CN114167190A - Hybrid vehicle battery micro short circuit identification method - Google Patents
Hybrid vehicle battery micro short circuit identification method Download PDFInfo
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- CN114167190A CN114167190A CN202111506820.4A CN202111506820A CN114167190A CN 114167190 A CN114167190 A CN 114167190A CN 202111506820 A CN202111506820 A CN 202111506820A CN 114167190 A CN114167190 A CN 114167190A
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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 on different dates by applying an identification algorithm, and converting the Uocmax and the Uocmin into corresponding SOCmax and SOCmin; splicing the converted SOCmax into a row of arrays, and splicing the SOCmin into another row of arrays; sorting the SOCmax arrays from large to small, sorting the SOCmin arrays according to the sorting mode of the SOCmax arrays, and recording the date to which each number in the SOCmin arrays belongs; recording a position serial number index (i) of the increase of the date to which the SOCmin belongs; calculating according to a formula to obtain the monthly self-discharge rate SDR of the battery; and judging the micro short circuit risk degree of the battery according to the SDR value. The method can more accurately identify the risks of the period with smaller self-discharge rate increase amplitude in the initial stage of the micro short circuit, and is not interfered by current integral 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 phenomenon that the self-discharge rate is gradually increased due to the fact that the battery cell in the power battery system is deteriorated due to factory manufacturing defects or after long-term use can be caused. 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 the voltage difference under the same SOC state (such as 100%), so that the micro-short circuit battery core is screened out. However, the SOC range of the battery system of the hybrid new energy vehicle is narrow, and is usually in the range of 40 to 60% SOC, and generally not higher than 80% SOC, and most vehicles do not have a fully charged state, and the SOC value of the vehicle in a static state is not fixed. The pressure differential changes at different SOC locations are not equivalent to corresponding capacity changes. Even if the capacity is not changed, the differential pressure changes with the change in the SOC. Therefore, the randomness of the SOC in the hybrid-driven parking state cannot identify the self-discharge rate change with a simple pressure difference change, and particularly cannot identify the change of the self-discharge rate at the initial stage of the micro short circuit when the increase amplitude of the self-discharge rate is small, so that the difficulty in identifying the micro short circuit of the battery cell of the hybrid battery system is increased.
At present, in the identification method of the micro short circuit of the power battery system, the micro short circuit internal resistance needs to be estimated in some methods, and the estimation of the micro circuit internal resistance needs to be established on the basis of current integration, so that the hybrid vehicle is not suitable for being used because the error of the current integration is larger than that of a pure electric vehicle; some hybrid vehicles need to record the change values of the charging capacity and the discharging capacity of the battery, or need to charge the battery to full power or discharge the battery to empty power, and are difficult to meet the requirements; some neural network technologies are relied on, and the method cannot achieve online high-efficiency calculation; some methods use the difference between the simulated value and the measured value of the battery model to determine, but the method is affected by the precision of the simulated value and has the possibility of misjudgment. Therefore, the hybrid vehicle micro short circuit identification method is provided.
Disclosure of Invention
The invention provides a hybrid vehicle battery micro-short circuit identification method, which overcomes the defects that the micro-short circuit of the battery core of the conventional hybrid power battery system cannot be identified by simple pressure difference change and the like.
The invention adopts the following technical scheme:
a hybrid vehicle battery micro short circuit identification method comprises the following steps:
the method comprises the following steps: collecting battery related parameters of at least two different dates of a vehicle;
step two: building a battery model, obtaining the highest monomer open-circuit voltage value Uocmax and the lowest monomer open-circuit voltage value Uocmin on different dates by applying an identification algorithm, and converting the highest monomer open-circuit voltage value Uocmax and the lowest monomer open-circuit voltage value Uocmin into corresponding SOCmax and SOCmin;
step three: splicing SOCmax obtained by converting two different dates into a row of array, and splicing SOCmin of the two different dates into another row of array;
step four: sorting SOCmax arrays in the third step from big to small, simultaneously sorting SOCmin arrays according to the sorting mode of the SOCmax arrays, and recording the attributive date of each number in the SOCmin arrays;
step five: recording a position serial number index (i) of the increase of the date to which the SOCmin belongs;
step six: calculating the monthly self-discharge rate SDR of the battery according to the formula (1):
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 first step collects battery related parameters of the vehicle on two different dates, and the difference between the two different dates does not exceed 30 days.
In a preferred embodiment, the number of the vehicles in the step one is three or more, any two of the vehicles in the step one are taken out to perform the operations from the step two to the step six to obtain a plurality of SDRs, and the SDRs are fitted to obtain a final SDR value.
