CN105974323B - A kind of algorithm model improving electric car SOC estimation precision - Google Patents
A kind of algorithm model improving electric car SOC estimation precision Download PDFInfo
- Publication number
- CN105974323B CN105974323B CN201610303779.3A CN201610303779A CN105974323B CN 105974323 B CN105974323 B CN 105974323B CN 201610303779 A CN201610303779 A CN 201610303779A CN 105974323 B CN105974323 B CN 105974323B
- Authority
- CN
- China
- Prior art keywords
- soc
- battery
- total capacity
- initial
- value
- Prior art date
Links
- 230000001186 cumulative Effects 0.000 claims abstract description 7
- 230000015556 catabolic process Effects 0.000 claims abstract description 4
- 230000004059 degradation Effects 0.000 claims abstract description 4
- 238000006731 degradation reactions Methods 0.000 claims abstract description 4
- 244000171263 Ribes grossularia Species 0.000 claims description 35
- 238000001914 filtration Methods 0.000 claims description 5
- 230000000875 corresponding Effects 0.000 claims description 3
- 238000005070 sampling Methods 0.000 claims description 3
- 238000000034 methods Methods 0.000 abstract description 5
- 238000006243 chemical reactions Methods 0.000 abstract description 3
- 238000010586 diagrams Methods 0.000 description 3
- 238000009825 accumulation Methods 0.000 description 2
- 238000004364 calculation methods Methods 0.000 description 2
- 230000001537 neural Effects 0.000 description 2
- 241001269238 Data Species 0.000 description 1
- 229910052493 LiFePO4 Inorganic materials 0.000 description 1
- 238000004458 analytical methods Methods 0.000 description 1
- 238000007599 discharging Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005868 electrolysis reactions Methods 0.000 description 1
- 239000003792 electrolytes Substances 0.000 description 1
- 238000005516 engineering processes Methods 0.000 description 1
- 238000002474 experimental methods Methods 0.000 description 1
- 239000007788 liquids Substances 0.000 description 1
- 230000005012 migration Effects 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000006011 modification reactions Methods 0.000 description 1
- MYMOFIZGZYHOMD-UHFFFAOYSA-N oxygen Chemical compound data:image/svg+xml;base64,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 data:image/svg+xml;base64,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 O=O MYMOFIZGZYHOMD-UHFFFAOYSA-N 0.000 description 1
- 229910052760 oxygen Inorganic materials 0.000 description 1
- 239000001301 oxygen Substances 0.000 description 1
Classifications
-
- 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]
- G01R31/385—Arrangements for measuring battery or accumulator variables
- G01R31/387—Determining ampere-hour charge capacity or SoC
Abstract
Description
Technical field
The present invention relates to electric vehicle engineering fields, and in particular to a kind of algorithm for improving electric car SOC estimation precision Model.
Background technique
Battery remaining power SOC (state of charge) estimation is a part important in BMS, as full-vehicle control One of tactful judgment criteria, SOC estimation receive combined influence (such as charge-discharge magnification, initial SOC, the environment temperature of several factors Degree, self discharge etc.).In addition, operating condition complicated in vehicle traveling process but also SOC estimation be difficult in practical applications it is accurate, So the estimation precision for improving battery SOC is a research hotspot of field of battery management.
Some SOC algorithms mainly have discharge test method, current integration method, open circuit voltage method, linear model method, neural network Method, Kalman filtering method etc..And the shortcomings that discharge test method is the SOC estimation in the case of can not adapting to curent change；Ampere-hour product The shortcomings that point-score, is to rely on initial SOC value and is easy to be influenced by the self discharge of battery itself；Open circuit voltage method is not suitable for Line SOC real-time monitoring is generally combined with other methods for assisting amendment precision；The shortcomings that linear analogue method is to be only applicable to SOC changes little system, more rare in practical applications；The shortcomings that neural network is that a large amount of reference datas is needed to carry out Training, estimation method are influenced very big by data and method；Kalman filtering algorithm is applicable in various battery systems, is especially suitable for electric current The power vehicle SOC estimation under big operating condition is fluctuated, but it is higher to algorithm design requirement, it is comparatively laborious, it is not easy to system realization.
