CN108333528B - SOC and SOT united state estimation method based on power battery electric-thermal coupling model - Google Patents
SOC and SOT united state estimation method based on power battery electric-thermal coupling model Download PDFInfo
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- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
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Abstract
The present invention relates to a kind of SOC and SOT united state estimation methods based on power battery electric-thermal coupling model, belong to technical field of battery management.This method are as follows: select power battery to be measured, establish electricity, the thermal model of the power battery, parameter needed for determining estimated driving force battery SOC and SOT；Trickle charge-discharge test and HPPC are carried out to tested power battery at different temperatures to test, and are established database of the equivalent circuit model parameter under the conditions of charge and discharge about temperature and SOC, are simulated the real steering vectors operating condition under different road conditions, establish database；It carries out parameter identification and obtains the characterisitic parameter of electricity, thermal model, the quantitative relationship under the conditions of acquisition charge and discharge between equivalent circuit model parameter and temperature and SOC；Equivalent-circuit model characterisitic parameter under the conditions of this models coupling PF algorithm, power battery charge and discharge is realized into power battery SOC and the estimation of SOT united state about the quantitative relation formula of temperature and SOC.
Description
Technical field
The invention belongs to technical field of battery management, it is related to SOC and the SOT connection based on power battery electric-thermal coupling model
Conjunction state estimation method.
Background technique
Important component of the power battery as EVs, HEVs and PHEVs carries out the SOC and SOT of power battery quasi-
Really and efficient estimation is particularly important, because the SOC close relation of power battery is to other states of battery such as temperature shape
The estimation of the states such as state, power rating (State ofPower, SOP) and health status (State ofHealth, SOH), and
Safety and reliability, efficiency for charge-discharge, power and the capacity of the SOT of power battery and battery, service life and cycle-index also have
Closely connection.But the real working condition of electric car is complicated, electric current, voltage, impedance measurement accuracy all limited to SOC and
The estimated accuracy of SOT.
At present to the main current integration method of SOC estimation method, open circuit voltage method, intelligent algorithm and the base of power battery
In the SOC estimation technique of model.Current integration method is to be widely used at present cell management system of electric automobile
Very simple a kind of SOC estimation method in (BatteryManagement System, BMS), but the estimation of this method
Precision depends primarily on the measurement accuracy and initial SOC value of electric current.Open circuit voltage method principle is simple, but hardly results in accurately
Open-circuit voltage.Artificial intelligence approach algorithm is complicated, needs to train a large amount of experimental data, it is impossible to be used for real vehicle.Based on mould
The SOC estimation method of type is that current research is most wide, is mainly based upon equivalent-circuit model design observer to estimate lithium ion
The estimated accuracy of the SOC of battery, this method are heavily dependent on model accuracy, vulnerable to factors such as temperature, discharge-rates
Influence, current many methods although it is contemplated that temperature adjustmemt, but do not account for OCV variation with temperature characteristic and
SOC and SOT online real-time Combined estimator.
Mainly there are following a few classes to the SOT estimation of power battery at present: utilizing the average temperature of simple thermal model estimation battery
Degree, such method calculation amount is small, but estimated accuracy cannot reflect actual battery temperature situation.Using numerical solution (if any
Limit first method, finite volume method etc.) Temperature Distribution of estimation battery, the estimation of such method is accurate, but calculates complicated, it is difficult to practical
Using.Using one-dimensional bifurcation thermal model, the measurement of mating surface temperature estimates that the Temperature Distribution of inside battery, such method calculate
Less, precision is higher, but needs to install a large amount of temperature sensor, it is difficult to application for amount.A kind of viable option
Exactly estimate that the Temperature Distribution of battery, such method can remove from battery using impedance measurement and in conjunction with suitable thermal model
Thermocouple is installed on monomer.This method is studied in the existing scholar of foreign countries, is come using heat-impedance model based on impedance measurement
Temperature Distribution inside battery cell is estimated and predicted.
Individually have much to the SOC of lithium battery or the SOT research estimated at present, but the two is combined and estimated
It counts and the method that can be applied to real vehicle BMS again then not yet occurs.
Summary of the invention
In view of this, the purpose of the present invention is to provide a kind of SOC and SOT based on power battery electric-thermal coupling model
United state estimation method.
