CN111044908B - OCV (open Circuit control) online calculation method based on microchip data and voltage filtering - Google Patents

Abstract

The invention discloses an OCV online calculation method based on microchip data and voltage filtering. The method comprises the following steps: firstly, collecting driving data with the temperature of more than 0 ℃, dividing the data into micro data segments, converting the discharge current value into a multiplying power form, calculating the variance, marking the micro data segments with the variance of more than a set threshold value M, and identifying the internal resistance of the marked micro data segments; then, filtering the discharge voltage data, and adopting a Kalman filtering algorithm when the filtering is carried out to the marked data segment; when filtering is carried out to the unmarked data segment, adopting a state equation algorithm; and finally, converting all OCV segments into 'OCV-slope' curve segments, fusing the segments into a whole in a statistical mode, and finally obtaining a new OCV curve by utilizing integration. The method has the advantages of low cost, simple calculation, high accuracy and high robustness.

Description

OCV (open Circuit control) online calculation method based on microchip data and voltage filtering

Technical Field

The invention relates to the technical field of new energy automobiles and battery management systems for energy storage, in particular to an OCV (open circuit voltage) online calculation method based on microchip data and voltage filtering.

Background

A difficulty with battery management systems as an important component of electric vehicles is the accurate estimation of state of charge, SOC, which is computed without departing from the open circuit voltage curve (OCV). The SOC-OCV curve is a very important curve of the battery in the SOC calibration process, and usually after the electric vehicle runs for a period of time and the vehicle is stationary for a period of time, the BMS calls the curve to correct the SOC value.

The conventional method for obtaining the OCV is to carry out an open-circuit voltage experiment test, the whole test is complicated, a lot of manpower and time are consumed, and the OCV state curve of a specific single battery in a test environment can only be obtained according to the test result of each time. Generally, when a battery cell leaves a factory, the OCV curve of each single battery is relatively close, but as the battery ages, the OCV curve of the battery changes, and meanwhile, due to different environments of each battery, the OCV curve of each battery is different.

The OCV plays an important role in calculating SOC, SOE and SOP, and since the OCV gradually changes with the use of the battery and the conventional testing method must be tested offline, a method for obtaining the OCV online is needed.

Disclosure of Invention

The invention aims to provide an OCV online calculation method based on microchip data and voltage filtering, which is low in cost, simple in calculation, high in accuracy and high in robustness.

The technical solution for realizing the purpose of the invention is as follows: an OCV online calculation method based on microchip data and voltage filtering comprises the following steps:

step 1, data screening: collecting driving data with the temperature of more than 0 ℃;

step 2, data marking: dividing data into micro data segments, converting the discharge current value into a multiplying power form, calculating the variance, and marking the micro data segments with the variance larger than a set threshold value M;

step 3, internal resistance identification: carrying out internal resistance identification on the marked micro data segment;

step 4, obtaining an OCV fragment: filtering the discharge voltage data, and adopting a Kalman filtering algorithm when the filtering is carried out to the marked data segment; when filtering is carried out to the unmarked data segment, adopting a state equation algorithm;

step 5, calculating OCV: and converting the OCV segment into an OCV-slope curve segment, statistically combining the segments into a complete OCV-slope curve, and finally obtaining a new OCV curve by utilizing integration.

Further, the data screening of step 1: collecting driving data with the temperature of more than 0 ℃, which comprises the following specific steps:

step 1.1, uploading real-time voltage and current data of the battery cell to a cloud end for storage in real time during vehicle networking;

step 1.2, selecting data of which the total discharge capacity exceeds 10 × rated capacity in the last two months, and if the total discharge capacity is less than 10 × rated capacity in the last two months, expanding the selected time range;

step 1.3, rejecting discharge data with the temperature less than 0 ℃;

and step 1.4, eliminating abnormal data reported and collected by the BMS.

Further, the data marking of step 2: dividing data into micro data segments, converting the discharge current value into a multiplying power form, calculating the variance, and marking the micro data segments with the variance larger than a set threshold value M, wherein the method specifically comprises the following steps:

step 2.1, dividing the discharged current data into 30s micro data segments without marking the data within 5min of initial discharge time;

step 2.2, converting the current into a multiplying power form, wherein the calculation formula is (1-1), I is the current, Ca is the rated capacity of the battery cell, and I is_CIs a multiplying factor value:

step 2.3, calculating the average multiplying power of the micro data fragments, wherein the micro data fragments with the average multiplying power smaller than 0.1C and the multiplying power larger than 0.7C do not need to be marked;

and 2.4, calculating the variance value of the rest micro data segments, and marking the data segments with the variance larger than a threshold value M, wherein the value range of the threshold value M is 0.1-0.5.