Summary of the invention
In view of the defects and deficiencies of the prior art, the present invention intends to provide a kind of raising electric car SOC to estimate The algorithm model of precision.
In order to solve the problems existing in background technology, the calculation of a kind of raising electric car SOC estimation precision of the invention Method model, its specific method are as follows:
(1), initial SOC error correction:
The difference between SOC that the SOC value and open-circuit voltage saved according to the last time is tabled look-up, saves with the last time The proportionate relationship between the difference of the total capacity calculated after temperature, self discharge correction factor is added in battery total capacity, finds out current The total capacity of battery；Then, the influence in conjunction with the total capacity of present battery and cycle life to cell degradation degree comes real The correction of existing initial time active volume, thus the initial SOC value after being corrected；
(2), SOC accumulated error corrects:
The relationship sample space caused between the factor of cumulative errors and SOC set up by battery off-line test, packet Discharge-rate and SOC containing temperature from SOC, the rate of charge of different charging currents and SOC, different discharge current sizes, Each corresponding relationship stands alone as a subprogram；SOC accumulated error is compensated with different correction factors, core side Method is current integration method；
On the other hand, the precision of SOC estimation additionally depends on the accuracy of current sampling data, calculates SOC dynamic in timing and becomes During change value, moving average filter and middle position value filtering method are carried out to electric current；
System obtains current average first, and passes through battery total capacity and initial SOC value that initial capacity is estimated, obtains To present battery active volume；
Secondly according to present current value, total capacity value, look into charge efficiency table and thermometer to present battery active volume into Row correction process, obtains present available capacity；
Current integration method is finally used, the capacity changed in certain time is calculated by current average, to obtain in real time Battery capacity SOC value.
The invention has the following beneficial effects: being estimated based on temperature correction, the correction of conversion coulombic efficiency, self discharge and SOH compensation Initial SOC, and the cumulative errors correcting algorithm combined using ampere-hour integral, open circuit voltage method, and obtained in conjunction with many experiments Empirical value, propose refinement cell operating status, according to impact factor compensate error SOC estimation scheme, to efficiently solve There are problems that initial error and cumulative errors in SOC estimation process.
Detailed description of the invention
Fig. 1 is the structural diagram of the present invention；
Fig. 2 is the flow chart of battery system initial capacity prediction model in the present invention；
Fig. 3 is the flow chart of battery system SOC calibration model in the present invention；
Fig. 4 is the schematic diagram that cell tester and BMS estimate SOC in embodiment；
Fig. 5 is the schematic diagram of SOC estimation error in embodiment.
Specific embodiment
With reference to the accompanying drawing, the present invention is further illustrated.
In order to make the objectives, technical solutions, and advantages of the present invention clearer, below in conjunction with attached drawing and specific implementation Mode, the present invention will be described in further detail.It should be appreciated that the specific embodiments described herein are only to explain this Invention, is not intended to limit the present invention.
As shown in Figure 1-3, present embodiment adopts the following technical scheme that
One, the correct influences factor:
1, temperature correction: temperature is an important factor for influencing battery SOC estimation, it is however generally that, the higher electrolysis liquid oxygen of temperature Change and accelerate, electrolyte conductivity increases, so that migration internal resistance reduces, so that the capacity that battery is released when low temperature than increasing Greatly, influence of the following formula compensation temperature to capacity is used in this algorithm:
QT=Q30[1+KT*(T-30)]
Wherein T is Current Temperatures, and battery capacity when QT is T temperature, KT is temperature coefficient, empirical value 0.006-0.008.
2, it converts coulombic efficiency: by the equivalent coulombic efficiency to 1/3C of the coulombic efficiency of different electric currents, defining pattern library Human relations efficiency is ηs, numerical value be equal to 1/3C from battery release electricity QsdBattery SOC is set to be restored to the preceding state institute of electric discharge with using 1/3C The electricity Q neededscRatio；It is respectively η that definition, which is charged and discharged coulombic efficiency,cAnd ηd, all it is with any electric current InCharge and discharge The ratio of electricity in journey.The following are three kinds of conversion coulombic efficiency calculation formula:
The coulombic efficiency of 1/3C multiplying power:
Charge coulombic efficiency:
Discharge coulombic efficiency:
3, self discharge: self discharge COEFFICIENT K is defined as the difference of open-circuit voltage and the ratio of time difference:
K=Δ OCV/ Δ t；
Cycle life SOH compensation: being measured in this system using the capacity that battery accumulation is released, and capacity is released in battery accumulation Reach 80% and be used as battery one cycle, the ratio of cycle life is decayed as battery life when cycle-index and battery dispatch from the factory Rate, correction factor of the battery cycle life attenuation rate as system start-up phase.