In order to achieve the above objectives, the invention provides the following technical scheme:
State-of-charge (State ofCharge, SOC) and state of temperature based on power battery electric-thermal coupling model
(State of Temperature, SOT) united state estimation method, method includes the following steps:
S1: selecting power battery to be measured, collects the technical parameter for arranging the power battery, establish the power battery electricity,
Thermal model, and model parameter needed for determining Combined estimator power battery SOC and SOT；
S2: trickle charge-discharge test and mixed pulses power characteristic are carried out to tested power battery at different temperatures
The equivalent-circuit model ginseng under the conditions of charge and discharge is established in (Hybrid Pulse Power Characteristic, HPPC) experiment
Count the experimental data base about temperature and SOC, the pure electric vehicle vapour under simcity, suburb, rural area and the different road conditions of high speed
Vehicle (Electric Vehicles, EVs), hybrid vehicle (Hybrid Electric Vehicles, HEVs) and plug-in
Hybrid vehicle (Plug-Hybrid Electric Vehicles, PHEVs) real steering vectors operating condition establishes the survey of real vehicle operating condition
Try experimental data base, including electric current, voltage, temperature and impedance data；
S3: carrying out parameter identification and obtain the characterisitic parameter of electricity, thermal model, is fitted under the conditions of obtaining charge and discharge by data
Quantitative relationship between equivalent circuit model parameter and temperature and SOC；
S4: by electric-thermal coupling model combination particle filter (Particle Filter, the PF) algorithm of power battery and
Equivalent-circuit model characterisitic parameter under the conditions of power battery charge and discharge is about the quantitative relation formula of temperature and SOC to realize power
Battery SOC and the estimation of SOT united state.
Further, in step sl, the thermal model of the power battery is the non-steady of one-dimensional (One-Dimension, 1-D)
State heat heat transfer model or one-dimensional concentration heat model, the electric model of the power battery are impedance model or equivalent circuit mould
The combination of one or more of type.
Further, the step S2 specifically:
S21: power battery to be measured is stood into 2h in 25 DEG C of isoperibol；
S22: charge and discharge are carried out to power battery with C/20 charge-discharge magnification, measure the open-circuit voltage of the power battery
The relation curve of (Open Circuit Voltage, OCV) and SOC and the active volume for determining the current generation power battery；
S23: electric current, voltage data that HPPC test obtains power battery under Current Temperatures are carried out；
S24: it every 10 DEG C of repetition step S21-S23 within the scope of the total temperature of the power battery, records under different temperatures
Electric current, voltage data；
S25: EVs, HEVs and PHEVs real steering vectors work under simcity, suburb, rural area and the different road conditions of high speed
Condition obtains the experimental datas such as electric current, voltage, temperature, the impedance of the power battery；
S26: the experimental data that will acquire summarizes and handles, and forms available experimental data base.
Further, the step S3 specifically:
S31: it using the experimental data obtained in step S2, recognizes to obtain the spy of electricity, thermal model using parameter identification method
Property parameter；
S32: using the experimental data obtained in step S2, equivalent circuit mould under the conditions of obtaining charge and discharge is fitted by data
Quantitative relationship between shape parameter and temperature and SOC.
Further, in step s 4, in step s 4, the PF algorithm can replace with Extended Kalman filter, without mark card
Kalman Filtering or H infinity filter optimal estimation algorithm.
Further, in step S31, the parameter identification method is least square method, but is not limited to the algorithm.
The beneficial effects of the present invention are: the mean temperature state that thermal model On-line Estimation obtains is supplied to electricity by the present invention
Then characterisitic parameter in Modifying model electric model utilizes high-precision SOC value meter to realize the SOC estimation of higher precision
Current open-circuit voltage, and then the heat production power of battery can be calculated, feed back the estimation that SOT is corrected into thermal model.This hair
Bright advantage has:
(1) it is established for Vehicular dynamic battery and is based on temperature and the modified electric-thermal coupling model of electric current, can accurately obtained
Electricity of the power battery within the scope of total temperature, thermal characteristics；
(2) consider relationship of the power battery between the equivalent circuit model parameter and temperature and SOC under the conditions of charge and discharge,
It can be realized the accurate estimation of SOC under real vehicle operating condition；
(3) the electric-thermal coupling model computation complexity is moderate, and the united state estimated accuracy of SOC and SOT are also enough to apply
Into the BMS of real vehicle；
(4) it proposes the electric-thermal coupling model based on power battery, in conjunction with non-linear filtering method, realizes power battery SOC
The online real-time combined estimation method with the bis- states of SOT.
