SOC algorithm with charging correction function based on ampere-hour integral method

Abstract

The invention discloses an SOC algorithm with a charging correction function based on an ampere-hour integral method, and the algorithm comprises the steps: constructing a correction contrast system, constructing a dynamic charging voltage and capacity comparison table, and enabling the capacity of the dynamic charging voltage and capacity comparison table to be SOCr; real-time capacity calculation construction including real-time capacity SOCt and a dynamic initial value SOC0: reading a capacity value as the SOC0 according to the current dynamic charging voltage and a capacity comparison table, and calculating the current real-time capacity SOCt according to the SOC0; and calculating a correction reference capacity, reading the current SOCr according to the dynamic charging voltage and the capacity comparison table, calculating the correction reference capacity SOCa, outputting the real-time capacity, updating the correction reference system, judging the difference value between the SOCa and the SOCt, outputting the real-time capacity SOCt, updating the dynamic charging voltage and the capacity comparison table or performing correction smoothing processing, and circularly substituting the real-time capacity SOCt and the dynamic charging voltage and the capacity comparison table for difference value judgment. According to the method, charging correction can be realized, the SOC estimation precision is improved, the SOC accumulated deviation is effectively reduced, the initial SOC precision is improved, and finally, the SOC estimation precision is kept within 5%.

Description

Technical Field

[0001] The invention relates to a SOC algorithm with a charging correction function based on an ampere-hour integration method, and belongs to the technical field of battery management system application algorithms. Background Art

[0002] The open circuit voltage of a battery refers to the potential difference between the positive and negative electrodes when the battery is at rest. Figure 3 As shown in the figure, the OCV-SOC curves of two lithium batteries are shown. Generally, after charging or discharging to a specific SOC, the battery is left to stand for a period of time, usually two hours or more, to measure the battery voltage, and the OCV-SOC curve is obtained. Generally speaking, even for the same SOC, the OCV obtained after charging and discharging is different. For ternary batteries, the correspondence between SOC and OCV is fine, strictly monotonic, and the OCV difference of different SOC is quite obvious. However, for lithium iron phosphate, there is a fairly long SOC range that is very flat, the OCV difference is very small, and it is not strictly monotonic, which is manifested as the same OCV value corresponding to multiple SOC points. Therefore, simply using the SOC-OCV correspondence to estimate SOC has problems such as difficulty in meeting the standing time requirements and insufficient accuracy.

[0003] Analysis of the corresponding relationship between charge and discharge rate and terminal voltage: In the case of battery dynamics, the battery voltage we measure is actually the terminal voltage of the battery. Figure 4 As shown in the figure, it is a simple first-order RC model of a battery, where the voltage source on the left represents OCV, R represents internal resistance, R1C1 represents polarization, and the output on the right is the terminal voltage. Therefore, once the battery is charged and discharged, the terminal voltage is far from the OCV.

[0004] Select a current rate and let the battery perform constant current charging or discharging at this rate. The curves obtained for charging and discharging at the same current are different, and a set of corresponding relationships between terminal voltage and SOC can be obtained. Figure 5 As shown in the figure, the terminal voltage curve of lithium iron phosphate battery at different rates at a certain temperature. In fact, except for the constant current charging stage of the battery, the working current of the battery rarely has a long constant current charging and discharging stage. As long as there is a different current switching, the difference in battery polarization will cause the terminal voltage to deviate from the predicted curve.

[0005] Influence of temperature factors: Batteries with different material systems exhibit different temperature characteristics. Take lithium iron phosphate as an example. Figure 6 The figure shows the relationship between SOC and internal resistance of the battery at different temperatures. In general, the internal resistance of the battery changes with the temperature and SOC, and is very sensitive to low temperatures. Similarly, parameters such as OCV and polarization will also change with temperature. This undoubtedly further increases the difficulty of prediction.

