CN106054081A - Lithium battery modeling method for SOC (State of Charge) estimation of electric vehicle power battery - Google Patents
Lithium battery modeling method for SOC (State of Charge) estimation of electric vehicle power battery Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
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Abstract
The invention discloses a lithium battery modeling method for SOC (State of Charge) estimation of an electric vehicle power battery. Each electrical parameter in a lithium battery Thevenin model is defined as a function of an environment variable by comprehensively considering the influence of an SOC environment factor, model parameters are obtained by a hybrid power pulse capacity characteristic (HPPC) experiment, practical parameter values of the battery model are obtained by test and calculation, and a parameter fitting method of the model is thus determined. Based on the shortcomings of the prior art, internal battery parameters at different temperatures and SOCs are measured and assessed, so that environment factors influencing parameter changes are analyzed, and a lithium battery Thevenin model with variable parameter is established.
Description
Technical field
The present invention relates to battery SOC modeling method field, a kind of estimate for electric automobile power battery SOC
Lithium battery modeling method.
Background technology
SOC, i.e. state of charge, refers to remaining battery state-of-charge, is commonly defined as present battery residue and holds
Amount and the ratio of nominal capacity, directly affect effectiveness and the judgement of electric automobile course continuation mileage of power battery pack Balance route
Reliability.But, the operating condition of electric automobile complexity brings bigger difficulty to the accurately estimation of SOC, sets up accurately
Lithium battery model be the key improving SOC estimation precision, at present conventional lithium battery equivalent-circuit model has Rint model, RC
Model, Thevenin model and PNGV model etc., wherein Thevenin model has explicit physical meaning, identification of Model Parameters in fact
Test advantages such as easily performing, be widely used in the mathematical modeling of battery, but its parameter is fixed, it is impossible to reflection battery is the most special
Property.
In existing SOC method of estimation, the consideration on the impact of environmental condition especially temperature is not comprehensive, it practice, electric
The parameter of pool model is continually changing with the change of ambient temperature, and its active volume difference under condition of different temperatures is very big, therefore
Temperature affects the degree of accuracy of battery model to a great extent, and then affects the estimated accuracy of SOC.And along with seasonal variations
With latitude difference, electric automobile running exists bigger variations in temperature, so using merely fixed model to remain
Can there is the biggest error in volume calculation, have algorithm to propose to be modified SOC by temperature compensation coefficient, or directly pass through
The SOC of estimation under different temperatures is converted by ampere-hour integral result.For said method, the impact of temperature is directly as correction
The factor acts on SOC estimated result, and the impact of battery model is still needed to study further by it.
Summary of the invention
It is an object of the invention to provide a kind of lithium battery modeling method estimated for electric automobile power battery SOC, with
Solve the circumscribed problem of the model scope of application in prior art algorithm.
In order to achieve the above object, the technical solution adopted in the present invention is:
A kind of lithium battery modeling method estimated for electric automobile power battery SOC, it is characterised in that: consider bag
Include the temperature impact in interior SOC environmental factors, each electric parameter in lithium battery Thevenin model is defined as environmental variable
Function, and by hybrid power pulse ability characteristics HPPC experiment obtain model parameter, first pass through test and be calculated
Battery model actual parameter value, and determine the parameter fitness method of model on this basis；
According to Thevenin model, there is polarization capacity Cpol in lithium battery interior and polarization resistance Rpol is in parallel, and with
Characterize the direct voltage source Uoc of open-circuit voltage, ohmic internal resistance Rohm and other internal resistances R0 tandem compound, wherein polarization capacity
Cpol, polarization resistance Rpol, ohmic internal resistance Rohm are variable element, relevant with battery SOC environmental factors, in model
Each electric parameter can by can actually detected to parameter calculate, by hybrid power pulse ability characteristics HPPC test
It is calculated model parameter；
Analysis experimental data acquisition varying environment, because of each electrical parameters of vegetarian refreshments, utilizes discrete data curve-fitting method,
Obtain the functional relationship of each electric parameter and environmental factors；It can be seen that be less than in the range of 30% at SOC, ohmic internal resistance
Rohm can raise suddenly, and when SOC is more than 30%, and SOC is on the impact of ohmic internal resistance Rohm inconspicuous, and temperature is to SOC
Impact be a continuous print process, therefore after analyzing influence factor, battery model parameter is carried out segment processing；
The mathematical model of lithium-ion-power cell is the basis of estimation battery SOC, and passing through set up model can obtain
The observational equation of battery, observational equation describes the functional relationship of SOC, charging and discharging currents, temperature factor and battery terminal voltage；Will
Battery actual capacity is set as an amount varied with temperature, and is advised by the change at different temperatures of research battery actual capacity
Rule, introduces temperature compensation coefficient ηTRevise the error between battery actual capacity and battery rated capacity under different temperatures.
