Modeling method for SOC-OCV curve non-plateau area of lithium iron phosphate battery
By using a piecewise quadratic function model and least squares fitting, the SOC-OCV curve of lithium iron phosphate batteries is constructed, which solves the problems of high testing cost and low accuracy caused by aging, and achieves efficient and accurate SOC calibration and aging adaptability.
Patent Information
- Application Number
- CN202511654187.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-12
- Publication Date
- 2026-02-17
AI Technical Summary
In existing technologies, the SOC-OCV curve of lithium iron phosphate batteries changes with aging, resulting in high testing costs, long testing cycles, low accuracy, and poor aging adaptability.
A piecewise quadratic function model is adopted, combined with least squares fitting, to construct the SOC-OCV curve in the non-plateau region. The parameters are obtained through static OCV testing, and an adaptive algorithm is embedded in the BMS for dynamic updating.
Significantly reduces testing time and cost, improves model accuracy, adapts to battery aging, and achieves efficient and accurate SOC calibration and aging adaptability.
Smart Images
Figure CN121543260A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of lithium iron phosphate battery technology, and in particular to a method for modeling the non-plateau region of the SOC-OCV curve of a lithium iron phosphate battery. Background Technology
[0002] Among numerous lithium-ion battery technologies, lithium iron phosphate (LiFePO4) batteries (LFP batteries) have gained widespread application in large-scale energy storage due to their excellent safety, relatively low cost, superior cycle stability (typically reaching thousands of cycles), and good high-temperature performance, becoming one of the mainstream choices. Accurately understanding the operating state of LFP batteries, especially their state of charge (SOC), is crucial for ensuring the safety of energy storage systems, improving energy management efficiency, optimizing system scheduling strategies, and extending battery life. SOC is a core parameter for assessing the current usable energy percentage of a battery, and its accurate estimation directly affects the realization of key functions such as system charge-discharge control, lifespan prediction, and fault warning. There is a specific correspondence between the open circuit voltage (OCV) and the SOC of a LFP battery, namely the SOC-OCV curve. This curve is an inherent "fingerprint" pattern of the LFP battery, reflecting its internal electrochemical characteristics. Compared to other types of lithium-ion batteries (such as ternary lithium batteries), the SOC-OCV curve of lithium iron phosphate batteries has a significant characteristic: a very flat voltage plateau exists in the middle SOC range (typically between 20% and 80% or wider). Within this plateau range, large changes in SOC correspond only to small fluctuations in OCV, making it impossible for OCV to accurately correspond to SOC. In contrast, the non-plateau region shows a clear and sensitive correspondence between OCV and SOC. This characteristic makes the OCV curve in the non-plateau period the most direct and reliable theoretical basis for SOC calibration and initial state estimation of lithium iron phosphate batteries.
[0003] In existing technologies, OCV and SOC curves are typically obtained through laboratory testing. However, as batteries age, their SOC-OCV curves change, and the trends differ across aging modes. Obtaining these curves through laboratory testing is costly and time-consuming because the OCV and SOC curves change with battery aging. This results in poor aging adaptability, and the flat voltage plateau in the mid-section of LFP batteries leads to low curve accuracy. Summary of the Invention
[0004] Therefore, based on the above background, the present invention provides a modeling method for the non-plateau region of the SOC-OCV curve of lithium iron phosphate batteries, which can not only significantly shorten the test time and reduce the test cost, but also improve the accuracy of the SOC-OCV curve model and has a certain degree of aging adaptability.
[0005] The technical solution provided by this invention is as follows:
[0006] A method for modeling the non-plateau region of the SOC-OCV curve of a lithium iron phosphate battery, characterized by the following steps:
[0007] S1: Based on the characteristics of lithium iron phosphate batteries, a segmented SOC-O CV curve model based on quadratic functions is established;
[0008] The SOC-OCV curve model based on quadratic functions includes SOC-OCV curves for the non-plateau period in the low SOC range and SOC-OCV curves for the non-plateau period in the high SOC range.
