Method for correcting soc of lithium iron phosphate battery and vehicle using the same

Abstract

The application relates to a method for correcting the SOC of a lithium iron phosphate battery and a vehicle using the method, and comprises the following steps: 1) obtaining the terminal voltage, the battery pack current and the battery parameters of a battery pack, calculating the OCV according to a first-order equivalent circuit model, and obtaining a first SOC value according to the corresponding relationship between the OCV and the SOC obtained in advance; the battery parameters include the inherent internal resistance value, the polarization capacitance value and the polarization resistance value; 2) obtaining the current battery pack static time and the battery pack current value, and also obtaining the current correction confidence coefficient according to the first SOC value and the correction corresponding relationship between the SOC, the battery static time, the battery pack current and the correction confidence coefficient obtained in advance; the battery static time is the time when the battery is in a state of not being charged and not being discharged; 3) calculating the SOC calculation value, correcting the SOC calculation value by the first SOC value through the current correction confidence coefficient, and obtaining the final SOC value.

Description

Technical Field

[0001] This invention relates to a method for correcting the state of charge (SOC) of a lithium iron phosphate battery and a vehicle using the method, belonging to the field of electric vehicle SOC prediction and online identification and updating of battery parameters. Background Technology

[0002] Electric vehicles, as a type of new energy vehicle, have advantages such as zero pollution and emissions, low noise, and economic practicality, making them the mainstream direction for the future development of the automotive industry. Compared with ternary lithium batteries, lithium iron phosphate batteries are lower in cost and safer. SOC (state of charge) is an important parameter for electric vehicles, referring to the percentage of the electric vehicle's capacity relative to its rated capacity. It is affected by factors such as battery pack temperature, voltage, and current, making it difficult to calculate accurately and in real time. Based on actual user needs, the SOC error is generally less than 5%, and under all operating conditions, the SOC error is guaranteed to be less than 10%. There is a functional relationship between SOC and the cell's open-circuit voltage (OCV) (OCV-SOC table). If the OCV is known, the initial value of SOC can be obtained by looking up the table. However, existing methods for calculating SOC have the following problems:

[0003] 1) Large SOC error: Due to the lack of consideration for factors such as the real-time full charge capacity of lithium iron phosphate battery cells, cumulative error, and imperfect real-time SOC correction algorithm, the SOC error is relatively large. If tested according to national standards or actual user operating conditions, it is difficult to guarantee that the SOC error is less than 10% under all operating conditions. Sometimes the error is very large, exceeding 30%, causing the vehicle to break down and resulting in a very poor user experience.

[0004] 2) Real-time OCV-SOC meter values ​​cannot be obtained on the vehicle. Generally, the OCV-SOC meter values ​​on the vehicle are obtained through laboratory testing. However, as the battery cells age, temperature changes, and operating conditions vary, the actual OCV-SOC meter values ​​will change and need to be updated in real time. However, the OCV-SOC meter values ​​cannot be obtained again on the vehicle.

[0005] 3) Correcting SOC is difficult. The linear correlation between OCV and SOC of lithium iron phosphate batteries is very poor. Only in the two stages of high and low charge (for some cells, the SOC in the middle stage is 50%-60%) is the OCV-SOC mapping better. There are few opportunities for correction and the correction error is large. Therefore, dynamic correction of SOC is difficult.

[0006] Therefore, the existing technology for calculating SOC suffers from the problem of large SOC errors due to the difficulty in correcting SOC. Summary of the Invention

[0007] The purpose of this invention is to provide a method for correcting the state of charge (SOC) of a lithium iron phosphate battery and a vehicle using this method, in order to solve the problem of large SOC errors caused by the difficulty in correcting SOC.

[0008] To achieve the above objectives, the present invention includes:

[0009] The present invention provides a method for correcting the state-of-charge (SOC) error of a lithium iron phosphate battery, comprising the following steps:

[0010] 1) Obtain the terminal voltage, battery pack current, and battery parameters of the battery pack. Calculate the OCV based on the first-order equivalent circuit model and obtain the first SOC value based on the pre-obtained correspondence between OCV and SOC. Battery parameters include: inherent internal resistance, polarization capacitance, and polarization resistance.

[0011] 2) Obtain the current battery pack resting time and battery pack current value. Also, based on the first SOC value, obtain the correction correspondence between SOC, battery resting time, battery pack current and correction reliability coefficient in advance, and obtain the current correction reliability coefficient. The battery resting time is the duration of the battery in a state of not charging or discharging.

