CN102955133A - Device for predicting discharge time of automotive battery by using clustering and method thereof - Google Patents

Device for predicting discharge time of automotive battery by using clustering and method thereof Download PDF

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Publication number
CN102955133A
CN102955133A CN201210306474XA CN201210306474A CN102955133A CN 102955133 A CN102955133 A CN 102955133A CN 201210306474X A CN201210306474X A CN 201210306474XA CN 201210306474 A CN201210306474 A CN 201210306474A CN 102955133 A CN102955133 A CN 102955133A
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discharge time
electrical leakage
clustering
automobile batteries
mean value
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权纯根
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Hyundai Mobis Co Ltd
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Hyundai Mobis Co Ltd
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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]
    • G01R31/396—Acquisition or processing of data for testing or for monitoring individual cells or groups of cells within a battery
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
    • B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R19/00—Arrangements for measuring currents or voltages or for indicating presence or sign thereof
    • G01R19/165—Indicating that current or voltage is either above or below a predetermined value or within or outside a predetermined range of values
    • 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]
    • G01R31/3644—Constructional arrangements
    • G01R31/3648—Constructional arrangements comprising digital calculation means, e.g. for performing an algorithm
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M10/00—Secondary cells; Manufacture thereof
    • H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
    • H01M10/48—Accumulators combined with arrangements for measuring, testing or indicating the condition of cells, e.g. the level or density of the electrolyte
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02E60/10—Energy storage using batteries

Abstract

The invention relates to a device for predicting discharge time of automotive battery by using clustering and a method thereof. The device and method, by collecting the leakage value, filter's collection of leakage values using the clustering technology, then filtered through the calculation of clustering technology average value of the leakage, and USES to calculate the average forecast of car battery discharge time. According to the device and method, USES the battery discharge time of clustering technology, anticipate a long time parking occurs when the battery discharge, battery discharge can be caused by the automobile electronic device misoperation prevention rather than cure.

