CN106054080B - A kind of combined estimation method of power battery charged state and health status - Google Patents

A kind of combined estimation method of power battery charged state and health status Download PDF

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CN106054080B
CN106054080B CN201610394956.3A CN201610394956A CN106054080B CN 106054080 B CN106054080 B CN 106054080B CN 201610394956 A CN201610394956 A CN 201610394956A CN 106054080 B CN106054080 B CN 106054080B
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battery
moment
state
matrix
soc
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CN106054080A (en
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冯代伟
黄大贵
陈岳航
葛森
吴献钢
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University of Electronic Science and Technology of China
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/392—Determining battery ageing or deterioration, e.g. state of health
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/385—Arrangements for measuring battery or accumulator variables
    • G01R31/387—Determining ampere-hour charge capacity or SoC

Abstract

The invention discloses the combined estimation method of a kind of novel power battery charged state (SOC) and health status (SOH), this method utilizes H by voltage, electric current and the temperature value at each moment in record battery use process in real time∞Filtering algorithm combination virtual battery model solves battery bias current noise, and uses H∞Filtering algorithm estimates the SOC of battery, using the SOH of EKF filter algorithm estimation battery, including battery actual capacity and internal resistance, and stores the running calculating data of algorithm.Entire algorithm calculates battery relevant parameter without making parametric assumption to battery use environment, using advanced filtering algorithm.Entire algorithm noise resisting ability is strong, and error is small, and algorithm flow is simple, there is preferable accuracy.

Description

A kind of combined estimation method of power battery charged state and health status
Technical field
The invention belongs to battery technology fields, and in particular to a kind of real-time estimation method of power battery interior state, Specifically related under the premise of guaranteeing that battery charge state real-time estimation is accurate, battery actual capacity and internal resistance parameter are realized Real-time estimation.
Background technique
Battery management system is the important component for ensureing the safe and stable traveling of electric car, and the charged shape of battery State (State of Charge, hereinafter referred to as SOC) estimation is the core of battery management system, and realizes battery charging and discharging pipe Reason, the basis of load balancing.However the nonlinearity of battery causes its SOC to be easy to be affected by various factors, so that quasi- Really estimation SOC becomes very difficult.
The health status (State of Health, hereinafter referred to as SOH) of battery is in the battery management system of electric car One of important performance indicator.SOH reflects the ageing process of battery in use, that is, the service life of battery.For The accurate estimation of SOH helps to ensure that battery performance, avoids influencing the normal operation of car body because of battery aging.
The research for SOH common at present generally refers to combine the Combined estimator algorithm of SOC estimation, considers cell tube The actual demand of reason system realizes the real-time estimation of SOH relevant parameter under the premise of guaranteeing SOC estimated accuracy.
Academia is directed to actual capacity and the internal resistance that the index that SOH is defined mainly includes battery, the two indexs are in battery Use process in, with the aging of battery big change can occur.With increasing for battery access times, battery is actual Releasable electricity constantly reduces, and then influences the operating time of battery.When the ratio between actual capacity and rated capacity are less than some threshold When value, scrapping for battery is also just indicated.Simultaneously because the chemical reaction of inside battery complexity, internal resistance constantly rise, cause Cell voltage is violent with the fluctuation of electric current, influences the stability of battery.
Individual SOC algorithm for estimating open circuit voltage method, current integration method and Extended Kalman filter method in traditional sense Deng.Some need estimated result after long-term stand to these algorithms, do not have real-time, some are easy accumulation in the presence of a harsh environment and make an uproar The error and sampling precision bring error of sound are difficult to obtain ideal when external bad environments, battery current variation are violent Effect.Importantly, these methods are difficult to estimate to combine with SOH, the observation of cell health state is caused centainly Difficulty.
