Fuzzy EKF-ah algorithm-based SOC estimation and correction method for lithium iron phosphate battery

Abstract

The present invention relates to the technical field of lithium battery state estimation, and in particular to a fuzzy EKF-AH algorithm-based state of charge (SOC) estimation and correction method for a lithium iron phosphate battery. The method comprises: by taking a certain startup of an energy storage device as a start, a BMS reading an SOC and a state of health (SOH) at the previous shutdown, and on the basis of a standby time, selecting an open circuit voltage (OCV) or an EKF algorithm to correct the SOC; using an EKF-AH algorithm to estimate the SOC, establishing, on the basis of fuzzy control, a fuzzy rule library associated with the SOC and the SOH, and dynamically adjusting a weight and a measurement noise deviation of the EKF-AH algorithm; and calculating estimation differences between the EKF algorithm and an ampere-hour integration method, and if the sum of the estimation differences is greater than a corresponding threshold, issuing an SOH correction warning. The present invention improves the estimation accuracy of the entire life cycle of the lithium iron phosphate battery, and assists the correction of the SOH.

Description

A lithium iron phosphate battery SOC estimation and correction method based on a fuzzy EKF-AH algorithm TECHNICAL FIELD

[0001] The present application relates to the technical field of lithium battery state estimation, and in particular to a lithium iron phosphate battery SOC estimation and correction method based on a fuzzy EKF-AH algorithm. BACKGROUND

[0002] In order to better cope with environmental pollution and energy crisis, the development of energy storage technology is attracting much attention. As one of the main power sources of energy storage systems, lithium-ion batteries play a crucial role in the field of energy storage and management. The state of charge (SOC) of the battery is an important indicator for evaluating the remaining energy of the battery, and directly affects the performance of the energy storage system, energy utilization efficiency and life prediction. Improving the energy efficiency and performance of the energy storage system, including the accuracy of SOC estimation, is of great significance for achieving the dual carbon goal and promoting energy transformation.

[0003] Currently, the mainstream SOC estimation methods include ampere-hour integration method, open circuit voltage method, Kalman filter method and machine learning algorithm, etc. The ampere-hour integration method is the most common SOC estimation method, which calculates the battery SOC by integrating the current over time. This method is greatly affected by the accuracy of the sensor, cannot eliminate the absolute error caused by the initial value error, and has the problem of cumulative process error over time. The open circuit voltage method estimates the remaining capacity of the battery by measuring the SOC value of the battery at different open circuit voltages. However, this method requires the battery to be stationary for a long time, which is not convenient for real-time monitoring, and the OCV plateau estimation of lithium iron phosphate battery is not accurate. The machine learning algorithm represented by neural network relies on a large amount of experimental data, and has poor robustness and battery universality. In contrast, the Kalman filter method has become a research hotspot for SOC estimation methods due to its high estimation accuracy, good robustness and ability to eliminate the influence of incorrect initial values.

[0004] The essence of the extended Kalman filter (EKF) for SOC estimation is to simulate the polarization characteristics of the battery through an equivalent circuit model, and then use the OCV and ampere-hour integration method to cooperatively estimate the SOC. Therefore, the EKF algorithm contains the advantages and disadvantages of the open circuit voltage method, and is not accurate in the plateau period of the battery (i.e. the slope of the SOC-OCV curve is too small), which can easily cause errors. At the same time, the traditional Kalman filter algorithm assumes that the measurement noise error γ is a fixed value, and does not consider the influence of battery life changes on the OCV characteristics of the battery, resulting in increasing estimation error. SUMMARY

[0005] The present application proposes a lithium iron phosphate battery SOC estimation and correction method based on a fuzzy EKF-AH algorithm to overcome the limitations of the prior art.

[0006] The present application estimates the SOC of the lithium iron phosphate battery by the EKF-AH algorithm combining the extended Kalman filter and ampere-hour integration, establishes the fuzzy rule base associated with the current state of charge SOC and the health status (lifetime) SOH, dynamically adjusts the weight of the EKF-AH algorithm and the measurement noise error γ, eliminates the initial value error and process cumulative error of the ampere-hour integration method, solves the inaccuracy problem of EKF in the platform period, improves the estimation accuracy of the lithium iron phosphate battery in the whole life cycle, and designs a SOC estimation and correction method for the lithium iron phosphate battery based on the fuzzy EKF-AH algorithm, including the following steps,

[0007] S1, taking a certain start of the energy storage device as the start, the BMS reads the SOC and SOH values at the last shutdown, denoted as SOC his , SOH his ;

[0008] S2, calculate the time difference between this start and the last shutdown as the battery standing time ΔT, and judge whether the battery standing time ΔT reaches the preset battery standing time T;

[0009] If the battery standing time ΔT≥T, go to step S3;

[0010] If the battery standing time ΔT<T, go to step S4;

[0011] S3, correct the SOC value using the open circuit voltage OCV to obtain a new SOC new , and calculate the difference ΔSOC=|SOC his -SOC new | between SOC his and SOC new , and set the SOH correction warning threshold of the battery as δ;

[0012] If ΔSOC>δ, it is determined that the SOH his estimation is wrong, and step S9 is executed;