Description

Utilize the clustering technological prediction automobile batteries devices and methods therefor of discharge time
Technical field
The present invention relates to a kind of automobile batteries Forecasting Methodology of discharge time, particularly, utilize the method for the discharge time of clustering technological prediction automobile batteries.
Background technology
Usually on the automobile various electronic installations are installed, also are equipped to the automobile batteries of electronic installation power supply.Recently, frequently for the automobile, the design of this automobile batteries is absolutely necessary for the use of electronic installation.When these automobile batteriess did not use long-time placement under the state of charging, because the charging charge of dark current battery all is discharged, corresponding electronic installation may be worked.Therefore, the various electronic installations of induction can be installed for the size of induced current on the automobile batteries, cut off required intelligent battery sensor (the Intelligent Battery Sensor of battery discharge (or cutting off the battery discharge that dark current causes); IBS).
Fig. 1 is the skeleton diagram of traditional automobile power source control system.As shown in Figure 1, traditional automobile power source control system can be by automobile batteries 10 and intelligent battery sensor 20 (Intelligent battery sensor; IBS) consist of.Battery 10 provides power supply for car-mounted electronic device 30.Intelligent battery sensor 20 is responsible for the dark current size that the induction automobile batteries offers various electronic installations 30, cuts off battery discharge.The battery discharge status that electronic control unit 40 is sensed based on intelligent battery sensor 20 is controlled various electronic installations 30.It is the internal temperature sensor that battery sensor 20 utilizes the inner shunt resistance 22 (Shunt Resistor) that possesses and automobile itself to install, measurements and calculations battery allowance, battery temperature and start battery etc. pass to electronic control unit 40 with its result.
In addition, above-mentioned conventional batteries sensor 20 only is used for the dark current of sensing the information of change pattern.Be traditional battery sensor 20 measured automobiles dark current, when the absolute value of automobile dark current surpasses the absolute value of critical current, then be set to general modfel, when the absolute value of dark current is lower than the absolute value of critical current, then be set as sleep pattern, save the dark current that consumes in the battery.Therefore, traditional battery sensor does not embody the prediction battery discharge time and carries out informationalized algorithm.Information discharge time of battery is extremely important to the driver during long term stop, thus need to predict and will predict the outcome and carry out informationization, so that the scheme that allows the driver take measures in advance.
Summary of the invention
The present invention is intended to provide a kind of automobile batteries prediction unit discharge time that utilizes automobile batteries Forecasting Methodology discharge time of clustering technology and utilize the clustering technology.In order to realize above-mentioned purpose, a kind of automobile batteries prediction unit discharge time of clustering technology that utilizes that the present invention proposes comprises: inside has shunt resistance, utilize the internal resistance value of automobile batteries and the shunt voltage at shunt resistance two ends, calculate the battery sensor section of the electrical leakage that occurs when stopping; And collect the electrical leakage calculate, and according to the clustering technology collected electrical leakage is filtered, calculate the mean value of electrical leakage after filtering, utilize the mean value that calculates, prediction section discharge time of prediction automobile batteries discharge time.
A kind of automobile batteries Forecasting Methodology discharge time of clustering technology of utilizing that the present invention proposes comprises: utilize the shunt magnitude of voltage at the shunt resistance two ends that automobile batteries internal resistance value and battery sensor inside possesses, the stage of calculating the electrical leakage that occurs when stopping; Collect the stage of the electrical leakage that calculates; According to the clustering technology electrical leakage of collecting is filtered, calculate the stage of electrical leakage mean value after filtering; The mean value that utilize to calculate, the discharge time of calculating automobile batteries is according to the stage of prediction of result automobile batteries discharge time of calculating.
The advantage that the present invention has is:
According to Forecasting Methodology of the present invention, adopt the algorithm of prediction battery discharge time, the battery discharge that occurs during the prior forecast long term stop, the malfunction of the auto electroincs that battery discharge can be caused prevents trouble before it happens.
Description of drawings
Fig. 1 is the skeleton diagram of traditional automobile power source control system;
Fig. 2 is that the integral body of relevant automobile batteries prediction unit discharge time in the one embodiment of the invention consists of block diagram;
Fig. 3 and Fig. 4 are the chart of the collected electrical leakage clustering process of the collection unit shown in Fig. 2;
Fig. 5 is the process flow diagram of automobile batteries Forecasting Methodology discharge time.
Embodiment
Describe one embodiment of the present invention in detail below in conjunction with accompanying drawing.
Fig. 2 is that the integral body of relevant automobile batteries prediction unit discharge time in the one embodiment of the invention consists of block diagram.As shown in Figure 2, relevant automobile batteries prediction unit discharge time 100 (hereinafter to be referred as device) is collected electrical leakage in one embodiment of the present invention, utilizes the clustering technology that the electrical leakage of collecting is filtered.Relevant apparatus 100 calculates by the electrical leakage mean value behind the clustering technical filter in one embodiment of the present invention, utilizes the mean value prediction automobile batteries discharge time of calculating.Particularly, the relevant apparatus 100 in one embodiment of the present invention is in order to carry out above-mentioned function, as shown in Figure 2, comprise battery sensor section 120 and discharge time prediction section 140.
Battery sensor section 120 forms electrical connection with automobile batteries 50, utilizes electric current and the voltage of automobile batteries, adopts the inside battery resistance of expressing in formula (1) computing formula (1).
V=I * R Internal resistance
Figure BDA00002055417200031
And battery sensor section 120 utilizes the internal resistance value R that calculates by formula (1) Internal resistanceThe shunt magnitude of voltage V at shunt resistance (Shunt Resistor) two ends that possess with inside Shunt voltage, the electrical leakage (I when utilizing formula (2) to calculate parking Leakage current).
Figure BDA00002055417200032
The above-mentioned electrical leakage I that discharge time, prediction section 140 collection battery sensor sections 120 were calculated Leakage current, utilize the mean value of collected electrical leakage, calculate the discharge time of automobile batteries 50.