Two, specific method:
1, initial SOC error correction:
The difference between SOC that the SOC value and open-circuit voltage saved according to the last time is tabled look-up, saves with the last time The proportionate relationship between the difference of the total capacity calculated after temperature, self discharge correction factor is added in battery total capacity, finds out current The total capacity of battery.Then, the influence in conjunction with the total capacity of present battery and cycle life to cell degradation degree comes real The correction of existing initial time active volume, thus the initial SOC value after being corrected.Its mathematical model are as follows:
SOC0(k)=SOC (k-1)；
SOC0` (k)=f (OCV (k))；
Q0(k)=Q (k-1)；
Q0` (k)=Q0(k)*f(Temp(k))*f(Time(k),Temp(k))；
Wherein, SOC is battery remaining power, and Q is total capacity, and f (OCV), f (Temp), f (Time, Temp), f (SOH) divide Not Wei open-circuit voltage, temperature, self discharge, cycle life and battery capacity functional relation.
2, SOC accumulated error corrects:
The relationship sample space caused between the factor of cumulative errors and SOC set up by battery off-line test, it is main Will comprising temperature and SOC, the rate of charge of different charging currents and SOC, difference discharge current size discharge-rate and SOC, each corresponding relationship stand alone as a subprogram.SOC accumulated error is compensated with different correction factors, core Heart method is current integration method.
On the other hand, the precision of SOC estimation additionally depends on the accuracy of current sampling data, in order to reach this purpose, During timing calculates SOC dynamic change value, moving average filter and middle position value filtering method are carried out to electric current.
System obtains current average first, and passes through battery total capacity and initial SOC value that initial capacity is estimated, obtains To present battery active volume.
Secondly according to present current value, total capacity value, look into charge efficiency (discharge-rate) table and thermometer to present battery Active volume is corrected processing, obtains present available capacity.
Current integration method is finally used, the capacity changed in certain time is calculated by current average, to obtain in real time Battery capacity SOC value.
Mathematical model is as follows:
Q0avail(k)=f (Temp (k)) * f (I (k)) * Q0(k)；
Qavail(k)=Q0avail(k)-I(k)*Δt；
AvailT (k)=Qavail(k)/(I(k))；
Wherein Q0availIt is the active volume that battery charging and discharging is initially, QavailIt is the real-time active volume of battery, AvailT is Time workable for battery, f (I) are the functional relations of electric current and battery capacity.
Embodiment
Experiment uses 18650 LiFePO4 battery cores, discharges under 1/3C multiplying power and room temperature, and charge and discharge cycles number 200 times. The BMS SOC estimated is compared with the practical SOC that cell tester records, by the analysis to experimental data, such as Fig. 4, figure Shown in 5, SOC estimation error is less than 3.8%.By the actual test on the electric car of different battery systems, which can Greatly to improve the SOC estimation precision under complex working condition.
The above is only used to illustrate the technical scheme of the present invention and not to limit it, and those of ordinary skill in the art are to this hair The other modifications or equivalent replacement that bright technical solution is made, as long as it does not depart from the spirit and scope of the technical scheme of the present invention, It is intended to be within the scope of the claims of the invention.