[0013] If ΔSOC≤δ, step S5 is executed;

[0014] S4, correct the SOC using the EKF algorithm, and the correction lasts for 1 min to obtain the corrected SOC new , and go to step S5;

[0015] S5, input the corrected SOC new as the initial value, estimate the SOC using the EKF algorithm and the ampere-hour integration method to obtain SOC EKF , SOC AH, and a fuzzy rule base associated with the state of charge (SOC) and the state of health (SOH) is established based on the fuzzy control, and the weight ω of the EKF-AH algorithm and the measurement noise error γ are dynamically adjusted to obtain an accurate SOC estimation value SOC NEW ;

[0016] S6, the estimated difference e(k) = |SOC EKF -SOC AH | of the EKF algorithm and the ampere-hour integral method is calculated,

[0017] When 10% < SOC NEW < 90%, i.e., the SOC is in the platform period, the warning threshold α is 2%;

[0018] When SOC NEW ≤ 10%, or SOC NEW ≥ 90%, i.e., the SOC is in the non-platform period, the warning threshold α is 5%;

[0019] S7, the sum of the estimated differences of the previous n times is obtained If the sum of the estimated differences e(k) is greater than the corresponding warning threshold α, go to step S9, otherwise go to step S8;

[0020] S8, the BMS updates the current SOC of the energy storage device, and assigns the accurate estimation value SOC NEW to SOC new , and goes to step S5;

[0021] S9, the BMS determines that the SOH his estimation is wrong, and issues a SOH correction warning.

[0022] Further, the OCV is used in S3 to correct and calculate the SOC value, which specifically includes the following contents,

[0023] S3.1, perform OCV-SOC test of the battery to obtain the relationship between the open circuit voltage OCV and the remaining capacity SOC under different temperatures and aging states;

[0024] S3.2, use MATLAB to fit to obtain a function relationship polynomial OCV = f(SOC) = a0 + a1SOC 1 + a2SOC 2 + … + a n SOC n ,

[0025] Where a n is the n-th order term coefficient;

[0026] S3.3, the open circuit voltage OCV voltage value read by the sensor after starting is substituted into the polynomial to obtain the SOC new .

[0027] Further, the S4 uses the EKF algorithm to correct the calculation of the SOC, specifically including the following,

[0028] S4.1, according to the polarization characteristics of the lithium battery, a Thevenin equivalent circuit model is established;

[0029] S4.2, the working equation of the above Thevenin equivalent circuit model is,

[0030] In the above formula, OCV is the open circuit voltage, R0 is the ohmic resistance of the battery, C is the polarization capacitance, R1 is the polarization resistance, U1 represents the voltage across the polarization resistance R1 and the polarization capacitance C, U s is the battery terminal voltage, i is the current of the battery, and the charge is positive and the discharge is negative;

[0031] S4.3, the above equation set is discretized, and the observation equation and the measurement equation are obtained by combining the extended Kalman filter as follows,

[0032] In the observation equation and the measurement equation, t is the unit sampling interval, Q max is the current maximum capacity of the battery, which is related to SOH, Q represents the process noise error matrix, and R represents the measurement noise error matrix;

[0033] The recursive estimation of the algorithm is as follows,

[0034] Prior estimation,

[0035] Kalman gain update,

[0036] Optimal estimation update,

[0037] is the prior estimation value of the observation state at the current time, P k - is the prior error covariance matrix at the current time, K k is the Kalman gain matrix, P k + is the optimal estimation and updated error covariance matrix at the current time, Q is the process noise error matrix, and R k is the measurement noise error matrix at the current time, U s is the voltage meter measurement value, This is an estimate of the voltage at the opposite end at the current moment;

[0038] S4.4, HPPC test is performed, and the battery is charged and discharged using a continuous pulse sequence current to obtain the dynamic characteristics of the battery under different SOC values. MATLAB is used to identify the battery parameters offline to obtain R0, R1, and C.

[0039] S4.5, Substituting the previously estimated SOC(k-1) and U1(k-1), we obtain SOC. new =SOC(k).

[0040] Furthermore, in S4, the EKF algorithm is used to correct the SOC, and its measurement noise error matrix R = 0.0003.

[0041] Furthermore, the dynamic adjustment of the EKF-AH algorithm weights ω and measurement noise error γ by fuzzy control in S5 specifically includes the following:

[0042] S5.1, Obtain SOC new SOH his Using the ampere-hour integration method and EKF to evaluate the SOC new SOC was estimated separately AH SOC EKF The discretization formula for the ampere-hour integral method is as follows: Q max =SOH his *Q 出厂 ,

[0043] In the above formula, t is the unit sampling interval, i is the current battery current (positive for charging, negative for discharging), and Q... max Q represents the battery's current maximum capacity. 出厂 Q represents the battery's maximum rated capacity at the time of manufacture. This capacity decreases as the battery is used. max The SOH level continues to decrease;

[0044] S5.2, in the fuzzy controller, the SOC new SOH his As input to the fuzzy controller, the EKF-AH algorithm weights ω and the measurement noise error γ are used as the system output;