Detailed description of the invention
In order to keep the purpose of the present invention, technical scheme and beneficial effects clearer, the present invention provides following attached drawing and carries out
Illustrate:
Fig. 1 is the whole method flow diagram of the present invention；
Fig. 2 is the details flow chart of step of embodiment of the present invention S1；
Fig. 3 is the equivalent-circuit model figure of power battery in the embodiment of the present invention；
Fig. 4 is the establishment process figure of impedance model of the embodiment of the present invention；
Fig. 5 is the thermal model schematic diagram of power battery in the embodiment of the present invention；
Fig. 6 is that experimental data obtains flow chart in step of embodiment of the present invention S2；
Fig. 7 is the details flow chart of step S3 in the embodiment of the present invention；
Fig. 8 is particle filter algorithm flow chart in step of embodiment of the present invention S4.
Specific embodiment
Below in conjunction with attached drawing, a preferred embodiment of the present invention will be described in detail.
Referring to Fig. 1, SOC the and SOT united state estimation method based on power battery electric-thermal coupling model be divided into it is following
Step:
S1: selecting power battery to be measured, compiles the technical parameter of the power battery, establish the power battery electricity,
Thermal model, and model parameter needed for determining Combined estimator power battery SOC and SOT；
S2: trickle (for example, C/20A) charge-discharge test is carried out to tested power battery at different temperatures and HPPC is real
It tests, establishes experimental data base of the equivalent circuit model parameter under the conditions of charge and discharge about temperature and SOC, simcity fierceness is driven
Operating condition (UrbanAssault Cycle, UAC) or Artemis hybrid vehicle operating condition (Artemis HEV) are sailed, it is dynamic to acquire this
The data such as electric current, voltage, temperature and the impedance of power battery；
S3: carrying out parameter identification and obtain the characterisitic parameter of electricity, thermal model, is fitted under the conditions of obtaining charge and discharge by data
Quantitative relationship between equivalent circuit model parameter and temperature and SOC；
S4: will be equivalent under the conditions of the electric-thermal coupling model combination PF algorithm of power battery and power battery charge and discharge
Circuit model characterisitic parameter realizes accurate power battery SOC and SOT united state about the quantitative relation formula of temperature and SOC
Estimation.
Referring to Fig. 2, step S1 specifically includes step S11~S13.
S11: selecting power battery to be measured, establishes continuous electricity, thermal model in the power battery time domain, and determine to combine and estimate
Model parameter needed for counting power battery SOC and SOT.Specifically,
The SOC of power battery is calculated by following formula:
Wherein SoC (t), I (t) respectively refer to the state-of-charge and electric current of power battery time-varying, and η is coulombic efficiency, QnIt is
The capacity of power battery can change with conditions such as circulating battery number, temperature.
The electric model of power battery includes equivalent-circuit model and impedance model.
Equivalent-circuit model is referring to Fig. 3, the ohmic internal resistance R that connectede, two include resistance Rs、Rl、Cs、ClPole
Change R-C pairs and open-circuit voltage OCV, mathematical model can indicate are as follows:
VT(t)=UOCV(SoC,t)-Vs(t)-Vl(t)-ReI(t)
Wherein, I (t) is the battery current of measurement, Vs(t)、VlIt (t) is the polarizing voltage of battery, τs=RsCsFor battery
Short-time constant, τl=RlClFor long-time constant, Rs、Rl、CsAnd ClFor the polarization resistance and polarization capacity of battery, UOCV(SoC,
T) it indicates battery open circuit voltage OCV, is the function of state-of-charge SOC and time, VT(t) expression formula is worn by equivalent circuit
Tieing up southern theorem can obtain, and be a nonlinear relation.
The foundation of impedance model referring to Fig. 4, approximating assumption power battery admittivity distribution situation radially are as follows:
Wherein a1、a2And a3It is first, second, and third coefficient about admittance and battery mean temperature fitting of a polynomialT (r) is the Temperature Distribution of battery radially.
