CN114167190B - Micro-short circuit identification method for hybrid vehicle battery - Google Patents
Micro-short circuit identification method for hybrid vehicle battery Download PDFInfo
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Abstract
A hybrid vehicle battery micro-short circuit identification method comprises the following steps: collecting battery related parameters of two different dates of a vehicle; building a battery model, obtaining Uocmax and Uocmin of different dates by applying an identification algorithm, and converting into corresponding SOCmax and SOCmin; splicing the SOCmax obtained by conversion into one array, and splicing SOCmin into another array; sequencing SOCmax arrays from large to small, sequencing SOCmin arrays according to a SOCmax array sequencing mode, and recording the date of each number in the SOCmin arrays; recording a position sequence number index (i) of which the date of the SOCmin is increased; calculating according to a formula to obtain a battery month self-discharge rate SDR; and judging the micro short circuit risk degree of the battery according to the SDR value. The invention can more accurately identify the risk of the period with smaller self-discharge rate increase amplitude in the early stage of micro-short circuit, and is not interfered by current integration errors.
Description
Technical Field
The invention relates to the technical field of new energy battery application, in particular to a hybrid vehicle battery micro-short circuit identification method.
Background
The battery core in the power battery system is deteriorated due to factory manufacturing defects or long-term use, so that the phenomenon that the micro-short circuit is gradually increased and the self-discharge rate is gradually increased is shown. The battery of the pure electric vehicle is always in a full-charge state, and the phenomenon of self-discharge increase can be monitored by comparing voltage differences under the same SOC state (such as 100%), so that the micro-short circuit battery core is screened. However, the battery system of the hybrid new energy vehicle has a relatively narrow SOC interval in which the SOC is typically 40 to 60% and typically not higher than 80% SOC, and many vehicles do not exist in a fully charged state, and the SOC value in the stationary state of the vehicle is not fixed. The change in differential pressure at different SOC locations is not equivalent to the corresponding change in capacity. Even if the capacity is unchanged, the differential pressure changes with the change in SOC. Therefore, the randomness of the SOC in the mixed parking state can not be used for identifying the change of the self-discharge rate by using simple pressure difference change, and particularly, the change of the self-discharge rate in a period with smaller increase amplitude in the initial stage of micro-short circuit can not be identified, so that the micro-short circuit identification difficulty of the battery core of the hybrid battery system is increased.
At present, in the identification method of the micro short circuit of the power battery system, some micro short circuit internal resistance needs to be estimated, the estimation of the micro circuit internal resistance needs to be established on the basis of current integration, and the error of the current integration is larger than that of a pure electric vehicle and is not applicable to a hybrid electric vehicle; some of the hybrid vehicles are required to record the change value of the charge capacity and the discharge capacity of the battery, or the battery is required to be charged to full power or discharged to empty power, so that the requirements of the hybrid vehicles are difficult to meet; some neural network technologies are used, and the method cannot achieve on-line efficient calculation; some are determined by using the difference between the simulation value and the actual measurement value of the battery model, but the method is affected by the accuracy of the simulation value and may have a misjudgment possibility. Therefore, we provide a method for identifying micro-short circuit of hybrid vehicle.
Disclosure of Invention
The invention provides a hybrid vehicle battery micro-short circuit identification method, which aims to overcome the defect that the micro-short circuit of the battery core of the traditional hybrid power battery system cannot be identified by simple pressure difference change.
The invention adopts the following technical scheme:
a hybrid vehicle battery micro-short circuit identification method comprises the following steps:
step one: collecting battery related parameters of at least two different dates of a vehicle;
step two: building a battery model, obtaining a highest single open circuit voltage value Uocmax and a lowest single open circuit voltage value Uocmin of different dates by applying an identification algorithm, and converting the highest single open circuit voltage value Uocmax and the lowest single open circuit voltage value Uocmin into corresponding SOCmax and SOCmin;
step three: splicing SOCmax obtained by converting two different dates into one array, and splicing SOCmin of the two different dates into another array;
step four: sequencing the SOCmax arrays from large to small in the third step, sequencing the SOCmin arrays according to a sequencing mode of the SOCmax arrays, and recording the date of each number in the SOCmin arrays;
step five: recording a position sequence number index (i) of which the date of the SOCmin is increased;
step six: calculating according to the formula (1) to obtain the cell month self-discharge rate SDR:
Step seven: and judging the micro short circuit risk degree of the battery according to the value of the SDR.
In a preferred embodiment, the step one collects battery related parameters of two different dates of the vehicle, and the difference between the two different dates is not more than 30 days.
In a preferred embodiment, the number of different dates of the first vehicle is three or more, any two of the three dates are taken to perform the operations from the second step to the sixth step, a plurality of SDRs are obtained, and then a final SDR value is obtained by fitting the plurality of SDRs.