Further, the internal resistance identification in step 3: and carrying out internal resistance identification on the marked micro data segment, specifically comprising the following steps:

step 3.1, in the labeled micro data segment, the OCV variation caused by SOC can be ignored, that is, the OCV in the micro data segment remains unchanged, so that the variation of the battery output voltage in the data segment is caused by the variation of the discharge rate, and therefore, the equivalent battery model is obtained as:

V＝I*r+OCV (1-2)

in the formula, r is the current internal resistance value of the battery, the internal resistance comprises the influences of polarization internal resistance and ohm internal resistance, I is the current value output by the automobile battery, OCV is the open-circuit voltage of the lithium battery, and V is the output voltage of the battery;

and 3.2, based on the formula, calculating the internal resistance r corresponding to each marked micro data segment by adopting a least square method.

Further, the OCV segment of step 4 is obtained: filtering the discharge voltage data, and adopting a Kalman filtering algorithm when the filtering is carried out to the marked data segment; when filtering is carried out to the unmarked data segment, an equation of state algorithm is adopted, which is specifically as follows:

step 4.1, establishing an OCV state equation according to an Ah integral formula:

in the formula, OCV_kFor the OCV variable to be calculated, the subscript k denotes the time of day, OCV_k-1The OCV variable at the last moment; i is_k-1The input quantity of current at the last moment, delta t is a calculation period, Ca is the rated capacity of the battery cell, and SOH is₀Is the initial estimated SOH value, OCV is a function of SOC, H_SOCIs the slope of OCV with respect to SOC, η_TIs the coefficient of temperature affecting capacity;

step 4.2, establishing an observation equation for OCV calculation according to the equivalent battery model of the battery:

V_k＝OCV_k+R_k*I_k

V_kis the current voltage of the battery, I_k、R_kRespectively the current and the current internal resistance;

step 4.3, in a section of complete discharge data filtering process, when filtering is carried out to a non-labeled data segment, calculating OCV by adopting a state equation algorithm, wherein the formula is as follows:

in the formula, k represents time;

when filtering proceeds to the marked data segment, the OCV is calculated using Kalman's filtering formula, which is:

where Q is the covariance of the error of the estimated equation of state, R is the covariance of the error of the estimated measurement equation, P₀＝0，OCV₀Cell voltage value, V, immediately after voltage application_kIs the current cell voltage acquisition value, r_kIs the internal resistance, I, of the tag data fragment identification_kIs the current value of the current collected current,

and

respectively represent OCV_kAnd P_kA transition value of (d);

and 4.4, repeating the steps 4.1-4.3 until all discharge curves of the battery cell discharge data are filtered, and obtaining OCV data sections in different voltage interval ranges.

Further, the OCV calculated in step 5: converting the OCV segment into an OCV-slope curve segment, statistically combining the segments into a complete OCV-slope curve, and finally obtaining a new OCV curve by utilizing integration, wherein the method comprises the following steps:

step 5.1, converting an original OCV curve provided by a battery cell manufacturer into an OCV-slope curve segment, wherein the ordinate is a slope value of the capacity relative to the OCV, the abscissa is the OCV, the minimum value of the OCV is an initial OCV point, and the maximum value of the OCV is a termination OCV point;

step 5.2, selecting a filtered OCV segment, and converting the OCV segment into an OCV-slope curve segment by calculating a capacity-OCV slope value of each voltage point, wherein the specific steps are as follows:

step 5.2.1, in one OCV segment data, selecting a voltage point, wherein the voltage point corresponds to a time node t, and the voltage corresponding to the electric quantity accumulating 1% of rated capacity from the time point forward is V_uThe voltage corresponding to the electric quantity accumulated backward by 1% of the rated capacity is V_dThen, the slope calculation formula at the time point is:

in the formula, C_tThe slope value of the selected voltage point is obtained;

step 5.2.2, converting an OCV segment into an OCV-slope curve segment in a mode of calculating the average slope of each voltage point through a formula (1-3);

step 5.3, repeating the step 5.2, and converting all OCV segments into OCV-slope curve segments;

step 5.4, by calculating the average value of the slope values of each voltage point, a plurality of OCV-slope curve segments are fused into a complete OCV-slope curve;

step 5.5, according to the fused OCV-slope curve, integrating within the range from the initial OCV point to the final OCV point to obtain a capacity value AhCa;

step 5.6, when the integrated capacity value reaches AhCa 5%, AhCa 10%, AhCa 15%. AhCa 100%, recording corresponding voltage points, wherein the voltage points respectively correspond to 5%, 10%, 15%. 100% of the SOC, and adding an OCV starting point corresponding to 0% of the SOC to obtain a new OCV curve.