[0045] Define three fuzzy subsets of SOC as Bigger(B), Middler(M), and Lower(L);

[0046] The three fuzzy subsets of SOH are defined as Old(O), NOW(M), and New(N);

[0047] The fuzzy subsets of omega and gamma are defined as Big (B), Mid (M) and Low (L) respectively;

[0048] According to the type and parameters of the membership function, the fuzzy inference rules are formulated through the design of the fuzzy controller;

[0049] S5.3, through the design of the fuzzy controller, the dynamic self-adaptive adjustment of the EKF-AH algorithm weight omega and the measurement noise error gamma is realized by combining the interval of the state of charge SOC and the size of the state of health SOH, and finally the adjusted measurement noise error gamma is transmitted to the measurement noise error matrix R in the Kalman filter gain coefficient, and the latest EKF-AH algorithm weight omega is used to calculate and obtain the accurate SOC estimation SOC NEW , see the formula as follows, SOC NEW (k) = omega SOC AH (k) + (1-omega) SOC EKF (k).

[0050] Further, the modified warning threshold value delta is 10%.

[0051] Compared with the prior art, the advantages of the present application are that,

[0052] 1. The EKF-AH algorithm combining extended Kalman filter and ampere-hour integral is used to estimate the SOC of the lithium iron phosphate battery, the initial value error and process cumulative error of the ampere-hour integral method are eliminated, the inaccuracy of EKF in the platform period is solved, and the SOC estimation accuracy is improved;

[0053] 2. The fuzzy rule base associated with the current state of charge SOC and the state of health (lifetime) SOH is established, the EKF-AH algorithm weight and the measurement noise error gamma are dynamically adjusted, and the estimation accuracy of the lithium iron phosphate battery in the whole life cycle is improved;

[0054] 3. Different threshold values are set according to whether the SOC is in the platform period, the cumulative estimation error of the two algorithms is compared with the threshold value, whether the SOH estimation is wrong is judged, and then the SOH is modified. BRIEF DESCRIPTION OF DRAWINGS

[0055] Fig. 1 is a flowchart of the SOC estimation and modification method of the lithium iron phosphate battery based on the fuzzy EKF-AH algorithm in the embodiment of the present application;

[0056] Fig. 2 is a Thevenin equivalent circuit model in the embodiment of the present application;

[0057] Fig. 3 is an OCV-SOC curve of the battery under different SOH in the embodiment of the present application;

[0058] Fig. 4 is the estimation effect of the EKF algorithm in the embodiment of the present application;

[0059] Figure 5 is the estimation effect of the EKF-AH algorithm in the embodiment of the application. DETAILED DESCRIPTION

[0060] In order to make the objects, technical solutions and advantages of the present application clearer, the technical solutions of the present application will be further described below. It should be noted that the embodiments described in the following examples do not represent all the embodiments consistent with the present disclosure.

[0061] A lithium iron phosphate battery SOC estimation and correction method based on a fuzzy EKF-AH algorithm, comprising the following steps,

[0062] S1, taking a certain start-up of the energy storage device as the starting point, the BMS reads the SOC and SOH values at the last shutdown, denoted as SOC his , SOH his ;

[0063] S2, calculate the time difference between the current start-up and the last shutdown as the battery standing time ΔT, and determine whether the battery standing time ΔT reaches the preset battery standing time T;

[0064] If the battery standing time ΔT≥T, go to step S3;

[0065] If the battery standing time ΔT<T, go to step S4;

[0066] S3, use the open circuit voltage OCV to correct and calculate the SOC value, get the new SOC new , and calculate the difference ΔSOC between SOC his and SOC new , ΔSOC = |SOC his -SOC new |, and set the SOH correction warning threshold of the battery as δ;

[0067] If ΔSOC>δ, determine that the SOH his estimation is wrong, then execute step S9;

[0068] If ΔSOC≤δ, execute step S5;

[0069] S4, use the EKF algorithm to correct and calculate the SOC, and correct for 5 minutes to get the corrected SOC new , and go to step S5;

[0070] S5, input the corrected SOC new as the initial value, use the EKF algorithm and ampere-hour integration method to estimate the SOC, get SOC EKF , SOC AH, and a fuzzy rule base associated with the state of charge (SOC) and the state of health (SOH) is established based on the fuzzy control, and the weight ω of the EKF-AH algorithm and the measurement noise error γ are dynamically adjusted to obtain an accurate SOC estimation value SOC NEW ;

[0071] S6, the estimated difference e(k) = |SOC EKF -SOC AH | of the EKF algorithm and the ampere-hour integral method is calculated, T-S fuzzy control rules are established, and different warning thresholds α are set according to whether the SOC is in a platform period,

[0072] When 10% < SOC NEW < 90%, that is, the SOC is in a platform period, the warning threshold α is 2%;

[0073] When SOC NEW ≤ 10% or SOC NEW ≥ 90%, that is, the SOC is not in a platform period, the warning threshold α is 5%;

[0074] S7, the sum of the estimated differences of the previous n times is obtained If the sum of the estimated differences e(k) is greater than the corresponding warning threshold α, go to step S9, otherwise go to step S8;

[0075] S8, the BMS updates the current SOC of the energy storage device, and assigns the accurate estimation value SOC NEW to SOC new , and goes to step S5;

[0076] S9, the BMS determines that the SOH his estimation is wrong, and issues a SOH correction warning.

