CN102569922A  Improved storage battery SOC estimation method based on consistency of unit cell  Google Patents
Improved storage battery SOC estimation method based on consistency of unit cell Download PDFInfo
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 CN102569922A CN102569922A CN2012100557016A CN201210055701A CN102569922A CN 102569922 A CN102569922 A CN 102569922A CN 2012100557016 A CN2012100557016 A CN 2012100557016A CN 201210055701 A CN201210055701 A CN 201210055701A CN 102569922 A CN102569922 A CN 102569922A
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 soc
 rule base
 cell
 fuzzy rule
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 238000010606 normalization Methods 0.000 claims description 5
 238000007796 conventional method Methods 0.000 claims description 4
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Classifications

 Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSSSECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSSREFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
 Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
 Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
 Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
 Y02E60/10—Energy storage using batteries
Abstract
The invention relates to an improved storage battery SOC (State Of Charge) estimation method based on the consistency of a unit cell. In the method, the training data set of a storage battery, SOC predicted data corrected on the basis of consistency, and a selfadoption neural network are use for building a fuzzy rule base with an optimized structure and parameters; and then the fuzzy rule base after offline learning is implanted into the fuzzy inference machine of an embedded controller of BMS (Battery Management System), so as to carry out online correction to storage battery SOC estimation. Compared with the prior art, the invention has the advantage that SOC differences in all unit cells inside the battery can be factually reflected on entire SOC estimation.
Description
Technical field
The present invention relates to a kind of batteries SOC and estimate to improve one's methods, especially relate to and a kind ofly estimate to improve one's methods based on the conforming batteries SOC of cell.
Background technology
(Battery Management System is BMS) to the inside battery state, particularly to stateofcharge (State ofcharge for traditional battery management system; When SOC) estimating; Often regard whole battery group as an integral body and estimate, in fact, owing to exist inconsistent between inner each monomer of battery pack; Therefore, replace each monomer to manage with integrality and have its irrationality.Such as, when the stateofcharge of inner certain batteries of battery pack is 20%, the stateofcharge of another joint is 80%, and obviously, one of this two batteries is put near crossing, and one near overcharging, and this moment, management system possibly be 50% to the state estimation value of battery pack.Because when car load uses whole battery group is treated as a battery, therefore, when hard intensity was discharged, 20% battery took place to put easily, and when hard intensity was charged, 80% battery overcharged easily.In the battery of more common in the past portable set was used in groups, because number of batteries in groups is fewer, applying working condition changed inviolent, so the influence that difference caused between the battery is not outstanding.But in the application of vehicle mounted dynamic battery, the quantity of onetenth Battery pack saves several ten thousand joints from tens and does not wait, and applying working condition changes very acutely in addition, is easy to occur aforesaid improper situation.This situation can make shorten the useful life of whole Battery pack, and possibly cause cell to lose efficacy in advance, even causes potential safety hazard, causes system's operation and maintenance cost to increase.This problem has become one of bottleneck problem of development of restriction electric vehicle industrialization and application.
And be limited by the volume of automobile controller and the restriction on the cost, each joint cell is all carried out parameter identification and unrealistic.Therefore, be necessary under present BMS system architecture, to provide a kind of SOC to estimate modification method based on battery pack internal consistency state.
Summary of the invention
The object of the invention is exactly for the defective that overcomes abovementioned prior art existence a kind of method that can the SOC difference of each cell in the battery pack be reflected in truly in the whole group SOC estimation to be provided; This method can be judged the degree of consistency of each cell on SOC in the battery pack, and carries out batteries SOC based on the consistency of cell and estimate to revise.
The object of the invention can be realized through following technical scheme: a kind ofly estimate to improve one's methods based on the conforming batteries SOC of cell; This method comprises based on the offline learning modeling process of the fuzzy rule base of adaptive neural network with based on the online SOC of fuzzy rule base estimates makeover process, and concrete steps are following:
At first utilize the training dataset of batteries model, make up to have based on the SOC prediction data of consistency correction and adaptive neural network and optimize structure and the fuzzy rule base of parameter;
Then the fuzzy rule base after the offline learning is implanted in the indistinct logic computer of embedded controller of BMS, batteries SOC is estimated to carry out online correction.
Described offline learning modeling process specifically may further comprise the steps:
1) through cell test set up can reflect cell in use dynamic characteristic the cell model and obtain the parameter and the distributions rule of cell;
2) according to cell modelling battery pack model;
3) adopt the input of exciting current as the battery pack model, emulation obtains the input data set of fuzzy rule base, and the predeterminated target output parameter is set;
4) input data set is input in the fuzzy rule base;
5) calculate the poor of fuzzy rule base output valve and predeterminated target output parameter; And with its study foundation as neural network BP training algorithm; Revise the weighted value of fuzzy rule base regular node, return step 4) up to the difference of the output of fuzzy rule base and predeterminated target output parameter less than preset threshold value;
6) offline learning finishes.