Compared with original technology, beneficial effects of the present invention is embodied in:
(1) in the Thevenin model of lithium battery, the parameters such as ohmic internal resistance, polarization resistance, polarization capacity are defined as
The function of environmental variable, line parameter matching of going forward side by side, improve the accuracy of battery model, extend the scope of application of model.
(2) parameter identification method used take into account the physical characteristic of lithium battery, and measurement result is accurate, and identification obtains
Parameter be more nearly actual value.
Accompanying drawing explanation
Fig. 1 is the Rohm that measures of the present invention and environmental factors relation.
Fig. 2 is sampling element hardware system structure figure in the specific embodiment of the invention.
Fig. 3 is the voltage sampling circuit figure in the specific embodiment of the invention to multi-section serial battery group.
Fig. 4 is temperature sampling circuit figure in the specific embodiment of the invention.
Detailed description of the invention
A kind of lithium battery modeling method estimated for electric automobile power battery SOC, considers including temperature
The impact of SOC environmental factors, each electric parameter in lithium battery Thevenin model is defined as the function of environmental variable, and leads to
Cross the experiment of mixing power pulse ability characteristics HPPC and obtain model parameter, first pass through test and be calculated battery model reality
Parameter value, and determine the parameter fitness method of model on this basis；
According to Thevenin model, there is polarization capacity Cpol in lithium battery interior and polarization resistance Rpol is in parallel, and with
Characterize the direct voltage source Uoc of open-circuit voltage, ohmic internal resistance Rohm and other internal resistances R0 tandem compound, wherein polarization capacity
Cpol, polarization resistance Rpol, ohmic internal resistance Rohm are variable element, relevant with battery SOC environmental factors, in model
Each electric parameter can by can actually detected to parameter calculate, by hybrid power pulse ability characteristics HPPC test
It is calculated model parameter；
Analysis experimental data acquisition varying environment, because of each electrical parameters of vegetarian refreshments, utilizes discrete data curve-fitting method,
Obtain the functional relationship of each electric parameter and environmental factors；It can be seen that be less than in the range of 30% at SOC, ohmic internal resistance
Rohm can raise suddenly, and when SOC is more than 30%, and SOC is on the impact of ohmic internal resistance Rohm inconspicuous, and temperature is to SOC
Impact be a continuous print process, therefore after analyzing influence factor, battery model parameter is carried out segment processing；
The mathematical model of lithium-ion-power cell is the basis of estimation battery SOC, and passing through set up model can obtain
The observational equation of battery, observational equation describes the functional relationship of SOC, charging and discharging currents, temperature factor and battery terminal voltage；
In tradition SOC based on EKF method of estimation, available total capacity Qreal of battery is counted as a constant value,
It practice, the temperature in environmental factors can affect Qreal, and then the precision of SOC estimation is produced impact.
Battery actual capacity is set as an amount varied with temperature, for inquiring into the temperature impact on Qreal, by grinding
Study carefully battery actual capacity Changing Pattern at different temperatures, introduce temperature compensation coefficient ηTRevise battery under different temperatures actual
Error between capacity and battery rated capacity.
Due to heat management system and the existence of battery discharge fever phenomenon, battery temperature meeting in electric automobile running
Changing, when ambient temperature is the most relatively low, battery temperature constantly can raise with the growth of the time of operation, and therefore battery is actual
The electricity that can release constantly raises, and therefore battery actual capacity is set as an amount varied with temperature.
Battery SOC is estimated to be divided into two parts, and Part I is the foundation of battery mathematical model, and Part II is hardware system
Design.
Modeling Link Model formula is as follows:
Uoc=U1+RohmIB+UB (1)
In formula, IB is the charging and discharging currents of battery, and U1 is the voltage at Rpol and Cpol two ends.By hybrid power arteries and veins in groups
Rush ability characteristics test and can be calculated the internal resistance of cell and temperature and the relation of SOC, close with what SOC changed according to the internal resistance of cell
Internal resistance of cell expression formula can be divided into two sections by the degree of cutting:
Cpol=τ/Rpol (4)
In formula, Rohm represents electrokinetic cell ohmic internal resistance, and Rpol represents battery polarization internal resistance, and Cpol represents battery polarization electricity
Holding, T represents cell operating conditions temperature, and SOC represents battery dump energy.When battery remaining power is more than 30%, battery mould
Shape parameter is affected the least by battery SOC change, can be reduced to the battery model function to ambient temperature, reduces algorithm complicated
Degree；When battery remaining power is less than 30%, battery model parameter has significantly change, therefore battery with battery SOC change
Model parameter is the binary crelation formula relevant to battery SOC and ambient temperature.