Compared with the prior art, the invention has the remarkable advantages that: (1) the OCV can be calculated on line, a battery pack does not need to be disassembled, a large amount of test equipment and environments are not needed, only the real-time running data of the automobile needs to be collected, and the method is simple and low in calculation cost; (2) by calculating the OCV on line, the current OCV condition of each battery can be updated in real time, so that assistance is provided for estimating the accuracy of the SOX, and the accuracy is high; (3) the method has the advantages of simple logic, stable OCV calculation result and high robustness, and a large amount of data are processed by adopting a statistical method.

Drawings

FIG. 1 is a schematic flow chart of an OCV online calculation method based on microchip data and voltage filtering according to the present invention.

FIG. 2 is a waveform diagram before and after discharge voltage data filtering according to an embodiment of the present invention.

FIG. 3 is a schematic view of multiple OCV segments in an embodiment of the present invention.

Fig. 4 is a segment diagram of an "OCV-slope" curve converted from an original OCV curve provided by a cell manufacturer in an embodiment of the present invention.

FIG. 5 is a graph of a segment of the "OCV-slope" curve in an example of the present invention.

FIG. 6 is a graph of "OCV-slope" after fusion in an embodiment of the present invention.

FIG. 7 is an integral graph of the "OCV-slope" curve in an example of the present invention.

Detailed Description

The invention relates to an OCV online calculation method based on microchip data and voltage filtering, which comprises the following steps of:

step 1, data screening: collecting driving data with the temperature of more than 0 ℃;

step 2, data marking: dividing data into micro data segments, converting the discharge current value into a multiplying power form, calculating the variance, and marking the micro data segments with the variance larger than a set threshold value M;

step 3, internal resistance identification: carrying out internal resistance identification on the marked micro data segment;

step 4, obtaining an OCV fragment: filtering the discharge voltage data, and adopting a Kalman filtering algorithm when the filtering is carried out to a marked data segment; when filtering is carried out to the unmarked data segment, adopting a state equation algorithm;

step 5, calculating OCV: and converting the OCV segment into an OCV-slope curve segment, statistically combining the segments into a complete OCV-slope curve, and finally obtaining a new OCV curve by utilizing integration.

Further, the data screening of step 1: collecting driving data with the temperature of more than 0 ℃, which comprises the following specific steps:

step 1.1, uploading real-time voltage and current data of the battery cell to a cloud end for storage in real time during the Internet of vehicles;

step 1.2, selecting data of which the total discharge capacity exceeds 10 × rated capacity in the last two months, and if the total discharge capacity is less than 10 × rated capacity in the last two months, expanding the selected time range;

step 1.3, rejecting discharge data with the temperature less than 0 ℃;

and step 1.4, eliminating abnormal data reported and collected by the BMS.

Further, the data marking of step 2: dividing data into micro data segments, converting the discharge current value into a multiplying power form, calculating the variance, and marking the micro data segments with the variance larger than a set threshold value M, wherein the method specifically comprises the following steps:

step 2.1, dividing the discharged current data into 30s micro data segments without marking the data within 5min of initial discharge time;

2.2, converting the current into a multiplying power form, wherein the calculation formula is (1-1), I is the current, Ca is the rated capacity of the battery cell, and I is_CIs a multiplying factor value:

step 2.3, calculating the average multiplying power of the micro data fragments, wherein the micro data fragments with the average multiplying power smaller than 0.1C and the multiplying power larger than 0.7C do not need to be marked;

and 2.4, calculating the variance value of the rest micro data segments, and marking the data segments with the variance larger than a threshold value M, wherein the value range of the threshold value M is 0.1-0.5.