There are two main classes for currently used SOH estimation mode:
1, the mode driven using off-line data, this method pass through the lot of experimental data of particular battery model, use The related algorithm of machine learning obtains the model equation of battery corresponding index from data, realizes health status prediction.This side Method is more demanding to the preparation of previous experiments data, computationally intensive, and obtained model is suitable only for corresponding battery class Type, without having scalability and versatility.This method can also generate biggish evaluated error when experimental data lacks.
2, the method tracked using on-line parameter.The variation of battery health index is considered as disorder parameter by this method, is made With specific control algolithm, tracking parameter variation.A kind of common parameter tracking algorithm is Dual Kalman filtering algorithm.This side The state space equation that method is decayed by establishing battery model and battery parameter, realizes the Combined estimator for being directed to SOC and SOH. This algorithm is more demanding to battery model, while having made excessively Utopian to be difficult to very it is assumed that resulting in it to external environment Benefit reason has the noise of biasing, equally easily leads to the accumulation of noise under specific circumstances, weakens the anti-of SOC and SOH estimation Interference performance.
Summary of the invention
The object of the present invention is to provide the joint estimate method of a kind of state-of-charge of novel power battery and health status, The present invention is broadly divided into two parts, is based on H∞The battery bias electric current of robust control filtering algorithm inhibits, and uses H∞Robust The unified algorithm of control filtering and Kalman filter carries out battery SOC estimation synchronous with SOH's.Above-mentioned purpose can be by following technologies Scheme is realized.
The technical scheme is that the combined estimation method of a kind of power battery charged state and health status, this method The following steps are included:
Step 1: virtual battery model is established, if k moment internal system state isPacket Include the partial pressure of first RC parallel connection link of battery state-of-charge SOC and k moment at k momentAnd the k moment The partial pressure of two RC parallel connection linksThen the internal system state at k+1 moment can be obtained by equationWherein ukThe input current I of expression system, simultaneously Theoretical output voltage y of the system at the k momentkMeetWherein Δ t is sampling Period, R1, R2Indicate resistance parameter in model, C1, C2Indicate capacitance parameter, resistance parameter and capacitance parameter all bases in model The setting of thing situation, OCV (SOC) indicate the non-linear relation between battery open circuit voltage and battery charge state, and η indicates charging Efficiency, OCV indicate battery open circuit voltage, R0Indicate battery；Battery capacity Cap and ohmic internal resistance R is set0For fixed value, battery Capacity C ap value is initial battery capacity Cap070~80%；
Step 2: establishing actual battery model, the composition of actual battery model is consistent with virtual battery model, and setting is practical The capacity of battery model is changed over time with ohmic internal resistance, and γ is arrangedk=(Capk R0,k) be the k moment relevant parameter, CapkTable Show system in the actual capacity at k moment, R0,kOhmic internal resistance of the expression system at the k moment, initial parameter γ0Pass through battery charge and discharge Electricity experiment obtains；
Step 3: obtaining battery both end voltage V, battery current I, battery temperature T；
Step 4: battery current I being inputted into virtual battery model, the first state of charge SOC is calculated by Coulomb integral1； I, V, T are input to the corresponding H of virtual battery model∞Filtering algorithm calculates the second state of charge SOC2, state-of-charge twice Value asks poor, calculates and obtains bias current noise, removes offset noise from battery current I, obtain filtered current value I '；
Step 5: by I ', V, T, inputting actual battery model, utilize H∞Filtering obtains battery third state of charge SOC3, And it records；