In discharge process, SOC state equation is
Qreal=Qfull/ηT (6)
WhereinSOC (t) is the instantaneous SOC value of t；SOC (0) is initial SOC value；When i (t) is t
Carve instantaneous current value；Qreal is that battery can use total capacity.Qfull is battery nominal total capacity, and η T is proportionality coefficient, is used for mending
Repay influence factor's impact on battery total capacity.This is that battery discharge directly carries out ampere-hour integration, but integration is by mistake in time
Difference constantly cumulative can cause SOC estimation difference bigger than normal, therefore uses expanded Kalman filtration algorithm (EKF) to eliminate cumulative error and carries
High arithmetic accuracy.
UB(SOC, T)=Uoc(SOC, T)-U1(SOC, T)-Rohm(SOC, T) IB (7)
yk=Uoc(xk)-U1(xk, T) and-Rohm(xk, T) and μk+vk (9)
Formula (7) is the observational equation of the battery Nonlinear state space model obtained according to the set up battery model of Fig. 1.?
In EKF algorithm, state variable xk of battery model is unique variable, characterizes the calculated SOC of kth time, observational variable yk table
Levy and characterize battery charge and discharge when kth time calculates by battery model kth time calculated battery terminal voltage UB, input variable μ k
Electricity electric current IB.The separate manufacturing firms model that formula (5) and (7) discretization can obtain battery is formula (8) and formula (9).Wherein Δ t is
In the sampling period, wk is system noise, and vk is observation noise, and the two is incoherent zero-mean Gauss white noise, and Uoc is battery
Open-circuit voltage, is only relevant with SOC variable.
Sampling element hardware system structure figure
For realizing battery model data collection, the amount that need to gather has cell voltage, temperature and charging and discharging currents.Hardware configuration
Figure is as shown in Figure 2.
Owing to electric automobile power battery series connection joint number is the most more, voltage acquisition uses the LTC6803-of Linear company
4 chips, detection 12 joint series-connected cell monomer voltage, and can realize the voltage of multi-section serial battery group is adopted with stacked architecture
Sample.Circuit structure is as shown in Figure 3.
Temperature acquisition uses 100k Ω NTC warming to battery temperature collection.With voltage reference to resistance and the string of NTC warming
Connection circuit is powered, and gathers warming and the series connection dividing potential drop of resistance, via signal conditioning circuit, is sent directly into ADC built-in for MCU and carries out
Voltage acquisition, calculates warming resistance value, can draw temperature value further according to warming temperature and resistance synopsis.Sample circuit such as figure
Shown in 4.
Current acquisition uses external Hall element, by coupling Hall element, obtains electric current, through Hall element
Signal condition, carries out charging and discharging currents collection.
Claims (1)
1. the lithium battery modeling method estimated for electric automobile power battery SOC, it is characterised in that: consider and include
Each electric parameter in lithium battery Thevenin model, in the impact of interior SOC environmental factors, is defined as environmental variable by temperature
Function, and obtain model parameter by the experiment of hybrid power pulse ability characteristics HPPC, first pass through test and be calculated electricity
Pool model actual parameter value, and determine the parameter fitness method of model on this basis；
According to Thevenin model, there is polarization capacity Cpol in lithium battery interior and polarization resistance Rpol is in parallel, and with sign
The direct voltage source Uoc of open-circuit voltage, ohmic internal resistance Rohm and other internal resistances R0 tandem compound, wherein polarization capacity Cpol, pole
Change resistance Rpol, ohmic internal resistance Rohm be variable element, relevant with battery SOC environmental factors, in model each electrically
Parameter can by can actually detected to parameter calculate, obtained by hybrid power pulse ability characteristics HPPC experimental calculation
To model parameter；
Analysis experimental data acquisition varying environment, because of each electrical parameters of vegetarian refreshments, utilizes discrete data curve-fitting method, obtains
Each electric parameter and the functional relationship of environmental factors；It can be seen that be less than in the range of 30% at SOC, ohmic internal resistance Rohm can dash forward
So raising, and when SOC is more than 30%, SOC is on the impact of ohmic internal resistance Rohm inconspicuous, and temperature is one on the impact of SOC
Individual continuous print process, therefore carries out segment processing to battery model parameter after analyzing influence factor；
The mathematical model of lithium-ion-power cell is the basis of estimation battery SOC, passes through set up model and can obtain battery
Observational equation, observational equation describes the functional relationship of SOC, charging and discharging currents, temperature factor and battery terminal voltage；By battery
Actual capacity is set as an amount varied with temperature, by research battery actual capacity Changing Pattern at different temperatures,
Introduce temperature compensation coefficient ηTRevise the error between battery actual capacity and battery rated capacity under different temperatures.