Further, the internal resistance identification in step 3: and carrying out internal resistance identification on the marked micro data segment, specifically comprising the following steps:

step 3.1, in the labeled micro data segment, the OCV variation caused by SOC can be ignored, that is, the OCV in the micro data segment remains unchanged, so that the variation of the battery output voltage in the data segment is caused by the variation of the discharge rate, and therefore, the equivalent battery model is obtained as:

V＝I*r+OCV (1-2)

wherein r is the current internal resistance value of the battery, the internal resistance comprises the influence of polarization internal resistance and ohm internal resistance, I is the current value output by the automobile battery, OCV is the open-circuit voltage of the lithium battery, and V is the output voltage of the battery;

and 3.2, based on the formula, calculating the internal resistance r corresponding to each marked micro data segment by adopting a least square method.

Further, the OCV segment of step 4 is obtained: filtering the discharge voltage data, and adopting a Kalman filtering algorithm when the filtering is carried out to the marked data segment; when filtering is carried out to the unmarked data segment, an equation of state algorithm is adopted, which is specifically as follows:

step 4.1, establishing an OCV state equation according to an Ah integral formula:

in the formula, OCV_kFor the OCV variable to be calculated, the subscript k denotes the time of day, OCV_k-1The OCV variable at the last moment; i is_k-1The input quantity of current at the last moment, delta t is a calculation period, Ca is the rated capacity of the battery cell, SOH₀Is the initial estimated SOH value, OCV is a function of SOC, H_SOCIs the slope of OCV with respect to SOC, η_TIs the coefficient of temperature affecting capacity;

step 4.2, establishing an observation equation for OCV calculation according to the equivalent battery model of the battery:

V_k＝OCV_k+R_k*I_k

V_kis the current voltage of the battery, I_k、R_kRespectively the current and the current internal resistance;

4.3, in the process of filtering a section of complete discharge data, when filtering is carried out to a non-labeled data segment, calculating the OCV by adopting a state equation algorithm, wherein the formula is as follows:

in the formula, k represents time;

when filtering is performed to mark the data segment, the OCV is calculated by using Kalman filtering formula:

where Q is the covariance of the error of the estimated state equation, R is the covariance of the error of the estimated measurement equation, P₀＝0，OCV₀Cell voltage value, V, at the moment when voltage is just applied_kIs the current cell voltage acquisition value, r_kIs the internal resistance, I, of the tag data fragment identification_kIs the current value of the current collected current,

and

respectively represent OCV_kAnd P_kA transition value of (d);

and 4.4, repeating the steps 4.1-4.3 until all discharge curves of the battery cell discharge data are filtered, and obtaining OCV data sections in different voltage interval ranges.

Further, the OCV calculated in step 5: converting the OCV segment into an OCV-slope curve segment, statistically combining the segments into a complete OCV-slope curve, and finally obtaining a new OCV curve by utilizing integration, wherein the method specifically comprises the following steps:

step 5.1, converting an original OCV curve provided by a battery cell manufacturer into an OCV-slope curve segment, wherein the ordinate is a slope value of the capacity relative to the OCV, the abscissa is the OCV, the minimum value of the OCV is an initial OCV point, and the maximum value of the OCV is a termination OCV point;

step 5.2, selecting a filtered OCV segment, and converting the OCV segment into an OCV-slope curve segment by calculating a capacity-OCV slope value of each voltage point, wherein the specific steps are as follows:

step 5.2.1, in one OCV segment data, selecting a voltage point, wherein the voltage point corresponds to a time node t, and the voltage corresponding to the electric quantity accumulating 1% of rated capacity from the time point forward is V_uThe voltage corresponding to the electric quantity accumulated backward by 1% of the rated capacity is V_dThen, the slope calculation formula at the time point is:

in the formula, C_tThe slope value of the selected voltage point is obtained;

step 5.2.2, converting an OCV segment into an OCV-slope curve segment in a mode of calculating the average slope of each voltage point through a formula (1-3);

step 5.3, repeating the step 5.2, and converting all OCV segments into OCV-slope curve segments;

step 5.4, by calculating the average value of the slope values of each voltage point, a plurality of OCV-slope curve segments are fused into a complete OCV-slope curve;

step 5.5, according to the fused OCV-slope curve, integrating within the range from the initial OCV point to the final OCV point to obtain a capacity value AhCa;

step 5.6, when the integrated capacity value reaches AhCa 5%, AhCa 10%, AhCa 15%. AhCa 100%, recording corresponding voltage points, wherein the voltage points respectively correspond to 5%, 10%, 15%. 100% of the SOC, and adding an OCV starting point corresponding to 0% of the SOC to obtain a new OCV curve.