[0077] Further, the OCV is used in S3 to correct and calculate the SOC value, which specifically includes the following contents,

[0078] S3.1, perform OCV-SOC test of the battery to obtain the relationship between the open circuit voltage OCV and the remaining capacity SOC under different temperatures and aging states;

[0079] S3.2, use MATLAB to fit to obtain a function relationship polynomial OCV = f(SOC) = a0 + a1SOC 1 +a2SOC 2 +…+a n SOC n ,

[0080] Where a n is the n-th order term coefficient;

[0081] S3.3, the open circuit voltage OCV voltage value read by the sensor after starting is substituted into the polynomial to obtain the SOC new .

[0082] Further, the S4 uses the EKF algorithm to correct the calculation of the SOC, specifically including the following,

[0083] S4.1, according to the polarization characteristics of the lithium battery, a Thevenin equivalent circuit model is established;

[0084] S4.2, the working equation of the above Thevenin equivalent circuit model is,

[0085] In the above formula, OCV is the open circuit voltage, R0 is the ohmic resistance of the battery, C is the polarization capacitance, R1 is the polarization resistance, U1 represents the voltage across the polarization resistance R1 and the polarization capacitance C, U s is the battery terminal voltage, i is the current of the battery, and the charge is positive and the discharge is negative;

[0086] S4.3, the above equation set is discretized, and the observation equation and the measurement equation are obtained by combining the extended Kalman filter as follows,

[0087] In the observation equation and the measurement equation, t is the unit sampling interval, Q max is the current maximum capacity of the battery, which is related to SOH, Q represents the process noise error matrix, and R represents the measurement noise error matrix;

[0088] The recursive estimation of the algorithm is as follows,

[0089] Prior estimation,

[0090] Kalman gain update,

[0091] Optimal estimation update,

[0092] is the prior estimation value of the observation state at the current time, P k - is the prior error covariance matrix at the current time, K k is the Kalman gain matrix, P k + is the optimal estimation and the updated error covariance matrix at the current time, Q is the process noise error matrix, and R k is the measurement noise error matrix at the current time, U s is the voltmeter measurement value, an estimated value of the voltage of the opposite end at the current moment;

[0093] S4.4, HPPC test is performed, a continuous pulse sequence current is used to charge and discharge the battery to obtain dynamic characteristics of the battery at different SOC values, MATLAB is used to perform offline identification of the battery parameters to obtain R0, R1 and C;

[0094] S4.5, the last estimated SOC(k-1) and U1(k-1) are substituted to obtain SOC new (k).

[0095] Further, the EKF algorithm is used in S4 to correct and calculate the SOC, and the measurement noise error matrix R is 0.0003.

[0096] Further, the EKF-AH algorithm weight ω and the measurement noise error γ are dynamically adjusted in S5 by using fuzzy control, and the specific contents include the following,

[0097] S5.1, SOC new and SOH his are obtained, new EKF is used to estimate SOC AH and SOC EKF respectively, the discretization formula of the ampere-hour integral method is as follows, Q max = SOH his * Q 出厂 ,

[0098] In the above formula, t is a unit sampling interval, i is the current battery current, positive for charging and negative for discharging, Q max is the current maximum capacity of the battery, Q 出厂 is the calibrated maximum capacity of the battery when it is shipped, which is continuously consumed and lost with the use of the battery, and Q max and SOH are continuously decreasing;

[0099] S5.2, in the fuzzy controller, SOC new and SOH his are taken as inputs of the fuzzy controller, and the EKF-AH algorithm weight ω and the measurement noise error γ are taken as outputs of the system;

[0100] Three fuzzy subsets of SOC are defined as Bigger (B), Middler (M) and Lower (L) respectively;

[0101] Three fuzzy subsets of SOH are defined as Old (O), NOW (M) and New (N) respectively;

[0102] Similarly, the fuzzy subsets of omega and gamma are defined as Big (B), Mid (M) and Low (L) respectively.

[0103] According to the type and parameters of the membership function, the fuzzy inference rules are formulated through the design of the fuzzy controller.

[0104] S5.3, through the design of the fuzzy controller, combining the interval of the state of charge SOC and the size of the state of health SOH, the dynamic self-adaptive adjustment of the EKF-AH algorithm weight omega and the measurement noise error gamma is realized by using the fuzzy controller, and finally the adjusted measurement noise error gamma is transmitted to the measurement noise error matrix R in the Kalman filter gain coefficient, and the latest EKF-AH algorithm weight omega is used to calculate and obtain the accurate SOC value SOC NEW , see the formula as follows, SOC NEW (k) = omega SOC AH (k) + (1-omega) SOC EKF (k).

[0105] Further, the modified warning threshold value delta is 10%.

[0106] Embodiment

[0107] Taking a lithium iron phosphate battery as the research object, the experimental test environment is PB5060LP28 STM32 DEMO, lithium iron phosphate battery (package), BMS_Studio host computer, and Yishengda battery detection system.

