method of claim 8, wherein performing the uncertainty prediction comprises: And performing the uncertainty prediction using the temperature measurement, the current measurement, and the voltage measurement in a mathematical model. The method of claim 9, wherein the correcting step, Calculating a gain factor; Calculating a corrected internal increased state prediction value using the gain factor, the voltage measurement, and the internal increased state prediction; And Computing a corrected uncertainty prediction value using the gain factor and the uncertainty prediction. The method of claim 10, wherein the applying step, Using the corrected internal 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. How to estimate the state. delete The method of claim 5 or 11, wherein the increased state vector, An electrochemical cell system comprising at least one of state of charge, voltage polarization level, hysteresis level, resistance, capacitance, polarization voltage time constant, polarization voltage harmonic element, hysteresis harmonic element, hysteresis rate constant and efficiency constant. How to estimate the current increased state. The method of claim 2, wherein performing the uncertainty prediction comprises: Determining a temperature; And And performing the uncertainty prediction using the temperature measurement, the current measurement, and the voltage measurement in a mathematical model. The method of claim 1, wherein performing the uncertainty prediction comprises: Determining a current measurement; Determining a voltage measurement; And And performing the internal increased state prediction using the current measurement and the voltage measurement in a mathematical model. The method of claim 15, wherein performing the uncertainty prediction comprises: Determining a temperature; And And performing the uncertainty prediction using the temperature measurement, the current measurement, and the voltage measurement in a mathematical model. The method of claim 16, wherein the correcting step, Calculating a gain factor; Calculating a corrected internal state prediction value using the gain factor, the voltage measurement value and the internal state prediction; And Computing a corrected uncertainty prediction value using the gain factor and the uncertainty prediction. 18. The method of claim 17, wherein applying: Using the corrected internal 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. How to estimate the state. delete The method of claim 1, The method of estimating a current increased state of an electrochemical cell system, further comprising ensuring convergence of one or more increased states for each physical value. 21. The method of claim 20, wherein assuring Replenishing a cell model output with said increased state that requires convergence. The method of claim 21, wherein the assuring And supplementing a measurement vector with a corresponding estimated value of the increased state based on a presently measured value. The method of claim 22, And a filter is used to adapt an increased state estimate based on the supplemented cell model output and the supplemented measurement vector. The method of claim 23, wherein the filter, At least one of a Kalman filter and an extended Kalman filter. An apparatus for estimating a current increased state of a cell pack system, the apparatus comprising: A component configured to perform internal increased state prediction of the cell; A component configured to perform an uncertainty prediction of the internal increased state prediction; A component configured to correct the internal increased state prediction and the uncertainty prediction; And A component configured to perform the internal increased state prediction, a component configured to perform the uncertainty prediction, and a correction configured to calculate the ongoing estimate for the increased state and the ongoing uncertainty for the increased state estimate. And a component configured to apply an algorithm to repeat the steps taken by the component. The component of claim 25, wherein the component configured to perform the internal state prediction is: A component configured to determine a current measurement; A component configured to determine a voltage measurement; And a component configured to perform the internal increased state prediction using the current measurement and the voltage measurement in a mathematical model. 27. The component of claim 26, wherein the component configured to perform the uncertainty prediction comprises: And a component configured to perform the uncertainty prediction using the current measurement and the voltage measurement in a mathematical model. The component of claim 27, wherein the component configured to correct is: A component configured to calculate a gain factor, A component configured to calculate a corrected internal increased state prediction value using the gain factor, the voltage measurement, and the internal increased state prediction; And And a component configured to calculate a corrected uncertainty prediction value using the gain factor and the uncertainty prediction. The component of claim 28, wherein the component configured to apply is: A cell pack system comprising a component 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 Apparatus for estimating the current increased state of the. delete delete 27. The component of claim 26, wherein the component is configured to perform the internal increased state prediction. A component configured to determine a temperature; And Further including a component configured to perform the internal increased state prediction using the temperature measurement, the current measurement, and the voltage measurement in a mathematical model. Estimating device. 