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Cited By (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106680722A (en) * | 2016-12-01 | 2017-05-17 | 威胜集团有限公司 | OCV-SOC curve real-time online prediction method and device |
| CN107064815A (en) * | 2017-03-31 | 2017-08-18 | 惠州市蓝微新源技术有限公司 | A kind of internal resistance of cell computational methods |
| CN107167743A (en) * | 2017-06-29 | 2017-09-15 | 北京新能源汽车股份有限公司 | Charge state estimation method and device based on electric vehicle |
| CN107870305A (en) * | 2017-12-04 | 2018-04-03 | 浙江大学城市学院 | The identification of lithium ion battery on-line parameter and SOH methods of estimation based on temperature parameter |
| CN108490361A (en) * | 2018-03-22 | 2018-09-04 | 深圳库博能源科技有限公司 | A kind of state-of-charge SoC computational methods based on high in the clouds feedback |
| CN109143092A (en) * | 2017-06-19 | 2019-01-04 | 宁德时代新能源科技股份有限公司 | Method and device for generating cell model and acquiring cell voltage and battery management system |
| CN109669131A (en) * | 2018-12-30 | 2019-04-23 | 浙江零跑科技有限公司 | Power battery SOC estimation method under a kind of work condition environment |
| CN110133505A (en) * | 2018-02-05 | 2019-08-16 | 南京湛研能源科技有限公司 | A kind of power battery charging and discharging state observation method based on variable parameter model |
| CN110188376A (en) * | 2019-04-12 | 2019-08-30 | 汉腾汽车有限公司 | A kind of power battery for hybrid electric vehicle initial quantity of electricity algorithm |
| CN110221219A (en) * | 2019-07-03 | 2019-09-10 | 中国民用航空飞行学院 | Airborne circumstance is got off the plane lithium battery SOC estimation method |
| CN111308363A (en) * | 2020-02-17 | 2020-06-19 | 中南大学 | Lithium battery state of charge estimation device and method based on self-adaptive model |
| WO2021035500A1 (en) * | 2019-08-27 | 2021-03-04 | 淄博火炬能源有限责任公司 | Online state of charge (soc) estimation system for 48v mild hybrid vehicle lithium ion battery |
| CN112557925A (en) * | 2020-11-11 | 2021-03-26 | 国联汽车动力电池研究院有限责任公司 | Lithium ion battery SOC estimation method and device |
| CN115856644A (en) * | 2023-02-28 | 2023-03-28 | 华东交通大学 | Energy storage battery modeling method |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103454592A (en) * | 2013-08-23 | 2013-12-18 | 中国科学院深圳先进技术研究院 | Method and system for estimating charge state of power battery |
| CN103744028A (en) * | 2013-12-25 | 2014-04-23 | 广西科技大学 | UKF-based storage battery SOC (state of charge) estimation method |
| CN104076293A (en) * | 2014-07-07 | 2014-10-01 | 北京交通大学 | Quantitative analysis method for observer-based SOC estimation errors of lithium batteries |
| CN104614676A (en) * | 2015-01-07 | 2015-05-13 | 王金全 | Method for modeling equivalent circuit model by considering pulse current response characteristic of energy storage battery |
| CN102981125B (en) * | 2012-11-30 | 2015-11-18 | 山东省科学院自动化研究所 | A kind of electrokinetic cell SOC method of estimation based on RC equivalent model |
| CN105277898A (en) * | 2015-10-27 | 2016-01-27 | 浙江大学 | Battery charge state detecting method |
| CN105301509A (en) * | 2015-11-12 | 2016-02-03 | 清华大学 | Combined estimation method for lithium ion battery state of charge, state of health and state of function |
-
2016
- 2016-06-17 CN CN201610444271.5A patent/CN106054081A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102981125B (en) * | 2012-11-30 | 2015-11-18 | 山东省科学院自动化研究所 | A kind of electrokinetic cell SOC method of estimation based on RC equivalent model |
| CN103454592A (en) * | 2013-08-23 | 2013-12-18 | 中国科学院深圳先进技术研究院 | Method and system for estimating charge state of power battery |