[0006] Influence of battery group efficiency: The above characteristics are the characteristics of a single battery cell. In actual whole-pack applications, the situation becomes more complicated due to the series-parallel combination of battery cells. There are inevitably differences in ohmic internal resistance, polarization internal resistance, self-discharge rate, initial capacity, etc. between different battery cells. A battery pack is composed of multiple battery cells in series. Assuming the rated capacity is 50Ah, balance is not considered. Due to the difference between the rated capacity and the initial capacity, in the actual discharge process, the battery with less power is discharged first, and the battery with more power is fully charged first. This results in a decrease in efficiency after grouping, and makes it more difficult to estimate the SOC after grouping.

[0007] In addition, there are many factors that make SOC estimation difficult, such as the actual BMS MCU or AFE measurement accuracy, sampling frequency, calendar / cycle life impact, etc.

[0008] The battery must be left to rest for a long time, but electric vehicles are frequently started, and the open circuit voltage is difficult to stabilize in a short time; the battery has a voltage platform, especially for lithium iron phosphate batteries, where the terminal voltage and SOC curves are approximately straight lines during the SOC30%-80% period; the battery is at different temperatures or different life periods, and although the open circuit voltage is the same, the actual SOC may be quite different; Figure 7 As shown, when we are using this electric bicycle, if the current SOC shows 100%, the voltage drops during acceleration and the battery level may show 80%. When the acceleration stops, the voltage rises again and the battery level will jump back to 100%.

[0009] The ampere-hour integration method estimates the battery SOC based on the initial SOC0. By calculating the integral of the charge and discharge current and the corresponding time within a certain period of time, the percentage of the changed power is calculated, and finally the difference between the initial SOC and the changed SOC, that is, the remaining power, is obtained. For example:

[0010]

[0011] Cmax: Battery capacity (Ah)

[0012] lnow: current (A)

[0013] t: time.

[0014] This method only uses the accumulated current of the battery in dynamic state as the basis for SOC estimation, and to a certain extent ignores the influence of the battery self-discharge rate, aging degree and charge and discharge rate on the battery SOC. Long-term use will also cause the measurement error to accumulate and expand continuously. The SOC0 at the initial moment is not easy to determine and there is a certain error in the accuracy. Moreover, as time goes by, the error will also accumulate and increase, so the accuracy of current measurement is very high. In addition, the error will increase under the condition of drastic current fluctuation. Since the capacity that the battery can release is affected by many factors such as the discharge rate, the temperature of the battery, and the number of battery charge and discharge cycles, the SOC representing the battery capacity state must also be related to these factors. When the discharge current changes, there will be ambiguity in the understanding of the SOC definition, making the SOC result obtained according to the definition seem to be self-contradictory; the actual application of multi-cell series and parallel combination is the weighted combination of all the above factors, which jointly affect the accuracy of SOC estimation. Therefore, it is still a big problem to accurately estimate SOC within the entire life cycle of the battery. Summary of the invention

[0015] The purpose of the present invention is to solve the deficiencies of the above-mentioned prior art. Aiming at the problem that the traditional ampere-hour integration method ignores the influence of self-discharge rate, aging degree and charge and discharge rate on battery SOC, resulting in large SOC error, a SOC algorithm with charging correction function based on the ampere-hour integration method is proposed.

[0016] In order to achieve the above object, the technical solution adopted by the present invention is:

[0017] SOC algorithm with charging correction function based on ampere-hour integration method, including fixed attenuation coefficient n of battery pack, capacity attenuation coefficient s of cycle number, capacity attenuation coefficient k of charge and discharge temperature, and capacity attenuation coefficient p of charge and discharge power;

[0018] S1 correction control system is constructed to construct a dynamic charging voltage and capacity comparison table, and the capacity of the dynamic charging voltage and capacity comparison table is SOCr;

[0019] S2 real-time capacity calculation construction, build real-time capacity calculation formula: Where SOCt is the real-time capacity, SOC0 is the dynamic initial value, i(t) is the positive and negative charge and discharge current at time t, dt is the accumulated time ratio, and Qn is the current full capacity value;

[0020] S3 calibration and comparison is started, the battery pack voltage data is sampled, the capacity value is read as SOC0 according to the current dynamic charging voltage and capacity comparison table, and the read SOC0 is substituted into step S2 to calculate the current real-time capacity SOCt;

[0021] S4 Calibration reference capacity calculation: Read the current SOCr according to the dynamic charging voltage and capacity comparison table, and calculate the calibration reference capacity SOCa. SOCa = [SOCr * Q0] / [Qn * (1 - (k + s + p))], where Q0 is the designed capacity and Qn is the current full capacity value.

