A state of charge correction method, a battery management device, a medium, and a controller

Abstract

The present application belongs to the technical field of battery management, and particularly relates to a state-of-charge correction method, a battery management device, a medium and a controller; according to the slope characteristics, the OCV-SOC curve is re-segmented in a target region, and then the SOC is corrected in the target region in combination with the working condition; the method and product disclosed in the embodiment of the present application estimate and correct the SOC of the static condition in combination with the voltage and SOC value, static time, average module temperature and other parameters of the monomer battery voltage at the upper and lower high voltage; the SOC under the running condition is also estimated and corrected through the first-order equivalent circuit of the battery cell; the present application effectively improves the remaining capacity estimation accuracy of the battery, especially the SOC of the lithium iron phosphate battery, before and after 60% of the OCV-SOC curve without introducing new hardware, and then improves the electric energy management level of the battery management device and the related controller.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of battery management, and particularly relates to a state of charge correction method, a battery management device, a medium and a controller. BACKGROUND

[0002] With the development of new energy automobile technology, the power battery provides power for vehicle driving components and intelligent cabin, and the accuracy of battery state of charge (SOC) evaluation plays an important role in user experience, vehicle performance and battery maintenance.

[0003] The most widely used method in the current automobile field is to obtain the initial value of the current driving cycle according to the voltage after the battery is at rest, the rest time and the temperature, and to calculate the change of SOC by the cumulative integral of the instantaneous current value obtained by the sensor, so that the SOC value is dynamically updated.

[0004] As shown in Figure 1 The open-circuit voltage corresponding to the interval of 30% to 95% of the remaining energy percentage of the lithium iron phosphate battery is relatively close, and the initial value cannot be obtained when the lithium iron phosphate battery is in the interval. If the vehicle cannot obtain the initial value for a long time, the SOC will jump due to the accumulation of errors of the current sensor, which reduces the user experience, and may cause the vehicle performance to decline and the battery life to decay too quickly in the long term. SUMMARY

[0005] The embodiment of the application discloses a state of charge correction method, comprising a first intrinsic information segmentation step and a second state of charge updating step; the first intrinsic information segmentation step obtains the first intrinsic curve of the open-circuit voltage (OCV) and the state of charge (SOC) of a target battery or battery pack, and divides the first intrinsic curve into N segments according to the slope dOCV / dSOC; the Mth segment in the N segments is a to-be-corrected segment, M and N are positive integers, N>M, and N is greater than or equal to 6; generally, N can be 6, and M can be 4.

[0006] Specifically, the slope dM of the Mth segment and the slope d(M-1) of the (M-1)th segment differ by a preset first value, and the slope dM of the Mth segment and the slope d(M+1) of the (M+1)th segment differ by a preset second value.

[0007] Further, the second state of charge updating step corrects the state of charge SOC of the target battery or battery pack in the Mth segment according to the load state of the target battery or battery pack; wherein the load state includes a first unloaded rest state and a second online driving state.

[0008] Wherein, if the load state is in the first empty static state; then the single battery voltage VSS when the target battery or battery pack accesses high voltage, the static time tst after the high voltage is disconnected, the average module temperature TAV, the state of charge SOCOFF when the high voltage is disconnected are obtained; and the state of charge in the Mth section is updated to obtain the corrected state of charge SOCNEW1; so that:

[0009] SOCNEW1=SOCOFF+(SOCOCV-SOCOFF)*qstOCV;

[0010] qstOCV is an update weight coefficient obtained according to the theoretical error value eVLT, the update weight coefficient qstOCV is 0 when the theoretical error value eVLT is greater than or equal to 5%, and the update weight coefficient qstOCV is -20*eVLT+100% when the theoretical error value eVLT is less than 5%.

[0011] Further, if the load state is in the second online driving state; then the current value SOCPRE of the state of charge and the state of charge estimation value SOCRun obtained by the offline model are corrected according to the temperature coefficient QT and the weight coefficient QRUN, and then the state of charge update value SOCNEW2 in the second online driving state is obtained; so that:

[0012] SOCNEW2=SOCPRE+(SOCRUN-SOCPRE)*QT*QRUN.