The invention is described in further detail below with reference to the figures and the specific embodiments.

Examples

With reference to fig. 1, the OCV online calculation method based on microchip data and voltage filtering of this embodiment includes the following steps:

step 1, data screening: collecting driving data with the temperature of more than 0 ℃, which comprises the following specific steps:

step 1.1, uploading real-time voltage and current data of a certain automobile battery cell to a cloud end for storage in real time during the Internet of vehicles;

step 1.2, extracting the cell data of the last two months from the cloud, wherein the cell data comprises current, monomer voltage and temperature data, accumulating the discharge electric quantity of the residual data, and if the accumulated electric quantity exceeds rated capacity by 10, indicating that the selected cell data is effective; if the accumulated electric quantity is less than the rated capacity x 10, properly expanding the selected time range;

step 1.3, rejecting discharge data with the temperature less than 0 ℃;

and step 1.4, eliminating abnormal data reported and collected by the BMS. When an automobile runs, if the temperature is low, the automobile starts a heating mechanism to heat a battery, so that most running working condition data are larger than 0 ℃, abnormal BMS data acquisition conditions are very rare conditions, and data can be removed in the data screening process, but the data are not removed much.

Step 2, data marking: dividing data into micro data segments of 30s, converting the discharge current value into a multiplying power form, calculating the variance, and marking the micro data segments with the variance larger than a set threshold value M, wherein the method specifically comprises the following steps:

and 2.1, screening the data of the target monomer battery cell, and dividing the selected data into micro-segments within the range of 30s according to time. And (4) data within 5min of initial discharge time, because the initial discharge polarization internal resistance does not fully show the partial pressure capacity, the battery data are not marked in the time period.

Step 2.2, converting the current into a multiplying power form, wherein the calculation formula is (1-1), I is the current, Ca is the rated capacity of the battery cell, and I is_CIs a multiplying factor value:

and 2.3, in order to eliminate the influence of over-high and over-low discharge multiplying power on internal resistance identification, marking the micro data segments with larger average multiplying power and smaller multiplying power. Calculating the average multiplying power of the micro data fragments, wherein the data fragments with the multiplying power smaller than 0.1C and the multiplying power larger than 0.7C are not marked;

and 2.4, calculating the variance value of the residual micro data segment, wherein the larger the variance is, the more complex the small segment of data is, the more sufficient the represented battery characteristic is, and the more accurate the internal resistance value identification is. And marking the data segments with the variance larger than the threshold M, wherein the value range of the threshold M is 0.1-0.5.

Under the normal use condition of the electric automobile, most driving working conditions are between 0.1 and 0.7C, and due to the complexity of the driving working conditions, a plurality of micro data segments are marked in each section of complete discharge data.

And 3, internal resistance identification: and carrying out internal resistance identification on the marked micro data segment, specifically comprising the following steps:

in step 3.1, the time range of the marked micro data segment is 30s, and the variance larger than M exists, so that the average multiplying power of the data segment is below 0.5C, and the SOC change rate does not exceed 0.42%. Since the SOC variation amplitude is small, SOC-induced OCV variation is negligible in the labeled micro data segment, i.e., OCV in the micro data segment remains unchanged, and therefore variation in the battery output voltage is caused by variation in the discharge rate within the data segment. The following formula can therefore be used as an equivalent cell model:

V＝I*r+OCV (1-2)

wherein r is the current internal resistance value of the battery, the internal resistance comprises the influence of polarization internal resistance and ohm internal resistance, I is the output current value of the automobile battery, and V is the output voltage of the battery;

and 3.2, based on the formula, calculating the internal resistance r corresponding to each marked micro data segment by adopting a least square method, wherein the OCV value of the data segment can be directly obtained by fitting the formula, but the OCV has high fluctuation and cannot be adopted.