[0022] S5 Real-time capacity output and calibration reference system update: Judge the difference between SOCa and SOCt:

[0023] When |SOCt - SOCa| < 5%, directly output the real-time capacity SOCt of step S3.

[0024] When |SOCt - SOCa| ≥ 5%, if SOCt > SOCa, then SOC0 = SOCt * [(SOCt - SOCa) / (100 - SOCa)], if SOCt < SOCa, then SOC0 = SOCt * [1 + (SOCt - SOCa) / (100 - SOCa)];

[0025] Substitute the calculated SOC0 into step S2 to obtain the calculated and adjusted SOCt, and perform the difference processing between the calculated and adjusted SOCt and SOCa. Loop like this until |SOCt - SOCa| < 5%. At this time, update the SOCr in the dynamic charging voltage and capacity comparison table to the currently calculated and adjusted SOCt.

[0026] Preferably, any battery cell in the battery pack has a differentiated temperature-voltage and capacity comparison table.

[0027] When starting for the first time, collect the data of the battery cells in the battery pack, read the capacity value according to the differentiated temperature-voltage and capacity comparison table as SOC0, and calculate the current real-time capacity SOCt.

[0028] Obtain SOCt through real-time capacity operation to initially generate the dynamic charging voltage and capacity comparison table.

[0029] Preferably, in step 2, when I(t) < 0.25A, the real-time capacity operation is paused; when I(t) ≥ 0.25A, the calculated real-time capacity SOCt is the effective capacity, and the effective capacity participates in the real-time capacity output and calibration reference system update.

[0030] Preferably, in step S2, set the threshold at time t in i(t). When t ≥ 60s, trigger step S5.

[0031] The beneficial effects of the present invention are mainly reflected in:

[0032] It improves the accuracy of SOC estimation, effectively reduces the cumulative deviation of SOC, improves the accuracy of the initial SOC, and finally keeps the accuracy of SOC estimation within 5%. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Other features, objects and advantages of the present application will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings:

[0034] Figure 1 The present invention is a schematic flow chart of the steps of the SOC algorithm with charging correction function based on the ampere-hour integration method.

[0035] Figure 2 It is an operation flow chart of the SOC algorithm with charging correction function based on the ampere-hour integration method of the present invention.

[0036] Figure 3 It is an OCV-SOC curve diagram in the prior art.

[0037] Figure 4 It is a first-order RC model diagram of a battery in the prior art.

[0038] Figure 5 It is a terminal voltage curve diagram at a certain temperature in the prior art.

[0039] Figure 6 It is a curve diagram showing the relationship between SOC and internal resistance of a battery at different temperatures in the prior art.

[0040] Figure 7 It is a curve diagram of SOC distortion during acceleration start in the prior art. DETAILED DESCRIPTION

[0041] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0042] The present application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the relevant inventions, rather than to limit the inventions. It should also be noted that, for ease of description, only the parts related to the relevant inventions are shown in the accompanying drawings. It should be noted that, in the absence of conflict, the embodiments in the present application and the features in the embodiments can be combined with each other.

[0043] The present invention provides a SOC algorithm with a charging correction function based on the ampere-hour integration method, including a fixed attenuation coefficient n of a battery pack, a cycle number capacity attenuation coefficient s, a charge and discharge temperature capacity attenuation coefficient k, and a charge and discharge power capacity attenuation coefficient p.

[0044] The fixed attenuation coefficient n of the battery pack is the factory fixed attenuation. The function of this parameter is mainly for battery packs with poor discharge capacity that leave enough discharge margin and charging redundancy that requires energy recovery. For battery packs with some capacity that cannot be fully utilized, after the program parameter setting conditions are met, the estimated redundancy is converted into a parameter ratio to make the SOC curve closer to the actual power expression.