Described fuzzy rule base comprises obfuscation layer, application of rules degree computation layer, application of rules degree normalization layer, defuzzification layer and output layer.
But described input data set is the characteristic quantity that the data of BMS realtime online collection are formed, but the data of described realtime online collection comprise the delta data of monomer battery voltage, battery voltage, battery pack current and cell SOC.
Described predeterminated target output parameter is whole group SOC estimated value poor with based on the revised SOC predicted value of consistency.
Described neural network BP training algorithm comprises the Hybrid Search algorithm that BP training algorithm and BP and least square method combine.
Described online SOC estimates that makeover process may further comprise the steps:
The fuzzy rule base that 1) will pass through the offline learning modeling process is implanted in the indistinct logic computer of embedded controller of BMS;
2) embedded controller is gathered the parameter of actual battery group in real time, and extracts the input of its characteristic quantity as fuzzy rule base, and fuzzy rule base calculates the correction that battery pack SOC estimates;
3) adopt conventional method to estimate the SOC estimated value of current actual battery group;
The SOC that 4) correction and the addition of SOC estimated value can be obtained revised actual battery group revises predicted value.
Described conventional method is Kalman filter method, current integration method, fuzzy reasoning method or neural net method.
Compared with prior art; The present invention can implement under the hardware system structure of present BMS; And can revise the state estimation of whole group SOC according to the virtual condition of consistency of battery pack; Judge the degree of consistency of each cell on SOC in the battery pack, thereby for the vehicle energy management strategy, judge that the degree of consistency and the variation tendency of battery pack provide more authentic and valid reference information.
Description of drawings
Fig. 1 can reflect the cell Mathematical Modeling of battery dynamic characteristic for the present invention and by its battery pack model of forming;
Fig. 2 is the constructed fuzzy rule base structural representation of the present invention;
Fig. 3 is fuzzy rule base modeling procedure and the training optimizing process flow chart that is used for the correction of SOC consistency;
Fig. 4 is the whole group of traditional battery pack SOC online prediction flow chart that the SOC estimation combines with the correction of consistency fuzzy reasoning.
Embodiment
Below in conjunction with accompanying drawing and specific embodiment the present invention is elaborated.
Embodiment
A kind ofly estimate to improve one's methods based on the conforming batteries SOC of cell, this method comprises based on the offline learning modeling process of the fuzzy rule base of adaptive neural network with based on the online SOC of fuzzy rule base estimates makeover process, and concrete steps are following:
At first utilize the training dataset of batteries model, make up to have based on the SOC prediction data of consistency correction and adaptive neural network and optimize structure and the fuzzy rule base of parameter;
Then the fuzzy rule base after the offline learning is implanted in the indistinct logic computer of embedded controller of BMS, batteries SOC is estimated to carry out online correction.
As shown in Figure 1, be the batteries model of the band distributed constant characteristic that is used to obtain training dataset.The described battery pack of model is for to be composed in series by the cell model.Uocv is the open circuit voltage of battery in the cell model; Resistance R
_{0}Be used for describing the battery ohmic internal resistance, R
_{1}, C
_{1}And R
_{2}, C
_{2}Be used for describing the polarity effect of battery.The physical and chemical process of lithium ion battery work is quite complicated; Describe clear also quite difficult; But; Can generally be divided into the motion of lithium ion in interelectrode transmission course and two parts of diffusion process on electrode the impedance that obstruction that receives when promptly in circuit model, describing the lithium ion activity with two RC links respectively or battery table reveal.With the less R of time constant
_{1}C
_{1}Link is described the impedance that receives when lithium ion transmits between electrode, with the bigger R of time constant
_{2}C
_{2}The impedance that link receives when describing the diffusion of lithium ion in electrode material; Because the latter's process is more complicated; Except the diffusion of lithium ion in the solid phase electrode material; The crystal structure of electrode material also can change owing to the accumulation gradually of lithium ion quantity, so the suffered hindering factor of the diffusion of lithium ion in electrode is more, also than slow many of its diffusion in electrolyte.The SOCOCV that considers battery only can bring the nonlinearity erron that can not put up with an electric capacity replacement, and therefore, OCV replaces with a nonlinear function among Fig. 1, and it is the function of SOC parameter.The nonlinear equivalent circuit model of this SOC of comprising is made up of linear segment and nonlinear partial, and has passed through the checking that operating mode discharges and recharges and electrochemical impedance spectroscopy is tested, and shows that it can reflect battery dynamic characteristic in the course of the work preferably.