[0012] The correction correspondence is obtained as follows: Through battery resting experiments, the terminal voltage of the battery pack before resting is recorded under different battery resting times, SOC, and battery pack currents, and the current initial voltage (OCV) of the current battery pack before resting is calculated; the voltage at the end of the battery pack after resting for the corresponding time is collected; the difference between the OCV before resting and the voltage at the end of resting reflects the correction reliability coefficient under the corresponding battery resting time, SOC, and battery pack current, and the difference between the OCV before resting and the voltage at the end of resting is inversely proportional to the correction reliability coefficient;

[0013] 3) Calculate the SOC value. Use the first SOC value to correct the SOC value using the current correction confidence coefficient to obtain the final SOC value. The principle of correction using the correction confidence coefficient is that the higher the correction confidence coefficient, the closer the final SOC value is to the first SOC value, and the lower the correction confidence coefficient, the closer the final SOC value is to the SOC value.

[0014] The benefits of this approach are as follows: The terminal voltage, current, and battery parameters of the battery pack are collected; the OCV is calculated based on a first-order equivalent circuit model; the initial SOC value is obtained based on the correspondence between OCV and SOC; a correction reliability coefficient is calculated for the initial SOC value; and the weight of the initial SOC value and the calculated SOC value in step 3) is calculated using the correction reliability coefficient to arrive at the final SOC value. The calculated SOC value in step 3) is obtained using existing technology. By continuously correcting the SOC value using the correction reliability coefficient, the displayed results become more accurate.

[0015] Furthermore, in step 1), the method for obtaining the battery parameters is to calculate the SOC value and the battery pack temperature, and obtain a corresponding set of battery parameters based on the pre-obtained correspondence between the battery pack temperature, SOC value and battery parameters.

[0016] Furthermore, the correspondence between OCV and SOC is updated. The update method is as follows: during the charging or discharging process of the battery pack, the battery pack terminal voltage, battery pack current, and battery pack temperature under the set SOC are collected, and the battery parameters are obtained based on the corresponding SOC and battery pack temperature; based on the battery parameters and the battery pack terminal voltage and battery pack current, the OCV under the corresponding SOC is calculated using a first-order equivalent circuit model, forming a correspondence between the set SOC and the corresponding calculated OCV; interpolation is used to supplement the corresponding OCV values ​​for other SOCs to obtain a new correspondence between OCV and SOC.

[0017] Furthermore, the correspondence between OCV and SOC is updated only when the battery pack meets the full charge and full discharge settings; the full charge and full discharge settings are that the charging or discharging process reaches or covers the set SOC range with good linear correlation between OCV and SOC.

[0018] The beneficial effect of doing this is that during the correction of SOC, the correspondence between SOC and OCV is constantly updated, making the correspondence between SOC and OCV better and greatly reducing the correction error.

[0019] Furthermore, in the process of updating the correspondence between OCV and SOC, the correspondence between battery pack temperature, SOC value and battery parameters is also updated. The update method is as follows: based on the new sets of OCV and SOC correspondences obtained during the OCV and SOC correspondence update process, as well as the corresponding battery pack terminal voltage, battery pack current and battery pack temperature, the corresponding SOC and battery parameters at the temperature are calculated according to the first-order equivalent circuit model.

[0020] The benefit of doing this is that by continuously updating the battery parameters, the OCV calculated from the battery parameters, battery terminal voltage, and battery temperature becomes more accurate.

[0021] Furthermore, in step 2), the correction correspondence includes at least a first correspondence between the battery resting time and battery pack current with good OCV-SOC linear correlation and the correction confidence coefficient, and a second correspondence between the battery resting time and battery pack current with poor OCV-SOC linear correlation and the correction confidence coefficient.

[0022] When obtaining the current correction confidence coefficient, if the pre-obtained SOC is in the SOC range with good OCV-SOC linear correlation, the current correction confidence coefficient is obtained according to the corresponding battery resting time, battery pack current and the first correspondence; if the pre-obtained SOC is in the SOC range with poor OCV-SOC linear correlation, the current correction confidence coefficient is obtained according to the corresponding battery resting time, battery pack current and the second correspondence.

[0023] Furthermore, in step 2), the battery resting time in the battery resting experiment shall not exceed 60 minutes; when obtaining the current correction confidence coefficient according to the correction correspondence, if the battery resting time exceeds 60 minutes, it shall still be regarded as 60 minutes.