Therefore, discharge time prediction section 140 comprise collection unit 142, filtrator section 144 and discharge time calculating part 146.
Collection unit 142 is collected the electrical leakage I that battery sensor 142 calculates Leakage current, the electrical leakage of collecting is delivered to filtrator section 144.The electrical leakage that filtrator section 144 collects calculates collected electrical leakage mean value.Especially, be equipped with on the automobile in the situation of any electronic installation, filtrator section 144 calculates the change of the electrical leakage mean value of any electronic installation generation of installing and is inclined to the electrical leakage mean value that obtains reflecting.Especially, filtrator section 144 is for calculating the electrical leakage mean value of reflection electrical leakage mean value change tendency, utilize the clustering technology that the electrical leakage of collecting is filtered, calculate the electrical leakage mean value after filtering, can reflect the change tendency of the electrical leakage mean value that any electronic installation produces.Leakage Current is to change according to vehicle condition.Especially, when on the automobile any electronic installation being installed, Leakage Current can be higher than mean value.Be that Leakage Current is the factor according to the state instantaneous variation of automobile, so need by suitable filtering technique process data (electrical leakage).Therefore, above-mentioned filtrator section 144 utilizes the data rule of inputting by above-mentioned collection unit 142 according to clustering (clustering) technology, and namely the rule of electrical leakage embodies suitable data filtering.The advantage of clustering technology is the heterogeneous pattern of analyzing from the data with like attribute, judges more exactly the tendentiousness of data.And the processing aspect of Large Volume Data also has faster processing speed than other pattern analysis algorithms.Such as Fig. 3 and Fig. 4, describe the clustering process of the electrical leakage of filtrator section execution in detail.
Discharge time, calculating part 146 utilization reflection electrical leakages changed the electrical leakage mean value of tendency, namely passed through the electrical leakage mean value I of clustering algorithm clustering Leakage current N, the discharge time of calculating automobile batteries 50.
As the following formula shown in (3), general battery rated capacity can be expressed as 20 hours discharge rates in 27 ℃ of electrolyte temperatures.
Battery capacity (Ah)=leakage current (I Leakage current N) * 20h (3)
Above-mentioned battery capacity is fixed value, thus being as the criterion discharge time, can be from formula (3) derived expression (4).
Figure BDA00002055417200041
Therefore, can calculate the complete discharge time of the battery that Leakage Current causes by (4).
But automobile batteries capacity minimizing 30% just can't start in the reality.Therefore, when the capacity that will be equivalent to battery 30% is adapted to formula (4), can't start the required time can express with formula (5).
Figure BDA00002055417200051
As mentioned above, information discharge time that the algorithm that discharge time, calculating part 146 showed by formula (5) calculates is delivered to electronic control unit 150 by vehicle-mounted communication modes such as LIN communication or CAN communications.
Electronic control unit 150 is estimated discharge time by 200 demonstrations of the display parts such as cluster (Cluster), or is used for the vehicles Leakage Current based on information discharge time.And expectation relevant information discharge time can also be used as the revisal data of the startability algorithm (SOF:State Of Function) of battery sensor section 120.
The clustering conditional curve figure of the electrical leakage that the collection unit as shown in Fig. 2 of Fig. 3 and Fig. 4 demonstration is collected.The Leakage Current of trying to achieve in the aforesaid formula (2) all can be different when each the parking, and is the same with curve map shown in Figure 3, with the data acquisition appearance of specific modality.In the clustering technology of the present invention, be not particularly limited, but can adopt the average clustering algorithm of K.As shown in Figure 3, the average clustering algorithm of K, its input field is divided into first to fourth field (Cluster_1, Cluster_2, Cluster_3, Cluster_4) of trooping, and distributes to the central value (data_center) of trooping.The central value of distributing can show with the coordinate figure of 2 dimensions.
The electrical leakage that tries to achieve in the formula (2) is input to the filter house 144 of Fig. 2 of built-in clustering algorithm through the collection unit 142 of Fig. 2, and curve map as shown in Figure 4 is the same, is under the jurisdiction of the pre-determined field of trooping.Show in the embodiment of Fig. 4 that any electrical leakage (data_1) with x1, y1 coordinate figure belongs to the second example of trooping field (Cluster_2).
Belong to the second electrical leakage (data_1) (x1 that troops field (Cluster_2), y1), with the second central value (data_center) (X1 that troops field (Cluster_2), Y1) together, be used for trying to achieve the new central value of this troop (Cluste r_2).At this moment, new central value coordinate can show with following formula (6).
Figure BDA00002055417200052
Therefore, the corresponding central value of coordinate according to formula (6) recomputates becomes the new mean value that this is trooped.Be the mean value I that new central value becomes aforesaid clustering Leakage Current Leakage current N
The Leakage Current data of input are used for trying to achieve central value again afterwards.The central value of trying to achieve so finally becomes this tendentious mean value of trooping of reflection.Therefore, the simple tendentiousness that has reflected the input data of on average comparing with the past can improve the reliability of the discharge time that will try to achieve.
Fig. 5 is automobile batteries Forecasting Methodology discharge time precedence diagram.As shown in Figure 5, at first start the process (S310) of calculating electrical leakage during parking.In this process, utilize the shunt magnitude of voltage at the shunt resistance two ends of the internal resistance value of automobile batteries and battery sensor inside, calculate the electrical leakage that occurs when stopping.
Next, collect the electrical leakage (S320) that calculates, according to clustering (clustering) technology such as the average clustering algorithms of K the electrical leakage of collecting is filtered, calculate the electrical leakage mean value (S330) after filtering.
Next, utilize the mean value calculation automobile batteries discharge time of calculating, according to prediction of result automobile batteries discharge time (S340) that calculates.
Afterwards, the discharge time of predicting, relevant information can be delivered to electronic control unit 150 by vehicle-mounted communication modes such as LIN communication or CAN communications, electronic control unit 150 is based on information discharge time, estimate discharge time by 200 demonstrations of the display parts such as cluster, or be used for adjusting the adjusting of automobile Leakage Current.And discharge scheduled time information is used for the revisal data of the startability algorithm (SOF:S tate Of Function) of battery sensor section 120.