As shown in Figure 2, be the universal architecture of constructed fuzzy rule base, include five layers: obfuscation layer, application of rules degree computation layer, application of rules degree normalization layer, defuzzification layer and output layer.The function of each layer is distinguished as follows: ground floor: the fuzzy membership function that calculates input
O
_{1，i}＝g
_{xi}(x，a
_{i}，b
_{i})；O
_{1，j}＝g
_{y(i2)}(y，c
_{j2}，d
_{j2})
X in the following formula, y are the node input, and g () is the degree of membership member function, can be used to calculate the degree of membership of current each input.A wherein
_{i}, b
_{i}, c
_{i}, d
_{i}Be called the former piece parameter, can be used to regulate the shape of degree of membership member function.
The second layer: computation rule relevance grade
O
_{2,1}=O
_{1,1}* O
_{1,3}=g
_{X1}(x, a
_{1}, b
_{1}) * g
_{Y1}(y, c
_{1}, d
_{1}) remember and make w
_{1}
O
_{2,2}=O
_{1,2}* O
_{1,4}=g
_{X2}(x, a
_{2}, b
_{2}) * g
_{Y2}(y, c
_{2}, d
_{2}) remember and make w
_{2}
This one deck major function is the output multiplication with ground floor, and with its product with w
_{1}And w
_{2}Output.
The 3rd layer: calculate relevance grade normalization
This one deck is used to calculate the w of i bar rule
_{i}Ratio with whole regular w value sums promptly triggers intensity level.
The 4th layer: computation rule output
In the formula, p
_{i}, q
_{i}, r
_{i}Be the parameter of this node, be commonly called the consequent parameter.In this layer, the triggering intensity level of each node and a single order polynomial multiplication
Layer 5: computing system output
The 4th layer of all signal z of output layer accumulation calculating
_{i}Total output, and finally form single output.
In implementation process, input variable shown in Figure 2 is actual to be by monomer battery voltage is vectorial, battery voltage is vectorial, battery pack current is vectorial and battery pack SOC estimated value vector extracts a plurality of input parameters that the back generates via characteristic index.
As shown in Figure 3, be the step of offline learning modeling process.At first, make up battery pack model as shown in Figure 1.The parameter of each cell can be through a batch test obtains to real target battery in the battery pack; Also can obtain the parameter distribution of a collection of battery through test, produce at random according to this distribution probability then.A kind of method in back is compared with the former can be so that data have generality more.Then, current excitation I is applied to carries out emulation on the battery pack model.The operating mode of current excitation I can be gathered from real vehicle, also can obtain through the vehicle dynamical system model emulation in the development phase.Because the parameter of all monomers is known in simulation model in the battery pack, therefore, can obtain the terminal voltage of each cell under current excitation and the delta data of SOC.The reference correction SOCt of SOC can obtain from the SOC information of each cell.Below be the method for a kind of SOC of obtaining with reference to correction:
SOC
_{t}(t)＝W
_{adj}(t)×SOC
_{a}(t)+(1W
_{adj}(t))×SOC
_{pack}(t)SOC
_{pack}(t)
SOC
_{a}(t)＝max(SOC
_{cell}(t))(W
_{adj}＞0)
SOC
_{a}(t)＝min(SOC
_{cell}(t))(W
_{adj}＜0)
dQ(t)＝SUM(Curr(twindows)：Curr(t))
The revised battery pack SOC target of SOCt
The battery pack SOC estimated value that SOCpackis traditional
Wadjadjusts weight
Conventional batteries SOC estimated value after the Wsocstandardization
The SOCaadjustment is with reference electrode core SOC
Windowsis electric weight cumulative time window in shortterm
MQis the electric standard value in shortterm
The method has been considered the influence of size of current and current SOC operation interval.Can be during practical implementation with reference to the method, but be not limited only to use the method.Again according to the structure of the structure fuzzy rule base of Fig. 2, here can be with the monomer battery voltage extreme difference, current battery pack SOC estimated value, BMS such as current excitation can online acquisition or estimated parameters normalization handle after as the input of rule base.Also can be not limited to abovementioned these collectable physical quantitys during practical implementation.At last, rule base is trained optimization, the output SOCt of rule base and the revise goal SOCtp of reference is compared, the definition error function through the method for iteration:
SOCtmodel output SOC correction value
The SOCtptraining data presets the SOC correction value
With the foundation of E as the BP back propagation learning, the weight of each node in the iteration modification rule storehouse, until E less than a certain preset termination condition position.So just accomplished the training optimizing process under a certain operating mode.