[0009] ①The non-plateau period curve in the low SOC range passes through the lower limit voltage of the cell, and its functional expression is shown in the following equation (1):
[0010]
[0011] In the formula, x1 and x2 both represent SOC, and y represents OCV; U min , where is the lower limit voltage of the battery cell; a1, a2, b1 and b2, c2 are parameters to be determined for the SOC-OCV curve during the non-plateau period in the low SOC range;
[0012] SOC0 is the SOC at the intersection of the two quadratic functions, taking the value... SOC1 is the boundary between the non-plateau period and the plateau period in the low SOC range;
[0013] ②The functional expression of the non-plateau period curve in the high SOC interval is shown in the following equation (2).
[0014]
[0015] In the formula, U max a3 represents the upper limit voltage of the battery cell; b3 and a3 are the SOC-OCV parameters to be determined during the non-plateau period in the high SOC range.
[0016] S2: Determine the values of parameters a1, a2, b1, b2, c2, a3, and b3 for the non-plateau period curve in the low SOC range and the SOC-OCV curve in the high SOC range of step S1.
[0017] S2.1 Static OCV testing is conducted in stages during the non-plateau period of the high SOC range and the non-plateau period of the low SOC range to obtain the resting point of the cell during the non-plateau period;
[0018] S2.2 Based on the stationary points obtained from the test in step S2.1, the least squares method is used for fitting to minimize the sum of squares of the vertical distances (residuals) between the SOC-OCV curve model based on the quadratic function and the stationary points of all non-plateau periods selected in step S2.1, so as to obtain the values of the parameters a1, a2, b1, b2, c2, a3 and b3 to be determined for the SOC-OCV curves of the non-plateau period in the low SOC interval and the non-plateau period in the high SOC interval;
[0019] The formula for the sum of squares of the vertical distance (residual) is shown in equation (3) below:
[0020]
[0021] Where: n is the number of stationary points; x i SOC at rest point; y i OCV is the static point; fx i ,β) is the fitting function; β=[β0,β1,…,β m ] T Let β0, β1, ..., β be the parameter vector. m They represent a1, a2, b1, b2, c2, a3, and b3, respectively.
[0022] S3: Obtain the SOC-OCV curve for the non-plateau period
[0023] Substituting the values of the parameters a1, a2, b1, b2, c2, a3, and b3 obtained in step S2.2 into the SOC-OCV curve model based on quadratic functions established in step S1, the specific expression describing the SOC-OCV relationship in the non-plateau period can be determined, and the SOC-OCV curve of the non-plateau period can be obtained.
[0024] S4: Based on the SOC-OCV curve obtained in step S3 during the non-plateau period, complete the SOC calibration under the battery resting condition;
[0025] S5: When the BMS detects that the deviation between the actual OCV and the OCV obtained from the SOC-OCV curve obtained in step S3 is greater than 2%, it triggers the repetition of steps S2 to S4.
[0026] Furthermore, the low SOC range is the range where the SOC is below 10%, and the high SOC range is the range where the SOC is above 95%.
[0027] Furthermore, the specific operational steps for conducting static OCV testing in stages during the non-plateau periods of the high SOC range and the low SOC range in step 2.1 are as follows:
[0028] S2.1.1 Discharge the battery to the lower limit voltage using a small current of 0.1C, then charge it to 100% SOC using a constant current of 0.1C, and let it rest for 2 hours; obtain the maximum usable capacity C of the current cell by ampere-hour integration. max See equation (4) below:
[0029] C max =Q in =I charge ×t charge =0.1C×t charge (4)
[0030] In the formula I charge The current used for constant current charging; t charge This refers to charging time;
[0031] S2.1.2 Perform static OCV testing during the non-plateau period.
[0032] ① Static OCV testing was performed during the non-plateau period of the high SOC range.