In a preferred embodiment, the battery related parameter includes battery current, or voltage of certain cells.
In a preferred embodiment, the battery model in the second step is any one of a battery equivalent circuit model, an electrochemical model and a fractional order model.
In a preferred embodiment, the identification algorithm in the second step is any algorithm capable of identifying and obtaining the open-circuit voltage, specifically any one of a least squares identification algorithm, a kalman filter algorithm, an H infinity algorithm and an intelligent machine learning optimization algorithm.
In a preferred embodiment, the highest and lowest cell open circuit voltage values of step two are converted into corresponding SOCmax and SOCmin by SOC-OCV curve.
In a preferred embodiment, the SOC-OCV curve is a corresponding relationship curve of a new battery or a corresponding relationship curve of a battery after aging.
From the above description of the invention, it is clear that the invention has the following advantages over the prior art:
1. according to the hybrid vehicle battery micro-short circuit identification method, the hybrid vehicle operation voltage data is converted into the open circuit voltage and then is converted into the state of charge, and the state of charge is spliced, sequenced and searched, so that the estimation of the month self-discharge rate is realized, the adopted data amount is more, and the erroneous judgment caused by errors in the estimation of the single-point SOC difference value is avoided; the estimation result is quantized, and the risk of the period with smaller self-discharge rate increase amplitude in the early stage of micro-short circuit can be more accurately identified, and the risk is not interfered by current integration errors.
2. The invention adopts the fluctuation and error of the open-circuit voltage identified by modeling, and reduces the dependence of the calculation result on the model and algorithm by carrying out the subsequent difference processing, so that the identification of micro-short circuit is reduced by the influence of the model and algorithm.
3. The algorithm flow of the invention has less time consumption, is suitable for online estimation, and the quantitative estimation result SDR can be commonly used with the self-discharge measurement standard in the production process of the battery cell.
Drawings
Fig. 1 is a flowchart of a first embodiment of the present invention.
Fig. 2 is a diagram showing the relationship of SOC-t in the first embodiment of the present invention.
Detailed Description
Specific embodiments of the present invention will be described below with reference to the accompanying drawings. Numerous details are set forth in the following description in order to provide a thorough understanding of the present invention, but it will be apparent to one skilled in the art that the present invention may be practiced without these details. Well-known components, methods and procedures are not described in detail.
Example 1
Referring to fig. 1, a hybrid vehicle battery micro-short circuit identification method includes the following steps:
step one: the battery current I1, highest cell voltage Vmax1, lowest cell voltage Vmin1 data for a day D1 operation is intercepted. The highest monomer voltage Vmax1 refers to the highest voltage in all the battery cells at each moment of D1; the lowest cell voltage Vmin1 is the lowest voltage among all the cells at each time of D1.
Step two: and (3) building a battery model, and applying an identification algorithm to identify and obtain Uocmax1 and Uocmin1.
Step three: uocmax1, uocmin1 were converted to SOCmax1, SOCmin1 by SOC-OCV curve.
Step four: intercepting battery current I2, highest cell voltage Vmax1 and lowest cell voltage Vmin1 data of another day D2, wherein the highest cell voltage Vmax2 refers to the highest voltage in all battery cells in each moment of D2; the lowest cell voltage Vmin2 is the lowest voltage among all the cells at each instant of D2. And repeating the second to third steps to obtain corresponding SOCmax2 and SOCmin2.
Step five: splicing SOCmax1 and SOCmax2 into a series of groups of SOCmax; SOCmin1 and SOCmin2 are spliced into a column group SOCmin.
Step six: sequencing SOCmax from big to small, sequencing SOCmin according to the SOCmax sequencing mode, and marking each attribution date in the SOCmin array after sequencing. As shown in fig. 2, the date corresponding to SOCmax1 and SOCmin1 in fig. 2 is 7 months and 11 days, and the date corresponding to SOCmax2 and SOCmin2 is 7 months and 26 days.
Step seven: after SOCmin sorting, when the date to which SOCmin belongs increases, the current position sequence number index (i) is recorded.
Step eight: obtaining a month self-discharge rate according to a formula (1):
wherein n is the total number of the position serial numbers recorded in the step seven; the settlement SDR result shown in fig. 2 was 0.028. The result represents that the self-discharge rate of the worst cell is 2.8% greater than that of the cell with the smallest self-discharge rate within one month, reaching about twice the self-discharge rate of a normal cell month.
Step nine: setting thresholds a1, a2 and a3, and judging that the battery is normal when SDR is smaller than the threshold a 1; when the threshold value a1 is less than SDR and less than the threshold value a2, judging that the battery micro short circuit is at first-level risk; when the threshold value a2 is less than SDR and less than the threshold value a3, judging that the battery micro short circuit is a secondary risk; when the threshold value a3< SDR, the battery micro-short circuit is judged to be at three levels of risks.