[0013] Wherein, the weight coefficient QRUN can be corrected according to the relationship between the theoretical value VTR and the measured value VDT of the target battery or battery pack voltage:

[0014] If VTR-VDT is greater than or equal to 3mV, then the weight coefficient QRUN=0 at this time;

[0015] If VTR-VDT is less than 3mV, then QRUN=-0.33*(VTR-VDT)+100%;

[0016] The temperature coefficient QT is 1 at 25 degrees Celsius, and the temperature coefficient QT increases with the decrease of temperature.

[0017] Further, the transient voltage and transient current of the target battery or battery pack at k time and k-1 time with a time interval of△t can be detected, and the model parameters of the cell first-order equivalent model are solved according to the cell first-order equivalent model.

[0018] Wherein, the transient voltage includes the model open circuit voltage V(k) at k time and the model open circuit voltage V(k-1) at k-1 time, and the transient current includes the model current I(k) at k time and the model current I(k-1) at k-1 time; and satisfy:

[0019] V(k) = I(k)R(0) + V(p,k) + V(OCV),

[0020] V(k-1) = I(k-1)R(0) + V(p,k-1) + V(OCV),

[0021] And according to the capacitance inductance characteristics can also be obtained:

[0022] V(k) = V(k-1)*θ1 + I(k)*θ2 + I(k-1)θ3 + θ4; Wherein,

[0023] θ1 = e^(-△t / τ);

[0024] θ2 = R(0);

[0025] θ3 = R(0)e^(-△t / τ) + R(p)(1-e^(-△t / τ));

[0026] θ4 = V(OCV)(1-e^(-△t / τ));

[0027] Wherein, the temperature correction coefficient QT is obtained based on the measured performance of the battery cell, and a binomial is fitted through test data:

[0028] When the temperature is greater than 25 degrees Celsius, it is 1, and when the temperature is less than 25 degrees Celsius, it conforms to the following formula:

[0029] y = 0.0002x^2 - 0.0076x + 1.0731, x is the module temperature, and y is the correction coefficient QT.

[0030] Correspondingly, the embodiment of the application also discloses a battery management device, comprising a first intrinsic information segmentation unit and a second state of charge updating unit; the first intrinsic information segmentation unit obtains the first intrinsic curve of the open circuit voltage OCV and the state of charge SOC of the target battery or battery pack, and divides the first intrinsic curve into N segments according to the slope dOCV / dSOC; the Mth segment in the N segments is a to-be-corrected segment, M and N are positive integers, N>M, and N is greater than or equal to 6.

[0031] Specifically, the slope dM of the Mth segment and the slope d(M-1) of the M-1th segment differ by a preset first value, and the slope dM of the Mth segment and the slope d(M+1) of the M+1th segment differ by a preset second value.

[0032] Further, the second state of charge updating unit can correct the state of charge SOC of the target battery or battery pack in the Mth segment according to the load state of the target battery or battery pack; wherein, the load state includes a first unloaded static state and a second online driving state.

[0033] If the load state is in the first no-load static state, then the individual cell voltage VSS when the target battery or battery pack is connected to high voltage, the static time tst after the high voltage is disconnected, the average module temperature TAV, and the state of charge SOCOFF when the high voltage is disconnected are obtained; and the state of charge in the Mth segment is updated to obtain the corrected state of charge SOCNEW1; so that:

[0034] SOCNEW1=SOCOFF+(SOCOCV-SOCOFF)*qstOCV;

[0035] qstOCV is an updated weighting coefficient obtained based on the theoretical error value eVLT. When the theoretical error value eVLT is greater than or equal to 5%, the updated weighting coefficient qstOCV takes the value of 0; when the theoretical error value eVLT is less than 5%, the updated weighting coefficient qstOCV takes the value of -20*eVLT+100%.

[0036] Furthermore, if the load state is in the second online operating state, then the current value of the state of charge (SOCPRE) and the estimated value of the state of charge (SOCRUN) obtained from the offline model are corrected according to the temperature coefficient QT and the weighting coefficient QRUN, thereby obtaining the updated value of the state of charge (SOCNEW2) in the second online operating state; so that:

[0037] SOCNEW2=SOCPRE+(SOCRUN-SOCPRE)*QT*QRUN.

[0038] The weighting coefficient QRUN can be corrected based on the relationship between the theoretical value VTR and the measured value VDT of the target battery or battery pack voltage.