Step 4, obtaining an OCV fragment: filtering the discharge voltage data, and adopting a Kalman filtering algorithm when the filtering is carried out to the marked data segment; when filtering is carried out to the unmarked data segment, an equation of state algorithm is adopted, which is specifically as follows:

step 4.1, establishing an OCV state equation according to an Ah integral formula:

in the formula, OCV_kFor the OCV variable to be calculated, the subscript k denotes the time of day, OCV_k-1The OCV variable at the last moment; i is_k-1In the previous moment, the current is used as the input quantity, delta t is the calculation period, Ca is the rated capacity value, SOH₀For an initial estimated SOH value, OCV (open circuit voltage) is a function of SOC, H_SOCIs the slope of OCV with respect to SOC, η_TIs the coefficient of temperature affecting capacity;

step 4.2, establishing an observation equation for OCV calculation according to the equivalent battery model of the battery:

V_k＝OCV_k+R_k*I_k

in the formula, V_kIs the current voltage of the battery, I_k、R_kRespectively the current and the internal resistance;

step 4.3, in a complete discharge data filtering process, when the filtering is carried out to a non-labeled data segment, the OCV is calculated by only adopting the equation of state to carry out filtering, and the formula is as follows:

in the formula, k represents time;

and 4.4, when the filtering is carried out to mark the data segment, calculating the OCV by adopting a Kalman filtering formula, wherein the formula is as follows:

where Q is the covariance of the error of the estimated equation of state, R is the covariance of the error of the estimated measurement equation, P₀＝0，OCV₀Cell voltage value, V, at the moment when voltage is just applied_kIs the current cell voltage acquisition value, r_kIs the internal resistance, I, of the tag data fragment identification_kIs the current value of the current collected current,

and

respectively represent OCV_kAnd P_kA transition value of (d); a complete segment of discharge data is filtered before and after, in the form shown in figure 2.

And 4.5, repeating the steps 4.1 to 4.4 until all the discharge curves of the battery cell data are filtered, and obtaining OCV data sections in different interval ranges, as shown in fig. 3.

Calculated OCV as described in step 5: converting the OCV segment into an OCV-slope curve segment, statistically combining the segments into a complete OCV-slope curve, and finally obtaining a new OCV curve by utilizing integration, wherein the method specifically comprises the following steps:

step 5.1, manufacturing a first OCV-slope curve according to the OCV curve provided by the battery cell manufacturer, wherein the form is shown in FIG. 4;

step 5.2, selecting a filtered OCV segment, and converting the OCV segment into an OCV-slope curve segment by calculating a capacity-OCV slope value of each voltage point, wherein the specific steps are as follows by combining with a graph 5:

step 5.2.1, in one OCV segment data, selecting a voltage point, wherein the voltage point corresponds to a time node t, and the voltage corresponding to the electric quantity accumulating 1% of rated capacity from the time point forward is V_uThe voltage corresponding to the electric quantity accumulated backward by 1% of the rated capacity is V_dThen, the slope calculation formula at the time point is:

in the formula, C_tThe slope value of the selected voltage point is obtained;

and 5.2.2, converting an OCV segment into an OCV-slope curve segment in a mode of calculating the average slope of each voltage point through the formula (1-3).

Step 5.3, repeating step 5.2-step 5.2.2, converting all OCV segments into 'OCV-slope' curve segments, and finally obtaining a result shown in FIG. 5, wherein the curve (i) is a slope curve converted from the OCV curve provided by the manufacturer, and the curve (ii) -fifth is a slope curve converted from the OCV segments obtained by filtering;

and 5.4, by calculating the average value of the slope values of each voltage point, a plurality of OCV-slope curve segments are fused into a complete OCV-slope curve, as shown in FIG. 6.

And 5.5, integrating in the range from the initial OCV point to the final OCV point according to the fused OCV-slope curve to obtain the capacity value AhCa. Assuming a fused "OCV-slope" curve, the starting OCV voltage point is 3.205 and the ending OCV voltage point 4.156. The slope curve is integrated starting at voltage point 3.205 and going to voltage point 4.156, where the integrated value is AhCa, which can be considered as the average capacity of the battery over the sampling period, as shown in figure 7.

Step 5.6, setting AhCa to 145Ah, when the integrated capacity value reaches AhCa 5%, AhCa 10%, AhCa 15%. AhCa 100%, recording corresponding voltage points, which correspond to 5%, 10%, 15%. 100% of the SOC, respectively, and then adding the initial OCV point corresponding to 0% of the SOC to obtain a new OCV curve, which is as follows:

calculate 5% of AhCa: 145 × 0.05 ═ 7.25Ah

The 10% of AhCa was calculated as: 145 × 0.1 ═ 14.5Ah

…

100% of AhCa was calculated as: 145 × 1 ═ 145Ah

According to the fused OCV-slope curve segment, integration is started from the initial OCV point of 3.205, when the integration amount is 7.25Ah, the OCV voltage point is 3.275, and the corresponding SOC is 5%;

when the integral quantity is 14.5Ah, the OCV voltage point is 3.32, and the corresponding SOC is 10 percent;

…

when the integrated amount is 145Ah, the OCV voltage point is 4.156, and the corresponding SOC is 100%.