During practical implementation,, can the data of different initial condition and different current excitation operating modes be merged and train in order to make the generalization ability of optimizing training back rule base better.Also can use the hybrid learning algorithm that combines least square to obtain convergence rate and better convergence result more fast.
As shown in Figure 4, for online SOC estimates the makeover process scheme.The SOC of whole Battery pack estimates and can realize through Kalman filter, also can obtain through current integration or additive method.The monomer battery voltage that the SOC estimated value of whole Battery pack and BMS gather in real time, battery voltage, battery pack current all will be as the inputs of consistency indistinct logic computer.Fuzzy rule base adopts method training shown in Figure 3 to obtain.Obtain based on current conforming SOC correction through indistinct logic computer, the estimated value addition with whole Battery pack SOC obtains final correction value then.
As stated, the present invention has formed a complete process steps from modeling to application.Because final what generate is the model that can be used for indistinct logic computer, therefore, the present invention can use with current BMS hardware in, and for solve the inconsistent SOC of the causing estimated bias of cell provide a kind of improve one's methods aforementioned.
Claims (8)
1. estimate to improve one's methods based on the conforming batteries SOC of cell for one kind; It is characterized in that; This method comprises based on the offline learning modeling process of the fuzzy rule base of adaptive neural network with based on the online SOC of fuzzy rule base estimates makeover process, and concrete steps are following:
At first utilize the training dataset of batteries model, make up to have based on the SOC prediction data of consistency correction and adaptive neural network and optimize structure and the fuzzy rule base of parameter;
Then the fuzzy rule base after the offline learning is implanted in the indistinct logic computer of embedded controller of BMS, batteries SOC is estimated to carry out online correction.
2. according to claim 1 a kind of based on the conforming batteries SOC estimation of cell modification method, it is characterized in that described offline learning modeling process specifically may further comprise the steps:
1) through cell test set up can reflect cell in use dynamic characteristic the cell model and obtain the parameter and the distributions rule of cell;
2) according to cell modelling battery pack model;
3) adopt the input of exciting current as the battery pack model, emulation obtains the input data set of fuzzy rule base, and the predeterminated target output parameter is set;
4) input data set is input in the fuzzy rule base;
5) calculate the poor of fuzzy rule base output valve and predeterminated target output parameter; And with its study foundation as neural network BP training algorithm; Revise the weighted value of fuzzy rule base regular node, return step 4) up to the difference of the output of fuzzy rule base and predeterminated target output parameter less than preset threshold value;
6) offline learning finishes.
3. according to claim 2 a kind of based on the conforming batteries SOC estimation of cell modification method; It is characterized in that described fuzzy rule base comprises obfuscation layer, application of rules degree computation layer, application of rules degree normalization layer, defuzzification layer and output layer.
4. according to claim 2 a kind of based on the conforming batteries SOC estimation of cell modification method; It is characterized in that; But described input data set is the characteristic quantity that the data of BMS realtime online collection are formed, but the data of described realtime online collection comprise the delta data of monomer battery voltage, battery voltage, battery pack current and cell SOC.
5. according to claim 2ly a kind ofly estimate modification method, it is characterized in that described predeterminated target output parameter is whole group SOC estimated value poor with based on the revised SOC predicted value of consistency based on the conforming batteries SOC of cell.
6. according to claim 2 a kind of based on the conforming batteries SOC estimation of cell modification method, it is characterized in that described neural network BP training algorithm comprises the Hybrid Search algorithm that BP training algorithm and BP and least square method combine.
7. according to claim 1 a kind of based on the conforming batteries SOC estimation of cell modification method, it is characterized in that described online SOC estimates that makeover process may further comprise the steps:
The fuzzy rule base that 1) will pass through the offline learning modeling process is implanted in the indistinct logic computer of embedded controller of BMS;
2) embedded controller is gathered the parameter of actual battery group in real time, and extracts the input of its characteristic quantity as fuzzy rule base, and fuzzy rule base calculates the correction that battery pack SOC estimates;
3) adopt conventional method to estimate the SOC estimated value of current actual battery group;
The SOC that 4) correction and the addition of SOC estimated value can be obtained revised actual battery group revises predicted value.
8. according to claim 7 a kind of based on the conforming batteries SOC estimation of cell modification method, it is characterized in that described conventional method is Kalman filter method, current integration method, fuzzy reasoning method or neural net method.
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US20080091363A1 (en) *  20061012  20080417  GyeJong Lim  Battery Management System (BMS) and driving method thereof 
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