Iron-lithium battery state of charge prediction method and device, storage medium and terminal

Abstract

The present invention discloses a method for predicting the state of charge of an iron-lithium battery, comprising: obtaining the voltage estimation error mean, voltage estimation error standard deviation, and voltage estimation error sliding average of the target iron-lithium battery at each sampling moment; respectively judging whether the voltage estimation error sliding average at the sampling moment is greater than a first condition value, and whether the voltage estimation error sliding average at the current sampling moment is less than a second condition value; if greater than, correcting the estimated state of charge to the first correction value; if less than, continuing to judge the sampling moment when the voltage estimation error sliding average is equal to the standard value, and correcting the estimated state of charge at the sampling moment to the second correction value; the first condition value expression is μ(t)+n*σ(t), and the second condition value expression is: μ(t)‑n*σ(t). The present invention utilizes the two charge and discharge platform characteristics of the open circuit voltage to judge the timing of correcting the estimated state of charge, and corrects the state of charge to the set value, thereby improving the prediction accuracy of the state of charge.

Description

Technical Field

[0001] The present invention relates to the technical field of lithium iron batteries, and in particular to a method and device for predicting the state of charge of a lithium iron battery, a storage medium, and a terminal. Background Art

[0002] The voltage of the iron-lithium battery during the constant current charge and discharge process is not constant, but there is a process of continuous rise or fall, stability, and then continuous rise or fall. Therefore, it can be seen that the iron-lithium battery has a stable process during the constant current charge and discharge process, which we call the charge and discharge platform. The charge and discharge process of the iron-lithium battery actually includes two charge and discharge platform periods.

[0003] Figure 1 The conventional discharge diagram of lithium iron battery at several different temperatures is given. Figure 1 It can be seen that when predicting the state of charge of an iron-lithium battery, if the estimated state of charge is higher than the actual state of charge, due to the error in the process of estimating the state of charge, when the actual state of charge is less than the end inflection point of the first charge and discharge platform, the estimated state of charge is still on the first charge and discharge platform, which will inevitably lead to an abnormal value in the voltage estimation error; similarly, if the estimated state of charge is lower than the actual state of charge, due to the error in the process of estimating the state of charge, when the actual state of charge is less than the starting inflection point of the second charge and discharge platform, the estimated state of charge is still on the first charge and discharge platform, which will also lead to an abnormal value in the voltage estimation error.

[0004] Existing methods for predicting the state of charge (SOC) of lithium-ion batteries primarily include the following: the ampere-hour integration method, which is simple and easy to implement but suffers from error accumulation and a lack of self-correction; the simple open-circuit voltage method, which is unsuitable for dynamic estimation; the closed-loop estimation method based on an equivalent circuit model, which, while highly accurate, requires high model accuracy and may experience a decrease in estimation accuracy after aging; and the machine learning-based method, which requires extensive data training and is not conducive to online estimation. In summary, existing SOC prediction methods for lithium-ion batteries all suffer from various problems. Summary of the Invention

[0005] The technical problem to be solved by the present invention is that the existing method for predicting the state of charge of a lithium iron battery cannot meet the requirements of real-time online, low data volume and high precision.

[0006] In order to solve the above technical problems, the present invention provides a method for predicting the state of charge of a lithium iron battery, comprising:

[0007] Collecting a real-time current value and a real-time temperature value of a target iron-lithium battery, and calculating a real-time estimated state of charge of the target iron-lithium battery based on the real-time current value;

[0008] Based on the real-time current value, the real-time temperature value and the real-time estimated state of charge, obtaining the real-time estimated terminal voltage of the target iron-lithium battery through a second-order RC equivalent circuit model;

[0009] Collecting the real-time terminal voltage of the target iron-lithium battery, and obtaining the voltage estimation error mean, voltage estimation error standard deviation, and voltage estimation error sliding average of the target iron-lithium battery at each sampling moment based on the real-time terminal voltage and the real-time estimated terminal voltage;