Step 6: by I ', V, T, inputting actual battery model, the health status of battery is obtained using Kalman filter, this is strong The capacity and ohmic internal resistance parameter γ that health state uses battery to change over timek=(Capk R0,k), use γkUpdate practical electricity Pool model, the benchmark model estimated as next sampling period state of charge, the health status of battery；
Further, the specific steps of the step 4:
Step 4.1: the first state of charge SOC is calculated by Coulomb integral1,k=SOC1,k-1+ I Δ t, wherein SOC1,kI.e. For first state of charge at k moment, from the SOC at k-1 moment1,k-1Recursion, the initial SOC at 0 moment1It is set by user It obtains, Δ t is the sampling period；
Step 4.2: calculating the second state of charge SOC2, need to safeguard any k moment inside battery shape in detailed process State variableIts internal state is as described in step 1, including battery SOC and two RC parallel connections The partial pressure of link, the initial value of internal state variableIt is arranged depending on battery virtual condition, and algorithmic procedure noise weight Q is set, Output noise weight V, algorithm evaluated error upper bound θ and Initial state estimation Error weight P0, this weight matrix will calculate Recursion updates in method operational process, is expressed as P at the k momentk, while according to virtual battery model, establish transfer matrixInput matrixIf any k moment inputs H∞The electricity of filtering Flow valuve I is uk, voltage value V is yk, η is charge efficiency, at the k moment executes following steps:
Step 4.2.1: the SOC estimation at current time is exportedWherein (1 0 0) M=,As step The inside battery state variable that needs are safeguarded is clearly indicated that in 4.2；
Step 4.2.2: output matrix is obtained to OCV-SOC relationship derivation Wherein the value of battery open circuit voltage and battery charge state SOC are in non-linear fixed relationship, this relationship uses functional form OCV (SOC) is indicated；
Step 4.2.3: setting matrixJ is unit matrix, and Q is algorithmic procedure noise Weight, PkThe Initial state estimation Error weight P updated for recursion continuous in algorithm operational process0, P is expressed as at the k momentk,Indicate output matrixTransposition, V indicate output noise weight；
Step 4.2.4: control gain is solvedPkFor recursion continuous in algorithm operational process update Initial state estimation Error weight P0, P is expressed as at the k momentk, LkIndicate calculated matrix in step 4.2.3 Indicate output matrixTransposition, V table Show output noise weight；
Step 4.2.5: the system mode at prediction k+1 moment, whereinEquation meets
All symbol meanings are equal It is explained in above step,Indicate k+1 moment internal system stateA indicates transfer MatrixB indicates input matrix Indicate control gainPkThe Initial state estimation Error weight P updated for recursion continuous in algorithm operational process0, in k Quarter is expressed as Pk, LkIndicate calculated matrix in step 4.2.3 Indicate output square Battle arrayTransposition, V indicate output noise weight；
Step 4.2.6: obtained in output step 4.2.1Wherein (1 0 0) M=,As step The inside battery state variable that needs are safeguarded is clearly indicated that in 4.2；
Step 4.3: obtaining filtered current valueWherein Cap represents virtual battery The electrical parameter of model, k are current sampling instant.
Further, in described 5 specific steps, need to safeguard any k moment actual battery internal state variableIts internal state is as described in step 1, including battery SOC and two RC parallel connection links Partial pressure, the initial value of internal state variableIt is arranged depending on battery virtual condition, and algorithmic procedure noise weight Q is set, exports Noise weight V, algorithm evaluated error upper bound θ and Initial state estimation Error weight P0, this weight matrix will transport in algorithm Recursion updates during row, is expressed as P at the k momentk, while according to virtual battery model, establish transfer matrixInput matrixThe wherein Cap in matrix BkAs battery Health status γkIn real-time parameter, if any k moment inputs H∞The current value I ' of filtering is uk, voltage value V is yk, η is in step It has clearly illustrated to execute following steps for charge efficiency, at the k moment in rapid 1: then having executed following steps:
Step 5.1: exporting the SOC estimation at current timeWherein (1 0 0) M=,It as needs to tie up The inside battery state variable of shield；
