CN110346734A - A kind of lithium-ion-power cell health status evaluation method based on machine learning - Google Patents
A kind of lithium-ion-power cell health status evaluation method based on machine learning Download PDFInfo
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Abstract
The invention discloses a kind of lithium-ion-power cell health status evaluation method based on machine learning, state-of-charge and health status for real-time estimation power battery.By establishing the equivalent-circuit model of lithium ion battery, parameter identification is carried out to it, resettles Uoc-SOC model, and estimate SOC.It is obtained using the training of a large amount of off-line datas with Uoc-SOC model parameter as input, maximum available is the neural network model of output.It carries out curve fitting to the Uoc and SOC of synchronization, obtain the parameter to be identified in model, it is entered into the neural network model that training obtains, obtain maximum available, and obtained Uoc-SOC model parameter and maximum available are returned into SOC estimation steps, update the parameter of its state equation and observational equation.The present invention proposes a kind of health state of lithium ion battery evaluation method, carries out estimation on line to cell health state, and carried out parameter update to SOC estimation, improves its estimation precision.
Description
Technical field
The present invention relates to electric automobile power battery management system state estimation technical fields, and in particular to power battery lotus
Electricity condition and health status joint estimate.
Background technique
As exhaustion, the environmental pollution increasingly of Global Oil resource are got worse, electric car is as a kind of energy conservation, ring
The vehicles of guarantor and sustainable development, have obtained the concern of people.Power resources of the power battery pack as electric car,
Its performance is always the emphasis studied.The battery of electric car when in use, need to make its work reasonable voltage, electric current,
In temperature range.Therefore, it is necessary to the uses to battery electronic on electric car effectively to be managed.To battery reality on electric car
The specific equipment for applying management is exactly battery management system (Battery Management System, BMS).It will not only guarantee electricity
Pond safely and reliably uses, and gives full play to the ability of battery and extend its service life, as battery entire car controller with
And the bridge linked up between driver, the charge and discharge of battery pack are controlled, and the base of electrokinetic cell system is reported to entire car controller
This parameter and fault message.The level of battery management system largely determines the performance of power battery pack.Therefore, one
A real-time, efficient battery management system is very important.
Battery management system (BMS) is the important ring of new-energy automobile dynamical system assembly and large-scale energy storage system exploitation
Section.Battery charge state (SOC) is used to characterize the remaining capacity of battery, the i.e. percentage of remaining capacity and rated capacity.Battery
State-of-charge (SOC) cannot be obtained directly from battery itself, can only pass through external characteristics parameter (such as voltage, electricity of measurement battery pack
Stream etc.) estimate to obtain indirectly.Electric automobile power battery in use, due to internal complicated electrochemical reaction, causes
Battery behavior embodies the non-linear of height, makes accurately to estimate that battery charge state (SOC) has great difficulty.It is non-for handling
The non-linear Kalman filtering (such as filtering of extension karr, Unscented kalman filtering) of linear problem is considered to be used to estimate SOC.
When using these algorithms, the relational expression of SOC and Uoc and the maximum available of battery, current method one can be related to
As do not consider its variation, default it for definite value.But in fact, with battery aging, the relationship of SOC and Uoc can become
Change, the maximum available of battery is also changing.If updating these variations not in time, the estimation error of SOC can become
It is increasing.
SOH refers to the health status of battery, i.e., the ratio of current maximum available and initial maximum active volume.With
Service time of battery increase, battery can gradually aging, occur internal resistance increase, battery capacity decaying phenomena such as.Battery capacity decaying
The reason of it is complicated, it is more to be related to factor, and change slow.Current still none accurate decline physical model.It is using
When model is established in machine learning, the selection of health factor has larger impact to its final precision, thus select suitable health because
Son is particularly significant.