[0010] determining in sequence whether the sliding average of the voltage estimation error at a single sampling moment meets a preset condition; if so, revising the estimated state of charge of the target iron-lithium battery at the corresponding sampling moment; otherwise, using the real-time estimated state of charge as the estimated state of charge of the target iron-lithium battery at the corresponding sampling moment;

[0011] The step of determining whether the voltage estimation error sliding average value at a certain sampling moment meets a preset condition, and if so, correcting the estimated state of charge of the target iron-lithium battery at the sampling moment includes:

[0012] Determine whether the voltage estimation error sliding average value at the sampling moment is greater than the first condition value, and at the same time determine whether the voltage estimation error sliding average value at the current sampling moment is less than the second condition value,

[0013] If the voltage estimation error sliding average at the sampling moment is greater than the first condition value, the sampling moment is used as a correction moment, and the estimated state of charge of the target iron-lithium battery at the correction moment is corrected to a first correction value, and at the sampling moment after the correction moment, the real-time estimated state of charge at the subsequent sampling moment is calculated using the first correction value as a percentage of the starting capacity;

[0014] If the voltage estimation error sliding average value at the sampling moment is less than the second condition value, the sampling moment is used as the standard moment, and the voltage estimation error mean value corresponding to the standard moment is used as the standard value. Then, it is judged in sequence whether the voltage estimation error sliding average values ​​corresponding to the sampling moments after the standard moment are equal to the standard value. When it is judged that the voltage estimation error sliding average value corresponding to a certain sampling moment after the standard moment is equal to the standard value, the sampling moment at which the voltage estimation error sliding average value is equal to the standard value is used as the correction moment, and the estimated state of charge of the target iron-lithium battery at the correction moment is corrected to the second correction value, and the real-time estimated state of charge at the subsequent sampling moments is calculated using the second correction value as the percentage of the starting capacity at the sampling moments after the correction moment;

[0015] The first conditional value expression is μ(t)+n*σ(t), and the second conditional value expression is: μ(t)-n*σ(t), where μ(t) represents the mean value of the voltage estimation error, σ(t) represents the standard deviation of the voltage estimation error, 0<n<5, and t represents the sampling time.

[0016] Preferably, A-3%≤x1≤A+3%, wherein A is the charge state of the target iron-lithium battery at the end inflection point of the first voltage platform, and x1 is the first correction value.

[0017] Preferably, B-3%≤x2≤B+3%, where B is the state of charge of the starting inflection point of the second voltage platform of the target iron-lithium battery, and x2 is the second correction value.

[0018] Preferably, the real-time estimated state of charge of the target lithium iron battery is calculated by an ampere-hour integration method.

[0019] Preferably, the calculation expression for calculating the real-time estimated state of charge at subsequent sampling moments using the first correction value as a percentage of the initial capacity is:

[0020]

[0021] Among them, SOC_AHI(t) represents the real-time estimated state of charge of the target iron-lithium battery, Cap_rated represents the rated capacity of the target iron-lithium battery, I_MEA(t) represents the real-time current value of the target iron-lithium battery, x1 is the first correction value, and t1 represents the correction time.

[0022] Preferably, the calculation expression for calculating the real-time estimated state of charge at subsequent sampling moments using the second correction value as a percentage of the initial capacity is:

[0023]

[0024] Among them, SOC_AHI(t) represents the real-time estimated state of charge of the target iron-lithium battery, Cap_rated represents the rated capacity of the target iron-lithium battery, I_MEA(t) represents the real-time current value of the target iron-lithium battery, x2 is the second correction value, and t1 represents the correction time.