The latest OCV curve was plotted in conjunction with the starting OCV point of 3.205, corresponding to an SOC of 0%, and the results are shown in table 1.

TABLE 1 OCV curves

SOC	OCV
			0％	3.205
5％	3.275
		10％	3.32
15％	3.357
		20％	3.388
25％	3.418
		30％	3.435
35％	3.449
		40％	3.466
45％	3.489
		50％	3.538
55％	3.57
		60％	3.624
65％	3.676
		70％	3.74
75％	3.782
		80％	3.842
85％	3.915
		90％	3.962
95％	4.023
		100％	4.156

In conclusion, the OCV can be calculated on line, the battery pack does not need to be disassembled, a large amount of test equipment and environment are not needed, only the real-time running data of the automobile needs to be collected, the method is simple, and the calculation cost is low; by calculating the OCV on line, the current OCV condition of each battery can be updated in real time, so that assistance is provided for estimating the accuracy of the SOX, and the accuracy is high; the method has the advantages of simple logic, stable OCV calculation result and high robustness, and a large amount of data are processed by adopting a statistical method.

Claims (5)

1. An OCV online calculation method based on microchip data and voltage filtering is characterized by comprising the following steps:

step 1, data screening: collecting driving data with the temperature of more than 0 ℃;

step 2, data marking: dividing data into micro data segments, converting the discharge current value into a multiplying power form, calculating the variance, and marking the micro data segments with the variance larger than a set threshold value M;

step 3, internal resistance identification: carrying out internal resistance identification on the marked micro data segment;

step 4, obtaining an OCV fragment: filtering the discharge voltage data, and adopting a Kalman filtering algorithm when the filtering is carried out to the marked data segment; when filtering is carried out to the unmarked data segment, adopting a state equation algorithm;

step 5, calculating OCV: converting the OCV segment into an OCV-slope curve segment, statistically combining the segments into a complete OCV-slope curve, and finally obtaining a new OCV curve by utilizing integration, wherein the method specifically comprises the following steps:

step 5.1, converting an original OCV curve provided by a battery cell manufacturer into an OCV-slope curve segment, wherein the ordinate is a slope value of the capacity relative to the OCV, the abscissa is the OCV, the minimum value of the OCV is an initial OCV point, and the maximum value of the OCV is a termination OCV point;

step 5.2, selecting a filtered OCV segment, and converting the OCV segment into an OCV-slope curve segment by calculating a capacity-OCV slope value of each voltage point, wherein the specific steps are as follows:

step 5.2.1, in one OCV segment data, selecting a voltage point, wherein the voltage point corresponds to a time node t, and the voltage corresponding to the electric quantity accumulating 1% of rated capacity from the time point forward is V_uThe voltage corresponding to the electric quantity accumulated backward by 1% of the rated capacity is V_dThen, the slope calculation formula at the time point is:

in the formula, C_tThe slope value of the selected voltage point is obtained;

step 5.2.2, converting an OCV segment into an OCV-slope curve segment in a mode of calculating the average slope of each voltage point through a formula (1-3);

step 5.3, repeating the step 5.2, and converting all OCV segments into OCV-slope curve segments;

step 5.4, by calculating the average value of the slope values of each voltage point, a plurality of OCV-slope curve segments are fused into a complete OCV-slope curve;

step 5.5, according to the fused OCV-slope curve, integrating in the range from the initial OCV point to the final OCV point to obtain a capacity value AhCa;

step 5.6, when the integrated capacity value reaches AhCa 5%, AhCa 10%, AhCa 15%. AhCa 100%, recording corresponding voltage points, wherein the voltage points respectively correspond to 5%, 10%, 15%. 100% of the SOC, and adding an OCV starting point corresponding to 0% of the SOC to obtain a new OCV curve.