[0024] Furthermore, in step 3), the second SOC value is obtained by multiplying the first SOC value and the correction confidence coefficient. The higher the correction confidence coefficient, the closer the final SOC is to the second SOC value, and the lower the correction confidence coefficient, the closer the final SOC value is to the calculated SOC value.

[0025] Furthermore, in step 3), the final SOC value is calculated using the following formula:

[0026] SOC final =SOC ah ×(1-kcorr)+SOCcorr×kcorr

[0027] Among them, SOC final This is the final SOC value; SOC ah is the first SOC value; kcorr is the current correction confidence coefficient; SOCcorr is the second SOC value.

[0028] The beneficial effect of doing this is that the final SOC value is obtained by calculating the weight of the first and second SOC values ​​calculated using existing technology, and by correcting the credibility coefficient, making the SOC value displayed in actual operation more accurate.

[0029] The present invention discloses a vehicle using a method for correcting the state of charge (SOC) of a lithium iron phosphate battery, wherein the vehicle displays the SOC using the aforementioned method for correcting the SOC of a lithium iron phosphate battery. Attached Figure Description

[0030] Figure 1 This is a flowchart of the SOC error correction process of the present invention;

[0031] Figure 2 This is the OCV-SOC mapping diagram at 25℃ according to the present invention. Detailed Implementation

[0032] The present invention will now be described in further detail with reference to the accompanying drawings.

[0033] Method Implementation Examples:

[0034] This invention provides a method for correcting the State of Charge (SOC) of a lithium iron phosphate battery. The method involves iteratively calculating a new SOC-OC correspondence with an existing OCV-SOC table using a first-order equivalent circuit model. This new SOC-OC correspondence replaces the data in the existing OCV-SOC table, forming a new OCV-SOC table. A SOC value is extracted from this new OCV-SOC table, and a temperature value is obtained. Based on the temperature and SOC values, the corresponding battery parameters are retrieved from an array table of battery temperature, capacity, and battery parameters. The obtained battery parameters are then used to calculate the cell open-circuit voltage (OCV) using the first-order equivalent circuit model. A new SOC value is obtained by querying the new OCV-SOC table based on the OCV. Next, a correction confidence coefficient for this new SOC value is calculated. A predicted corrected SOC value is obtained using the new SOC value and the correction confidence coefficient. Finally, the predicted corrected SOC value is compared with the SOC calculated using ampere-hour integration and the open-circuit voltage. ah To obtain a final SOC final Values ​​and display. For example... Figure 1 As shown, the specific method is as follows:

[0035] 1) Predicting SOC value:

[0036] After the BMS is woken up and powered on, the temperature-charge-battery parameter table in the historical storage pool is read from the BMS. Different temperatures and charges correspond to different battery parameters. Then, the u(t)Pack voltage and i(t)Pack current are collected, and OCV(t) is calculated according to the following formula (first-order equivalent circuit model).

[0037]

[0038] Where OCV(t) is the real-time cell open-circuit voltage, u(t) is the Pack voltage, i(t) is the Pack current, Rs is the inherent resistance of the battery, Cp is the polarization capacitance, Rp is the polarization resistance, and t is the temperature.

[0039] For different temperatures and power levels, a set of Rs, Cp, and Rp are generated. The OCV under different temperatures and power levels is obtained using the above formula. Then, the temperature and OCV-SOC parameter table in the historical storage pool is read, and the SOC value is obtained by linear interpolation using the calculated OCV.

[0040] Regarding the parameters in the historical storage value pool temperature and capacity table and the battery parameter table, if reading fails after the BMS is powered on, the default value will be selected. The default value comes from the parameter values ​​obtained from the battery pack experiment.

[0041] 2) Iterative OCV-SOC table and first-order equivalent circuit model:

[0042] Iterate through the OCV-SOC table and the first-order equivalent circuit model, and reconstruct a new OCV-SOC curve based on data from a complete or near-complete charging and discharging process. Calculate the OCV values ​​under different SOCs based on the parameters within the first-order equivalent circuit model, obtain new OCV-SOC data at different temperatures by querying the new OCV-SOC table, and replace the new data in the existing OCV-SOC table with the new OCV-SOC data at different temperatures.