[0025] Preferably, the voltage estimation error mean expression is:

[0026]

[0027] Wherein, μ(t) represents the mean voltage estimation error, μ(t-Δt) represents the mean voltage estimation error at the previous sampling moment; Err(t) represents the voltage estimation error of the target iron-lithium battery;

[0028] The voltage estimation error standard deviation expression is:

[0029]

[0030] Where, σ(t) represents the standard deviation of voltage estimation error, U MEA (t) represents the real-time terminal voltage of the target iron-lithium battery, and V(t-Δt) is the variance of the target iron-lithium battery voltage estimation error at the previous sampling moment;

[0031] The voltage estimation error sliding average expression is:

[0032]

[0033] Where Err_SW(t) represents the sliding average of the voltage estimation error, t i =t-ΔT, ΔT is the time length of the sliding window, and ΔT is an integer multiple of Δt, and Δt is the sampling interval.

[0034] In order to solve the above technical problems, the present invention also provides a device for predicting the state of charge of an iron-lithium battery, comprising a real-time estimated state of charge acquisition module, a real-time estimated terminal voltage acquisition module, an error calculation module and a correction judgment module;

[0035] The real-time estimated state of charge acquisition module is used to collect the real-time current value and real-time temperature value of the target iron-lithium battery, and calculate the real-time estimated state of charge of the target iron-lithium battery based on the real-time current value;

[0036] The real-time estimated terminal voltage acquisition module is used to obtain the real-time estimated terminal voltage of the target iron-lithium battery through a second-order RC equivalent circuit model based on the real-time current value, the real-time temperature value and the real-time estimated state of charge;

[0037] The error calculation module is used to collect the real-time terminal voltage of the target iron-lithium battery, and obtain the voltage estimation error mean, voltage estimation error standard deviation and voltage estimation error sliding average of the target iron-lithium battery at each sampling moment based on the real-time terminal voltage and the real-time estimated terminal voltage;

[0038] The correction judgment module is used to sequentially judge whether the sliding average of the voltage estimation error at a single sampling moment meets a preset condition, and if so, to correct the estimated state of charge of the target iron-lithium battery at the corresponding sampling moment; otherwise, the real-time estimated state of charge is used as the estimated state of charge of the target iron-lithium battery at the corresponding sampling moment;

[0039] The step of determining whether the voltage estimation error sliding average value at a certain sampling moment meets a preset condition, and if so, correcting the estimated state of charge of the target iron-lithium battery at the sampling moment includes:

[0040] Determine whether the voltage estimation error sliding average value at the sampling moment is greater than the first condition value, and at the same time determine whether the voltage estimation error sliding average value at the current sampling moment is less than the second condition value,

[0041] If the voltage estimation error sliding average at the sampling moment is greater than the first condition value, the sampling moment is used as a correction moment, and the estimated state of charge of the target iron-lithium battery at the correction moment is corrected to a first correction value, and at the sampling moment after the correction moment, the real-time estimated state of charge at the subsequent sampling moment is calculated using the first correction value as a percentage of the starting capacity;

[0042] If the voltage estimation error sliding average value at the sampling moment is less than the second condition value, the sampling moment is used as the standard moment, and the voltage estimation error mean value corresponding to the standard moment is used as the standard value. Then, it is judged in sequence whether the voltage estimation error sliding average values ​​corresponding to the sampling moments after the standard moment are equal to the standard value. When it is judged that the voltage estimation error sliding average value corresponding to a certain sampling moment after the standard moment is equal to the standard value, the sampling moment at which the voltage estimation error sliding average value is equal to the standard value is used as the correction moment, and the estimated state of charge of the target iron-lithium battery at the correction moment is corrected to the second correction value, and the real-time estimated state of charge at the subsequent sampling moments is calculated using the second correction value as the percentage of the starting capacity at the sampling moments after the correction moment;

[0043] The first conditional value expression is μ(t)+n*σ(t), and the second conditional value expression is: μ(t)-n*σ(t), where μ(t) represents the mean value of the voltage estimation error, σ(t) represents the standard deviation of the voltage estimation error, 0<n<5, and t represents the sampling time.

[0044] In order to solve the above technical problems, the present invention further provides a computer storage medium, wherein the computer storage medium stores a computer program, and the computer program is executed by a processor such as the method for predicting the state of charge of a lithium iron battery.