2. The OCV online calculation method based on microchip data and voltage filtering as claimed in claim 1, wherein the data screening of step 1: collecting driving data with the temperature of more than 0 ℃, which comprises the following specific steps:

step 1.1, uploading real-time voltage and current data of the battery cell to a cloud end for storage in real time during vehicle networking;

step 1.2, selecting data of which the total discharge capacity exceeds 10 × rated capacity in the last two months, and if the total discharge capacity is less than 10 × rated capacity in the last two months, expanding the selected time range;

step 1.3, rejecting discharge data with the temperature less than 0 ℃;

and step 1.4, eliminating abnormal data reported and collected by the BMS.

3. The OCV online calculation method based on microchip data and voltage filtering according to claim 1, wherein the data of step 2 is labeled as follows: dividing data into micro data segments, converting the discharge current value into a multiplying power form, calculating the variance, and marking the micro data segments with the variance larger than a set threshold value M, wherein the method specifically comprises the following steps:

step 2.1, dividing the discharged current data into 30s micro data segments without marking the data within 5min of initial discharge time;

2.2, converting the current into a multiplying power form, wherein the calculation formula is (1-1), I is the current, Ca is the rated capacity of the battery cell, and I is_CIs a multiplying factor value:

step 2.3, calculating the average multiplying power of the micro data fragments, wherein the micro data fragments with the average multiplying power smaller than 0.1C and the multiplying power larger than 0.7C do not need to be marked;

and 2.4, calculating the variance value of the rest micro data segments, and marking the data segments with the variance larger than a threshold value M, wherein the value range of the threshold value M is 0.1-0.5.

4. The OCV online calculation method based on microchip data and voltage filtering as claimed in claim 1, wherein the internal resistance identification of step 3 is as follows: and carrying out internal resistance identification on the marked micro data segment, specifically comprising the following steps:

step 3.1, in the labeled micro data segment, the OCV variation caused by SOC can be ignored, that is, the OCV in the micro data segment remains unchanged, so that the variation of the battery output voltage in the data segment is caused by the variation of the discharge rate, and therefore, the equivalent battery model is obtained as:

V＝I*r+OCV (1-2)

in the formula, r is the current internal resistance value of the battery, the internal resistance comprises the influences of polarization internal resistance and ohm internal resistance, I is the current value output by the automobile battery, OCV is the open-circuit voltage of the lithium battery, and V is the output voltage of the battery;

and 3.2, based on the formula, calculating the internal resistance r corresponding to each marked micro data segment by adopting a least square method.

5. The OCV online calculation method based on microchip data and voltage filtering according to claim 1, wherein the OCV section of step 4 obtains: filtering the discharge voltage data, and adopting a Kalman filtering algorithm when the filtering is carried out to the marked data segment; when filtering is carried out to the unmarked data segment, an equation of state algorithm is adopted, which is specifically as follows:

step 4.1, establishing an OCV state equation according to an Ah integral formula:

in the formula, OCV_kFor the OCV variable to be calculated, the subscript k denotes the time of day, OCV_k-1Is the last momentOCV variable of (d); i is_k-1The input quantity of current at the last moment, delta t is a calculation period, Ca is the rated capacity of the battery cell, and SOH is₀Is the initial estimated SOH value, OCV is a function of SOC, H_SOCIs the slope of OCV with respect to SOC, η_TIs the coefficient of temperature affecting capacity;

step 4.2, establishing an observation equation for OCV calculation according to the equivalent battery model of the battery:

V_k＝OCV_k+R_k*I_k

V_kis the current voltage of the battery, I_k、R_kRespectively the current and the current internal resistance;

4.3, in the process of filtering a section of complete discharge data, when filtering is carried out to a non-labeled data segment, calculating the OCV by adopting a state equation algorithm, wherein the formula is as follows:

in the formula, k represents time;

when filtering is performed to mark the data segment, the OCV is calculated by using Kalman filtering formula:

where Q is the covariance of the error of the estimated equation of state, R is the covariance of the error of the estimated measurement equation, P₀＝0，OCV₀Cell voltage value, V, immediately after voltage application_kIs the current cell voltage acquisition value, r_kIs the internal resistance, I, of the tag data fragment identification_kIs the current value of the current collected current,

and

respectively represent OCV_kAnd P_kA transition value of (d);

and 4.4, repeating the steps 4.1-4.3 until all discharge curves of the battery cell discharge data are filtered, and obtaining OCV data sections in different voltage interval ranges.

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