Claims (7)

1. one kind is utilized the clustering technological prediction automobile batteries method of discharge time, it is characterized in that, comprises following several stages:
Utilize the shunt magnitude of voltage at the shunt resistance two ends of the internal resistance value of automobile batteries and battery sensor inside, calculate the stage of the electrical leakage that occurs when stopping;
Collect the stage of the electrical leakage of above-mentioned calculating;
According to the clustering technology electrical leakage of collecting is filtered, calculate the stage of the electrical leakage mean value after filtering; And
Utilize the mean value that calculates, calculate the discharge time of automobile batteries, according to the result of calculation prediction automobile batteries stage of discharge time.
2. the clustering technological prediction automobile batteries method of discharge time of utilizing according to claim 1 is characterized in that, according to the mean value of the above-mentioned electrical leakage of clustering technique computes is:
When any electronic installation was installed on the automobile, the change tendency of the electrical leakage mean value that produces according to any electronic installation of above-mentioned installation obtained the reflection value.
3. the clustering technological prediction automobile batteries method of discharge time of utilizing according to claim 2 is characterized in that, described clustering technology is the average clustering algorithm of K.
4. the clustering technological prediction automobile batteries method of discharge time of utilizing according to claim 1 is characterized in that, calculates automobile batteries and also comprises after the stage of discharge time:
Calculate automobile batteries relevant information discharge time, be delivered to the stage of electronic control unit by automotive interior communication; And
The stage that shows automobile batteries relevant information discharge time that receives from electronic control unit by display unit in the mode of vision.
5. the clustering technological prediction automobile batteries method of discharge time of utilizing according to claim 4, it is characterized in that, also comprise: it's the stage of the data of electronic control unit revisal battery sensor startability algorithm is past automobile batteries relevant information exchange discharge time.
6. automobile batteries prediction unit discharge time that utilizes the clustering technology is characterized in that, comprising:
Inside has shunt resistance, utilizes the internal resistance value of automobile batteries and the shunt magnitude of voltage at shunt resistance two ends, calculates the battery sensor section of the electrical leakage that occurs when stopping; And
Collect the electrical leakage that calculates, according to the clustering technology electrical leakage of collecting is filtered, the mean value of electrical leakage utilized the mean value that calculates after calculating was filtered, and predicted automobile batteries prediction section discharge time of discharge time.
7. automobile batteries prediction unit discharge time that utilizes the clustering technology according to claim 6 is characterized in that, above-mentioned discharge time, prediction section comprised,
The collection unit of the electrical leakage that calculates;
According to the clustering technology of the average clustering algorithm of K, the electrical leakage that the electrical leakage collection unit is collected filters, and calculates the filter house of electrical leakage mean value after filtering;
Utilize the electrical leakage mean value of above-mentioned calculating, calculate automobile batteries calculating part discharge time of discharge time.
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CN106461715A (en) * 2014-04-30 2017-02-22 宝马股份公司 Method for identifying subtle quiescent current errors
CN107658921A (en) * 2017-09-08 2018-02-02 成都瓦力特新能源技术有限公司 Communication base station fixed sources of energy management system and its management method
CN104515967B (en) * 2013-10-08 2018-04-20 现代摩比斯株式会社 The voltage channel of car battery sensor is from compensating device and its method
CN109917294A (en) * 2019-03-25 2019-06-21 深圳艾威仕汽车检测设备有限公司 Vehicle battery electric leakage monitoring method based on big data analysis

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Application publication date: 20130306