[0039] If VTR-VDT is greater than or equal to 3 millivolts, then the weighting factor QRUN=0.

[0040] If VTR-VDT is less than 3 millivolts, then QRUN = -0.33 * (VTR-VDT) + 100%;

[0041] Its temperature coefficient QT is 1 at 25 degrees Celsius, and this temperature coefficient QT increases as the temperature decreases.

[0042] Specifically, M can be 4; detect the transient voltage and transient current of the target battery or battery pack at time k and time k-1 with a time interval of Δt, and solve for the model parameters of the first-order equivalent model of the battery cell based on the first-order equivalent model of the battery cell.

[0043] The transient voltage includes the model open-circuit voltage V(k) at time k and the model open-circuit voltage V(k-1) at time k-1; the transient current includes the model current I(k) at time k and the model current I(k-1) at time k-1; and satisfies:

[0044] V(k)=I(k)R(0)+V(p,k)+V(OCV),

[0045] V(k-1)=I(k-1)R(0)+V(p,k-1)+V(OCV),

[0046] And according to the capacitance inductance characteristics can also be obtained:

[0047] V(k)=V(k-1)*θ1+I(k)*θ2+I(k-1)θ3+θ4;Wherein,

[0048] θ1= e^(-△t / τ);

[0049] θ2=R(0);

[0050] θ3=R(0)e^(-△t / τ)+R(p)(1-e^(-△t / τ));

[0051] θ4= V(OCV)(1-e^(-△t / τ));

[0052] Wherein, the temperature correction coefficient QT is obtained based on the actual performance of the battery cell, and a binomial is fitted through test data:

[0053] When the temperature is greater than 25 degrees Celsius, it is 1, and when the temperature is less than 25 degrees Celsius, it conforms to the following formula:

[0054] y = 0.0002x^2 - 0.0076x + 1.0731, x is the module temperature, and y is the correction coefficient QT.

[0055] Wherein, τ is a time constant, reflecting the speed of response change in the circuit; V(k), V(k-1) can be calculated according to the sensor measurement, and I(k), I(k-1) can be obtained according to the current sensor measurement; through the above equivalent model, V(OCV), R(0), R(p) and time constant τ can be obtained.

[0056] Correspondingly, the embodiment of the application also discloses a computer storage medium and a corresponding controller; the computer storage medium comprises a storage medium body for storing a computer program; when the computer program is executed by a microprocessor, any state of charge correction method as above can be realized; similarly, the controller comprises any battery management device and / or any computer storage medium as above.

[0057] The reliability of the prior art is poor, and the remaining energy may jump or deviate from the actual battery performance. For household vehicles, if the upper limit of charging is set, for example, the vehicle is used for a long time in the 30%-80% SOC interval. For commercial vehicles, the standing time can be considered as zero.

[0058] The present application corrects the SOC of the standing and driving conditions by fine division of the OCV-SOC curve, without the need for additional new hardware, providing more opportunities for lithium iron phosphate battery remaining capacity calibration, and improving the accuracy of remaining energy estimation.

[0059] The present application is economically feasible and can improve the reliability of remaining capacity estimation, reducing the risk of battery performance degradation and poor customer experience due to inaccurate capacity. Of course, the present application is also applicable to non-lithium iron phosphate batteries.

[0060] In summary, the present application re-segments the OCV-SOC curve in the target region according to the slope characteristics, and then corrects the SOC in the target region combined with the working conditions. The method and product disclosed in the present application estimate and correct the SOC of the standing condition combined with the voltage and SOC values of the single battery voltage at high and low voltage, standing time, average module temperature and other parameters. The SOC in the running condition is also estimated and corrected by the first-order equivalent circuit of the battery cell. Without introducing new hardware, the present application effectively improves the accuracy of remaining capacity estimation of the battery, especially the lithium iron phosphate battery, before and after 60% of the OCV-SOC curve, and improves the power management level of the battery management device and related controller.