[0033] Discharged at a constant current to 90% with n% SOC intervals, and left to stand for 2 hours at each stage. The stable voltage OCV point was recorded. The current SOC was calculated using the ampere-hour integration method. The formula for the ampere-hour integration method is shown in the following formula (5):
[0034]
[0035] In the formula, SOC t Let SOC be the current time value. t-1 Let SOC be the state of charge at the previous moment, I be the current of the constant current discharge, and dt be the time from t-1 to t.
[0036] ② Static OCV testing was performed during the non-plateau period in the low SOC range.
[0037] With n% SOC as the interval, constant current discharge was performed from 10% SOC to 2% SOC, with each stage left to stand for 2 hours, and the stable voltage OCV point was recorded.
[0038] Furthermore, the value of n ranges from 1 to 5.
[0039] Furthermore, the specific steps for SOC calibration in step S4 are as follows:
[0040] S4.1 screens for static operating conditions during battery online operation:
[0041] S4.2 Obtain the individual unit voltage U at the current resting moment;
[0042] S4.3 Substitute the individual unit voltage U into the non-plateau SOC-OCV curve obtained in step S3 to obtain the current SOC.
[0043] Furthermore, the static condition in step S4.1 is to simultaneously satisfy cluster current < first threshold and voltage change rate < second threshold.
[0044] Furthermore, the first threshold is set to 10A, and the second threshold is set to 2mV / 5min.
[0045] Based on the same inventive concept, the present invention also provides an electronic device, including a memory and a processor; the memory is used to store a computer program; the processor is used to implement, when executing the computer program, a method for modeling the non-plateau region of the SOC-OCV curve of a lithium iron phosphate battery as described above.
[0046] Based on the same inventive concept, the present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements a method for modeling the non-plateau region of the SOC-OCV curve of a lithium iron phosphate battery as described above.
[0047] The beneficial effects achieved by this invention are as follows:
[0048] This invention focuses on the high-sensitivity voltage range in the non-plateau region and constructs an adaptive piecewise quadratic function model. It only requires sampling at rest points in the low SOC (<10%) and high SOC (>95%) non-plateau regions, which significantly solves the problems of long testing time (reducing testing time by more than 70%) and high cost of traditional construction methods. It also uses the least squares method to optimize the parameters of the piecewise quadratic function, avoiding plateau region interference and improving model accuracy. At the same time, by embedding an adaptive algorithm in the BMS, it automatically triggers model refitting when the deviation between the actual OCV and the model exceeds 2%, realizing dynamic updates throughout the entire life cycle. This makes SOC calibration more efficient, accurate and adaptable to different aging states, with strong aging adaptability, and provides a universal solution for the safe operation, life optimization and cross-vendor battery management of energy storage systems. Attached Figure Description
[0049] Appendix Figure 1 This is a flowchart of the method for modeling the non-plateau region of the SOC-OCV curve of the lithium iron phosphate battery according to the present invention.
[0050] Appendix Figure 2 This is a sample diagram of the SOC-OCV curve of the constructed lithium iron phosphate battery. The solid lines on the left and right sides of the figure represent the non-plateau region curves constructed using this method. Detailed Implementation
[0051] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0052] Example 1: A method for modeling the non-plateau region of the SOC-OCV curve of a lithium iron phosphate battery, comprising the following steps:
[0053] S1: Based on the characteristics of lithium iron phosphate batteries, a segmented SOC-OCV curve model based on quadratic functions is established;
[0054] The SOC-OCV curve model based on quadratic functions includes SOC-OCV curves for the non-plateau period in the low SOC range and SOC-OCV curves for the non-plateau period in the high SOC range.
[0055] Specifically, the low SOC range mentioned herein and in its context refers to the range where the SOC is below 10%, and the high SOC range refers to the range where the SOC is above 95%.