[0043] Iteration of the OCV-SOC table and the first-order equivalent circuit model can only be performed under certain iterative conditions. This invention assumes that the iterative conditions are met only when the battery is fully charged and fully discharged.

[0044] The linear correlation between OCV and SOC in lithium iron phosphate batteries is very poor. Only in the high and low capacity stages (and in some cells, the mid-stage SOC at 50%-60%) does the OCV-SOC mapping show relatively good results. Figure 2 The diagram shows the OCV-SOC mapping relationship at 25℃. OCV-SOC has few opportunities for correction and large correction errors, making dynamic SOC correction difficult and making it hard to capture moments when the OCV-SOC linear correlation is good after a period of rest. To increase the chances of full charge and full discharge, if the resting time exceeds a set time or the Pack current is very small and the duration reaches a set value (in this invention, the resting time is 30 minutes, and the absolute value of the Pack current i(t) < 1A), then OCV-SOC is considered to be within a good linear range (a good linear range is when the slopes of SOC and OCV are similar or even equal within the same range). Figure 2 The OCV is greater than 3.345V (SOC > 95%) or the OCV is less than 3.285V (SOC < 25%). Figure 2 The selected area (where OCV increases significantly with SOC) is considered to be fully charged and fully discharged.

[0045] The definition of a complete or near-complete charging and discharging process is as follows: The resting time exceeds 30 minutes or the pack current i(t) < 1A lasts for more than 30 minutes, and the OCV is within a relatively good time range. A relatively accurate initial SOC value is obtained by consulting the OCV-SOC table. During charging, the cell voltage u(t) and i(t)Pa are recorded at SOC values ​​of 0%, 5%, 10%, 15%, 20%, 25%, 30%, 50%, 55%, 60%, 90%, 95%, and 100%, respectively. Thirteen sets of data were collected, including ck current and temperature values ​​(the data was determined based on the linear correlation between cell OCV and SOC, and could be increased or decreased). For discharge, the cell voltage u(t), pack current i(t), and temperature values ​​were recorded at SOC values ​​of 100%, 95%, 90%, 60%, 55%, 50%, 30%, 25%, 20%, 15%, 10%, 5%, and 0%, for a total of 13 sets of data (the number of data sets was determined based on the linear correlation between cell OCV and SOC, and could be increased or decreased). If not all 13 sets of data were collected, they were linearly fitted from existing data, ultimately requiring 13 sets. During the sequential power-on and power-off process of the BMS, the iteration condition was met only when the battery reached full charge and full discharge (charging: SOC > 95%; discharging: SOC < 25%). If this condition was not met, the iteration was not performed.

[0046] If the iteration conditions are met, the process proceeds iteratively. Using the first-order equivalent circuit model and the battery parameters obtained at the time of cell manufacturing—inherent internal resistance Rs0, polarization capacitance Cp0, and polarization resistance Rp0—as initial values, the OCV value at that temperature is calculated, resulting in a new set of OCV-SOC data. Using the latest set of OCV-SOC data and the system's real-time full-charge capacity, parameter identification is performed to obtain the latest battery parameters (inherent resistance Rs, polarization capacitance Cp, and polarization resistance Rp) at that temperature, which are then updated and stored in the historical record. The earliest data in the historical data is continuously replaced, with several sets being replaced sequentially (in this invention, the earliest 7 sets of historical data are replaced) to obtain the latest 7 real-time full-charge capacities of the system at that temperature. The maximum and minimum values ​​are subtracted, and the average value is calculated to obtain the latest real-time full-charge capacity of the system. Furthermore, the OCV is calculated based on the latest battery parameters, and the latest OCV-SOC data is obtained through the OCV, updated, and stored in the historical record.

[0047] Specifically:

[0048] After a BMS power-on, if the resting time exceeds 1 hour, the cell terminal voltage U b=3.189V (less than 3.285V, SOC = 9%), so the initial SOC value is 9%. Then, based on the historically stored real-time full-charge capacity of the system, the full-charge capacity of the system at that moment is obtained. When the vehicle starts charging, both the real-time SOC and capacity begin to increase. Then, the cell voltage u(t), pack current i(t), and temperature are recorded at SOC values ​​of 10%, 15%, 20%, 25%, 30%, 50%, 55%, 60%, 90%, 95%, and 100%. Based on the existing inherent resistance value Rs, polarization capacitance value Cp, and polarization resistance value Rp of the battery, the corresponding OCV is calculated using a first-order equivalent circuit model.