Step 5.2: output matrix is obtained to OCV-SOC relationship derivationIts The value and battery charge state SOC of middle battery open circuit voltage are in non-linear fixed relationship, this relationship uses functional form OCV (SOC) it indicates；
Step 5.3: setting matrixJ indicates unit matrix, and as described above, Q is Algorithmic procedure noise weight, PkThe Initial state estimation Error weight P updated for recursion continuous in algorithm operational process0, in k Moment is expressed as Pk,Indicate output matrixTransposition, V indicate output noise Weight；
Step 5.4: solving control gainPkIt is updated for recursion continuous in algorithm operational process Initial state estimation Error weight P0, P is expressed as at the k momentk, LkIndicate calculated matrix in step 5.3 Indicate output matrixTransposition, V Indicate output noise weight；
Step 5.5: the system mode at prediction k+1 moment, whereinEquation meetsPay attention to R here0,kUsing battery Health status γkIn real-time parameter, all symbol meanings are explained in above step,Indicate etching system when k+1 Internal stateA indicates transfer matrixB indicates input Matrix Indicate control gainPkFor in algorithm operational process not The Initial state estimation Error weight P that disconnected recursion updates0, P is expressed as at the k momentk, LkIndicate calculated matrix in step 5.3 Indicate output matrixTransposition, V table Show output noise weight；
Step 5.6: obtained in output step 5.1Wherein (1 0 0) M=,It is as bright in step 5 Really demonstrate the need for the actual battery internal state variable of maintenance；
Further, the step 6 method particularly includes:
Step 6.1: executing the forecast period of EKF filter, need to safeguard electricity at any k moment in control process The prior estimate of pond internal state parameterWith Posterior estimatorSettingFor the parameter of system initial state, electricity is preset Pond parameter decaying noise covariance Qγ, initial battery parameter evaluated error covarianceIf appointing The current value I ' that the meaning k moment inputs Kalman filter is uk, voltage value V is yk, then following steps are executed at the k moment:
Step 6.1.1: willIt is assigned to
Step 6.1.2: covariance matrix is updated
Step 6.2: calculating output voltage equation to battery parameterTotal differentialSpecific step Rapid includes that initial companion matrix Φ is arranged0=0, Θ0=0, the two matrixes continuous recursion in algorithm operational process updates, in k Moment is expressed as ΦkAnd Θk, following steps are executed at the k moment:
Step 6.2.1: setting matrixThrough the k-1 moment in step 5.4 The control gain arrivedExecute operationWherein Specific R1, C1, R2, C2Value in actual battery model determine, ΘkWhat continuous recursion updated as in algorithm operational process is auxiliary Help matrix；
Step 6.2.2: setting observing matrixWherein battery open circuit voltage Value and battery charge state SOC be in non-linear fixed relationship, this relationship using functional form OCV (SOC) expression, simultaneously SettingExecute operation Φk=Gk+CkΘk, wherein SOC3By step It is obtained in rapid 5；
Step 6.2.3: settingIt substitutes intoIt finds outOperation obtainsWhereinIt indicatesIn first element, i.e. the prior estimate of battery actual capacity；
Step 6.3: executing the more new stage of EKF filter, first setting voltage evaluated error covariance matrix Rγ, following steps are executed at any k moment:
Step 6.3.1: gain matrix is solvedWhereinIt is prior estimate The covariance matrix of error, RγIt is the voltage evaluated error covariance matrix pre-set；
Step 6.3.2: Posterior estimator is solvedWhereinFor what is obtained in step 5 Present battery internal state variable,According to present input current uk, stateAnd cell health stateMeter Cell output voltage is calculated, is metWhereinIt is the internal system state estimation that step 5 obtains, ohmic internal resistance R0,kAsBe with The prior estimate of the cell health state component of time changeSecond element in vector.
Detailed description of the invention
Fig. 1 is the equivalent second-order model of battery, and wherein capacitor C is used to indicate the open-circuit voltage of battery.
Fig. 2 is the basic flow chart of algorithm.
Fig. 3 is the basic control block diagram of algorithm
Specific embodiment
For the technology contents that the present invention will be described in detail, algorithm characteristic realizes purpose and effect, below in conjunction with specific reality Mode is applied system operational process is described in detail.