[0056] ①The non-plateau period curve in the low SOC range passes through the lower limit voltage of the cell, and its functional expression is shown in the following equation (1):
[0057]
[0058] In the formula, x1 and x2 both represent SOC, and y represents OCV; U min , where is the lower limit voltage of the battery cell; a1, a2, b1 and b2, c2 are parameters to be determined for the SOC-OCV curve during the non-plateau period in the low SOC range;
[0059] SOC0 is the SOC at the intersection of the two quadratic functions; SOC1 is the boundary between the non-plateau period and the plateau period in the low SOC range; according to the experience of the original OCV-SOC curve of lithium iron phosphate battery, it is usually taken as 10%.
[0060] The SOC-OCV in the non-plateau period of the low SOC interval is simplified to two quadratic functions. To ensure the smoothness of the SOC-OCV, the slopes of the two quadratic functions should be the same at their intersection. Therefore:
[0061] y′1=y′2
[0062] y'1 and y'2 are the slopes of the functions y1 and y2, respectively;
[0063]
[0064] That is, 2a1x1 + b1 = 2a2x2 + b2;
[0065] Then determine the coordinates of their intersection point:
[0066] Right now:
[0067] ②The functional expression of the non-plateau period curve in the high SOC interval is shown in the following equation (2).
[0068]
[0069] In the formula, U max a3 represents the upper limit voltage of the battery cell; b3 and a3 are the SOC-OCV parameters to be determined during the non-plateau period in the high SOC range.
[0070] S2: Determine the values of parameters a1, a2, b1, b2, c2, a3, and b3 for the non-plateau period curve in the low SOC range and the SOC-OCV curve in the high SOC range of step S1.
[0071] S2.1 Static OCV testing is conducted in stages during the non-plateau period of the high SOC range and the non-plateau period of the low SOC range to obtain the resting point of the cell during the non-plateau period;
[0072] The specific steps for the static OCV test in this process are as follows:
[0073] S2.1.1 Discharge the battery to the lower limit voltage using a small current of 0.1C, then charge it to 100% SOC using a constant current of 0.1C, and let it rest for 2 hours; obtain the maximum usable capacity C of the current cell by ampere-hour integration. max See equation (4) below:
[0074] C max =Q in =I charge ×t charge =0.1C×t charge (4)
[0075] In the formula I charge The current used for constant current charging; t charge This refers to charging time;
[0076] Specifically, the current that a battery can be fully charged in 1 hour using constant current charging is 1C = 280A for a 280Ah battery cell.
[0077] S2.1.2 Perform static OCV testing during the non-plateau period.
[0078] ① Static OCV testing was performed during the non-plateau period of the high SOC range.
[0079] Discharged at a constant current to 90% with n% SOC intervals, and left to stand for 2 hours at each stage. The stable voltage OCV point was recorded. The current SOC was calculated using the ampere-hour integration method. The formula for the ampere-hour integration method is shown in the following formula (5):
[0080]
[0081] In the formula, SOC t Let SOC be the current time value. t-1 Let SOC be the state of charge at the previous moment, I be the current of the constant current discharge, and dt be the time from t-1 to t.
[0082] ② Static OCV testing was performed during the non-plateau period in the low SOC range.
[0083] With n% SOC as the interval, constant current discharge was performed from 10% SOC to 2% SOC, with each stage left to stand for 2 hours, and the stable voltage OCV point was recorded.
[0084] The value of n ranges from 1 to 5.
[0085] S2.2 Based on the stationary points obtained from the test in step S2.1, the least squares method is used for fitting to minimize the sum of squares of the vertical distances (residuals) between the SOC-OCV curve model based on the quadratic function and the stationary points of all non-plateau periods selected in step S2.1, so as to obtain the values of the parameters a1, a2, b1, b2, c2, a3 and b3 to be determined for the SOC-OCV curves of the non-plateau period in the low SOC interval and the non-plateau period in the high SOC interval;
[0086] The formula for the sum of squares of the vertical distance (residual) is shown in equation (3) below:
[0087]
[0088] Where: n is the number of stationary points; x i SOC at rest point; y i OCV is the static point; fx i ,β) is the fitting function; β=[β0,β1,…,β m ] T Let β0, β1, ..., β be the parameter vector. m They represent a1, a2, b1, b2, c2, a3, and b3, respectively.