[0061] It should be noted that the terms "first", "second", and the like used in this text are only used to describe the elements in the technical solution, and do not constitute a limitation on the technical solution, nor can they be understood as an indication or implication of the importance of the corresponding elements; elements with "first", "second" and the like indicate that at least one element is included in the corresponding technical solution. BRIEF DESCRIPTION OF DRAWINGS

[0062] In order to more clearly illustrate the technical solutions of the present application, and to facilitate further understanding of the technical effects, technical features and purposes of the present application, the present application will be described in detail below with reference to the accompanying drawings, which constitute an essential part of the specification and are used to illustrate the technical solutions of the present application together with the embodiments of the present application, but do not constitute a limitation on the present application.

[0063] The same reference numerals in the drawings represent the same components, specifically:

[0064] Figure 1 OCV-SOC curve of lithium iron phosphate in the related art.

[0065] Figure 2 OCV-SOC curve of lithium iron phosphate in the related art.

[0066] Figure 3 SOC updating process before and after standing of the embodiment of the application.

[0067] Figure 4 First-order equivalent circuit model of the battery cell in the embodiment of the application.

[0068] Figure 5 First-order equivalent circuit model of the battery cell in the embodiment of the application.

[0069] Figure 6 Online driving condition SOC process in the embodiment of the application.

[0070] Figure 7 Process schematic diagram of the method embodiment of the application.

[0071] Figure 8 Component structure schematic diagram of the device embodiment of the application.

[0072] Figure 9 Product layout structure schematic diagram of the application Figure 1 .

[0073] Figure 10 Product layout structure schematic diagram of the application Figure 2 .

[0074] Figure 11 Product layout structure schematic diagram of the application Figure 3 .

[0075] Figure 12 Product layout structure schematic diagram of the application Figure 4 .

[0076] Wherein:

[0077] 001-SOC first segment data

[0078] 002-SOC second segment data

[0079] 003-SOC third segment data

[0080] 004-SOC fourth segment data

[0081] 005-SOC fifth segment data

[0082] 006-SOC sixth segment data

[0083] 010-first intrinsic curve

[0084] 011-SOC update value;

[0085] 100 - Steps for segmenting the first intrinsic information;

[0086] 200 - Second state of charge update steps;

[0087] 210 - Long-term static condition;

[0088] 220 - Online driving conditions;

[0089] 600 - Battery Management Device;

[0090] 610 - First intrinsic information segmentation unit;

[0091] 620 - Second state of charge update unit;

[0092] 621 - Long-term static information processing unit;

[0093] 622 - Online vehicle information processing unit;

[0094] 900 - Vehicles;

[0095] 901 - Controller;

[0096] 903 - Computer storage media;

[0097] 999-battery pack. Detailed Implementation

[0098] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Of course, the specific embodiments described below are merely illustrative of the technical solutions of the present invention, and not intended to limit the invention. Furthermore, the parts described in the embodiments or drawings are merely illustrative examples of relevant parts of the present invention, and not the entirety of the invention.

[0099] like Figure 7 and Figures 9 to 12 The illustrated state of charge (SOC) correction method includes a first intrinsic information segmentation step 100 and a second SOC update step 200. The first intrinsic information segmentation step 100 acquires a first intrinsic curve 010 of the open-circuit voltage OCV and the SOC of the target battery or battery pack 999, and segments the first intrinsic curve 010 according to the slope dOCV / dSOC. Figure 2 6 segments shown.

[0100] Specifically, the slope d4 of the fourth segment differs from the slope d3 of the third segment by a preset first value, and the slope d4 of the fourth segment differs from the slope d5 of the fifth segment by a preset second value.

[0101] exist Figure 2In the SOC is about 60% of the improved technical solutions are specifically given: at this time, if the vehicle is in a long time after the update SOC value; to assess the initial value of the remaining percentage of battery energy needs to obtain long time stationary SOC, single cell voltage value; and based on the open circuit voltage of the obtained iron phosphate battery SOC interval, the accurate division of the SOC percentage, based on the docv / dsoc is different, the SOC interval is divided as follows:

[0102] S1: 0% - 10%

[0103] S2: 10% - 30%

[0104] S3: 30% - 55%

[0105] S4: 55% - 65%

[0106] S5: 65% - 97%

[0107] S6: 97% - 100%

[0108] In the prior art, the iron phosphate battery only uses S1, S2 and S6 segments for initialization, covering the SOC range higher than 97% and lower than 30%; and the normal use range of the vehicle is usually between 30%-97%, which is difficult to initialize, especially for winter conditions.