[0045] In order to solve the above technical problems, the present invention also provides a terminal, including: a processor and a memory; the memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory, so that the electronic device executes the iron-lithium battery charge state prediction method as described.

[0046] Compared with the prior art, one or more embodiments of the above solutions may have the following advantages or beneficial effects:

[0047] The method for predicting the state of charge of a lithium iron battery provided by an embodiment of the present invention is applied. By collecting and calculating the voltage estimation error and other values ​​of the target lithium iron battery at each sampling moment, and judging the charge and discharge progress of the lithium iron battery based on the sliding average of the voltage estimation error, the two charge and discharge platform characteristics of the open circuit voltage are used to judge the timing of correcting the estimated state of charge, and the state of charge is corrected to the set value, thereby improving the prediction accuracy of the state of charge. Further, by simultaneously judging the two conditions of the estimated state of charge being higher than the actual state of charge and the estimated state of charge being lower than the actual state of charge, it is ensured that the state of charge prediction process realizes real-time monitoring of the above two conditions. The method of the present invention can also meet the needs of real-time online use, and does not require the amount of data required for monitoring, further expanding its scope of use.

[0048] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purposes and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0050] Figure 1 The conventional discharge diagram of iron-lithium battery at several different temperatures is given;

[0051] Figure 2 A schematic diagram showing a flow chart of a method for predicting a battery state of charge according to a first embodiment of the present invention is shown;

[0052] Figure 3 It shows a schematic diagram of correction when the real-time estimated state of charge is 10% higher than the first embodiment of the present invention;

[0053] Figure 4 It shows a schematic diagram of correction when the real-time estimated state of charge is 10% lower than that in the first embodiment of the present invention;

[0054] Figure 5 A schematic diagram showing the structure of a battery state of charge prediction device according to a second embodiment of the present invention is shown;

[0055] Figure 6 It shows a schematic structural diagram of a terminal according to a fourth embodiment of the present invention. DETAILED DESCRIPTION

[0056] The following describes the embodiments of the present invention in detail with reference to the accompanying drawings and examples, so that the present invention can fully understand how to apply technical means to solve technical problems and achieve technical effects, and thus implement the invention accordingly. It should be noted that, as long as no conflict exists, the various embodiments of the present invention and the various features of the embodiments can be combined with each other, and the resulting technical solutions are all within the scope of protection of the present invention.

[0057] Figure 1 The conventional discharge diagram of lithium iron battery at several different temperatures is given. Figure 1 It can be seen that when predicting the state of charge of an iron-lithium battery, if the estimated state of charge is higher than the actual state of charge, due to the error in the process of estimating the state of charge, when the actual state of charge is less than the end inflection point of the first charge and discharge platform, the estimated state of charge is still on the first charge and discharge platform, which will inevitably lead to an abnormal value in the voltage estimation error; similarly, if the estimated state of charge is lower than the actual state of charge, due to the error in the process of estimating the state of charge, when the actual state of charge is less than the starting inflection point of the second charge and discharge platform, the estimated state of charge is still on the first charge and discharge platform, which will also lead to an abnormal value in the voltage estimation error.

[0058] Example 1

[0059] In order to solve the technical problems existing in the prior art, an embodiment of the present invention provides a method for predicting the state of charge of an iron-lithium battery.

[0060] Figure 2 FIG2 shows a flow chart of a method for predicting the state of charge of a battery according to an embodiment of the present invention; FIG3 shows a flow chart of a method for predicting the state of charge of a battery according to an embodiment of the present invention; Figure 2 As shown, the method for predicting the state of charge of a lithium iron battery according to an embodiment of the present invention includes the following steps.

[0061] Step S101 : collecting a real-time current value and a real-time temperature value of a target iron-lithium battery, and calculating a real-time estimated state of charge of the target iron-lithium battery based on the real-time current value.