[0045] The capacity attenuation coefficient s for the number of cycles, the capacity attenuation coefficient k for the charge and discharge temperature, and the capacity attenuation coefficient p for the charge and discharge power are generally extracted from experimental data.

[0046] The capacity attenuation coefficient s of the number of cycles is calculated by taking 95% of the cumulative charge capacity and 95% of the cumulative discharge capacity as one cycle. The capacity ratio coefficient is calculated based on the number of cycles and the capacity attenuation. Generally, the single cell test data table of the cell manufacturer is referenced. For example;

[0047]

[0048] The capacity attenuation coefficient k of charge and discharge temperature is calculated by using the typical data of capacity test within each ℃ under constant temperature conditions to calculate the capacity ratio coefficient with 25℃. Generally, it refers to the single cell test data table of the cell manufacturer. For example:

[0049]

[0050] The charge and discharge power capacity attenuation coefficient p is calculated by the influence of different charge and discharge currents on the maximum charge and discharge capacity of the battery. The actual current request current of the vehicle's operating conditions can be used to obtain a full capacity at the starting temperature of 25 degrees, and then compared with the theoretical design capacity, it is converted into the battery's charge and discharge rate capacity ratio. Finally, different rate capacity ratios are used to make a charge and discharge rate capacity coefficient table. For example:

[0051]

[0052] like Figure 1 and Figure 2 As shown, the SOC algorithm with charging correction function based on the ampere-hour integration method includes:

[0053] A calibration control system is constructed to construct a dynamic charging voltage and capacity comparison table, and the capacity of the dynamic charging voltage and capacity comparison table is SOCr;

[0054] Real-time capacity calculation construction, build real-time capacity calculation formula: Where SOCt is the real-time capacity, SOC0 is the dynamic initial value, i(t) is the positive and negative charge and discharge current at time t, dt is the accumulated time ratio, and Qn is the current full capacity value;

[0055] The calibration control is started, the voltage data of the battery pack is sampled, and the capacity value is read according to the current dynamic charging voltage and capacity comparison table as SOC0. The read SOC0 is substituted into step S2 to calculate the current real-time capacity SOCt.

[0056] The calibration reference capacity is calculated. The current SOCr is read according to the dynamic charging voltage and capacity comparison table, and the calibration reference capacity SOCa is calculated. SOCa = [SOCr * Q0] / [Qn * (1 - (k + s + p))], where Q0 is the designed capacity and Qn is the current full capacity value.

[0057] The real-time capacity output and the calibration control system are updated, and the difference between SOCa and SOCt is judged:

[0058] When |SOCt - SOCa| < 5%, the real-time capacity SOCt in step S3 is directly output.

[0059] When |SOCt - SOCa| ≥ 5%, if SOCt > SOCa, then SOC0 = SOCt * [(SOCt - SOCa) / (100 - SOCa)], if SOCt < SOCa, then SOC0 = SOCt * [1 + (SOCt - SOCa) / (100 - SOCa)].

[0060] The calculated SOC0 is substituted into step S2 to obtain the calculated and adjusted SOCt, and the difference between the calculated and adjusted SOCt and SOCa is processed. This loop continues until |SOCt - SOCa| < 5%. At this time, the SOCr in the dynamic charging voltage and capacity comparison table is updated to the currently calculated and adjusted SOCt.

[0061] Specific implementation process and principle description:

[0062] First, a dynamic charging voltage and capacity comparison table is constructed, and the capacity of the dynamic charging voltage and capacity comparison table is SOCr.