[0109] The method and product of the application distinguish the slope of the S4 segment after fine division of the SOC-OCV curve, and the slope is different from the slopes of S3 and S5 by a given difference, so that OCV initialization can be performed.

[0110] According to the above division method, the voltage interval of the initialization is widened, and the S4 interval is added to the previous S1, S2 and S6 intervals; the stationary voltage V after meeting certain conditions is in the S4 interval, and the initial SOC value is determined according to the voltage at the corresponding SOC value.

[0111] Further, the second state of charge updating step 200 corrects the state of charge SOC of the target battery or battery pack 999 in the fourth segment according to the load state of the target battery or battery pack 999; wherein the load state includes a first idle state and a second online driving state.

[0112] Wherein, if the load state is in the first idle state as shown in Figure 3 The single cell voltage VSS of the target battery or battery pack 999 connected to high voltage, the stationary time tst after disconnecting the high voltage, the average module temperature TAV, and the state of charge SOCOFF when the high voltage is disconnected are obtained; and the state of charge in the fourth segment is updated to obtain the corrected state of charge SOCNEW1; so that:

[0113] SOCNEW1 = SOC OFF + (SOC OCV - SOC OFF) * qstOCV;

[0114] qstOCV is an updated weight coefficient obtained according to the theoretical error value eVLT, the updated weight coefficient qstOCV is 0 when the theoretical error value eVLT is greater than or equal to 5%; the updated weight coefficient qstOCV is -20*eVLT+100% when the theoretical error value eVLT is less than 5%.

[0115] In addition, if the load state is in the second online driving state as shown in Figure 6 , the current value SOCPRE of the state of charge and the state of charge estimation value SOC RUN obtained by the offline model are corrected according to the temperature coefficient QT and the weight coefficient QRUN, and then the state of charge update value SOCNEW2 in the second online driving state is obtained; so that:

[0116] SOCNEW2 = SOCPRE + (SOC RUN - SOCPRE) * QT * QRUN.

[0117] Specifically, the weight coefficient QRUN can be corrected according to the relationship between the theoretical value VTR and the measured value VDT of the target battery or battery pack 999 voltage;

[0118] If VTR-VDT is greater than or equal to 3 millivolts, then the weight coefficient QRUN=0 at this time;

[0119] If VTR-VDT is less than 3 millivolts, then QRUN=-0.33*(VTR-VDT)+100%;

[0120] The temperature coefficient QT is 1 at 25 degrees Celsius, and the temperature coefficient QT increases with the decrease of temperature.

[0121] Further, the transient voltage and transient current of the target battery or battery pack 999 at k time and k-1 time separated by time △t can be detected, and the model parameters of the cell first-order equivalent model can be solved according to the cell first-order equivalent model as shown in Figure 4 .

[0122] As shown in Figure 5 , the transient voltage includes the model open circuit voltage V(k) at k time and the model open circuit voltage V(k-1) at k-1 time, and the transient current includes the model current I(k) at k time and the model current I(k-1) at k-1 time; and satisfy:

[0123] V(k) = I(k)R(0) + V(p,k) + V(OCV),

[0124] V(k-1)=I(k-1)R(0)+V(p,k-1)+V(OCV),

[0125] Furthermore, based on the characteristics of capacitors and inductors, we can also obtain:

[0126] V(k)=V(k-1)*θ1+I(k)*θ2+I(k-1)θ3+θ4; where,

[0127] θ1 = e^(-Δt / τ);

[0128] θ2=R(0;

[0129] θ3=R(0)e^(-△t / τ)+R(p)(1- e^(-△t / τ));

[0130] θ4= V(OCV)(1- e^(-Δt / τ));

[0131] The temperature correction coefficient QT is obtained based on the actual performance of the battery cell, and is fitted with a binomial formula using test data:

[0132] The value is 1 when the temperature is above 25 degrees Celsius, and conforms to the following formula when the temperature is below 25 degrees Celsius:

[0133] y = 0.0002x^2 - 0.0076x + 1.0731, where x is the module temperature and y is the correction coefficient QT.

[0134] Furthermore, such as Figure 8 The battery management device 600 shown includes a first intrinsic information segmentation unit 610 and a second state of charge update unit 620; wherein, the first intrinsic information segmentation unit 610 acquires a first intrinsic curve 010 of the open-circuit voltage OCV and state of charge SOC of the target battery or battery pack 999, and segments the first intrinsic curve 010 into segments according to the slope dOCV / dSOC. Figure 2 6 segments shown.