[0049] As shown in Table 1, assuming the current charge reaches 97% SOC, achieving full charge and discharge conditions, record the u(t) cell voltage, i(t) pack current, and temperature at SOC values ​​of 10%, 15%, 20%, 25%, 30%, 50%, 55%, 60%, 90%, and 95%. Then, based on the existing battery's inherent internal resistance Rs, polarization capacitance Cp, and polarization resistance Rp, calculate the corresponding OCV using a first-order equivalent circuit model. However, for "SOC = 0%, 5%, and 100%", the values ​​need to be obtained through linear interpolation using the existing data. When SOC = 95%, SOC = 97%, and the corresponding capacity values ​​are known, the u(t) cell voltage, i(t) pack current, and temperature at SOC = 100% can be obtained through linear interpolation, and the corresponding OCV can then be calculated based on the battery parameters. A total of 13 complete sets of SOC-OCV data for the battery cell and a new system full charge capacity (SOC = 100% system capacity) are obtained as new SOC-OCV arrays. These 7 new SOC-OCV arrays are then used to replace the earliest 7 SOC-OCV arrays in the historical data stored in the chip.

[0050] Regarding how to obtain the new system full charge capacity: obtain 7 real-time system full charge capacities, remove the maximum and minimum values, and then calculate the average value to obtain the new system full charge capacity.

[0051] Table 1. Data from 13 sets of SOC-OCV

[0052]

[0053] 3) Calculate the SOC correction confidence coefficient:

[0054] According to the battery standing time, SOC, and i(t)Pack current, the SOC correction reliability coefficient kcorr(t) is calculated, with a range from 0.01 to 0.99. A larger coefficient indicates higher reliability of the corrected SOC value. When the battery standing time exceeds 1 hour, the terminal voltage Ub of the cell is approximately equal to its open-circuit voltage OCV. The smaller the difference between the terminal voltage Ub of the cell and its open-circuit voltage OCV, the higher the reliability of COV, and the higher the reliability of OCV, the greater the reliability of SOC.

[0055] The SOC correction reliability coefficient is obtained through experiments. The terminal voltage Ub value of the cell before standing is recorded under different battery standing times, SOC, and i(t)Pack currents respectively; OCV is calculated through a first-order equivalent circuit model, then the cell is left to stand for more than 1 hour, and the terminal voltage Ub value after standing is recorded. The smaller the error between the terminal voltage Ub of the cell and the open-circuit voltage OCV of the cell, the more reliable the SOC correction coefficient is.

[0056] The same method is used to record data under different conditions respectively, and then a 3-dimensional array table with 3 inputs (battery standing time, SOC, i(t)Pack current) and 1 output (SOC correction reliability coefficient) is obtained. In order to reduce the amount of data and calculation and improve practical availability, dimensionality reduction processing is performed to reduce the 3 dimensions to 2 dimensions.

[0057] Specifically, a 2-dimensional array table with 2 inputs (battery standing time, i(t)Pack current) and 1 output (SOC correction reliability coefficient) is generated for each SOC value. SOC is divided into a good OCV-SOC linear mapping interval (SOC < 25%, 50% < SOC < 60%, SOC > 95%) and a poor OCV-SOC linear mapping interval (intervals other than the good interval). In the good OCV-SOC linear mapping interval, different SOCs correspond to different SOC correction reliability coefficients, so a mathematical statistical method, such as averaging, is used to obtain a SOC correction reliability coefficient that can represent the SOC correction reliability coefficient in this interval; in the poor OCV-SOC linear mapping interval, different SOCs correspond to different SOC correction reliability coefficients, so a mathematical statistical method, such as averaging, is used to obtain a SOC correction reliability coefficient that can represent the SOC correction reliability coefficient in this interval.

[0058] As shown in Table 2, it is the 2-dimensional array table with 2 inputs (battery standing time, i(t)Pack current) and 1 output (SOC correction reliability coefficient) for the good OCV-SOC linear mapping interval; as shown in Table 3, it is the 2-dimensional array table with 2 inputs (battery standing time, i(t)Pack current) and 1 output (SOC correction reliability coefficient) for the poor OCV-SOC linear mapping interval.

[0059] Using two inputs (duration and i(t)Pack current), a linear lookup is performed in Table 2 or Table 3. Table 2 is consulted when the SOC is in a region with a good OCV-SOC linear mapping; Table 3 is consulted when the SOC is in a region with a poor OCV-SOC linear mapping. The final corrected reliability coefficient kcorr(t) value is obtained.