In the present embodiment, a threshold a1=0.02 is set; threshold a2=0.04; threshold a3=0.06. According to the settlement value of the eight SDR, the micro short circuit risk degree of the battery can be judged to be a first level.
The battery model in the second step is any one of a battery equivalent circuit model, an electrochemical model and a fractional order model. The identification algorithm is an algorithm capable of identifying and obtaining open-circuit voltage, and specifically is any one of a least square identification algorithm, a Kalman filtering algorithm, an H infinity algorithm and an intelligent machine learning optimization algorithm.
The SOC-OCV curve is a corresponding relation curve of a new battery or a corresponding relation curve of a battery after aging.
The fifth to seventh steps may be performed in other manners, for example, directly searching for the corresponding sorting position without stitching.
Example two
The first collection of the step of this embodiment is the battery current, the highest cell voltage and the lowest cell voltage of three or more different dates. And selecting any two different dates as a group, dividing all the dates into a plurality of groups, performing the operations of the first embodiment to the eighth embodiment on each group to obtain a plurality of SDRs, fitting the SDRs to obtain a final SDR value, and finally executing the step nine of the first embodiment on the final SDR value to judge the micro-short circuit risk degree of the battery.
In addition, in other embodiments, the highest cell voltage and the lowest cell voltage in the data of the battery operation of a certain day are intercepted, and the voltage value of certain battery cells can also be adopted.
The foregoing is merely illustrative of specific embodiments of the present invention, but the design concept of the present invention is not limited thereto, and any insubstantial modification of the present invention by using the design concept shall fall within the scope of the present invention.
Claims (8)
1. The micro-short circuit identification method for the hybrid vehicle battery is characterized by comprising the following steps of:
step one: collecting at least two battery related parameters of a vehicle on different dates, wherein the battery related parameters comprise battery current, highest single voltage and lowest single voltage;
step two: building a battery model, obtaining a highest single open circuit voltage value Uocmax and a lowest single open circuit voltage value Uocmin of different dates by applying an identification algorithm, and converting the highest single open circuit voltage value Uocmax and the lowest single open circuit voltage value Uocmin into corresponding SOCmax and SOCmin;
step three: splicing SOCmax obtained by converting two different dates into one array, and splicing SOCmin of the two different dates into another array;
step four: sequencing the SOCmax arrays from large to small in the third step, sequencing the SOCmin arrays according to a sequencing mode of the SOCmax arrays, and recording the date of each number in the SOCmin arrays;
step five: recording a position sequence number index (i) of which the date of the SOCmin is increased;
step six: calculating according to the formula (1) to obtain the cell month self-discharge rate SDR:
step seven: and judging the micro short circuit risk degree of the battery according to the value of the SDR.
2. The hybrid vehicle battery micro-short circuit identification method as set forth in claim 1, wherein: and step one, acquiring battery related parameters of two different dates of the vehicle, wherein the difference value of the two different dates is not more than 30 days.
3. The hybrid vehicle battery micro-short circuit identification method as set forth in claim 1, wherein: and step one, carrying out operations from step two to step six on any two of three or more different dates of the vehicle, obtaining a plurality of SDRs, and fitting the SDRs to obtain a final SDR value.
4. The hybrid vehicle battery micro-short circuit identification method as set forth in claim 1, wherein: the battery model in the second step is any one of a battery equivalent circuit model, an electrochemical model and a fractional order model.
5. The hybrid vehicle battery micro-short circuit identification method as set forth in claim 1, wherein: the identification algorithm in the second step is an algorithm capable of identifying and obtaining open-circuit voltage, and specifically is any one of a least square identification algorithm, a Kalman filtering algorithm, an H infinity algorithm and an intelligent machine learning optimization algorithm.
6. The hybrid vehicle battery micro-short circuit identification method as set forth in claim 1, wherein: and the highest single open circuit voltage value and the lowest single open circuit voltage value in the second step are converted into corresponding SOCmax and SOCmin through the SOC-OCV curve.
7. The hybrid vehicle battery micro-short circuit identification method as set forth in claim 6, wherein: the SOC-OCV curve is a corresponding relation curve of a new battery or a corresponding relation curve of a battery after aging.
8. The hybrid vehicle battery micro-short circuit identification method as set forth in claim 1, wherein: the specific process of the step seven is as follows: setting thresholds a1, a2 and a3, and judging that the battery is normal when SDR is smaller than the threshold a 1; when the threshold value a1 is less than SDR and less than the threshold value a2, judging that the battery micro short circuit is at first-level risk; when the threshold value a2 is less than SDR and less than the threshold value a3, judging that the battery micro short circuit is a secondary risk; when the threshold value a3< SDR, the battery micro-short circuit is judged to be at three levels of risks.
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