[0135] The second state of charge update unit 620 corrects the state of charge (SOC) of the target battery or battery pack 999 in the fourth stage according to the load state of the target battery or battery pack 999; wherein the load state includes a first no-load static state and a second online driving state.

[0136] If the load state is in the first no-load static state, then the individual cell voltage VSS when the target battery or battery pack 999 is connected to the high voltage, the static time tst after the high voltage is disconnected, the average module temperature TAV, and the state of charge SOCOFF when the high voltage is disconnected are obtained; and the state of charge in the fourth segment is updated to obtain the corrected state of charge SOCNEW1; so that:

[0137] SOCNEW1 = SOC OFF + (SOC OCV - SOC OFF) * qstOCV;

[0138] qstOCV is an updated weight coefficient obtained according to a theoretical error value eVLT, the updated weight coefficient qstOCV is 0 when the theoretical error value eVLT is greater than or equal to 5%, and the updated weight coefficient qstOCV is -20*eVLT+100% when the theoretical error value eVLT is less than 5%.

[0139] Further, if the load state is in the second online driving state, the current value SOCPRE of the state of charge and the state of charge estimation value SOC RUN obtained by the offline model are corrected according to the temperature coefficient QT and the weight coefficient QRUN, and then the state of charge update value SOCNEW2 in the second online driving state is obtained, so that:

[0140] SOCNEW2 = SOCPRE + (SOC RUN - SOCPRE) * QT * QRUN.

[0141] The weight coefficient QRUN can be corrected according to the relationship between the theoretical value VTR and the measured value VDT of the target battery or battery pack 999 voltage.

[0142] If VTR-VDT is greater than or equal to 3 millivolts, then the weight coefficient QRUN=0 at this time;

[0143] If VTR-VDT is less than 3 millivolts, then QRUN=-0.33*(VTR-VDT)+100%;

[0144] The temperature coefficient QT is 1 at 25 degrees Celsius, and the temperature coefficient QT increases with the decrease of temperature.

[0145] Specifically, for M=4, the transient voltage and transient current of the target battery or battery pack 999 at k time and k-1 time are detected, and the model parameters of the cell first-order equivalent model are solved according to the cell first-order equivalent model as shown in Figure 4

[0146] The transient voltage includes the model open circuit voltage V(k) at k time and the model open circuit voltage V(k-1) at k-1 time, and the transient current includes the model current I(k) at k time and the model current I(k-1) at k-1 time; and satisfy:

[0147] V(k) = I(k)R(0) + V(p,k) + V(OCV),

[0148] V(k-1) = I(k-1)R(0) + V(p,k-1) + V(OCV), ​

[0149] Furthermore, based on the characteristics of capacitors and inductors, we can also obtain:

[0150] V(k)=V(k-1)*θ1+I(k)*θ2+I(k-1)θ3+θ4; where,

[0151] θ1 = e^(-Δt / τ);

[0152] θ2=R(0;

[0153] θ3=R(0)e^(-△t / τ)+R(p)(1- e^(-△t / τ));

[0154] θ4= V(OCV)(1- e^(-Δt / τ));

[0155] The temperature correction coefficient QT is obtained based on the actual performance of the battery cell, and is fitted with a binomial formula using test data:

[0156] The value is 1 when the temperature is above 25 degrees Celsius, and conforms to the following formula when the temperature is below 25 degrees Celsius:

[0157] y = 0.0002x^2 - 0.0076x + 1.0731, where x is the module temperature and y is the correction coefficient QT.

[0158] Furthermore, such as Figures 9 to 12 The computer storage medium 903 shown includes a storage medium body for storing a computer program; when the computer program is executed by the microprocessor, it can implement any of the above-mentioned state of charge correction methods; similarly, its controller 901 includes any of the above-mentioned battery management devices 600 and / or any of the above-mentioned computer storage medium 903; the technical solutions will not be described in detail here.