When system starts, associated batteries model parameter is set according to battery size and corresponding virtual battery model is joined Number, it is assumed that present battery parameter is as shown in table 1；
Then consider that actual battery parameter aging performance, setting virtual battery capacity are the 90% of initial capacity, virtual battery Internal resistance is 2 times of initial internal resistance, there is 2 virtual battery model relevant physical parameter of table；
By multicycle constant current pulse charge, discharge test in laboratory environment, the corresponding OCV of different SOC is obtained Value, these values have been pre-set among system, and for arbitrary OCV, corresponding SOC can be obtained by interpolation method, Its precision is related to number experimental period, for every class battery, it is assumed that and pass through experiment and obtains its OCV and SOC relationship, for Each OCV finds connected a pair of of OCVk≤ OCV≤OCVk+1, interpolation can be obtained corresponding SOC value.Consideration demand, this In provide the first two point of OCV-SOC table: SOC:0.00,0.15；OCV (V): 2.62,2.73.
The above are initialization procedures normal when system starting, for unknown battery size or user for certain Reason needs to modify parameter, then can be with manual modification information above, including battery relevant physical parameter and the corresponding pass OCV-SOC System, this relationship at least need to provide OCV value when battery is full of and is vented, the i.e. correspondence of SOC=0 and SOC=100% Open-circuit voltage values
System then begins to setting and algorithm solves relevant noise parameter, including H∞Filtering algorithm carries out SOC estimation, with And Kalman filter algorithm carries out the relevant parameter of SOH estimation.According to the noise complexity of system, electric current, voltage noise are set Weight function, system itself is according to pre-set current sensor and voltage sensor error, for all kinds of batteries, if Corresponding algorithm start-up parameter is determined.Simultaneously in view of the precision of peripheral hardware sensor may be adjusted with replacement, system Provided with different noise gears, user can as required, adjustment current sensor and the permitted maximum of voltage sensor Coverage error.Under normal circumstances, H∞The effect of robust control filtering is fine, even inconsistent in actual noise and setting noise When, it can still obtain very good effect.Here for hypothesis purpose, the parameter of setting such as table 3.
It is provided with after battery model state space parameter and algorithm noise parameter, system will start setting up current electricity Pond internal state estimated value, if SOC value when last time shuts down is not lost, the SOC value that system shuts down last time is as current SOC estimation, user can voluntarily specify estimated value, otherwise by reading the current SOC of current time open-circuit voltage interpolation acquisition Estimation.It is now assumed that system starts for the first time, therefore the initial SOC that system is directly arranged is 0, two links of simultaneity factor Partial pressure is similarly 0, i.e.,
Parameters all at this time is respectively provided with completion, and system formally starts.System will be according to the algorithm provided before totally 7 later It is step by step rapid to execute, current voltage and current value are measured in each sampled point, obtain each moment by a series of matrix operations SOC and SOH estimated value.The calculating process for executing one cycle step is as follows:
It is provided first using H∞Filtering algorithm solve battery SOC basic calculation, by taking actual battery parameter as an example into Row calculates:
Assuming that the current value currently acquired is 1A, voltage value 2.8V:
1, the SOC estimation at current time is exported
2, observing matrix is constructedCurrently estimateFrom OCV-SOC table is tabled look-up acquisitionThe OCV of this section variation is considered as broken line at this time, derivation can be with It obtains
3, system communication cycle is set as 1s, according to battery status space equation, is substituted into data and is found out respective battery parameter Transfer matrix
4, matrix is setIt substitutes into solve and obtain
5, control gain is solved
6, the system mode at next moment is predicted:
Wherein
Substitute into R0It solves
7, control matrix is updated
8, algorithm counts device increases k=k+1；
Therefore for algorithm after primary update, the SOC value of estimation is about 0.03%.
The calculating process in system operation is given below:
Initial parameter data are set first,
1, using virtual battery parameter model, in conjunction with above-mentioned H∞Filtering algorithm, finding out primary updated estimation SOC value is 0.0320%.
