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 PDF

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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
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China
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soc
battery
total capacity
initial
value
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CN201610303779.3A
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Chinese (zh)
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CN105974323A (en
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吉跃华
袁伟宏
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深圳市鑫成泰科技有限公司
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    • GPHYSICS
    • G01MEASURING; TESTING
    • G01RMEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/385Arrangements for measuring battery or accumulator variables
    • G01R31/387Determining ampere-hour charge capacity or SoC

Abstract

The invention discloses a kind of algorithm models for improving electric car SOC estimation precision, it is related to electric vehicle engineering field;Its specific method is 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, the proportionate relationship between the difference of the total capacity calculated after temperature, self discharge correction factor is added with the battery total capacity that the last time saves, finds out the total capacity of present battery;The influence of total capacity and cycle life to cell degradation degree then in conjunction with present battery, the correction of Lai Shixian initial time active volume, the initial SOC value after being corrected;(2), SOC accumulated error corrects;The present invention is based on temperature correction, the correction of conversion coulombic efficiency, self discharge and SOH compensation to estimate initial SOC, and the cumulative errors correcting algorithm combined using ampere-hour integral, open circuit voltage method, solve the problems, such as that there are initial errors and cumulative errors in SOC estimation process.

Description

A kind of algorithm model improving electric car SOC estimation precision

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)

1. a kind of algorithm model for improving electric car SOC estimation precision, it is characterised in that: its specific method is 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, the battery saved with the last time The proportionate relationship between the difference of the total capacity calculated after temperature, self discharge correction factor is added in total capacity, finds out present battery Total capacity;Then, the influence in conjunction with the total capacity of present battery and cycle life to cell degradation degree, to realize just The correction for the moment active volume that begins, 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 includes temperature The discharge-rate and SOC of the rate of charge and SOC, different discharge current sizes from SOC, different charging currents are spent, it is each Kind corresponding relationship stands alone as a subprogram;SOC accumulated error is compensated with different correction factors, core methed 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 change value in timing During, 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, is worked as Preceding battery active volume;
Secondly according to present current value, total capacity value, look into charge efficiency table and thermometer school carried out to present battery active volume Positive processing, obtains present available capacity;
Current integration method is finally used, the capacity changed in certain time is calculated by current average, to obtain real-time electricity Tankage SOC value.
CN201610303779.3A 2016-05-09 2016-05-09 A kind of algorithm model improving electric car SOC estimation precision CN105974323B (en)

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Families Citing this family (17)

* Cited by examiner, † Cited by third party
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)

* Cited by examiner, † Cited by third party
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)

* Cited by examiner, † Cited by third party
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

Patent Citations (5)

* Cited by examiner, † Cited by third party
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)

* Cited by examiner, † Cited by third party
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

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