[0108] As shown in FIG. 1, a SOC estimation and correction method for a lithium iron phosphate battery based on a fuzzy EKF-AH algorithm includes the following specific steps,

[0109] S1, taking a certain start of the energy storage device as the starting point, the BMS reads the SOC and SOH values at the last shutdown, denoted as SOC his , SOH his .

[0110] S2, calculate the time difference between this start and the last shutdown as the battery standing time AT, and judge whether the battery standing time AT reaches the preset battery standing time T; in this embodiment, taking a lithium iron phosphate battery as an example, T = 2h,

[0111] If the battery standing time AT is greater than or equal to 2h, go to S3;

[0112] If the battery standing time AT is less than 2h, go to S4;

[0113] S3, since the battery standing time is long enough, the open circuit voltage OCV is used to correct and calculate the SOC value, to obtain a new SOC new , and calculate SOC his and SOCnew The difference ΔSOC = |SOC his -SOC new |, and the SOH correction warning threshold of the battery is δ, and the SOH correction warning threshold δ of the embodiment is 10%;

[0114] If ΔSOC > 10%, it is considered that SOH his estimation error, step S9 is performed;

[0115] If ΔSOC ≤ 10%, step S5 is performed;

[0116] In step S3, the SOC value is corrected and calculated using the open circuit voltage OCV, which specifically includes the following contents,

[0117] S3.1, the battery OCV-SOC test is performed to obtain the relationship between the open circuit voltage OCV and the remaining capacity SOC under different temperatures and aging states;

[0118] S3.2, the function relationship polynomial OCV = f(SOC) = a0 + a1SOC 1 +a2SOC 2 +…+a n SOC n ,

[0119] Where a n is the coefficient of the n-th power term;

[0120] S3.3, the voltage value of the open circuit voltage OCV read by the sensor after starting is substituted into the polynomial to obtain SOC new .

[0121] The open circuit voltage OCV will continuously decrease during the static period due to the influence of aging, weather, self-discharge, etc. Therefore, SOC correction is needed to reduce the estimation error caused by the initial value error. However, if the SOC correction value is greater than the threshold, and the threshold is set to 10% in this embodiment, it is considered that the aging problem of the battery is verified, and SOH correction is needed to determine the fault.

[0122] S4, since the static time of the battery is short, the EKF algorithm is used to correct and calculate the SOC value, and the measurement noise correction lasts for 1 min to obtain the corrected SOC new , and go to S5.

[0123] In step S4, the EKF algorithm is used to correct and calculate the SOC, which specifically includes the following contents,

[0124] S4.1, according to the polarization characteristics of the lithium battery, a Thevenin equivalent circuit model is established, as shown in FIG. 2;

[0125] S4.2, the working equation of the above Thevenin equivalent circuit model is:

[0126] In the above formula, OCV is the open circuit voltage, R0 is the ohmic internal resistance of the battery, C is the polarization capacitance, R1 is the polarization resistance, U1 represents the voltage across the polarization resistance R1 and the polarization capacitance C, U s is the battery terminal voltage, i is the current of the battery, and the charging is positive and the discharging is negative;

[0127] S4.3, discretize the above equation set, and combine the extended Kalman filter to obtain the observation equation and the measurement equation as follows,

[0128] In the observation equation and the measurement equation, t is the unit sampling interval, Q max is the current maximum capacity of the battery, which is related to SOH, and Q represents the process noise error matrix, and R represents the measurement noise error matrix;

[0129] The recursive estimation of the algorithm is as follows,

[0130] Prior estimation,

[0131] Kalman gain update,

[0132] Optimal estimation update,

[0133] is the prior estimation value of the observation state at the current time, P k - is the prior error covariance matrix at the current time, K k is the Kalman gain matrix, P k + is the optimal estimation at the current time and the updated error covariance matrix, Q is the process noise error matrix, and R k is the measurement noise error matrix at the current time, U s is the voltage meter measurement value, is the estimation value of the terminal voltage at the current time;

[0134] S4.4, perform HPPC test, use continuous pulse sequence current to charge and discharge the battery to obtain the dynamic characteristics of the battery at different SOC values, use MATLAB to perform offline identification of the battery parameters to obtain R0, R1 and C;

[0135] S4.5, substitute the estimated SOC(k-1) and U1(k-1) of the last time to obtain SOC new= SOC(k).

[0136] In order to simplify the explanation, the offline identification parameter method is used in the embodiment, and therefore the S4.4 can be improved into various online identification parameter methods, and the results still belong to the protection scope of the patent.

[0137] The S4 uses the EKF algorithm to correct and calculate the SOC, and in order to improve the correction ability of the observation value, the measurement noise error matrix R of this step is 0.0003.

[0138] S5, input the corrected SOC new As the initial value, the SOC is estimated by using the EKF algorithm and the ampere-hour integration method, and the SOC EKF AH is obtained. NEW , and the fuzzy rule base associated with the state of charge SOC and the state of health SOH is established in combination with the fuzzy control, the EKF-AH algorithm weight ω and the measurement noise error γ are dynamically adjusted, and the accurate SOC estimation SOC new is obtained.