[0089] S3: Obtain the SOC-OCV curve for the non-plateau period
[0090] Substituting the values of the parameters a1, a2, b1, b2, c2, a3, and b3 obtained in step S2.2 into the SOC-OCV curve model based on quadratic functions established in step S1, the specific expression describing the SOC-OCV relationship in the non-plateau period can be determined, and the SOC-OCV curve of the non-plateau period can be obtained.
[0091] S4: Based on the SOC-OCV curve obtained in step S3 during the non-plateau period, complete the SOC calibration under the battery resting condition (under the resting condition, the battery voltage is approximately equal to OCV, and the SOC can be calibrated by the OCV-SOC curve at this time, this process is called SOC calibration).
[0092] The specific steps for SOC calibration in this process are as follows:
[0093] S4.1 During the online operation of the battery, a static condition is selected. The static condition is that the cluster current is less than a first threshold and the voltage change rate is less than a second threshold. The first threshold is 10A and the second threshold is 2mV / 5min.
[0094] S4.2 Obtain the individual unit voltage U at the current resting moment;
[0095] S4.3 Substitute the individual unit voltage U into the non-plateau SOC-OCV curve obtained in step S3 to obtain the current SOC.
[0096] S5: When the BMS detects that the deviation between the actual OCV and the OCV obtained from the SOC-OCV curve obtained in step S3 is greater than 2%, it triggers the repetition of steps S2 to S4.
[0097] Appendix Figure 2 The attached figure shows the SOC-OCV curve of a lithium iron phosphate battery constructed during practical applications. The solid lines on the left and right sides represent the non-plateau region curve model constructed using this method.
[0098] The present invention and its embodiments have been described above. This description is not restrictive, and the embodiments shown are only one of the embodiments of the present invention. The actual structure is not limited to this. In conclusion, if those skilled in the art are inspired by this description and design similar structures and embodiments without departing from the spirit of the present invention, they should all fall within the protection scope of the present invention.
Claims
1. A method for modeling the non-plateau region of the SOC-OCV curve of a lithium iron phosphate battery, characterized in that, Comprising the following steps: S1: Based on the characteristics of lithium iron phosphate battery, segmental establishment of SOC-OCV curve model based on quadratic function; The SOC-OCV curve model based on quadratic function includes low SOC interval non-platform period curve and high SOC interval non-platform period SOC-OCV curve; ①The low SOC interval non-platform period curve passes through the lower limit voltage of the cell, and its function expression is as follows (1): In the formula, x1 and x2 both represent SOC, and y represents OCV; U min is the lower limit voltage of the battery cell; a1, a2, b1, and b2, and c2 are parameters to be determined for the SOC-OCV curve in the non-platform period of the low SOC interval. SOC0 is the SOC at the intersection of the two quadratic functions, taking values SOC1 is the boundary value between the non-platform period and the platform period in the low SOC range. ②The function expression of the high SOC interval non-platform period curve is as follows (2) In the formula, U max is the upper limit voltage of the battery cell; a3 and b3 are SOC-OCV parameters to be determined in the non-platform period of the high SOC interval. S2: Determine the values of the to-be-determined parameters a1, a2, b1, b2, c2, a3 and b3 of the low SOC interval non-platform period curve and the high SOC interval non-platform period SOC-OCV curve in step S1; S2.1: Static OCV test on high SOC interval non-platform period and low SOC interval non-platform period in stages, and obtain the static points of the cell in the non-platform period; S2.2: Based on the static points obtained in step S2.1, use the least square method to fit, so that the sum of squares of the vertical distance (residual) between the SOC-OCV curve model based on quadratic function and all the static points of the non-platform period selected in step S2.1 is minimized, to obtain the values of the to-be-determined parameters a1, a2, b1, b2, c2, a3 and b3 of the low SOC interval non-platform period curve and the high SOC interval non-platform period SOC-OCV curve; The sum of squares of the vertical distance (residual) is as follows (3): where: n is the number of rest points; x i is the rest point SOC; y i is the rest point OCV; fx i , β) is the fitting function; β = [β0, β1,..., β m ] T is the parameter vector, β0, β1,..., β m represent a1, a2, b1, b2, c2, a3, and b3, respectively; S3: Obtain the non-platform region SOC-OCV curve Substitute the values of the to-be-determined parameters a1, a2, b1, b2, c2, a3 and b3 obtained in step S2.2 into the SOC-OCV curve model based on quadratic function established in step S1, to determine the specific expression of the SOC-OCV relationship in the non-platform period, and obtain the SOC-OCV curve in the non-platform period; S4: Based on the SOC-OCV curve in the non-platform period obtained in step S3, complete SOC calibration under the battery static condition; S5: When the BMS detects that the deviation between the actual OCV and the OCV obtained through step S3 is >2%, trigger the repetition of steps S2 to S4.