[0062] Specifically, the current value and temperature value of the target iron-lithium battery are collected in real time to serve as the real-time current value and real-time temperature value of the target iron-lithium battery. The method for collecting the real-time current value and real-time temperature value is a conventional technical means in this field and will not be described in detail here. At the same time, based on the collected real-time current value, the real-time estimated state of charge of the target iron-lithium battery is calculated using the ampere-hour integration method. The expression of the real-time estimated state of charge before the state of charge correction is as follows:

[0063]

[0064] Among them, SOC_AHI(t) represents the real-time estimated state of charge of the target iron-lithium battery, Cap_init represents the initial capacity of the target iron-lithium battery when it is powered on, Cap_rated represents the rated capacity of the target iron-lithium battery, I_MEA(t) represents the real-time current value of the target iron-lithium battery, and t represents the sampling time.

[0065] Step S102 : Based on the real-time current value, the real-time temperature value and the real-time estimated state of charge, the real-time estimated terminal voltage of the target lithium iron battery is obtained through a second-order RC equivalent circuit model.

[0066] Specifically, the real-time current value, real-time temperature value and real-time estimated state of charge are input into the second-order RC equivalent circuit model to obtain the real-time estimated terminal voltage of the target iron-lithium battery. The expression of the second-order RC equivalent circuit model is:

[0067]

[0068] Among them, U_CAL(t) represents the real-time estimated terminal voltage of the target iron-lithium battery, U oc (t) represents the open circuit voltage, and U oc (t) = U oc (I_MEA(t), T_MEA(t), SOC_AHI(t)), R0(t) represents the ohmic internal resistance, and R0(t)=R0(I_MEA(t), T_MEA(t), SOC_AHI(t)), R1(t) represents the concentration polarization internal resistance, and R1(t)=R1(I_MEA(t), T_MEA(t), SOC_AHI(t)), R2(t) represents the electrochemical polarization internal resistance, and R2(t)=R2(I_MEA(t), T _MEA(t), SOC_AHI(t)), τ1(t) represents time constant 1, and τ1(t) = τ1(I_MEA(t), T_MEA(t), SOC_AHI(t)), τ2(t) represents time constant 2, and τ2(t) = τ2(I_MEA(t), T_MEA(t), SOC_AHI(t)), I is the real-time current value of the target iron-lithium battery I_MEA(t), T_MEA(t) represents the real-time temperature value of the target iron-lithium battery. It should be noted that t is the relative sampling time after power-on, and t' is the relative time that the current state remains in a certain state after switching.

[0069] Step S103 , collecting the real-time terminal voltage of the target iron-lithium battery, and obtaining the voltage estimation error mean, voltage estimation error standard deviation, and voltage estimation error sliding average of the target iron-lithium battery at each sampling moment based on the real-time terminal voltage and the real-time estimated terminal voltage.

[0070] Specifically, the terminal voltage of the target iron-lithium battery is sampled in real time and used as the real-time terminal voltage of the target iron-lithium battery. Then, based on the collected real-time terminal voltage and the calculated real-time estimated terminal voltage, the voltage estimation error mean of the target iron-lithium battery at each sampling moment is calculated. Then, based on the voltage estimation error mean of the target iron-lithium battery at each sampling moment, the voltage estimation error mean, voltage estimation error standard deviation, and voltage estimation error sliding average at the corresponding sampling moment are calculated. That is, through the above calculation, the voltage estimation error mean, voltage estimation error standard deviation, and voltage estimation error sliding average of the target iron-lithium battery at each sampling moment can be obtained.

[0071] The voltage estimation error is further calculated as: Err(t) = U_CAL(t) - U_MEA(t)

[0072] Among them, U_CAL(t) represents the real-time estimated terminal voltage of the target iron-lithium battery, and U_MEA(t) represents the real-time terminal voltage of the target iron-lithium battery.