[0060] Table 2. Good linear mapping intervals between OCV-SOC

[0061]

[0062] Table 3. Poor linear mapping intervals between OCV-SOC

[0063]

[0064]

[0065] After predicting the SOC value based on the OCV-SOC table and the first-order equivalent circuit model, the predicted SOC value needs to be corrected. The SOC value displayed in real time on an electric vehicle is calculated using ampere-hour integration and open-circuit voltage. However, in actual operation, the SOC varies depending on the specific circumstances, so real-time correction of the SOC value is necessary.

[0066] In actual operation, the electric vehicle displays the SOC value obtained from the historical OCV-SOC table in real time. At the same time, the OCV-SOC data in step 2) updates the OCV-SOC table in real time. The SOC value is predicted based on the OCV-SOC table and the first-order equivalent circuit model. The predicted SOC value is then corrected to ensure its accuracy.

[0067] The durations in Tables 2 and 3 represent the battery resting time within the electric vehicle, and the Pack current represents the current flowing through the battery, where C is the charging rate. This resting time is not the time the electric vehicle is completely stationary, but rather the time spent in various stopping situations encountered during operation (such as traffic lights, rest stops, etc.). Different situations result in different battery resting times. The State of Charge (SOC) is corrected within these resting times to obtain the corresponding correction confidence coefficient. For example, when the SOC is 20%, it falls within the range of good OCV-SOC linear mapping. Referring to Table 2, if the resting time is 5 minutes and the i(t)Pack current is 0.5A, the correction confidence coefficient is 0.65.

[0068] 4) Predicting corrected SOC value:

[0069] Based on the SOC value obtained in step 1) and the correction confidence coefficient kcorr(t) obtained in step 3), multiply the two to finally obtain the correction SOCcorr value.

[0070] 5) Calculate the final SOC final value:

[0071] The predicted SOC value is then corrected a second time based on the correction confidence coefficient. Finally, the SOC value predicted using the ampere-hour integral and open-circuit voltage methods in existing technologies is combined to calculate the final SOC. final The value is calculated using the following formula:

[0072] SOC final =SOC ah ×(1-kcorr)+SOCcorr×kcorr

[0073] Among them, SOC final This represents the final SOC value; SOC ah This represents the SOC value predicted using the ampere-hour integration and open-circuit voltage method in the prior art; kcorr represents the calculated SOC correction confidence coefficient; and SOCcorr represents the corrected SOC value.

[0074] In actual electric vehicle operation, variations in battery life, temperature changes, and operating conditions can lead to significant errors in the actual State of Charge (SOC). Existing technologies use the ampere-hour integration method and open-circuit voltage method to predict the SOC value, which is then displayed in real-time on the electric vehicle. However, due to varying real-world conditions, the SOC exhibits considerable error and requires dynamic correction. This invention iteratively obtains a new, real-time updated historical OCV-SOC table using an existing OCV-SOC table and a first-order equivalent circuit model. The SOC value is predicted using this new historical OCV-SOC table and the first-order equivalent circuit model. The obtained SOC value is then evaluated for reliability according to Tables 2 and 3, and a correction reliability coefficient is obtained from the tables to determine the accuracy of the predicted SOC value. The predicted SOC value is then corrected using the correction coefficient to obtain the corrected SOC value (SOCcorr). The SOC value predicted using the ampere-hour integration and open-circuit voltage method is then used to correct the SOC value. ah The SOC is obtained by weighting the predicted corrected SOC value (SOCcorr) and the corrected confidence coefficient. final This value makes the SOC value displayed during actual operation more accurate.

[0075] Vehicle Example:

[0076] The vehicle in this embodiment uses a method for correcting the SOC of a lithium iron phosphate battery. The method for correcting the SOC of a lithium iron phosphate battery has been described clearly enough in the method embodiments and will not be repeated here.