[0063] For example:

[0064] 53996, 53774, 53636, 53531, 53444, 53367, 53301, 53243, 53193, 53141, / / 99% - 90%

[0065] 53093, 53041, 52983, 52918, 52840, 52753, 52648, 52539, 52422, 52297, / / 89% - 80%

[0066] 52168,52040,51910,51777,51648,51522,51393,51267,51141,51010, / / 79%~70%

[0067] 50870,50729,50567,50409,50264,50131,50010,49899,49782,49676, / / 69%~60%

[0068] 49563,49454,49338,49224,49105,48988,48865,48746,48624,48505, / / 59%~50%

[0069] 48384,48265,48148,48033,47924,47809,47699,47595,47480,47366, / / 49%~40%

[0070] 47269,47133,47014,46899,46784,46678,46567,46440,46306,46171, / / 39%~30%

[0071] 46042,45894,45767,45673,45593,45508,45411,45299,45089,44704, / / 29%~20%

[0072] 44285,43870,43477,43089,42740,42417,42152,41927,41729,41540, / / 19%~10%

[0073] 41332,41131,40911,40659,40371,40029,39640,39211,38631,37918, / / 9%~0%.

[0074] SOCr is 0% to 99%, and each percentage corresponds to a voltage value of a dynamic charging voltage.

[0075] Its dynamic voltage value is in dynamic comparison with the corresponding SOCr. Charging correction can be performed through the calculation in this case, so that the dynamic charging voltage and capacity comparison table provides a more accurate sampling SOC.

[0076] Real-time capacity calculation construction, build real-time capacity calculation formula: Where SOCt is the real-time capacity, SOC0 is the dynamic initial value, i(t) is the charging and discharging current with positive and negative directions at time t, dt is the cumulative time ratio, and Qn is the current full capacity value.

[0077] This real-time capacity calculation formula is used to perform current integration calculation to update the real-time capacity. The current integration calculation takes into account the cycle number capacity attenuation coefficient s, the charge and discharge temperature capacity attenuation coefficient k, and the charge and discharge power capacity attenuation coefficient p; thus making the calculated real-time capacity closer to the reference value of the true SOC.

[0078] The calibration reference is started, the battery pack voltage data is sampled, and the capacity value is read according to the current dynamic charging voltage and capacity comparison table as SOC0, and the read SOC0 is substituted into step S2 to calculate the current real-time capacity SOCt.

[0079] That is, the read data is used to participate in the real-time capacity calculation.

[0080] The calibration reference capacity is calculated. The current SOCr is read according to the dynamic charging voltage and capacity comparison table, and the calibration reference capacity SOCa is calculated. SOCa = [SOCr * Q0] / [Qn * (1 - (k + s + p))], where Q0 is the design capacity and Qn is the current full capacity value. This satisfies the operation provided by the SOC reference item.

[0081] The real-time capacity output and the calibration reference system are updated, and the difference between SOCa and SOCt is judged:

[0082] When |SOCt - SOCa| < 5%, the real-time capacity SOCt of step S3 is directly output;

[0083] When |SOCt - SOCa| ≥ 5%, if SOCt > SOCa, then SOC0 = SOCt * [(SOCt - SOCa) / (100 - SOCa)], if SOCt < SOCa, then SOC0 = SOCt * [1 + (SOCt - SOCa) / (100 - SOCa)];

[0084] The calculated SOC0 is substituted into step S2 to obtain the calculated and adjusted SOCt, and the difference between the calculated and adjusted SOCt and SOCa is processed. This cycle continues until |SOCt - SOCa| < 5%. At this time, the SOCr in the dynamic charging voltage and capacity comparison table is updated to the current calculated and adjusted SOCt.

[0085] This satisfies the accuracy of the output real-time capacity SOCt, and its general SOC accuracy is within 5%. At the same time, it meets the requirement of correcting and updating the dynamic charging voltage and capacity comparison table, and eliminates the cumulative error.

[0086] It should be noted that the above-mentioned fixed attenuation coefficient n, cycle number capacity attenuation coefficient s, charge and discharge temperature capacity attenuation coefficient k, charge and discharge power capacity attenuation coefficient p, dynamic charging voltage and capacity comparison table are all built into the calculation software in digital format, and the comparison reading, calculation, and comparison table are dynamically updated through the software. The software integration and operation are not described in detail here, and it is only necessary to meet the requirements of realizing the calculation of the present invention.