[0073] The mean expression of voltage estimation error is:

[0074]

[0075] Wherein, μ(t) represents the mean voltage estimation error of the target iron-lithium battery, μ(t-Δt) represents the mean voltage estimation error at the previous sampling moment, and Err(t) represents the voltage estimation error of the target iron-lithium battery.

[0076] The voltage estimation error standard deviation expression is:

[0077]

[0078] Wherein, σ(t) represents the standard deviation of the voltage estimation error of the target Fe-lithium battery, U_MEA(t) represents the real-time terminal voltage of the target Fe-lithium battery, and V(t-Δt) is the variance of the voltage estimation error of the target Fe-lithium battery at the previous sampling moment;

[0079] The sliding average expression of voltage estimation error is:

[0080]

[0081] Among them, Err_SW(t) represents the voltage estimation error sliding average of the target iron-lithium battery, t i =t-ΔT, ΔT is the time length of the sliding window, and ΔT is an integer multiple of Δt, and Δt is the sampling interval.

[0082] Step S104, determine in turn whether the sliding average of the voltage estimation error at a single sampling moment meets the preset conditions. If so, correct the estimated state of charge of the target iron-lithium battery at the corresponding sampling moment; otherwise, use the real-time estimated state of charge as the estimated state of charge of the target iron-lithium battery at the corresponding sampling moment.

[0083] Specifically, this embodiment needs to determine whether the estimated state of charge of the target iron-lithium battery at the corresponding moment needs to be corrected based on the sliding average of the voltage estimation error corresponding to each sampling moment obtained previously. If necessary, it is corrected according to the settings. If not, the real-time estimated state of charge corresponding to the current sampling moment is directly used as the estimated state of charge of the target iron-lithium battery at the current sampling moment.

[0084] It should be noted that if this method is used in real time online, the present invention will execute the above steps after sampling at each sampling moment; if this method is based on the prediction of the state of charge of the target iron-lithium battery based on past data, the above steps need to be executed for each sampling moment in chronological order.

[0085] The specific process of determining whether the sliding average value of the voltage estimation error at a single sampling moment meets the preset conditions is as follows:

[0086] To facilitate the distinction, we assume that the targeted sampling moment is the target sampling moment;

[0087] Determine whether the sliding average of the voltage estimation error at the target sampling moment is greater than the first condition value, and also determine whether the sliding average of the voltage estimation error at the target sampling moment is less than the second condition value; when the sliding average of the voltage estimation error at the target sampling moment is greater than the first condition value, it means that the sliding average of the voltage estimation error at the target sampling moment meets the preset condition, and the target sampling moment needs to be used as the correction moment, that is, it is determined that the target sampling moment is the correction moment of the iron-lithium battery state of charge; then the estimated state of charge of the target iron-lithium battery at the correction moment is corrected to the first correction value, and it is also set that when calculating the real-time estimated state of charge of the target iron-lithium battery at the sampling moment after the correction moment, the first correction value needs to be used as the starting capacity percentage to calculate the real-time estimated state of charge of the subsequent sampling moments.

[0088] It should be noted that when determining whether the sliding average of the voltage estimation error at each sampling moment is greater than the first condition value, we only need to select the sampling moment when the first voltage estimation error sliding average is greater than the first condition value, and correct the estimated state of charge corresponding to the sampling moment; because if the estimated state of charge is not corrected, the sliding average of the voltage estimation error corresponding to the sampling moments after the sampling moment when the first voltage estimation error sliding average is greater than the first condition value will all be greater than the first condition value, and if the estimated state of charge at the sampling moment when the first voltage estimation error sliding average is greater than the first condition value is corrected, and since the estimated state of charge at subsequent sampling moments is calculated based on the first correction value as a percentage of the starting capacity, the real-time estimated state of charge at subsequent sampling moments, therefore, the sliding average of the voltage estimation error corresponding to the sampling moments after the sampling moment when the first voltage estimation error sliding average is greater than the first condition value will no longer be greater than the first condition value.