Summary of the invention
To solve problems of the prior art, state-of-charge (SOC) and health status (SOH) of the present invention to battery
Joint estimate is carried out, by the coefficient and maximum possible capacity of real-time update Uoc and SOC relational expression, improves SOC estimation precision,
Reduce estimation error；And the input using Uoc and the coefficient of SOC relational expression as health factor, as BP neural network model.It should
Estimation and control of the invention for entire battery status, the capacity for improving the service life of battery and giving full play to battery have weight
Big meaning.
The present invention adopts the following technical solutions, realizes above-mentioned technical purpose.
A kind of lithium-ion-power cell health status evaluation method based on machine learning, comprising the following steps:
Step (1), establishes the equivalent-circuit model of lithium-ion-power cell, and recognizes the unknown parameter in model；
Step (2), establishes Uoc-SOC model:Wherein ai、bi、ciIt is
Parameter to be identified, U in modelOCIndicate the open-circuit voltage of battery, SOC is battery charge state；
Step (3), estimates state-of-charge SOC；
Step (4) obtains a by off-line data by curve matchingi、bi、ciValue；
Step (5), to ai、bi、ciAnd maximum available C is normalized, and obtains a'i、b'i、c'iAs defeated
Enter, C' conduct output, using machine learning algorithm to a'i、b'i、c'iAnd corresponding C' is trained, and is finally obtained with a'i、b
'i、c'iTo input the machine learning model for C' being output；
Step (6) carries out curve fitting to obtain input value a to the Uoc and SOC of synchronizationi、bi、ci, defeated after normalization
Enter to machine mould, obtains maximum available C；
Step (7), by a obtained in step (6)i、bi、ciAnd C value returns to step (3), updates state equation and observation
Corresponding parameter in equation.
Further, the equivalent-circuit model selects Thevenin equivalent-circuit model or Order RC equivalent-circuit model.
Further, the Uoc-SOC model UOC=K0+K1z+K2z2+K3z3+K4z4+K5z5+K6z6OrOrOrReplacement, wherein z is state-of-charge SOC, K0、K1、K2K3、
K4、K5、K6、α1、α2For parameter to be identified in model.
Further, the state-of-charge SOC is estimated using expanded Kalman filtration algorithm, the polarizing voltage of battery and
The state equation of state-of-charge isWherein
Up、Rp、CpIndicate that battery polarization voltage, resistance, capacitor, η are coulombic efficiency, Δ T is sampling time interval, ILFor battery charging and discharging stream,
ωkFor system noise；End voltage observational equation be
Wherein υkTo measure noise.
The beneficial effects of the present invention are: the lithium-ion-power cell health status proposed by the present invention based on machine learning is estimated
Calculation method, parameter and present battery when can estimate state-of-charge using modelling with real-time update in Uoc-SOC model are most
Big active volume improves the estimation precision of SOC.In addition, the present invention is strong using the parameter in Uoc-SOC model as data-driven
The health factor of health state estimating method has higher estimation precision；And in joint estimate, in updating estimation SOC
While Uoc-SOC Model Parameter, the input that the parameter of update is estimated as SOH, SOC estimation, SOH estimation share same
Group parameter, reduces calculation amount.
Detailed description of the invention
Fig. 1 is lithium-ion-power cell model equivalent circuit diagram；
Fig. 2 is the flow chart of the lithium-ion-power cell health status evaluation method the present invention is based on machine learning.
Specific embodiment
The specific technical solution of the present invention will be further described in conjunction with attached drawing below, but protection of the invention
Range is not limited to this.
A kind of lithium-ion-power cell health status evaluation method based on machine learning, comprising the following steps:
Step (1), establishes the equivalent-circuit model of lithium-ion-power cell, can select Thevenin equivalent circuit mould
Type or Order RC equivalent-circuit model, the present embodiment by taking Thevenin equivalent circuit as an example (such as Fig. 1), wherein UOCIndicate battery
Open-circuit voltage, UtIndicate the end voltage of battery, R0For the ohmic internal resistance of battery, Up、Rp、CpIndicate battery polarization voltage, resistance,
Capacitor；ILFor battery charging and discharging stream.According to circuit diagram in Fig. 1, using the above equivalent-circuit model of electrotechnics theory analysis,
Establish the continuous time equation of battery model:
Ut=Uoc-ILR0-Up (1)
Transmission function is obtained by Laplace transform:
Bilinear transformation enables?