[0139] As shown in FIG. 3, the OCV-SOC curve of the lithium iron phosphate battery has a platform period, and the OCV-SOC curves at different SOHs are different.

[0140] Based on the actual test results, [10%, 90%] is selected as the SOC platform period in the embodiment, the OCV in this interval does not change obviously, which leads to the decrease of the OCV correction and the EKF estimation accuracy. At the same time, with the aging of the battery and the decrease of the SOH, the OCV of the battery in the non-platform period will change, and therefore the aging degree of the battery characterized by the state of health (lifetime) SOH needs to be considered.

[0141] The S5 specifically includes the following contents,

[0142] The fuzzy control dynamically adjusts the EKF-AH algorithm weight ω and the measurement noise error γ in the S5, and specifically includes the following contents,

[0143] S5.1, obtain the SOC new and the SOH his , and estimate the SOC new by using the ampere-hour integration method and the EKF, respectively, to obtain the SOC AH and the SOC EKF , and the discrete formula of the ampere-hour integration method is as follows, Q max = SOH his * Q 出厂 ,

[0144] In the formula, t is a unit sampling interval, i is the current of the battery, the charge is positive, the discharge is negative, and Q maxQ is the current maximum capacity of the battery 出厂 Q is the nominal maximum capacity of the battery at the time of leaving the factory, which is gradually lost as the battery is used max and SOH is constantly falling;

[0145] S5.2, in the fuzzy controller, SOC new , SOH his as the input of the fuzzy controller, and the EKF-AH algorithm weight ω and the measurement noise error γ as the output of the system;

[0146] Three fuzzy subsets of SOC are defined as Bigger (B), Middler (M) and Lower (L) respectively;

[0147] Three fuzzy subsets of SOH are defined as Old (O), NOW (M) and New (N) respectively;

[0148] Similarly, the fuzzy subsets of ω and γ are defined as Big (B), Mid (M) and Low (L) respectively;

[0149] According to the type and parameters of the membership function, the fuzzy inference rules are formulated through the design of the fuzzy controller, as shown in Table 1.

[0150] In this embodiment, the first input variable of the fuzzy control is the state of charge SOC, and the fuzzy subsets are defined as [L, M, B], which correspond to three intervals of SOC, [0%, 10%], (10%, 90%), [90%, 100%], i.e. non-platform period, platform period, non-platform period.

[0151] The second variable is the health status (lifetime) SOH, and the fuzzy subsets are defined as [O, M, N], which correspond to three intervals of SOH, [70%, 85%], (85%, 95%), [95%, 100%], representing the old, middle and new degrees of battery aging.

[0152] Batteries with SOH less than 70% are usually recycled, so 70% is taken as the lower limit, and the fuzzy domain and interval division can be adjusted according to the actual situation.

[0153] Similarly, ω and γ are classified in the same way, and this paragraph is only an example, and the specific division needs to be measured for different specifications of batteries.

[0154] Table 1 Fuzzy Rule Base Table

[0155] S5.3, through the design of the fuzzy controller, combining the interval of the state of charge SOC and the size of the state of health SOH, the dynamic self-adaptive adjustment of the EKF-AH algorithm weight ω and the measurement noise error γ is realized by using the fuzzy controller, and finally the adjusted measurement noise error γ is transmitted to the measurement noise error matrix R in the Kalman filter gain coefficient. The latest EKF-AH algorithm weight ω is used to calculate and obtain the accurate SOC estimation SOC NEW , see the formula as follows, SOC NEW (k) = ωSOC AH (k) + (1-ω)SOC EKF (k).

[0156] The larger ω is, the greater the AH algorithm weight is, and the higher the credibility of the ampere-hour integration method is. According to Table 1, when the battery is in the platform period, ω and γ are selected as Big (B), because in this case, the nonlinearity of the OCV-SOC curve is poor, and the algorithm trusts the ampere-hour integration method more;

[0157] When the battery aging causes SOH to decrease, the selection of ω and γ will become larger, from L to M, because in this case, the OCV-SOC curve changes compared with the factory battery, which causes the OCV estimation part of the Kalman filter part to be inaccurate, and the algorithm trusts the ampere-hour integration method more;

[0158] In this embodiment, since the test battery is in good health, SOH = N, so when it is in the platform period, i.e. SOC = M, ω (B) = 0.7, γ (B) = 0.03; when it is in the non-platform period, i.e. SOC = L or B, ω (L) = 0.45, γ (L) = 0.0003.

[0159] S6, calculate the estimation difference e (k) = |SOC EKF -SOC AH | of the EKF algorithm and the ampere-hour integration method, establish T-S fuzzy control rules, and set different warning thresholds α according to whether the SOC is in the platform period,

[0160] When 10% < SOC NEW < 90%, i.e. the SOC is in the platform period, the warning threshold α is 2%;

[0161] When SOC NEW ≤ 10%, or SOC NEW ≥ 90%, i.e. the SOC is in the non-platform period, the warning threshold α is 5%;

[0162] S7, sum the estimation differences of the previous n times to obtain If the sum of the estimation differences e (k) is greater than the corresponding warning threshold α, go to step S9, otherwise go to step S8;

[0163] S8, BMS updates the current SOC of the energy storage device, and the accurate estimated SOC is assigned to SOC NEW assigned to SOC new , and goes to step S5.