Claims (1)
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610303779.3A CN105974323B (en) | 2016-05-09 | 2016-05-09 | A kind of algorithm model improving electric car SOC estimation precision |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610303779.3A CN105974323B (en) | 2016-05-09 | 2016-05-09 | A kind of algorithm model improving electric car SOC estimation precision |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN105974323A CN105974323A (en) | 2016-09-28 |
| CN105974323B true CN105974323B (en) | 2019-04-12 |
Family
ID=56991442
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610303779.3A CN105974323B (en) | 2016-05-09 | 2016-05-09 | A kind of algorithm model improving electric car SOC estimation precision |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN105974323B (en) |
Families Citing this family (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106646243A (en) * | 2016-11-09 | 2017-05-10 | 珠海格力电器股份有限公司 | Storage battery state of charge calculation method and device |
| CN108614216B (en) * | 2016-12-12 | 2020-12-25 | 财团法人车辆研究测试中心 | Battery health state estimation device and method |
| CN110249233A (en) * | 2017-02-09 | 2019-09-17 | Abb瑞士股份有限公司 | Health status for battery is estimated |
| CN106585422B (en) * | 2017-02-17 | 2020-07-17 | 合肥国轩高科动力能源有限公司 | SOH estimation method for power battery |
| CN107450026A (en) * | 2017-07-06 | 2017-12-08 | 惠州晟芯源科技有限公司 | A kind of unmanned plane electrokinetic cell SOC estimation method |
| CN107991618A (en) * | 2017-11-21 | 2018-05-04 | 惠州市蓝微电子有限公司 | A kind of SOC estimation method and device |
| CN107991623B (en) * | 2017-11-27 | 2020-05-08 | 山东大学 | Battery ampere-hour integral SOC estimation method considering temperature and aging degree |
| TWI628453B (en) * | 2018-01-19 | 2018-07-01 | 新盛力科技股份有限公司 | Method for estimated battery capacity |
| CN108680867B (en) * | 2018-05-21 | 2020-07-10 | 合肥工业大学 | Online SOC calibration method of all-vanadium redox flow battery based on capacity correction |
| CN109407013B (en) * | 2018-09-29 | 2020-12-18 | 广州小鹏汽车科技有限公司 | Standby battery electric quantity state monitoring circuit and method |
| CN109061521A (en) * | 2018-10-30 | 2018-12-21 | 四川长虹电源有限责任公司 | Battery group state-of-charge calculation method and system |
| CN109521370A (en) * | 2018-11-20 | 2019-03-26 | 厦门科灿信息技术有限公司 | A kind of battery SOC acquisition methods, system, device and readable storage medium storing program for executing |
| CN109633451A (en) * | 2018-12-19 | 2019-04-16 | 东莞钜威动力技术有限公司 | Energy-storage system is from trajectory parameters scaling method and SOC estimation method |
| CN109581242A (en) * | 2018-12-28 | 2019-04-05 | 蜂巢能源科技有限公司 | The method for estimating error and system of state-of-charge SOC |
| CN109633457A (en) * | 2019-02-22 | 2019-04-16 | 上海度普新能源科技有限公司 | A kind of acquisition methods and acquisition system of remaining capacity |
| CN109782210A (en) * | 2019-03-11 | 2019-05-21 | 威马智慧出行科技（上海）有限公司 | The current sensor bearing calibration of cell management system of electric automobile and electronic equipment |
| CN110058177A (en) * | 2019-05-06 | 2019-07-26 | 奇瑞新能源汽车技术有限公司 | A kind of power battery electricity SOC modification method |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102062841A (en) * | 2009-11-11 | 2011-05-18 | 北汽福田汽车股份有限公司 | Estimation method and system of state of charge (SOC) of power battery |
| CN102662148A (en) * | 2012-05-09 | 2012-09-12 | 中国农业大学 | On-line feedback battery state of charge (SOC) predicting method |
| CN103018679A (en) * | 2012-12-10 | 2013-04-03 | 中国科学院广州能源研究所 | Estimation method of initial state of charge (SOC0) of lead-acid cell |
| CN103529396A (en) * | 2013-10-25 | 2014-01-22 | 重庆长安汽车股份有限公司 | Estimating method for initial value of stage of charge of high-accuracy lithium ion battery |
| CN104241713A (en) * | 2013-06-18 | 2014-12-24 | 北汽福田汽车股份有限公司 | SOC (state of charge) correcting method and SOC correcting device of power battery |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9128162B2 (en) * | 2012-09-19 | 2015-09-08 | Apple Inc. | Estimating state of charge (SOC) and uncertainty from relaxing voltage measurements in a battery |
| JP2015038444A (en) * | 2013-08-19 | 2015-02-26 | 株式会社東芝 | Secondary battery remaining capacity estimation method and secondary battery remaining capacity estimation apparatus |