33. The component of claim 32, wherein the component configured to perform the uncertainty prediction comprises: And further comprising a component configured to perform the uncertainty prediction using the temperature measurement, the current measurement and the voltage measurement in a mathematical model. . 34. The component of claim 33, wherein the component configured to correct is: A component configured to calculate a gain factor; A component configured to calculate a corrected internal increased state prediction value using the gain factor, the voltage measurement, and the internal increased state prediction; And And a component configured to calculate a corrected uncertainty prediction using the gain factor and the uncertainty prediction. 35. The component of claim 34, wherein the component configured to apply is: A current increase of the cell pack system, characterized in that the algorithm comprises a component configured to use the corrected internal state prediction value and the corrected uncertainty prediction value to obtain a predicted value for a repeated next time step. Device for estimating a lost state. delete The method of claim 29 or 35, wherein the increased state vector, Current of a cell pack system comprising at least one of state of charge, voltage polarization level, hysteresis level, resistance, capacitance, polarization voltage time constant, polarization voltage harmonic element, hysteresis harmonic element, hysteresis rate constant and efficiency constant Device for estimating increased state. 27. The component of claim 26, wherein the component configured to perform the uncertainty prediction comprises: A component configured to determine a temperature; And And further comprising a component configured to perform the uncertainty prediction using the temperature measurement, the current measurement and the voltage measurement in a mathematical model. . The component of claim 25, wherein the component is configured to perform the uncertainty prediction. A component configured to determine a current measurement; A component configured to determine a voltage measurement; And And a component configured to perform the uncertainty prediction using the current measurement and the voltage measurement in a mathematical model. 40. The component of claim 39, wherein the component configured to perform the uncertainty prediction comprises: A component configured to determine a temperature; And And further comprising a component configured to perform the uncertainty prediction using the temperature measurement, the current measurement and the voltage measurement in a mathematical model. . 41. The component of claim 40, wherein the component configured to correct is: A component configured to calculate a gain factor; A component configured to calculate a corrected internal state prediction using the gain factor, the voltage measurement, and the internal state prediction; And And a component configured to calculate a corrected uncertainty prediction value using the gain factor and the uncertainty prediction. 42. The component of claim 41, wherein the component configured to apply is: A component configured to use the corrected internal increased state prediction value, the corrected internal 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. And estimating a current increased state of the cell pack system. delete The method of claim 25, Further comprising components configured to ensure convergence of one or more increased states for each physical value. 45. The component of claim 44, wherein the component is configured to ensure convergence. And a component configured to supplement a cell model output with the one or more increased states where convergence is desired. 46. The component of claim 45, wherein the component is configured to ensure convergence. And further comprising a component configured to supplement each measurement vector with a corresponding estimate of the one or more increased states based on a current measured value. The method of claim 46, And a component configured to implement a filter to adapt an increased state estimate based on the supplemented cell model output and the supplemented measurement vector. The method of claim 47, wherein the filter, 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; And Means for calculating an ongoing estimate for the increased state and an ongoing uncertainty for the increased state estimate by applying an algorithm that repeats the internal increased state prediction, the uncertainty prediction, and the correction. A system for estimating the current increased state of an electrochemical cell. 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; And Performing the internal increased state prediction, performing the uncertainty prediction, and correcting the steps to yield an ongoing estimate for the increased state and an ongoing uncertainty for the increased state estimate. A storage medium coded with machine-readable computer program code comprising instructions for causing a computer to perform a method for estimating a current increased state of an electrochemical cell comprising applying an algorithm. 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; And Performing the internal increased state prediction, performing the uncertainty prediction, and correcting the steps to calculate an ongoing estimate for the increased state and an ongoing uncertainty for the increased state estimate. A computer data signal realized as a computer-readable medium 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.

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