The foundation of thermal model referring to Fig. 5, after to power battery reasonable assumption,
Establish the governing equation of battery are as follows:
Its boundary condition are as follows:
Wherein t indicates moment, ρ, cp、ktRespectively indicate volume averag density, specific heat capacity and thermal conductivity, VbIndicate battery
Volume, roIndicate the maximum radius of battery, Q is the heat generation rate of battery, and h is convection transfer rate, T∞For heat-transfer medium temperature.
S12: the calculating formula and equivalent-circuit model of power battery SOC in discretization step S11, and thermal model is carried out
Depression of order processing, is translated into the state-space expression of control guiding.
The calculating formula of power battery SOC and equivalent-circuit model discretization are obtained into following state-space expression:
State equation:
Output equation: VT(k)=UOCV(SoC(k))-Vs(k)-Vl(k)-ReI(k)+vk
Wherein Δ t indicates the sampling interval, and k indicates sampling instant, wk、vkRespectively process noise and measurement noise.
Following state-space expression is obtained after the thermal model depression of order of power battery is handled:
Y=Cx+Du
WhereinU=[Q T∞]T, y=[Tc Ts]TRespectively the state of control system, output and input.
Sytem matrix A, B, C, D are defined as follows:
Wherein, α=kt/ρcp, it is the thermal diffusivity of battery.
Step S13: battery-based impedance operator carries out reasonable assumption to battery, obtains impedance about the average temperature of battery
The nonlinear function of degree, temperature gradient and environment temperature.
The real part of impedance indicates:
Referring to Fig. 6, step S2 specifically includes step S21~S26.
S21: power battery to be measured is stood into 2h in 25 DEG C of isoperibol；
S22: charge and discharge are carried out to power battery with C/20 charge-discharge magnification, measure the pass of the OCV and SOC of the power battery
It is curve and the active volume for determining the current generation power battery；
S23: electric current, voltage data that HPPC test obtains power battery under Current Temperatures are carried out；
S24: it every 10 DEG C of repetition step S21-S23 within the scope of the total temperature of the power battery, records under different temperatures
Electric current, voltage data；
S25: simulation UAC or Artemis HEV real steering vectors operating condition, acquire the electric current of the power battery, voltage, temperature and
Measure the experimental datas such as impedance；
S26: the experimental data obtained before this step is summarized and is handled, available experimental data base is formed.
Referring to Fig. 7, step S3 specifically includes step S31~S33.
S31: using the experimental data obtained in step S2, determine the OCV under the conditions of charge and discharge about between temperature and SOC
Quantitative relationship；
S32: using quantitative relationship obtained in the experimental data and S31 obtained in step S2, using parameter identification side
Method recognizes to obtain the characterisitic parameter of electricity, thermal model, and step S32 includes S321~S322, specifically,
S321: characterisitic parameter R in equivalent-circuit modele、Rs、Rl、Cs、ClIdentification process are as follows:
The end voltage of power battery can describe in complex frequency domain are as follows:
Wherein s is complex frequency domain symbol.
According to principle of least square method, the following equation of equation differential configuration can use:
Wherein, z (k)=UOCV(k,Tk)-VT(k)
θ=[k1k2k3k4k5]
In formula, z (k) is output matrix, and θ is intermediate variable matrix, to need the vector recognized,For input matrix.
Then the spy in battery electric model can be obtained using recurrence least square (Recursive Least Squares, RLS) algorithm
Property parameter.
S322: characterisitic parameter h, k in thermal modelt、cpIdentification process are as follows:
Optimization objective function used can be expressed as follows:
Wherein NfFor pendulous frequency in this experiment, θ*Corresponding battery parameter value when for Euclidean distance minimum.E (k, θ) can
In the form of the vector differentials being expressed as:
E (k, θ)=[Tc,e(k,θ)Ts,e(k,θ)]T-[Tc,exp(k)Ts,exp(k)]T
Wherein θ=[kt cp h]T, Tc,e(k, θ) and Ts,e(k, θ) respectively indicates DIE Temperature and the model of surface temperature is estimated
Evaluation, Tc,exp(k) and Ts,exp(k) measured value of DIE Temperature and surface temperature is respectively indicated.Utilize the optimization work in MATLAB
Vector space Euclidean distance minimum can be realized in tool box culvert number fmincon, so that identification obtains the characterisitic parameter of thermal model.