[0159] It should be noted that the above embodiments are only for more clearly illustrating the technical solution of the present invention. Those skilled in the art will understand that the implementation of the present invention is not limited to the above content. Any obvious changes, substitutions or replacements made based on the above content do not exceed the scope of the technical solution of the present invention. Other implementations will also fall within the scope of the present invention without departing from the concept of the present invention.

Claims

1. A state-of-charge correction method characterized by, The method comprises a first intrinsic information segmentation step (100) and a second state of charge updating step (200). The first intrinsic information segmentation step (100) obtains a first intrinsic curve (010) of open circuit voltage (OCV) and state of charge (SOC) of a target battery or battery pack (999), and divides the first intrinsic curve (010) into N segments according to the slope dOCV / dSOC. The Mth segment in the N segments is a segment to be corrected, M and N are positive integers, N>M, and N is greater than or equal to 6. The slope dM of the Mth segment and the slope d(M-1) of the (M-1)th segment differ by a preset first value, and the slope dM of the Mth segment and the slope d(M+1) of the (M+1)th segment differ by a preset second value. The second state of charge updating step (200) corrects the state of charge SOC of the target battery or battery pack (999) in the Mth segment according to the load state of the target battery or battery pack (999). The load state includes a first unloaded static state and a second online driving state. If the load state is in the first unloaded static state, the single battery voltage VSS when the target battery or battery pack (999) is connected to high voltage, the static time tst after the high voltage is disconnected, the average module temperature TAV, and the state of charge SOCOFF when the high voltage is disconnected are obtained. The state of charge in the Mth segment is updated to obtain the corrected state of charge SOCNEW1, so that: SOCNEW1=SOCOFF+(SOCOCV-SOCOFF)*qstOCV; qstOCV is an updating weight coefficient obtained according to a theoretical error value eVLT. When the theoretical error value eVLT is greater than or equal to 5%, the updating weight coefficient qstOCV is 0. When the theoretical error value eVLT is less than 5%, the updating weight coefficient qstOCV is -20*eVLT+100%; If the load state is in the second online driving state, the current value SOCPRE of the state of charge and the state of charge estimation value SOCNEW2 obtained from the offline model are corrected according to the temperature coefficient QT and the weight coefficient QRUN, so that: SOCNEW2=SOCPRE+(SOCRUN-SOCPRE)*QT*QRUN.

2. The state-of-charge correction method of claim 1, wherein: The weight coefficient QRUN is corrected according to the relationship between the theoretical value VTR and the measured value VDT of the voltage of the target battery or battery pack (999). If VTR-VDT is greater than or equal to 3 millivolts, then the weight coefficient QRUN=0 at this time. If VTR-VDT is less than 3 millivolts, then QRUN=-0.33*(VTR-VDT)+100%. The temperature coefficient QT is 1 at 25 degrees Celsius, and the temperature coefficient QT increases as the temperature decreases.

3. The state-of-charge correction method according to claim 1 or 2, wherein: Detecting the transient voltage and transient current of the target battery or the battery pack (999) at k time and k-1 time with a time interval of△t, and solving the model parameters of the cell first-order equivalent model according to the cell first-order equivalent model; the transient voltage includes the model open circuit voltage V(k) at k time and the model open circuit voltage V(k-1) at k-1 time, and the transient current includes the model current I(k) at k time and the model current I(k-1) at k-1 time; and satisfy: V(k)=I(k)R(0)+V(p,k)+V(OCV), V(k-1)=I(k-1)R(0)+V(p,k-1)+V(OCV), And according to the capacitance and inductance characteristics, the following can also be obtained: V(k)=V(k-1)*θ1+I(k)*θ2+I(k-1)θ3+θ4; wherein, θ1= e^(-△t / τ); θ2=R(0); θ3=R(0)e^(-△t / τ)+R(p)(1- e^(-△t / τ)); θ4= V(OCV)(1- e^(-△t / τ)); Wherein, the temperature correction coefficient QT is obtained based on the actual performance of the cell, and a binomial is fitted through test data: When the temperature is greater than 25 degrees Celsius, it is 1, and when the temperature is less than 25 degrees Celsius, it meets the following formula: y = 0.0002x^2 - 0.0076x + 1.0731, x is the module temperature, and y is the correction coefficient QT.