Claims

1. A method for correcting the state of charge (SOC) of a lithium iron phosphate battery, characterized in that, Includes the following steps: 1) Obtain the terminal voltage, battery pack current and battery parameters of the battery pack, calculate OCV according to the first-order equivalent circuit model, and obtain the first SOC value according to the pre-obtained correspondence between OCV and SOC. The battery parameters include: inherent internal resistance, polarization capacitance, and polarization resistance. 2) Obtain the current battery pack resting time and battery pack current value. Also, based on the first SOC value, obtain the correction correspondence between SOC, battery resting time, battery pack current and correction reliability coefficient in advance to obtain the current correction reliability coefficient. The battery resting time is the duration of the battery in a state of neither charging nor discharging. The correction correspondence is obtained as follows: Through battery resting experiments, the terminal voltage of the battery pack before resting is recorded under different battery resting times, SOC, and battery pack currents, and the current pre-resting OCV of the current battery pack is calculated; the resting end voltage of the battery pack is collected after the corresponding resting time; the difference between the pre-resting OCV and the resting end voltage reflects the correction reliability coefficient under the corresponding battery resting time, SOC, and battery pack current, and the difference between the pre-resting OCV and the resting end voltage is inversely proportional to the correction reliability coefficient; 3) Calculate the SOC calculated value. Use the first SOC value to correct the SOC calculated value using the current correction confidence coefficient to obtain the final SOC value. The principle of correction using the correction confidence coefficient is that the second SOC value is obtained by multiplying the first SOC value and the correction confidence coefficient. The higher the correction confidence coefficient, the closer the final SOC value is to the second SOC value. The lower the correction confidence coefficient, the closer the final SOC value is to the SOC calculated value.

2. The method for correcting the SOC of a lithium iron phosphate battery according to claim 1, characterized in that, In step 1), the method for obtaining the battery parameters is to calculate the SOC value and the battery pack temperature, and obtain a corresponding set of battery parameters based on the pre-obtained correspondence between the battery pack temperature, SOC value and battery parameters.

3. The method for correcting the SOC of a lithium iron phosphate battery according to claim 2, characterized in that, The correspondence between OCV and SOC is also updated. The update method is as follows: During the charging or discharging process of the battery pack, the battery pack terminal voltage, battery pack current, and battery pack temperature under the set SOC are collected, and the battery parameters are obtained according to the corresponding SOC and battery pack temperature; Based on the battery parameters and the battery pack terminal voltage and battery pack current, the OCV under the corresponding SOC is calculated using a first-order equivalent circuit model, forming a correspondence between the set SOC and the corresponding calculated OCV; The corresponding OCV values ​​for other SOCs are supplemented using the interpolation method to obtain a new correspondence between OCV and SOC.

4. The method for correcting the SOC of a lithium iron phosphate battery according to claim 3, characterized in that, The correspondence between OCV and SOC is updated only when the battery pack meets the full charge and full discharge settings. The full charge and full discharge settings are that the charging or discharging process reaches or covers the SOC range with good linear correlation between OCV and SOC.

5. The method for correcting the SOC of a lithium iron phosphate battery according to claim 3, characterized in that, During the process of updating the correspondence between OCV and SOC, the correspondence between battery pack temperature, SOC value and battery parameters is also updated. The update method is as follows: based on the new sets of OCV and SOC correspondences obtained during the OCV and SOC correspondence update process, as well as the corresponding battery pack terminal voltage, battery pack current and battery pack temperature, the corresponding SOC and battery parameters at the temperature are calculated according to the first-order equivalent circuit model.

6. The method for correcting the SOC of a lithium iron phosphate battery according to claim 1, characterized in that, In step 2), the correction correspondence includes at least a first correspondence between the battery resting time and battery pack current with good OCV-SOC linear correlation and the correction confidence coefficient, and a second correspondence between the battery resting time and battery pack current with poor OCV-SOC linear correlation and the correction confidence coefficient. When obtaining the current correction confidence coefficient, if the pre-obtained SOC is in the SOC range with good OCV-SOC linear correlation, the current correction confidence coefficient is obtained according to the corresponding battery resting time, battery pack current and the first correspondence; if the pre-obtained SOC is in the SOC range with poor OCV-SOC linear correlation, the current correction confidence coefficient is obtained according to the corresponding battery resting time, battery pack current and the second correspondence.

7. The method for correcting the SOC of a lithium iron phosphate battery according to claim 1, characterized in that, In step 2), the battery resting time in the battery resting experiment shall not exceed 60 minutes; when obtaining the current correction confidence coefficient according to the correction correspondence, if the battery resting time exceeds 60 minutes, it shall still be regarded as 60 minutes.