2, using Coulomb integral method, finding out primary updated estimation SOC value is I × Δ T/Cap × 100%= 0.0139%
3, finding out bias current size is
4, it therefore actually enters SOC and SOH and estimates that the electric current in model is
5, H is reused∞It is 0.0503% that filtering algorithm, which finds out actual SOC value,.
6, SOH data are estimated using Kalman filter, firstly, setting prior estimate
7, corresponding matrix is established,
Pay attention to and the H of virtual battery model∞Filtering algorithm is different, and B matrix is the difference with γ and changes, γk,1I.e. For capacity C ap.
8, it calculates
9, it calculates
10, Φ is calculatedk=Gk+CkΘk=(0 00 2.173)
11, matrix is substituted into
12, it finds out
13, covariance matrix is updated
13、
14, Posterior estimator is finally solvedTherefore in this period system internal resistance It is estimated to be small size rise.
Whole system is by estimating the SOC estimation at each moment, Yi Ji electricity constantly calculating above-mentioned matrix operation process Pond inner parameter, thus real
Show real-time SOC and SOH to estimate.
After system finishing operation, system saves the SOC value finally estimated in memory, and next subsystem is waited to transport It is called when row, improves the initial estimation precision of SOC estimation next time.
Joint estimate algorithm of the invention is in estimation process, and entire algorithm anti-noise jamming ability is strong, and calculating process It is easy to describe, parameter setting is simple, and user is suitble to use.
The present invention elaborates specific implementation method by taking the above battery parameter as an example, the battery that the present invention is not limited to be illustrated Type and design parameter.
1 battery relevant physical parameter of table
2 virtual battery model relevant physical parameter of table
3 algorithm correlated noise parameter of table

Claims (3)

1. the combined estimation method of a kind of power battery charged state and health status, method includes the following steps:
Step 1: virtual battery model is established, if k moment internal system state isIncluding k The partial pressure of first RC parallel connection link of battery charge state SOC and k moment at momentAnd second RC of k moment is simultaneously Join the partial pressure of linkThen the internal system state at k+1 moment can be obtained by equationWherein ukThe input current I of expression system, together When system the k moment theoretical output voltage ykMeetWherein Δ t is to adopt Sample period, R1, R2Indicate resistance parameter in model, C1, C2Indicate capacitance parameter, resistance parameter and capacitance parameter all roots in model It is set according to thing situation, OCV (SOC) indicates the non-linear relation between battery open circuit voltage and battery charge state, and η expression is filled Electrical efficiency, OCV indicate battery open circuit voltage, R0Indicate the internal resistance of battery；The internal resistance R of battery capacity Cap and battery is set0It is solid Definite value, battery capacity Cap value are initial battery capacity Cap070~80%；
Step 2: establishing actual battery model, the composition of actual battery model is consistent with virtual battery model, and actual battery is arranged The capacity of model is changed over time with ohmic internal resistance, and γ is arrangedk=(Capk R0,k) be the k moment relevant parameter, CapkIndicate system The actual capacity united at the k moment, R0,kInternal resistance of the expression system in the battery at k moment, initial parameter γ0Pass through battery charging and discharging Experiment obtains；
Step 3: obtaining battery both end voltage V, battery current I, battery temperature T；
Step 4: battery current I being inputted into virtual battery model, the first state of charge SOC is calculated by Coulomb integral1；By I, V, T is input to the corresponding H of virtual battery model∞Filtering algorithm calculates the second state of charge SOC2, state of charge is asked twice Difference calculates and obtains bias current noise, removes offset noise from battery current I, obtain filtered current value I '；
Step 5: by I ', V, T, inputting actual battery model, utilize H∞Filtering obtains battery third state of charge SOC3, and remember Record；