It enables
Formula (4) can simplify are as follows:
UL, k=a1UL, k-1+UOC, k-a1UOC, k-1+a2IL, k+a3IL, k-1 (5)
Again because sampling time T is very short, then:
ΔUOC, k=UOC, k-UOC, k-1≈0 (6)
Then formula (6) can simplify are as follows:
UT, k=(1-a1)UOC, k+a1UT, k-1+a2IL, k+a3IL, k-1 (7)
Then parameter identification is carried out using least square method of recursion, formula (7) can be write as:
Wherein ΦLs, kIt is the data matrix of system, θLs, kIt is parameter matrix.
Step (2), establishes Uoc-SOC model:
Using battery testing cabinet, cycling life test is carried out to certain 18650 type lithium ion battery, obtains different cycle-indexes
Under corresponding SOC and Uoc value, carry out curve fitting to obtained data, obtain the relationship of Uoc-SOC: wherein ai、bi、ciFor
Parameter to be identified in model, these parameters to be identified change with the variation of cell health state, and then can be with
Health factor as characterization health status.
Uoc-SOC model can also be used with drag:
UOC=K0+K1z+K2z2+K3z3+K4z4+K5z5+K6z6 (11)
Wherein z is state-of-charge SOC, K0、K1、K2K3、K4、K5、K6、α1、α2For parameter to be identified in model, these are distinguished
The parameter of knowledge changes with the variation of cell health state, can also be used as the health factor of characterization health status.
Below by taking formula (9) as an example, n=3 is taken, it is fitted, the parameter obtained under different cycle-indexes is as follows:
| Cycle-index | a1 | b1 | c1 | a2 | b2 | c2 | a3 | b3 | c3 |
| 1 | 3.936 | 1.242 | 0.8202 | 0.095 | 0.5856 | 0.1909 | 3.004 | -0.2176 | 0.7787 |
| 25 | 2.985 | 1.196 | 0.3515 | 2.863 | 0.7151 | 0.4342 | 3.12 | 0.04232 | 0.5155 |
| 50 | 3.51 | 1.196 | 0.4159 | 2.654 | 0.6447 | 0.4176 | 3.061 | 0.02453 | 0.482 |
| 75 | 3.471 | 1.219 | 0.6137 | 0.1646 | 0.6018 | 0.2187 | 3.353 | 0.1179 | 0.8297 |
As it can be seen that parameter to be identified converts, can be as health factor under different cycle-indexes.
Step (3) estimates that state-of-charge SOC, the present embodiment estimate SOC by taking expanded Kalman filtration algorithm as an example
It calculates, the polarizing voltage of lithium ion battery and state equation such as formula (10), the observational equation such as formula (11) of end voltage of state-of-charge.
Wherein: ωkFor system noise, it is assumed that it meets Gaussian Profile, υkTo measure noise, also assume that it meets Gauss point
Cloth, η are coulombic efficiency, and Δ T is sampling time interval.
Step (4), obtains a large amount of off-line datas: test battery at different temperatures, under different health status with different
Uoc and SOC data when discharge-rate (such as 0.1C, 0.5C, 1C, 2C) discharges, and in various dynamic operation conditions (as new mark Europe is followed
Ring test-NEDC, Metro cycle-UDDS) under Uoc and SOC data；The Uoc-SOC model of formula (9) is selected, is considered
To model accuracy and computation complexity, n=3 is taken, then by the method for curve matching, obtains ai、bi、ciValue.