[0164] S9, BMS determines SOH his estimation error, and issues an SOH correction warning.

[0165] Before the algorithm fusion, the EKF algorithm and the ampere-hour integration method must have estimation errors, and the error difference of the two algorithms in different SOC intervals is different.

[0166] Because the EKF algorithm in the platform period will trust the ampere-hour integration method more, a larger measurement noise error γ is selected, and the theoretical algorithm error should be smaller, so a smaller warning threshold α = 2% is selected.

[0167] Similarly, the SOC-OCV curve slope in the non-platform period is larger, and the EKF algorithm needs to select a smaller measurement noise error γ, and the theoretical algorithm error should be larger, so the warning threshold α = 5% is selected.

[0168] Because both algorithms depend on the ampere-hour integration method to different degrees, and the ampere-hour integration method is affected by SOH. Therefore, if the error of the two algorithms is greater than the threshold, it indicates that the current displayed battery health SOH result has an error, and needs to be re-estimated and verified.

[0169] As shown in FIGS. 4 and 5, the embodiment performs a charging test, and estimates the SOC by using the single EKF algorithm and the EKF-AH algorithm respectively, and the latter uses parameter dynamic adjustment. The estimation error, the SOC estimation result, and the terminal voltage estimation result are included in the figure. The more accurate the estimation result is, the more the actual value and the estimated value are overlapped. As can be seen from FIG. 4, the estimation error of the single EKF algorithm is larger, so the actual value and the estimated value are not overlapped. As can be seen from FIG. 5, the EKF-AH algorithm has good robustness, the curves are highly overlapped, and is more accurate.

[0170] The results show that the estimation error of the single EKF algorithm is stable at 3%, and the estimation error based on the fuzzy EKF-AH algorithm is stable at about 0.2%.

[0171] In summary, the EKF-AH algorithm combined with extended Kalman filter and ampere-hour integration is used to estimate the SOC of lithium iron phosphate battery, which eliminates the initial value error and process cumulative error of ampere-hour integration method, solves the inaccuracy of EKF in the platform period; the fuzzy rule base associated with the current state of charge SOC and the state of health (life) SOH is established, the weight of EKF-AH algorithm and the measurement noise error γ are dynamically adjusted, and the estimation accuracy of the whole life cycle of lithium iron phosphate battery is improved; according to whether the SOC is in the platform period, different thresholds are set, the cumulative estimation error of the two algorithms is compared with the threshold size, whether the SOH estimation is wrong is judged, and then the SOH is corrected.

[0172] The above is only the preferred embodiment of the present application, and does not limit the present application in any way. Any person skilled in the art can make any form of equivalent replacement, modification or change to the technical solutions and technical contents disclosed in the present application without departing from the scope of the technical solutions of the present application, which still falls within the protection scope of the present application.