[0087] In a specific embodiment, Figure 2 As shown, any cell in the battery pack has a differentiated temperature voltage and capacity comparison table; under experimental conditions, the voltage and capacity comparison table of the cell at different temperatures is obtained, for example:

[0088]

[0089] When starting up for the first time, collect the data of the cells in the battery pack, read the capacity value as SOC0 according to the differentiated temperature voltage and capacity comparison table, and calculate the current real-time capacity SOCt;

[0090] The SOCt is obtained through real-time capacity calculation, and the dynamic charging voltage and capacity comparison table is initially generated.

[0091] Such a design allows for faster initialization, meeting the requirements for efficient initialization of differentiated battery packs. It can also enable fast startup operations after power failure and restart, and enable efficient establishment of a dynamic charging voltage and capacity comparison table.

[0092] In a specific embodiment, when I(t) < 0.25A, the real-time capacity calculation is suspended; when I(t) ≥ 0.25A, the calculated real-time capacity SOCt is the effective capacity, and the effective capacity participates in the real-time capacity output and the correction control system update.

[0093] That is, there are abnormal conditions such as weak current after power-on. When a weak current lower than 0.25A is involved in the calculation, the calculation of its SOCt will be abnormal, affecting the real-time capacity output and the update of the correction control system. Therefore, weak current limitation is performed, so that the real-time capacity calculation is accurate and reliable, and the update judgment and response are more accurate and efficient.

[0094] In a specific embodiment, a threshold value of time t in i(t) is set, and when t≥60s, the real-time capacity output and correction control system update step is triggered, that is, time t runs along the periodic time axis, that is, the cumulative charging time exceeds 60s to provide a more reliable real-time capacity, and an initial segment limitation is performed, so that the SOCt provided by the calculation is more accurate and reliable.

[0095] In addition, the present invention also has many details to optimize. For example: voltage acquisition optimization, the front-end chip requires an integrated chip with high single-string sampling accuracy, and the error is preferably within ±4mV; the sampling frequency between battery strings is preferably within 500uS; the cascaded BMS needs to calibrate the single-string voltage to avoid equipment acquisition errors; the sampling circuit also has an equalization function to improve the consistency of the module single string; the program adds a voltage data filtering algorithm to improve the reliability of the voltage sampling data.

[0096] For current sampling, sampling resistors, shunts, and Hall sensors should be used reasonably according to the current range of actual working conditions to ensure that the current acquisition accuracy within the continuous current range is within ±1% FS while taking into account that the maximum continuous current acquisition accuracy does not exceed ±3% FS; the temperature drift problem of sampling resistors and shunts should be handled properly (using power redundancy, multi-channel parallel connection, increasing heat dissipation capacity, and adding temperature drift coefficients to the program for calculation); the current zero drift problem in the static state should be reasonably set (the effective current judgment threshold should be reasonably increased according to the actual working conditions. Generally, current detection within ±0.5A is collected through other circuits. The working current is greater than ±0.25A for SOC effective calculation current to avoid being affected by invalid current and mistakenly starting SOC calculation). The program adds a current filtering algorithm to improve the reliability of current sampling data.

[0097] Temperature sampling: improve the stability of the resistance of the temperature sampling circuit line and ensure the reliability of the resistance collected by the NTC; improve the accuracy of temperature sampling, and the error should be within ±1°C; the program adds a temperature filtering algorithm to improve the reliability of temperature sampling data. Learning capacity: To improve the accuracy of the AH amount automatically calculated by the charging and discharging current direction according to the time of less than 1S under the working current condition, it is necessary to improve the accuracy of the SOC initial value and the current and temperature sampling, appropriately speed up the current sampling frequency, and optimize the processing of the accumulated amount of the peak surge current so that it is not disturbed by it and does not lose flow.

[0098] It also has terminal SOC virtualization. At the charging terminal, when the real-time SOCt value reaches 95%, the remaining time for full charge is inferred by learning the relationship between the capacity and the current full capacity. The above formula calculation is stopped, the capacity learning is retained, and the last 4% of SOC is evenly distributed according to the remaining time and virtualized to SOCt. When the full charge conditions are met, SOCt is updated to 100% to prevent premature or sudden entry into 100% from causing misjudgment and SOC jumps to customers.