8. The method for correcting the SOC of a lithium iron phosphate battery according to claim 1, characterized in that, In step 1), the formula for calculating OCV based on the first-order equivalent circuit model is as follows: in, This is the real-time open-circuit voltage of the battery cell. Pack voltage, Pack current, This is the inherent resistance value inside the battery. This is the polarization capacitance value. t represents the polarization resistance value, and t represents the temperature.

9. The method for correcting the state of charge (SOC) of a lithium iron phosphate battery according to claim 1, characterized in that, In step 3), the final SOC value is calculated using the following formula: in, This is the final SOC value; The SOC value is the calculated value; kcorr is the current correction confidence coefficient. This is the second SOC value.

10. A vehicle using a method for correcting the state of charge (SOC) of a lithium iron phosphate battery, characterized in that, The vehicle displays the SOC using the method for correcting the SOC of the lithium iron phosphate battery as described in claim 1.

11. The vehicle using the corrected lithium iron phosphate battery SOC method according to claim 10, characterized in that, In step 1), the method for obtaining the battery parameters is to calculate the SOC value and the battery pack temperature, and obtain a corresponding set of battery parameters based on the pre-obtained correspondence between the battery pack temperature, SOC value and battery parameters.

12. The vehicle using the corrected lithium iron phosphate battery SOC method according to claim 11, characterized in that, The correspondence between OCV and SOC is also updated. The update method is as follows: During the charging or discharging process of the battery pack, the battery pack terminal voltage, battery pack current, and battery pack temperature under the set SOC are collected, and the battery parameters are obtained according to the corresponding SOC and battery pack temperature; Based on the battery parameters and the battery pack terminal voltage and battery pack current, the OCV under the corresponding SOC is calculated using a first-order equivalent circuit model, forming a correspondence between the set SOC and the corresponding calculated OCV; The corresponding OCV values ​​for other SOCs are supplemented using the interpolation method to obtain a new correspondence between OCV and SOC.

13. The vehicle using the corrected lithium iron phosphate battery SOC method according to claim 12, characterized in that, The correspondence between OCV and SOC is updated only when the battery pack meets the full charge and full discharge settings. The full charge and full discharge settings are that the charging or discharging process reaches or covers the SOC range with good linear correlation between OCV and SOC.

14. The vehicle using the corrected lithium iron phosphate battery SOC method according to claim 12, characterized in that, During the process of updating the correspondence between OCV and SOC, the correspondence between battery pack temperature, SOC value and battery parameters is also updated. The update method is as follows: based on the new sets of OCV and SOC correspondences obtained during the OCV and SOC correspondence update process, as well as the corresponding battery pack terminal voltage, battery pack current and battery pack temperature, the corresponding SOC and battery parameters at the temperature are calculated according to the first-order equivalent circuit model.

15. The vehicle using the corrected lithium iron phosphate battery SOC method according to claim 10, characterized in that, In step 2), the correction correspondence includes at least a first correspondence between the battery resting time and battery pack current with good OCV-SOC linear correlation and the correction confidence coefficient, and a second correspondence between the battery resting time and battery pack current with poor OCV-SOC linear correlation and the correction confidence coefficient. When obtaining the current correction confidence coefficient, if the pre-obtained SOC is in the SOC range with good OCV-SOC linear correlation, the current correction confidence coefficient is obtained according to the corresponding battery resting time, battery pack current and the first correspondence; if the pre-obtained SOC is in the SOC range with poor OCV-SOC linear correlation, the current correction confidence coefficient is obtained according to the corresponding battery resting time, battery pack current and the second correspondence.

16. The vehicle using the corrected lithium iron phosphate battery SOC method according to claim 10, characterized in that, In step 2), the battery resting time in the battery resting experiment shall not exceed 60 minutes; when obtaining the current correction confidence coefficient according to the correction correspondence, if the battery resting time exceeds 60 minutes, it shall still be regarded as 60 minutes.

17. The vehicle using the corrected lithium iron phosphate battery SOC method according to claim 10, characterized in that, In step 1), the formula for calculating OCV based on the first-order equivalent circuit model is as follows: in, This is the real-time open-circuit voltage of the battery cell. Pack voltage, Pack current, This is the inherent resistance value inside the battery. This is the polarization capacitance value. t represents the polarization resistance value, and t represents the temperature.

18. The vehicle using the corrected lithium iron phosphate battery SOC method according to claim 10, characterized in that, In step 3), the final SOC value is calculated using the following formula: in, This is the final SOC value; The SOC value is the calculated value; kcorr is the current correction confidence coefficient. This is the second SOC value.

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