Claims

1. A lithium iron phosphate battery SOC estimation and correction method based on a fuzzy EKF-AH algorithm, characterized in that, The method comprises the following steps, S1, taking the certain time of starting of the energy storage device as the beginning, the BMS reads the SOC, SOH values at the last time of shutdown, denoted as SOC his , SOH his ; S2, calculate the time difference between the current start-up and the last shutdown as the battery resting time ΔT, and determine whether the battery resting time ΔT reaches the preset battery resting time T; If the battery resting time ΔT≥T, go to step S3; If the battery resting time ΔT<T, go to step S4; S3, using open circuit voltage OCV to correct the SOC value, get new SOC new , and calculate SOC his The difference between SOC new ΔSOC = |SOC his -SOC new |, set the SOH correction warning threshold of the battery as δ; If ΔSOC > δ, SOH is determined his If the estimation is erroneous, step S9 is executed. If ΔSOC≤δ, execute step S5; S4, using the EKF algorithm to correct the SOC, the correction lasts for 1 min, and the corrected SOC is obtained new , go to step S5; S5, input SOC new As an initial value, the SOC of the next state is estimated using the EKF algorithm and the ampere-hour integration method to obtain the SOC EKF , SOC AH , and a fuzzy rule base associated with the state of charge SOC and the state of health SOH is established in combination with fuzzy control to dynamically adjust the EKF-AH algorithm weight ω and the measurement noise error γ to obtain an accurate SOC estimation value SOC NEW ; S6, calculate the estimated difference e(k) = |SOC EKF -SOC AH |, establish T-S fuzzy control rules, set different warning thresholds a according to whether SOC is in the platform period, When 10% < SOC NEW When 90% < SOC, i.e. SOC is in the plateau, the warning threshold a takes the value of 2%. When SOC NEW ≤ 10%, or SOC NEW ≥ 90%, i.e. SOC is in non-plateau period, the warning threshold a takes the value of 5%; S7, summing the difference values of the previous n estimations to obtain If the sum of the estimated difference e(k) is greater than the corresponding warning threshold α, go to step S9, otherwise go to step S8; S8, the BMS updates the current SOC of the energy storage device, and the accurate estimated SOC NEW is assigned to SOC new , and the process goes to step S5. S9, BMS determines SOH his Estimate error, issue SOH correction warning. 2.The lithium iron phosphate battery SOC estimation and correction method based on the fuzzy EKF-AH algorithm of claim 1, characterized in that, The OCV is used to correct and calculate the SOC value in S3, which specifically includes the following contents, S3.1, perform OCV-SOC test to obtain the relationship between the open circuit voltage OCV and the remaining capacity SOC under different temperatures and aging states; S3.2, use MATLAB to fit the function relationship polynomial of the open circuit voltage OCV and the SOC OCV = f(SOC) = a0+ a1SOC + a2SOC +... + a 1 SOC 2 +... + a n SOC n , where a n is the coefficient of the nth power term; S3.3, the voltage value of the open circuit voltage OCV read by the sensor after starting is substituted into the polynomial to obtain the SOC new . 3.The lithium iron phosphate battery SOC estimation and correction method based on the fuzzy EKF-AH algorithm of claim 1, characterized in that, The EKF algorithm is used to correct and calculate the SOC in S4, which specifically includes the following contents, S4.1, according to the polarization characteristics of the lithium battery, establish the Thevenin equivalent circuit model; S4.2, the working equation of the Thevenin equivalent circuit model is, In the above formula, OCV is the open circuit voltage, R0 is the ohmic internal resistance of the battery, C is the polarization capacitance, R1 is the polarization resistance, U1 represents the voltage across the polarization resistance R1 and the polarization capacitance C, U s is the terminal voltage of the battery, i is the current of the battery, and the charge is positive and the discharge is negative. S4.3, the above equation set is discretized, and combined with the extended Kalman filter to obtain the observation equation and measurement equation as follows, In the observation equation and the measurement equation, t is a unit sampling interval, Q max is the current maximum capacity of the battery, which is related to SOH, Q represents a process noise error matrix, and R represents a measurement noise error matrix; The recursive estimation of the algorithm is as follows, priori estimates, Kalman gain update, optimal estimate update, P is the prior estimate of the observation state at the current time instant k - K is the prior error covariance matrix at the current time instant k K is the Kalman gain matrix, P k + is the optimal estimate for the current time instant and the updated error covariance matrix, Q is the process noise error matrix, R k is the measurement noise error matrix for the current time instant, U s is the voltmeter measurement value, is the estimated value of the terminal voltage at the current time; S4.4, perform HPPC test, use a continuous pulse sequence current to charge and discharge the battery to obtain the dynamic characteristics of the battery under different SOC values, use MATLAB to perform offline identification of the battery parameters to obtain R0, R1 and C; S4.5, substituting the last estimated SOC(k-1), U1(k-1), to obtain SOC new = SOC(k).

4. The lithium iron phosphate battery SOC estimation and correction method based on the fuzzy EKF-AH algorithm according to claim 3, characterized in that, The measurement noise error matrix R of the EKF algorithm used to correct and calculate the SOC in S4 is 0.0003.

5. The lithium iron phosphate battery SOC estimation and correction method based on the fuzzy EKF-AH algorithm according to claim 1, characterized in that, The fuzzy control dynamically adjusts the EKF-AH algorithm weight ω and the measurement noise error γ in S5, which specifically includes the following contents, S5.1, obtain SOC new , SOH his , SOC new estimated using ampere-hour integration method and EKF AH , SOC EKF The discrete formula of ampere-hour integration method is as follows, Q max = SOH his * Q 出厂 , In the above equation, t is the unit sampling interval, i is the current battery current, positive for charging, negative for discharging, Q max is the current maximum capacity of the battery, Q 出厂 is the nominal maximum capacity of the battery at the time of manufacture, which is depleted as the battery is used, Q max and SOH is constantly decreasing; S5.2, in the fuzzy controller, the SOC new , SOH his As inputs to the fuzzy controller, the EKF-AH algorithm weight ω and the measurement noise error γ are taken as the output of the system; Three fuzzy subsets of SOC are defined as Bigger (B), Middler (M) and Lower (L) respectively; Three fuzzy subsets of SOH are defined as Old (O), NOW (M) and New (N) respectively; Similarly, the fuzzy subsets of ω and γ are defined as Big (B), Mid (M) and Low (L) respectively; According to the type and parameter settings of the membership function, the fuzzy inference rules are formulated through the design of the fuzzy controller; S5.3, through the design of fuzzy controller, combining the interval of state of charge SOC and the size of state of health SOH, using fuzzy controller to realize the dynamic self-adaptive adjustment of EKF-AH algorithm weight ω and measurement noise error γ, finally the adjusted measurement noise error γ is transmitted to the measurement noise error matrix R in Kalman filter gain coefficient, using the latest EKF-AH algorithm weight ω to calculate and obtain the accurate SOC estimation SOC NEW , see the formula as follows, SOC NEW (k) = ωSOC AH (k) + (1 - ω)SOC EKF (k).

6. The lithium iron phosphate battery SOC estimation and correction method based on the fuzzy EKF-AH algorithm according to claim 1, characterized in that, The correction warning threshold δ is 10%.

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