Step 6: by I ', V, T, inputting actual battery model, the health status of battery, the health shape are obtained using Kalman filter The capacity and ohmic internal resistance parameter γ that state uses battery to change over timek=(Capk R0,k), use γkUpdate actual battery mould Type, the benchmark model estimated as next sampling period state of charge, the health status of battery；
It is characterized in that the step 6 method particularly includes:
Step 6.1: executing the forecast period of EKF filter, need to safeguard in battery at any k moment in control process The prior estimate of portion's state parameterWith Posterior estimatorSettingFor the parameter of system initial state, battery ginseng is preset Number decaying noise covariance Qγ, initial battery parameter evaluated error covarianceIf when any k The current value I ' for carving input Kalman filter is uk, voltage value V is yk, then following steps are executed at the k moment:
Step 6.1.1: willIt is assigned to
Step 6.1.2: covariance matrix is updated
Step 6.2: calculating output voltage equation to battery parameterTotal differentialSpecific steps packet Include the initial companion matrix Φ of setting0=0, Θ0=0, the two matrixes continuous recursion in algorithm operational process updates, at the k moment It is expressed as ΦkAnd Θk, following steps are executed at the k moment:
Step 6.2.1: setting matrixThrough the k-1 moment obtained in the step 5.4 Control gainExecute operationWhereinSpecifically R1, C1, R2, C2Value in actual battery model determine, ΘkThe auxiliary moment that continuous recursion updates as in algorithm operational process Battle array；
Step 6.2.2: setting observing matrixThe wherein value of battery open circuit voltage It is in non-linear fixed relationship with battery charge state SOC, this relationship is indicated using functional form OCV (SOC), is arranged simultaneouslyExecute operation Φk=Gk+CkΘk, wherein SOC3By step 5 Middle acquisition；
Step 6.2.3: settingIt substitutes intoIt finds outOperation obtains WhereinIt indicatesIn first element, i.e. the prior estimate of battery actual capacity；
Step 6.3: executing the more new stage of EKF filter, first setting voltage evaluated error covariance matrix Rγ, Any k moment executes following steps:
Step 6.3.1: gain matrix is solvedWhereinIt is prior estimate error Covariance matrix, RγIt is the voltage evaluated error covariance matrix pre-set；
Step 6.3.2: Posterior estimator is solvedWhereinIt is current for what is obtained in step 5 Inside battery state variable,According to present input current uk, stateAnd cell health stateCalculate electricity Pond output voltage meetsWhereinIt is the internal system state estimation that step 5 obtains, ohmic internal resistance R0,kAsBe with The prior estimate of the cell health state component of time changeSecond element in vector.
2. a kind of combined estimation method of power battery charged state and health status as described in claim 1, feature exist In the specific steps of the step 4:
Step 4.1: the first state of charge SOC is calculated by Coulomb integral1,k=SOC1,k-1+ I Δ t, wherein SOC1,kWhen as k The first state of charge carved, from the SOC at k-1 moment1,k-1Recursion, the initial SOC at 0 moment1It sets to obtain by user, Δ t is the sampling period；
Step 4.2: calculating the second state of charge SOC2, need to safeguard any k moment inside battery state variable in detailed processIts internal state is as described in step 1, including battery charge state SOC and two RC are simultaneously Join the partial pressure of link, the initial value of internal state variableIt is arranged depending on battery virtual condition, and algorithmic procedure noise weight is set Q, output noise weight W, algorithm evaluated error upper bound θ and Initial state estimation Error weight P0, this weight matrix will be Recursion updates in algorithm operational process, is expressed as P at the k momentk, while according to virtual battery model, establish transfer matrixInput matrixIf any k moment inputs H∞The electricity of filtering Flow valuve I is uk, voltage value V is yk, η is charge efficiency, at the k moment executes following steps:
Step 4.2.1: the SOC estimation at current time is exportedWherein (1 0 0) M=,As step 4.2 In clearly indicate that the inside battery state variable that needs are safeguarded；