Step (5), due to (i.e. current maximum available C is different) a under different health statusi、bi、ciValue it is different, lead to
Cross correlation analysis, selection and the biggish a of SOH correlationi、bi、ciValue as input, using C as export；To improve training
Precision, using minimax normalization method to a of inputi、bi、ciIt is normalized with the C of output, obtains a'i、b'i、c'iWith
C'；Neural network (by taking BP neural network as an example) algorithm is used, to a'i、b'i、c'i, C'(i=1,2,3；) learning training is carried out,
It finally obtains with a'i、b'i、c'iIt is the BP neural network model of output for input, C'.
Step (6) can be obtained real-time synchronization Uoc and SOC data by step (1), (3), choose 10%-90%
SOC and corresponding Uoc in range, are ordinate by abscissa, Uoc of SOC, obtain input value a by curve matchingi、bi、
ci, to ai、bi、ciIt evolves after normalizing, the BP neural network model that input step (5) obtains, and returned to the C' of output is counter
One changes, and obtains the current maximum available C of lithium battery.
Step (7), by a obtained in step (6)i、bi、ciAnd C value returns to step (3), updates state equation and observation
Corresponding parameter in equation.
Step (8) after step (7) has updated parameter, executes step (1), (3), (5), (6), (7), carries out one under battery
The health status of secondary circulation is estimated.
It should be noted that being explained although content disclosed in this invention is expounded through the foregoing embodiment
It states content and is not considered as limitation of the present invention.The modification that professional and technical personnel in the field make technical solution of the present invention
And equivalent replacement, it should all cover in scope of the invention as claimed.
Claims (4)
1. a kind of lithium-ion-power cell health status evaluation method based on machine learning, which is characterized in that including following step
It is rapid:
Step (1), establishes the equivalent-circuit model of lithium-ion-power cell, and recognizes the unknown parameter in model；
Step (2), establishes Uoc-SOC model:Wherein ai、bi、ciIt is model
In parameter to be identified, UOCIndicate the open-circuit voltage of battery, SOC is battery charge state；
Step (3), estimates state-of-charge SOC；
Step (4) obtains a by off-line data by curve matchingi、bi、ciValue；
Step (5), to ai、bi、ciAnd maximum available C is normalized, and obtains a 'i、b′i、c′iAs input, C'
As output, using machine learning algorithm to a 'i、b′i、c′iAnd corresponding C' is trained, and is finally obtained with a 'i、b′i、c′i
To input the machine learning model for C' being output；
Step (6) carries out curve fitting to obtain input value a to the Uoc and SOC of synchronizationi、bi、ci, it is input to after normalization
Machine mould obtains maximum available C；
Step (7), by a obtained in step (6)i、bi、ciAnd C value returns to step (3), updates state equation and observational equation
In corresponding parameter.
2. a kind of lithium-ion-power cell health status evaluation method based on machine learning according to claim 1,
It is characterized in that, the equivalent-circuit model selects Thevenin equivalent-circuit model or Order RC equivalent-circuit model.
3. a kind of lithium-ion-power cell health status evaluation method based on machine learning according to claim 1,
It is characterized in that, the Uoc-SOC model UOC=K0+K1z+K2z2+K3z3+K4z4+K5z5+K6z6OrOrOrReplacement, wherein z is state-of-charge SOC, K0、K1、K2 K3、
K4、K5、K6、α1、α2For parameter to be identified in model.
4. a kind of lithium-ion-power cell health status evaluation method based on machine learning according to claim 1,
It is characterized in that, the state-of-charge SOC is estimated using expanded Kalman filtration algorithm, the polarizing voltage of battery and charged shape
The state equation of state isWherein Up、Rp、Cp
Indicate that battery polarization voltage, resistance, capacitor, η are coulombic efficiency, Δ T is sampling time interval, ILFor battery charging and discharging stream, ωk
For system noise；End voltage observational equation beWherein υk
To measure noise.
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