S33: quasi- by data based on the parameter identification method in the experimental data and step S321 obtained in step S2
Close the quantitative relationship under the conditions of obtaining charge and discharge between electrical model parameters and temperature and SOC.
S4, referring to Fig. 8, using the relation data between the open-circuit voltage and temperature and SOC under the conditions of charge and discharge as opening
Road voltage data library is looked into when running for particle filter observer and is taken, is constantly iterated based on electric-thermal coupling model, through overweight
The property wanted sample phase and resampling stage obtain the estimated value of particle filter observer.In particle filter algorithm, p is usually used
(Xk|Xk-1) indicate state transition model, with p (Zk|Xk) indicate state observation model, essence just correspond to state equation and
Observational equation.Specifically, step S4 includes step S41-S46.
S41: initialization relevant parameter, such as sampling number, sampling period, process-noise variance and measurement noise variance,
Population etc..
S42: the data such as measurement data of load sensor, such as battery surface temperature, DIE Temperature and measurement impedance,
In real-time online observation process, which be can be omitted, and sensor acquisition data can be directly entered step after processing
S44。
S43: initialization particle filter observer, such as initialization particle assembly, particle weights array etc..
S44: particle assembly importance sampling stage, the step include step S441~S444, specifically,
S441: particle importance sampling
It indicates to obey the reference conditions probability distribution in k moment particle assembly, wherein
Z1:k={ Z1,Z2,···,Zk}。
S442: particle weights are calculated
The weight calculation formula comes from sequential importance sampling, whereinIt is i-th in k moment particle assembly
The observation probability distribution of particle,For the prior probability at the k moment that i-th of particle is calculated from the k-1 moment
Distribution,For the probability density function of reference distribution, and assume that the state estimation of every step is optimal estimation,
The function only depends on Xk-1And Zk。
S443: in current sample time, iterative step S441~S442.
S444: particle weights normalized
WhereinFor the weight of i-th of particle in the particle assembly under each sampling instant,For each sampling
When the particle assembly inscribed in i-th of particle normalized weight.
S45: resampling stage, the step include step S451~S452, specifically,
S451: according to APPROXIMATE DISTRIBUTIONGenerate N number of random sample setSampling policy according to selection
Weight is calculated, and normalizes weightThen to particle assemblyIt is eliminated and is replicated.
S452: in current sample time, each particle weights are reset
WhereinFor the normalized weight for inscribing i-th of particle when k,For the weight of the current time particle, N is to produce
Raw random sample number.
S46: calculating particle filter output, and formula is as follows
Wherein p (X0:k|Z1:k) it is posterior probability density function, δ (dX0:k) it is Dirac-delta function.
S47: iterative step S44~S46, repeat the importance sampling of particle assembly at every sampling moment and adopts again
Sample process, and calculate particle filter estimated value.
The algorithm of the observer is better than Extended Kalman filter, and particle filter is based on probability statistics, to the mistake of system
There is no limit for journey noise and measurement noise.And it should be noted that algorithm flow described in step S4 is that elementary particle filtering is calculated
Its algorithm can be expanded to spreading kalman for different accuracy of observation requirements for actual battery management system by method
Particle filter (Extended Kalman Particle Filter, EPF), Unscented kalman particle filter (Unscented
Kalman Particle Filter, UPF) or adaptive particle filter (Adaptive Particle Filter, APF).
The action and effect of embodiment
SOC and SOT united state estimation side based on power battery electric-thermal coupling model involved according to the present invention
The mean temperature state that thermal model On-line Estimation obtains is supplied to the characterisitic parameter in electric model amendment electric model by method, thus
The SOC estimation for realizing higher precision, then can calculate current open-circuit voltage, and then can count using high-precision SOC value
The heat production power of battery is calculated, the estimation for correcting SOT into thermal model is fed back.
Using SOC the and SOT united state estimation method based on power battery electric-thermal coupling model invention the advantages of
Have:
1) it is established for Vehicular dynamic battery and is based on temperature and the modified electric-thermal coupling model of electric current, can accurately obtained
Electricity of the power battery within the scope of total temperature, thermal characteristics；
2) relationship between the equivalent circuit model parameter and temperature and SOC under the conditions of consideration power battery charge and discharge, energy
Enough realize the accurate estimation of SOC under real vehicle operating condition；
3) the electric-thermal coupling model computation complexity is moderate, and the united state estimated accuracy of SOC and SOT are also enough to apply
Into the BMS of real vehicle；
4) it proposes the electric-thermal coupling model based on power battery, in conjunction with non-linear filtering method, realizes power battery SOC
The online real-time combined estimation method with the bis- states of SOT.