2. The method for modeling the non-plateau region of the SOC-OCV curve of a lithium iron phosphate battery according to claim 1, characterized in that, The low SOC interval is the interval where SOC is below 10%, and the high SOC interval is the interval where SOC is above 95%.
3. The method for modeling the non-plateau region of the SOC-OCV curve of a lithium iron phosphate battery according to claim 2, characterized in that, The specific operation steps of step 2.1: static OCV test on high SOC interval non-platform period and low SOC interval non-platform period in stages are as follows: S2.1.1 After discharging the battery at a small current of 0.1C to the lower limit voltage, charging it at 0.1C to 100% SOC, and standing for 2 hours, the maximum available capacity C of the current battery is obtained by integrating the ampere-hours max , see equation (4) below: C max = Q in = I charge x t charge = 0.1 C x t charge (4) wherein I charge is the current of the constant current charge; t charge is the charging time; S2.1.2: Static OCV test on non-platform period ①Static OCV test on high SOC interval non-platform period Take n% SOC as interval, constant current discharge to 90%, each stage static for 2 hours, record stable voltage OCV point, and use ampere-hour integral method to calculate current SOC, and the calculation formula is as follows (5): where SOC t is the current time SOC, SOC t-1 is the previous time SOC, I is the current of constant current discharge, and dt is the time from t-1 to t. ②Static OCV test on low SOC interval non-platform period Take n% SOC as interval, constant current discharge from SOC 10% to SOC 2%, each stage static for 2 hours, and record stable voltage OCV point.
4. The method for modeling the non-plateau region of the SOC-OCV curve of a lithium iron phosphate battery according to claim 3, characterized in that, The value range of n is 1-5.
5. The method for modeling the non-plateau region of the SOC-OCV curve of a lithium iron phosphate battery according to claim 1, characterized in that, The specific operation steps of step S4: SOC calibration are as follows: S4.1 In the process of battery online operation, a static condition is screened out; S4.2 A single cell voltage U at a current static moment is obtained; S4.3 The single cell voltage U is brought into the non-platform region SOC-OCV curve obtained in step S3 to obtain a current SOC.
6. The method for modeling the non-plateau region of the SOC-OCV curve of a lithium iron phosphate battery according to claim 5, characterized in that, The static condition of step S4.1 is that a cluster current < a first threshold value and a voltage change rate < a second threshold value are simultaneously satisfied.
7. The method for modeling the non-plateau region of the SOC-OCV curve of a lithium iron phosphate battery according to claim 6, characterized in that, The first threshold value is 10 A, and the second threshold value is 2 mV / 5 min.
8. An electronic device, comprising: The memory is used for storing a computer program; and the processor is used for realizing the modeling method of the non-platform region SOC-OCV curve of the lithium iron phosphate battery according to any one of claims 1 to 7 when the computer program is executed.
9. A computer-readable storage medium, characterized in that: The storage medium has the computer program stored thereon, and the computer program realizes the modeling method of the non-platform region SOC-OCV curve of the lithium iron phosphate battery according to any one of claims 1 to 7 when the computer program is executed by the processor.