In a preferred embodiment, the battery-related parameter includes a battery current, or a voltage of some of the 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 all algorithms capable of identifying and obtaining the open-circuit voltage, and specifically is any one of a least square identification algorithm, a kalman filter algorithm, an H infinite algorithm, and an intelligent machine learning optimization algorithm.
In a preferred embodiment, the highest cell open-circuit voltage value and the lowest cell open-circuit voltage value in the second step are converted into corresponding SOCmax and SOCmin through an SOC-OCV curve.
In a preferred embodiment, the SOC-OCV curve is a relationship curve corresponding to a new battery, or a relationship curve corresponding to an aged battery.
As can be seen from the above description of the present invention, compared with the prior art, the present invention has the following advantages:
1. according to the hybrid vehicle battery micro short circuit identification method, the hybrid vehicle running voltage data is converted into the state of charge after being converted into the open-circuit voltage, the state of charge is spliced, sequenced and searched, the estimation of the monthly self-discharge rate is realized, the adopted data volume is large, and the misjudgment caused by errors in the estimation of the single-point SOC difference value is avoided; the estimation result is quantized, the risk of the period with small self-discharge rate increase amplitude at the initial stage of the micro short circuit can be accurately identified, and the interference of current integral errors is avoided.
2. The invention adopts the open-circuit voltage identified by modeling to have fluctuation and error, and reduces the dependency of the calculation result on the model and the algorithm by performing difference processing subsequently, so that the identification of the micro short circuit is reduced by the influence of the model and the algorithm.
3. The algorithm flow of the invention consumes less time and is suitable for on-line estimation, and the quantitative estimation result SDR can be universal 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 relationship diagram of SOC-t according to a first embodiment of the present invention.
Detailed Description
The following describes embodiments of the present invention with reference to the drawings. Numerous details are set forth below in order to provide a thorough understanding of the present invention, but it will be apparent to those skilled in the art that the present invention may be practiced without these details. Well-known components, methods and processes are not described in detail below.
Example one
A hybrid vehicle battery micro short circuit identification method, referring to FIG. 1, comprises the following steps:
the method comprises the following steps: and intercepting data of battery current I1, highest cell voltage Vmax1 and lowest cell voltage Vmin1 of operation of D1 on a certain day. The maximum cell voltage Vmax1 is the maximum voltage of all the battery cells at each time of D1; the highest cell voltage Vmin1 is the lowest voltage among all the battery cells at each time point D1.
Step two: and (3) building a battery model, and identifying by using an identification algorithm to obtain Uocmax1 and Uocmin 1.
Step three: uocmax1, Uocmin1 were converted to SOCmax1, SOCmin1 by SOC-OCV curves.
Step four: intercepting data of battery current I2, highest cell voltage Vmax1 and lowest cell voltage Vmin1 of D2 running on another day, wherein the highest cell voltage Vmax2 refers to the highest voltage in all the battery cells at each moment of D2; the highest cell voltage Vmin2 is the lowest voltage among all the battery cells at each time point D2. And repeating the steps two to three to obtain the corresponding SOCmax2 and SOCmin 2.
Step five: splicing SOCmax1 and SOCmax2 into a row of array SOCmax; SOCmin1 and SOCmin2 are spliced into a row of array SOCmin.
Step six: and sequencing SOCmax from large to small, sequencing SOCmin in a SOCmax sequencing mode, and marking the attribution date of each number in the SOCmin array after sequencing. As shown in FIG. 2, the dates SOCmax1 and SOCmin1 in FIG. 2 correspond to 11 days 7 months, and the dates SOCmax2 and SOCmin2 correspond to 26 days 7 months.
Step seven: after the SOCmin is sorted, when the date to which the SOCmin belongs is increased, the current position serial number index (i) is recorded.
Step eight: the monthly self-discharge rate is found according to the formula (1):
wherein n is the total number of the position serial numbers recorded in the seventh step; the settlement SDR result shown in fig. 2 is 0.028. The results represent the worst cell self-discharge rate within one month, which is 2.8% greater than the cell with the lowest self-discharge rate, reaching about twice the monthly self-discharge rate of a normal cell.
Step nine: setting thresholds a1, a2 and a3, and judging that the battery is normal when SDR < threshold a 1; when the threshold a1< SDR < threshold a2, determining that the battery is in a first-level risk of micro-short circuit; when the threshold a2< SDR < threshold a3, determining that the battery micro short circuit is a secondary risk; when the threshold a3< SDR, the battery micro-short is judged to be a three-level risk.