4. A battery management apparatus (600) comprising a first intrinsic information segmenting unit (610), a second state of charge updating unit (620); wherein, The first intrinsic information segmentation unit (610) obtains the first intrinsic curve (010) of the open circuit voltage OCV and the state of charge SOC of the target battery or the battery pack (999), and divides the first intrinsic curve (010) into N segments according to the slope dOCV / dSOC; the Mth segment in the N segments is a to-be-corrected segment, M and N are positive integers, N>M, and N is greater than or equal to 6; the slope dM of the Mth segment and the slope d(M-1) of the M-1th segment differ by a preset first value, and the slope dM of the Mth segment and the slope d(M+1) of the M+1th segment differ by a preset second value; the second state of charge updating unit (620) corrects the state of charge SOC of the target battery or the battery pack (999) in the Mth segment according to the load state of the target battery or the battery pack (999); wherein, the load state includes a first unloaded static state and a second online driving state; If the load state is in the first unloaded static state; then the single battery voltage VSS when the target battery or the battery pack (999) is connected to high voltage, the static time tst after disconnecting high voltage, the average module temperature TAV, and the state of charge SOCoff when disconnecting high voltage are obtained; and the state of charge in the Mth segment is updated to obtain the corrected state of charge SOCNEW1; so that: SOCNEW1=SOCOFF+(SOCOCV-SOCOFF)*qstOCV; qstOCV is an updated weighting coefficient obtained based on the theoretical error value eVLT. The updated weighting coefficient qstOCV takes a value of 0 when the theoretical error value eVLT is greater than or equal to 5%; the updated weighting coefficient qstOCV takes a value of -20*eVLT+100% when the theoretical error value eVLT is less than 5%. If the load state is in the second online driving state, then the current value of the state of charge (SOCPRE) and the estimated value of the state of charge (SOCRUN) obtained from the offline model are corrected according to the temperature coefficient QT and the weighting coefficient QRUN, thereby obtaining the updated value of the state of charge (SOCNEW2) in the second online driving state; so that: SOCNEW2=SOCPRE+(SOCRUN-SOCPRE)*QT*QRUN.

5. The battery management device (600) of claim 4, wherein: The weighting coefficient QRUN is corrected based on the relationship between the theoretical value VTR and the measured value VDT of the target battery or battery pack (999); If VTR-VDT is greater than or equal to 3 millivolts, then the weighting coefficient QRUN = 0. If VTR-VDT is less than 3 millivolts, then QRUN = -0.33 * (VTR-VDT) + 100%; The temperature coefficient QT is 1 at 25 degrees Celsius, and the temperature coefficient QT increases as the temperature decreases.

6. The battery management device (600) of claim 4 or 5, wherein: M=4; detect the transient voltage and transient current of the target battery or the battery pack (999) at time k and time k-1 with a time interval of Δt, and solve the model parameters of the first-order equivalent model of the battery cell according to the first-order equivalent model of the battery cell; the transient voltage includes the model open-circuit voltage V(k) at time k and the model open-circuit voltage V(k-1) at time k-1, and the transient current includes the model current I(k) at time k and the model current I(k-1) at time k-1; and satisfy: V(k)=I(k)R(0)+V(p,k)+V(OCV), V(k-1)=I(k-1)R(0)+V(p,k-1)+V(OCV), Furthermore, based on the characteristics of capacitors and inductors, we can also obtain: V(k)=V(k-1)*θ1+I(k)*θ2+I(k-1)θ3+θ4; where, θ1 = e^(-Δt / τ); θ2=R(0; θ3=R(0)e^(-△t / τ)+R(p)(1- e^(-△t / τ)); θ4= V(OCV)(1- e^(-Δt / τ)); The temperature correction coefficient QT is obtained based on the actual performance of the battery cell, and is fitted with a binomial formula using test data: The value is 1 when the temperature is above 25 degrees Celsius, and conforms to the following formula when the temperature is below 25 degrees Celsius: y = 0.0002x^2 - 0.0076x + 1.0731, where x is the module temperature and y is the correction coefficient QT.

7. A computer storage medium (903) comprising a storage medium body for storing a computer program; wherein the computer program, when executed by a microprocessor, implements the state of charge correction method as described in any one of claims 1 to 3.

8. A controller (901) comprising a battery management device (600) as claimed in any of claims 4 to 6 and / or a computer storage medium (903) as claimed in claim 7.

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