[0099] At the end of discharge, when the learned capacity is close to 5%, the remaining time for full charge is inferred through the relationship between the learned capacity and the current full capacity, and the last 5% of SOC is virtualized to the real-time SOCt at 5 times the speed of the remaining time. When SOCt is 0%, the actual remaining 3% capacity of the battery is left for the vehicle to take corresponding actions.

[0100] From the above description, it can be found that the SOC estimation accuracy is improved, the SOC cumulative deviation is effectively reduced, the initial SOC accuracy is improved, and ultimately the SOC estimation accuracy is kept within 5%.

[0101] The term "comprise" or any other similar term is intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus / device that includes a list of elements includes not only those elements but also other elements not expressly listed, or also includes elements inherent to such process, method, article, or apparatus / device.

[0102] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

Claims

1. The SOC algorithm with a charging correction function based on the ampere-hour integration method includes the fixed attenuation coefficient n of the battery pack, the capacity attenuation coefficient s of the cycle number, the capacity attenuation coefficient k of the charge and discharge temperature, and the capacity attenuation coefficient p of the charge and discharge power. It is characterized in that: S1 Construction of the correction reference system: Construct a dynamic charging voltage and capacity comparison table, and the capacity of the dynamic charging voltage and capacity comparison table is SOCr. S2 real-time capacity calculation construction, build real-time capacity calculation formula: Where SOCt is the real-time capacity, SOC0 is the dynamic initial value, i(t) is the positive and negative charge and discharge current at time t, dt is the accumulated time ratio, and Qn is the current full capacity value; S3 Start of the correction reference: Sample the battery pack voltage data, read the capacity value according to the current dynamic charging voltage and capacity comparison table as SOC0, and substitute the read SOC0 into step S2 to calculate the current real-time capacity SOCt. S4 Calculation of the correction reference capacity: Read the current SOCr according to the dynamic charging voltage and capacity comparison table, and calculate the correction reference capacity SOCa. SOCa = [SOCr * Q0] / [Qn * (1 - (k + s + p))], where Q0 is the designed capacity and Qn is the current full capacity value. S5 Output of the real-time capacity and update of the correction reference system: Judge the difference between SOCa and SOCt: When |SOCt - SOCa| < 5%, directly output the real-time capacity SOCt in step S3. When |SOCt - SOCa| ≥ 5%, perform correction smoothing processing. When SOCt > SOCa, then SOC0 = SOCt * [(SOCt - SOCa) / (100 - SOCa)]; when SOCt < SOCa, then SOC0 = SOCt * [1 + (SOCt - SOCa) / (100 - SOCa)]. Substitute the calculated SOC0 into step S2 to obtain the calculated and adjusted SOCt, and process the difference between the calculated and adjusted SOCt and SOCa. Loop like this until |SOCt - SOCa| < 5%. At this time, update the SOCr in the dynamic charging voltage and capacity comparison table to the currently calculated and adjusted SOCt.

2. The SOC algorithm with a charging correction function based on the ampere-hour integration method according to claim 1, characterized in that: Any battery cell in the battery pack has a differentiated temperature voltage and capacity comparison table. When starting for the first time, collect the data of the battery cells in the battery pack, select the battery cell with the lowest voltage, read the capacity value according to the differentiated temperature voltage and capacity comparison table as SOC0, and calculate the current real-time capacity SOCt. Obtain SOCt through real-time capacity operation to initially generate a dynamic charging voltage and capacity comparison table.

3. The SOC algorithm with a charging correction function based on the ampere-hour integration method according to claim 1, characterized in that: In step 2, when I(t) < 0.25A, the real-time capacity operation is paused; when I(t) ≥ 0.25A, the calculated real-time capacity SOCt is the effective capacity, and the effective capacity participates in the output of the real-time capacity and the update of the correction reference system.

4. The SOC algorithm with a charging correction function based on the ampere-hour integration method according to claim 1, characterized in that: In step S2, set the threshold at time t in i(t). When t ≥ 60s, trigger step S5.

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