| CN103744028A (en) * | 2013-12-25 | 2014-04-23 | 广西科技大学 | UKF-based storage battery SOC (state of charge) estimation method |
| CN104076293A (en) * | 2014-07-07 | 2014-10-01 | 北京交通大学 | Quantitative analysis method for observer-based SOC estimation errors of lithium batteries |
| CN104614676A (en) * | 2015-01-07 | 2015-05-13 | 王金全 | Method for modeling equivalent circuit model by considering pulse current response characteristic of energy storage battery |
| CN105277898A (en) * | 2015-10-27 | 2016-01-27 | 浙江大学 | Battery charge state detecting method |
| CN105301509A (en) * | 2015-11-12 | 2016-02-03 | 清华大学 | Combined estimation method for lithium ion battery state of charge, state of health and state of function |
Non-Patent Citations (2)
| Title |
|---|
| 孙冬等: "基于离散滑模观测器的锂电池荷电状态估计", 《中国电机工程学报》 * |
| 魏增福等: "锂电池动态***Thevenin模型研究", 《电源技术》 * |
Cited By (19)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106680722A (en) * | 2016-12-01 | 2017-05-17 | 威胜集团有限公司 | OCV-SOC curve real-time online prediction method and device |
| CN107064815A (en) * | 2017-03-31 | 2017-08-18 | 惠州市蓝微新源技术有限公司 | A kind of internal resistance of cell computational methods |
| CN109143092A (en) * | 2017-06-19 | 2019-01-04 | 宁德时代新能源科技股份有限公司 | Method and device for generating cell model and acquiring cell voltage and battery management system |
| CN107167743A (en) * | 2017-06-29 | 2017-09-15 | 北京新能源汽车股份有限公司 | Charge state estimation method and device based on electric vehicle |
| CN107870305A (en) * | 2017-12-04 | 2018-04-03 | 浙江大学城市学院 | The identification of lithium ion battery on-line parameter and SOH methods of estimation based on temperature parameter |
| CN107870305B (en) * | 2017-12-04 | 2019-10-18 | 浙江大学城市学院 | The identification of lithium ion battery on-line parameter and SOH estimation method based on temperature parameter |
| CN110133505A (en) * | 2018-02-05 | 2019-08-16 | 南京湛研能源科技有限公司 | A kind of power battery charging and discharging state observation method based on variable parameter model |
| CN108490361B (en) * | 2018-03-22 | 2020-07-24 | 深圳库博能源科技有限公司 | Cloud feedback-based SOC (state of charge) calculation method |
| CN108490361A (en) * | 2018-03-22 | 2018-09-04 | 深圳库博能源科技有限公司 | A kind of state-of-charge SoC computational methods based on high in the clouds feedback |
| CN109669131A (en) * | 2018-12-30 | 2019-04-23 | 浙江零跑科技有限公司 | Power battery SOC estimation method under a kind of work condition environment |
| CN109669131B (en) * | 2018-12-30 | 2021-03-26 | 浙江零跑科技有限公司 | SOC estimation method of power battery under working condition environment |
| CN110188376A (en) * | 2019-04-12 | 2019-08-30 | 汉腾汽车有限公司 | A kind of power battery for hybrid electric vehicle initial quantity of electricity algorithm |
| CN110221219A (en) * | 2019-07-03 | 2019-09-10 | 中国民用航空飞行学院 | Airborne circumstance is got off the plane lithium battery SOC estimation method |
| WO2021035500A1 (en) * | 2019-08-27 | 2021-03-04 | 淄博火炬能源有限责任公司 | Online state of charge (soc) estimation system for 48v mild hybrid vehicle lithium ion battery |
| CN112601968A (en) * | 2019-08-27 | 2021-04-02 | 淄博火炬能源有限责任公司 | Charge state online estimation system for 48V light-mixed automobile lithium ion battery |
| CN111308363A (en) * | 2020-02-17 | 2020-06-19 | 中南大学 | Lithium battery state of charge estimation device and method based on self-adaptive model |
| CN112557925A (en) * | 2020-11-11 | 2021-03-26 | 国联汽车动力电池研究院有限责任公司 | Lithium ion battery SOC estimation method and device |
| CN115856644A (en) * | 2023-02-28 | 2023-03-28 | 华东交通大学 | Energy storage battery modeling method |
| CN115856644B (en) * | 2023-02-28 | 2023-05-05 | 华东交通大学 | Modeling method of energy storage battery |
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