In the present embodiment, the threshold value a1=0.02 is set; threshold a2= 0.04; threshold a3= 0.06. And judging the micro short circuit risk degree of the battery to be first grade according to the settlement value of the eight SDR.
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 any algorithm capable of identifying to obtain the open-circuit voltage, and specifically is any one of a least square identification algorithm, a Kalman filtering algorithm, an H infinite 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 aged battery.
The fifth step to the seventh step may also be executed in other forms, such as directly searching the corresponding sorting position without splicing.
Example two
In step one of the present embodiment, the battery current, the highest cell voltage and the lowest cell voltage are collected on three or more different days. 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 risk degree of the micro short circuit of the battery.
In addition, in other embodiments, the highest cell voltage and the lowest cell voltage in the data of the battery operation on a certain day are intercepted, and may also be voltage values of some battery cells.
The above description is only an embodiment of the present invention, but the design concept of the present invention is not limited thereto, and any insubstantial modifications made by using the design concept should fall within the scope of infringing the present invention.
Claims (9)
1. A hybrid vehicle battery micro short circuit identification method is characterized by comprising the following steps:
the method comprises the following steps: collecting battery related parameters of at least two different dates of a vehicle;
step two: building a battery model, obtaining the highest monomer open-circuit voltage value Uocmax and the lowest monomer open-circuit voltage value Uocmin on different dates by applying an identification algorithm, and converting the highest monomer open-circuit voltage value Uocmax and the lowest monomer open-circuit voltage value Uocmin into corresponding SOCmax and SOCmin;
step three: splicing SOCmax obtained by converting two different dates into a row of array, and splicing SOCmin of the two different dates into another row of array;
step four: sorting SOCmax arrays in the third step from big to small, simultaneously sorting SOCmin arrays according to the sorting mode of the SOCmax arrays, and recording the attributive date of each number in the SOCmin arrays;
step five: recording a position serial number index (i) of the increase of the date to which the SOCmin belongs;
step six: calculating the monthly self-discharge rate SDR of the battery according to the formula (1):
n is the sum of the numbers of the position sequence numbers recorded in the step five, and D2-D1 are the difference values of two different dates;
step seven: and judging the micro short circuit risk degree of the battery according to the value of the SDR.
2. A hybrid vehicle battery micro-short recognition method as set forth in claim 1, wherein: the first step is to collect the battery related parameters of the vehicle on two different dates, and the difference value of the two different dates is no more than 30 days.
3. A hybrid vehicle battery micro-short recognition method as set forth in claim 1, wherein: and (3) selecting any two of the vehicles in the step one, performing the operations from the step two to the step six to obtain a plurality of SDRs, and fitting the SDRs to obtain a final SDR value.
4. A hybrid vehicle battery micro-short recognition method as set forth in claim 1, wherein: the battery-related parameters include battery current, highest cell voltage, lowest cell voltage, or voltage of certain cells.
5. A hybrid vehicle battery micro-short recognition method as set forth in claim 1, wherein: and the battery model in the second step is any one of a battery equivalent circuit model, an electrochemical model and a fractional order model.
6. A hybrid vehicle battery micro-short recognition method as set forth in claim 1, wherein: the identification algorithm in the second step is all algorithms capable of identifying and obtaining the open-circuit voltage, and specifically is any one of a least square identification algorithm, a Kalman filtering algorithm, an H infinite algorithm and an intelligent machine learning optimization algorithm.
7. A hybrid vehicle battery micro-short recognition method as set forth in claim 1, wherein: and converting the highest single open-circuit voltage value and the lowest single open-circuit voltage value in the second step into corresponding SOCmax and SOCmin through an SOC-OCV curve.
8. A hybrid vehicle battery micro-short recognition method as set forth in claim 7, wherein: the SOC-OCV curve is a corresponding relation curve of a new battery or a corresponding relation curve of a aged battery.
9. A hybrid vehicle battery micro-short recognition method as set forth in claim 1, wherein: the concrete process of the seventh step is as follows: setting thresholds a1, a2 and a3, and judging that the battery is normal when SDR < threshold a 1; when the threshold a1< SDR < threshold a2, determining that the battery is in a first-level risk of micro-short circuit; when the threshold a2< SDR < threshold a3, determining that the battery micro short circuit is a secondary risk; when the threshold a3< SDR, the battery micro-short is judged to be a three-level risk.
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