[0089] When the sliding average of the voltage estimation error at the target sampling moment is less than the second condition value, it does not mean that the sliding average of the voltage estimation error at the target sampling moment meets the preset conditions. It only means that the first sampling moment whose sliding average of the voltage estimation error is less than the second condition value is selected in sequence from many sampling moments, and the target sampling moment needs to be used as the standard moment, and the voltage estimation error mean corresponding to the standard moment needs to be used as the standard value; and the sampling moments after the standard moment do not need to judge the first condition value or the second condition value. It is only necessary to judge whether the sliding average of the voltage estimation error corresponding to the sampling moments after the standard moment is equal to the standard value in sequence. If it is judged that the sliding average of the voltage estimation error corresponding to a certain sampling moment after the standard moment is equal to the standard value, it means that the sliding average of the voltage estimation error at the sampling moment when the sliding average of the voltage estimation error is equal to the standard value meets the preset conditions, and the sampling moment when the sliding average of the voltage estimation error is equal to the standard value needs to be used as the correction moment, and the estimated state of charge of the target iron-lithium battery at the correction moment is corrected to the second correction value; at the same time, it is also set that when calculating the real-time estimated state of charge of the target iron-lithium battery at the sampling moment after the correction moment, the real-time estimated state of charge of the subsequent sampling moments needs to be calculated using the second correction value as the percentage of the starting capacity. If, in the process of determining whether the sliding average value of the voltage estimation error is equal to the standard value, it is determined that the sliding average value of the voltage estimation error at certain sampling moments is not equal to the standard value, then corresponding judgments are performed on the following sampling moments in the sampling order until a sampling moment is determined at which the sliding average value of the voltage estimation error is equal to the standard value.

[0090] Similarly, it should be noted that when determining whether the sliding average of the voltage estimation error at each sampling moment is less than the second condition value, we only need to select the first sampling moment when the sliding average of the voltage estimation error is less than the second condition value, and use the voltage estimation error mean corresponding to the sampling moment as the standard value. Then, there is no need to determine whether the sampling moment after the first sampling moment when the sliding average of the voltage estimation error is less than the second condition value is less than the second condition value. It is only necessary to determine whether the sliding average of the voltage estimation error is equal to the standard value at the sampling moment after the first sampling moment when the sliding average of the voltage estimation error is less than the second condition value. Similarly, this determination process only needs to select the first sampling moment when the sliding average of the voltage estimation error is equal to the standard value in chronological order, and correct the estimated state of charge corresponding to the sampling moment. In the above process, the standard value is determined only once, and there is no need to determine the standard value for the sampling moments after the first one that meet the voltage estimation error sliding average value less than the second condition value. In this process, only when it is determined that the sliding average of the voltage estimation error is equal to the standard value can the sampling moment when the sliding average of the voltage estimation error is equal to the standard value be used as the moment that meets the preset conditions. It should be further explained that, for the sampling moments at which the voltage estimation error sliding average is less than the second conditional value but does not satisfy the condition that the voltage estimation error sliding average is equal to the standard value, the corresponding real-time estimated state of charge is used as the estimated state of charge of the target iron-lithium battery at the corresponding sampling moment. If the estimated state of charge at the first sampling moment at which the voltage estimation error sliding average is equal to the standard value is corrected, and since the estimated state of charge at subsequent sampling moments is calculated using the second correction value as a percentage of the starting capacity, the voltage estimation error sliding average corresponding to the sampling moments after the first sampling moment at which the voltage estimation error sliding average is equal to the standard value will no longer be less than the second conditional value.

[0091] If the sliding average value of the voltage estimation error at the target sampling moment is neither greater than the first condition value nor less than the second condition value, it means that the sliding average value of the voltage estimation error at the target sampling moment does not meet the preset conditions, and the real-time estimated state of charge is used as the estimated state of charge of the target iron-lithium battery at the corresponding sampling moment.