| KR101529515B1 (en) * | 2014-06-12 | 2015-06-17 | 국방과학연구소 | Apparatus and method for estimating battery charge state using mmae-ekf |
-
2016
- 2016-05-09 CN CN201610303779.3A patent/CN105974323B/en active IP Right Grant
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102062841A (en) * | 2009-11-11 | 2011-05-18 | 北汽福田汽车股份有限公司 | Estimation method and system of state of charge (SOC) of power battery |
| CN102662148A (en) * | 2012-05-09 | 2012-09-12 | 中国农业大学 | On-line feedback battery state of charge (SOC) predicting method |
| CN103018679A (en) * | 2012-12-10 | 2013-04-03 | 中国科学院广州能源研究所 | Estimation method of initial state of charge (SOC0) of lead-acid cell |
| CN104241713A (en) * | 2013-06-18 | 2014-12-24 | 北汽福田汽车股份有限公司 | SOC (state of charge) correcting method and SOC correcting device of power battery |
| CN103529396A (en) * | 2013-10-25 | 2014-01-22 | 重庆长安汽车股份有限公司 | Estimating method for initial value of stage of charge of high-accuracy lithium ion battery |
Non-Patent Citations (1)
| Title |
|---|
| 基于安时积分法的电池SOC估算误差校正;鲍慧 等;《计算机仿真》;20131130;第30卷(第11期);第148-151页 |
Also Published As
| Publication number | Publication date |
|---|---|
| CN105974323A (en) | 2016-09-28 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Wang et al. | Modeling and state-of-charge prediction of lithium-ion battery and ultracapacitor hybrids with a co-estimator | |
| CN107690585B (en) | Method and apparatus for determining the state of health and state of charge of a lithium sulfur battery | |
| Zou et al. | Combined state of charge and state of health estimation over lithium-ion battery cell cycle lifespan for electric vehicles | |
| Zheng et al. | Investigating the error sources of the online state of charge estimation methods for lithium-ion batteries in electric vehicles | |
| CN105607009B (en) | A kind of power battery SOC methods of estimation and system based on dynamic parameter model | |
| Wei et al. | Enhanced online model identification and state of charge estimation for lithium-ion battery with a FBCRLS based observer | |
| Wang et al. | A method for state-of-charge estimation of LiFePO4 batteries at dynamic currents and temperatures using particle filter | |
| Sun et al. | Estimation of state-of-charge and state-of-power capability of lithium-ion battery considering varying health conditions | |
| Tian et al. | A modified model based state of charge estimation of power lithium-ion batteries using unscented Kalman filter | |
| Mastali et al. | Battery state of the charge estimation using Kalman filtering | |
| CN104502858B (en) | Electrokinetic cell SOC methods of estimation and system based on backward difference discrete model | |
| CN103399276B (en) | A kind of capacity of lithium ion battery is estimated and cycles left life-span prediction method | |
| CN103852727B (en) | Method and device for estimating power battery charge state on line | |
| Wei et al. | Adaptive estimation of state of charge and capacity with online identified battery model for vanadium redox flow battery | |
| CN103630843B (en) | Battery status estimating unit and estimation method, battery control device, battery system | |
| Li et al. | State of charge estimation for LiMn2O4 power battery based on strong tracking sigma point Kalman filter | |
| JP5818878B2 (en) | Lithium ion battery charge state calculation method | |
| He et al. | Comparison study on the battery models used for the energy management of batteries in electric vehicles | |
| CN103616647B (en) | A kind of estimation method of battery dump energy for cell management system of electric automobile | |
| US8975897B2 (en) | State-of-charge estimating apparatus | |
| CN105093122B (en) | Emergency light battery SOC method of estimation based on the adaptive SQKF of strong tracking | |
| CN104360285B (en) | A kind of battery capacity modification method based on improved ampere-hour integration method | |
| CN103969587B (en) | A kind of hybrid vehicle electrokinetic cell SOC estimation method | |
| CN105068011B (en) | A method of for estimating and correcting LiMn2O4 series-connected cell packet SOC value | |
| CN105353313B (en) | Method and device for estimating state of charge of battery |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |