CN108333528A - SOC and SOT united state methods of estimation based on power battery electric-thermal coupling model - Google Patents
SOC and SOT united state methods of estimation 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 methods of estimation based on power battery electro thermal coupling model, belong to technical field of battery management.This method is：Power battery to be measured is selected, electricity, the thermal model of the power battery is established, determines parameter needed for estimated driving force battery SOC and SOT；Trickle charge-discharge test is carried out to tested power battery at different temperatures and HPPC is tested, database of the equivalent circuit model parameter under the conditions of charge and discharge about temperature and SOC is established, simulates the real steering vectors operating mode 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；By the equivalent-circuit model characterisitic parameter under the conditions of this models coupling PF algorithms, power battery charge and discharge about the quantitative relation formula of temperature and SOC to realize that power battery SOC and SOT united states estimate.
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 method of estimation.
Background technology
Important component of the power battery as EVs, HEVs and PHEVs carries out the SOC and SOT of power battery accurate
Really and efficient estimation is particularly important, because the SOC close relations of power battery are 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
The SOT of power battery and the safety and reliability of battery, efficiency for charge-discharge, power and capacity, service life and cycle-index also have
Closely contact.But the real working condition of electric vehicle 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 methods of estimation, open circuit voltage method, intelligent algorithm and the base of power battery
In the SOC estimations 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 methods of estimation in (BatteryManagement System, BMS), but the estimation of this method
Precision depends primarily on the measurement accuracy of electric current and initial SOC value.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 methods of estimation of type are that current research is most wide, are 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, easily by 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
Real-time Combined estimator online SOC and SOT.
Mainly there are following a few classes to the SOT of power battery estimations at present：The average temperature of battery is estimated using simple thermal model
Degree, such method calculation amount is small, but estimated accuracy cannot reflect actual battery temperature situation.Using numerical solution (if any
The first method of limit, finite volume method etc.) estimation battery Temperature Distribution, the estimation of such method is accurate, but calculates complicated, it is difficult to practical
Using.Using one-dimensional bifurcation thermal model, the Temperature Distribution of inside battery is estimated in the measurement of mating surface temperature, such method calculates
Amount is little, and precision is higher, but needs to install a large amount of temperature sensor, it is difficult to application.A kind of viable option
Exactly estimate the Temperature Distribution of battery using impedance measurement and in conjunction with suitable thermal model, such method can remove from battery
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.
Invention content
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 method of estimation.
In order to achieve the above objectives, the present invention provides the following technical solutions：
State-of-charge (State ofCharge, SOC) based on power battery electric-thermal coupling model and state of temperature
(State of Temperature, SOT) united state method of estimation, this approach includes the following steps：
S1：Select power battery to be measured, collect arrange the power battery technical parameter, establish the power battery electricity,
Thermal model, and determine the model parameter needed for 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
(Hybrid Pulse Power Characteristic, HPPC) is tested, and establishes the equivalent-circuit model ginseng under the conditions of charge and discharge
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 mode establishes the survey of real vehicle operating mode
Try experimental data base, including electric current, voltage, temperature and impedance data；
S3：It carries out parameter identification and obtains the characterisitic parameter of electricity, thermal model, be fitted by data under the conditions of obtaining charge and discharge
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 states.
Further, in step sl, the thermal model of the power battery is the non-steady of one-dimensional (One-Dimension, 1-D)
The electric model of state heat heat transfer model or one-dimensional concentration heat model, the power battery is impedance model or equivalent circuit mould
The combination of one or more of type.
Further, the step S2 is specially：
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 magnifications, 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：Carry out electric current, voltage data that HPPC tests obtain power battery under Current Temperatures；
S24：Every 10 DEG C of repetition step S21-S23 within the scope of the total temperature of the power battery, record 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 of acquisition is summarized and handled, available experimental data base is formed.
Further, the step S3 is specially：
S31：Using the experimental data obtained in step S2, recognize 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 algorithms 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 estimations of higher precision
Current open-circuit voltage, and then the heat production power of battery can be calculated, it feeds back in thermal model and corrects the estimation of SOT.This hair
Bright advantage has：
(1) it is directed to Vehicular dynamic battery to establish based on temperature and the modified electric-thermal coupling model of electric current, can accurately obtain
Electricity of the power battery within the scope of total temperature, thermal characteristics；
(2) consider the relationship between equivalent circuit model parameter and temperature and SOC of the power battery under the conditions of charge and discharge,
It can realize the accurate estimation of SOC under real vehicle operating mode；
(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.
Description of the drawings
In order to keep the purpose of the present invention, technical solution and advantageous effect clearer, the present invention provides following attached drawing and carries out
Explanation：
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 establishes procedure chart for 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 implementation mode
Below in conjunction with attached drawing, the preferred embodiment of the present invention is described in detail.
Referring to Fig. 1, SOC the and SOT united state methods of estimation based on power battery electric-thermal coupling model be divided into it is following
Step：
S1：Select power battery to be measured, compile the technical parameter of the power battery, establish the power battery electricity,
Thermal model, and determine the model parameter needed for 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 mode (UrbanAssault Cycle, UAC) or Artemis hybrid vehicles operating mode (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：It carries out parameter identification and obtains the characterisitic parameter of electricity, thermal model, be fitted by data under the conditions of obtaining charge and discharge
Quantitative relationship between equivalent circuit model parameter and temperature and SOC；
S4：It will be equivalent under the conditions of the electric-thermal coupling model combination PF algorithms 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：Power battery to be measured is selected, establishes continuous electricity, thermal model in the power battery time domain, and determine to combine and estimate
Count the model parameter needed for 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 be expressed as：
VT(t)=UOCV(SoC,t)-Vs(t)-Vl(t)-ReI(t)
Wherein, I (t) is the battery current measured, Vs(t)、Vl(t) it 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 state-of-charge SOC and the function of time, VT(t) expression formula is worn by equivalent circuit
The southern theorem of dimension can obtain, and be a nonlinear relation.
The foundation of impedance model is referring to Fig. 4, the admittivity distribution situation radially of approximating assumption power battery is：
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,
The governing equation for establishing battery is：
Its boundary condition is：
Wherein t indicates moment, ρ, cp、ktVolume averag density, specific heat capacity and thermal conductivity, V are indicated respectivelybIndicate battery
Volume, roIndicate that the maximum radius of battery, Q are the heat generation rate of battery, 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 that control is oriented to.
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 that sampling interval, k indicate 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.System
System matrix A, B, C, D are defined as follows：
Wherein, α=kt/ρcp, it is the thermal diffusivity of battery.
Step S13：Impedance operator based on battery 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 magnifications, measure the pass of the OCV and SOC of the power battery
It is curve and determines the active volume of the current generation power battery；
S23：Carry out electric current, voltage data that HPPC tests obtain power battery under Current Temperatures；
S24：Every 10 DEG C of repetition step S21-S23 within the scope of the total temperature of the power battery, record under different temperatures
Electric current, voltage data；
S25：Simulate UAC or Artemis HEV real steering vectors operating modes, 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 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 the 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 be：
The terminal voltage of power battery can be described in complex frequency domain：
Wherein s is complex frequency domain symbol.
According to principle of least square method, the equation that equation differential configuration can be utilized following：
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 it can be obtained the spy in battery electric model using recurrence least square (Recursive Least Squares, RLS) algorithm
Property parameter.
S322：Characterisitic parameter h, k in thermal modelt、cpIdentification process be：
Optimization object function used can indicate as follows：
Wherein NfFor pendulous frequency in this experiment, θ*For Euclidean distance minimum when corresponding battery parameter value.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) indicates that the model of DIE Temperature and surface temperature is estimated respectively
Evaluation, Tc,exp(k) and Ts,exp(k) measured value of DIE Temperature and surface temperature is indicated respectively.Utilize the optimization work in MATLAB
Vector space Euclidean distance minimum can be realized in tool box culvert number fmincon, to which identification obtains the characterisitic parameter of thermal model.
S33：It is quasi- by data based on the parameter identification method in the experimental data and step S321 obtained in step S2
Close the quantitative relationship between electrical model parameters and temperature and SOC under the conditions of obtaining charge and discharge.
S4, referring to Fig. 8, using the open-circuit voltage under the conditions of charge and discharge and the relation data between temperature and SOC as opening
Road voltage data library is looked into when being run 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 the measurement data of load sensor, such as battery surface temperature, DIE Temperature and measurement impedance,
In real-time online observation process, which can be omitted, and sensor gathered data can be directly entered step after processing
S44。
S43：Initialize 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 assemblies, wherein
Z1:k={ Z1,Z2,···,Zk}。
S442：Calculate particle weights
The weight calculation formula comes from sequential importance sampling, whereinIt is i-th in k moment particle assemblies
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 often walked 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 weightsThen to particle assemblyIt is eliminated and is replicated.
S452：In current sample time, each particle weights are reset
WhereinTo inscribe the normalized weight of i-th of particle when k,For the weight of the current time particle, N is production
Raw random sample number.
S46：Particle filter output is calculated, formula is as follows
Wherein p (X0:k|Z1:k) it is posterior probability density function, δ (dX0:k) it is Dirac-delta functions.
S47：Iterative step S44~S46 repeats the importance sampling of particle assembly and adopts again at every sampling moment
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 elementary particle filtering calculation
Method can be directed to different accuracy of observation requirements, its algorithm is expanded to spreading kalman for actual battery management system
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 effect of embodiment
According to SOC the and SOT united states estimation side according to the present invention based on power battery electric-thermal coupling model
The mean temperature state that thermal model On-line Estimation obtains is supplied to electric model to correct the characterisitic parameter in electric model by method, to
The SOC estimations for realizing higher precision, then can calculate current open-circuit voltage, and then can count using high-precision SOC value
The heat production power for calculating battery feeds back in thermal model and corrects the estimation of SOT.
Using SOC the and SOT united state methods of estimation based on power battery electric-thermal coupling model invention the advantages of
Have：
1) it is directed to Vehicular dynamic battery to establish based on temperature and the modified electric-thermal coupling model of electric current, can accurately obtain
Electricity of the power battery within the scope of total temperature, thermal characteristics；
2) equivalent circuit model parameter under the conditions of consideration power battery charge and discharge and the relationship between temperature and SOC, energy
Enough realize the accurate estimation of SOC under real vehicle operating mode；
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 illustrate, preferred embodiment above is merely illustrative of the technical solution of the present invention and unrestricted, 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 (6)
1. the state-of-charge (State ofCharge, SOC) based on power battery electric-thermal coupling model and state of temperature (State
Of Temperature, SOT) united state method of estimation, it is characterised in that：This approach includes the following steps：
S1：Power battery to be measured is selected, the technical parameter for arranging the power battery is collected, establishes electricity, the hot-die of the power battery
Type, and determine the model parameter needed for Combined estimator power battery SOC and SOT；
S2：Trickle charge-discharge test and mixed pulses power characteristic (Hybrid are carried out to tested power battery at different temperatures
Pulse Power Characteristic, HPPC) experiment, the equivalent circuit model parameter under the conditions of charge and discharge is established about temperature
Spend the experimental data base with SOC, the pure electric automobile under simcity, suburb, rural area and the different road conditions of high speed
(Electric Vehicles, EVs), hybrid vehicle (Hybrid Electric Vehicles, HEVs) and plug-in mixed
Power vehicle (Plug-Hybrid Electric Vehicles, PHEVs) real steering vectors operating mode is closed, real vehicle working condition measurement is established
Experimental data base, including electric current, voltage, temperature and impedance data；
S3：It carries out parameter identification and obtains the characterisitic parameter of electricity, thermal model, be fitted by data equivalent under the conditions of obtaining charge and discharge
Quantitative relationship between circuit model parameters and temperature and SOC；
S4：By electric-thermal coupling model combination particle filter (Particle Filter, the PF) algorithm and power of power battery
Equivalent-circuit model characterisitic parameter under the conditions of battery charging and discharging is about the quantitative relation formula of temperature and SOC to realize power battery
SOC and SOT united states are estimated.
2. SOC the and SOT united state methods of estimation according to claim 1 based on power battery electric-thermal coupling model,
It is characterized in that：In step sl, the thermal model of the power battery is that the unstable state of one-dimensional (One-Dimension, 1-D) is given birth to
The electric model of hot heat transfer model or one-dimensional concentration heat model, the power battery is in impedance model or equivalent-circuit model
One or more of combinations.
3. SOC the and SOT united state methods of estimation according to claim 1 based on power battery electric-thermal coupling model,
It is characterized in that：The step S2 is specially：
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 magnifications, measure the open-circuit voltage (Open of the power battery
Circuit Voltage, OCV) and SOC relation curve and determine the current generation power battery active volume；
S23：Carry out electric current, voltage data that HPPC tests obtain power battery under Current Temperatures；
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 mode under different road conditions obtain the power battery electric current,
The experimental datas such as voltage, temperature, impedance；
S26：The experimental data of acquisition is summarized and handled, available experimental data base is formed.
4. SOC the and SOT united state methods of estimation according to claim 1 based on power battery electric-thermal coupling model,
It is characterized in that：The step S3 is specially：
S31：Using the experimental data obtained in step S2, recognize 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
Number and the quantitative relationship between temperature and SOC.
5. SOC the and SOT united state methods of estimation according to claim 1 based on power battery electric-thermal coupling model,
It is characterized in that：In step s 4, the PF algorithms can replace with Extended Kalman filter, Unscented kalman filtering or H infinity
Filter optimal estimation algorithm.
6. SOC the and SOT united state methods of estimation according to claim 4 based on power battery electric-thermal coupling model,
It is characterized in that：In step S31, the parameter identification method is least square method, but is not limited to the algorithm.
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