Step 4.2.2: output matrix is obtained to OCV-SOC relationship derivationWherein The value and battery charge state SOC of battery open circuit voltage are in non-linear fixed relationship, this relationship uses functional form OCV (SOC) it indicates；
Step 4.2.3: setting matrixJ is unit matrix, PkFor in algorithm operational process The Initial state estimation Error weight P that continuous recursion updates0, P is expressed as at the k momentk,Indicate output matrixTransposition；
Step 4.2.4: control gain is solvedPkIt is updated for recursion continuous in algorithm operational process first Beginning state estimation Error weight P0, P is expressed as at the k momentk, LkIndicate calculated matrix in step 4.2.3 Indicate output matrixTransposition, W Indicate output noise weight；
Step 4.2.5: the system mode at prediction k+1 moment, whereinEquation meets
All symbol meanings are above It is explained in step,Indicate k+1 moment internal system stateA indicates transfer matrixB indicates input matrix Indicate control gainPkThe Initial state estimation Error weight P updated for recursion continuous in algorithm operational process0, in k Quarter is expressed as Pk, LkIndicate calculated matrix in step 4.2.3 Indicate output square Battle arrayTransposition；
Step 4.2.6: obtained in output step 4.2.1Wherein (1 0 0) M=,As in step 4.2 Clearly indicate that the inside battery state variable that needs are safeguarded；
Step 4.3: obtaining filtered current valueWherein Cap represents virtual battery model Electrical parameter, k are current sampling instant.
3. a kind of combined estimation method of power battery charged state and health status as described in claim 1, feature exist In described 5 specific steps, need to safeguard any k moment actual battery internal state variableIts internal state is as described in step 1, including battery SOC and two RC parallel connection links Partial pressure, the initial value of internal state variableIt is arranged depending on battery virtual condition, and algorithmic procedure noise weight Q is set, output is made an uproar Sound weight W, algorithm evaluated error upper bound θ and Initial state estimation Error weight P0, this weight matrix will run in algorithm Recursion updates in the process, is expressed as P at the k momentk, while according to virtual battery model, establish transfer matrixInput matrixThe wherein Cap in matrix BkAs battery Health status γkIn real-time parameter, if any k moment inputs H∞The current value I ' of filtering is uk, voltage value V is yk, η is in step It has clearly illustrated to execute following steps for charge efficiency, at the k moment in rapid 1: then having executed following steps:
Step 5.1: exporting the SOC estimation at current timeWherein (1 0 0) M=,As need to safeguard Inside battery state variable；
Step 5.2: output matrix is obtained to OCV-SOC relationship derivationIt is wherein electric The value and battery charge state SOC of pond open-circuit voltage are in non-linear fixed relationship, this relationship uses functional form OCV (SOC) it indicates；
Step 5.3: setting matrixJ indicates unit matrix, and as described above, Q is algorithm Process noise weight, PkThe Initial state estimation Error weight P updated for recursion continuous in algorithm operational process0, at the k moment It is expressed as Pk,Indicate output matrixTransposition, W indicate output noise weight；
Step 5.4: solving control gainPkIt is updated for recursion continuous in algorithm operational process initial State estimation Error weight P0, P is expressed as at the k momentk, LkIndicate calculated matrix in step 5.3 Indicate output matrixTransposition, W Indicate output noise weight；
Step 5.5: the system mode at prediction k+1 moment, whereinEquation meetsPay attention to R here0,kUsing battery Health status γkIn real-time parameter, all symbol meanings are explained in above step,Indicate etching system when k+1 Internal stateA indicates transfer matrixB indicates input Matrix Indicate control gainPkFor in algorithm operational process not The Initial state estimation Error weight P that disconnected recursion updates0, P is expressed as at the k momentk, LkIndicate calculated matrix in step 5.3 Indicate output matrixTransposition, W Indicate output noise weight；
Step 5.6: obtained in output step 5.1Wherein (1 0 0) M=,As clearly indicated that in step 5 The actual battery internal state variable for needing to safeguard.
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