Finally, it is stated that preferred embodiment above is only used to illustrate the technical scheme of the present invention and not to limit it, although logical
It crosses above preferred embodiment the present invention is described in detail, however, those skilled in the art should understand that, can be
Various changes are made to it in form and in details, without departing from claims of the present invention limited range.
Claims (5)
1. state-of-charge SOC and state of temperature SOT united state estimation method based on power battery electric-thermal coupling model,
It is characterized in that: method includes the following steps:
S1: selecting power battery to be measured, collects the technical parameter for arranging the power battery, establishes electricity, the hot-die of the power battery
Type, and model parameter needed for determining Combined estimator power battery SOC and SOT；
S2: trickle charge-discharge test is carried out to tested power battery at different temperatures and mixed pulses power characteristic HPPC is real
Test, establish experimental data base of the equivalent circuit model parameter under the conditions of charge and discharge about temperature and SOC, simcity, suburb,
Pure electric automobile EVs, hybrid vehicle HEVs and plug-in hybrid-power automobile under rural road conditions different with high speed
PHEVs real steering vectors operating condition establishes real vehicle working condition measurement experimental data base, including electric current, voltage, temperature and impedance data；
S3: carrying out parameter identification and obtain the characterisitic parameter of electricity, thermal model, is fitted by data equivalent under the conditions of obtaining charge and discharge
Quantitative relationship between circuit model parameters and temperature and SOC；
S4: under the conditions of the electric-thermal coupling model combination particle filter PF algorithm of power battery and power battery charge and discharge
Equivalent-circuit model characterisitic parameter realizes that power battery SOC and SOT united state estimate about the quantitative relation formula of temperature and SOC
Meter；
The step S2 specifically:
S21: power battery to be measured is stood into 2h in 25 DEG C of isoperibol；
S22: charge and discharge are carried out to power battery with C/20 charge-discharge magnification, measure the open-circuit voltage OCV and SOC of the power battery
Relation curve and determine the current generation power battery active volume；
S23: electric current, voltage data that HPPC test obtains power battery under Current Temperatures are carried out；
S24: every 10 DEG C of repetition step S21-S23 within the scope of the total temperature of the power battery, the electricity under different temperatures is recorded
Stream, voltage data；
S25: simulate EVs, HEVs and PHEVs real steering vectors operating condition under different road conditions obtain the power battery electric current,
The experimental datas such as voltage, temperature, impedance；
S26: the experimental data that will acquire summarizes and handles, and forms available experimental data base.
2. SOC the and SOT united state estimation method according to claim 1 based on power battery electric-thermal coupling model,
It is characterized by: in step sl, the thermal model of the power battery is the unstable state heat heat transfer model or one-dimensional of one-dimensional 1-D
Concentration heat model, the electric model of the power battery is the group of one or more of impedance model or equivalent-circuit model
It closes.
3. SOC the and SOT united state estimation method according to claim 1 based on power battery electric-thermal coupling model,
It is characterized by:
The step S3 specifically:
S31: it using the experimental data obtained in step S2, recognizes to obtain the characteristic ginseng of electricity, thermal model using parameter identification method
Number；
S32: using the experimental data obtained in step S2, it is fitted equivalent-circuit model under the conditions of obtaining charge and discharge by data and joins
The several and quantitative relationship between temperature and SOC.
4. SOC the and SOT united state estimation method according to claim 1 based on power battery electric-thermal coupling model,
It is characterized by: in step s 4, the PF algorithm can replace with Extended Kalman filter, Unscented kalman filtering or H infinity
Filter optimal estimation algorithm.
5. SOC the and SOT united state estimation method according to claim 1 based on power battery electric-thermal coupling model,
It is characterized by: the parameter identification method is least square method, but is not limited to the algorithm in step S31.
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| CN109799463A (en) * | 2019-01-18 | 2019-05-24 | 上海卡鲁自动化科技有限公司 | The estimation and prediction technique of power battery SOC/SOH/SOP under actual operating mode based on big data method |
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