[0092] It should be noted that the first conditional value expression is μ(t)+n*σ(t), and the second conditional value expression is: μ(t)-n*σ(t), where μ(t) represents the mean value of the voltage estimation error and σ(t) represents the standard deviation of the voltage estimation error. That is, the sliding average value of the voltage estimation error at each sampling moment must be judged against the judgment condition formed by the corresponding mean value of the voltage estimation error and the standard deviation value of the voltage estimation error. Where 0<n<5, and preferably, n is 3, that is, the first conditional value expression is μ(t)+3*σ(t), and the second conditional value expression is: μ(t)-3*σ(t).

[0093] At the same time, the first correction value and the second correction value also have a value range. The value range of the first correction value is: A-3%≤x1≤A+3%, where A is the charge state of the end inflection point of the first voltage platform of the target iron-lithium battery, and x1 is the first correction value; the value range of the second correction value is: B-3%≤x2≤B+3%, where B is the charge state of the starting inflection point of the second voltage platform of the target iron-lithium battery, and x2 is the second correction value. The method for obtaining the end inflection point of the first voltage platform of the target iron-lithium battery and the starting inflection point of the second voltage platform of the target iron-lithium battery can be set by those skilled in the art based on the actual situation of the target iron-lithium battery. The method for obtaining the above-mentioned inflection points is a conventional technical means in this field, and the present invention will not be further elaborated on this.

[0094] The calculation expression for calculating the real-time estimated state of charge at subsequent sampling moments using the first correction value as a percentage of the initial capacity is:

[0095]

[0096] Among them, SOC_Var(t) represents the real-time estimated state of charge of the target iron-lithium battery, Cap_rated represents the rated capacity of the target iron-lithium battery, I_MEA(t) represents the real-time current value of the target iron-lithium battery, x1 is the first correction value, and t1 represents the correction time.

[0097] The calculation expression for calculating the real-time estimated state of charge at subsequent sampling moments using the second correction value as a percentage of the initial capacity is:

[0098]

[0099] Among them, SOC_Var(t) represents the real-time estimated state of charge of the target iron-lithium battery, Cap_rated represents the rated capacity of the target iron-lithium battery, I_MEA(t) represents the real-time current value expression of the target iron-lithium battery, x2 is the second correction value, and t1 represents the correction time.

[0100] In order to further illustrate the real-time effect of the method for predicting the state of charge of a lithium iron battery according to the present invention, Figure 1 The schematic diagram of conventional discharge of a lithium iron battery at several different temperatures shown in the figure is used as an example to provide a specific correction explanation. When the estimated state of charge of the target lithium iron battery is higher than the actual state of charge, when the state of charge is discharged to below 67%, the inflection point at the end of the first discharge platform, the estimated state of charge is still within the first discharge platform, which causes an abnormal value in the voltage estimation error; at this time, when it is determined that the sliding average of the voltage estimation error is greater than μ(t)+3*σ(t), it is determined that the estimated state of charge is too high, and the estimated state of charge needs to be corrected to 65%, and then 65% is used as the starting capacity percentage to calculate the real-time estimated state of charge at the subsequent sampling moments. Taking the estimated state of charge being 10% higher as an example, under the NEDC operating condition cyclic discharge, the corresponding correction results are as follows Figure 3 shown.

[0101] Similarly, when the estimated state of charge of the target iron-lithium battery is lower than the actual state of charge, the estimated state of charge is less than 62%, but the actual state of charge is greater than 67% (that is, it is in the first discharge platform), the voltage estimation error has an abnormal value; at this time, when the sliding average of the voltage estimation error is less than μ(t)+3*σ(t), the error mean at that moment is stored and recorded as the standard value. Since the actual state of charge and the estimated state of charge are both less than 62%, the voltage estimation error sliding average converges to the standard value. Therefore, when the voltage estimation error sliding average reaches the standard value, it is determined that the estimated state of charge is too low, and the estimated state of charge needs to be corrected to 60%. Subsequently, 60% is used as the starting capacity percentage to calculate the real-time estimated state of charge at the subsequent sampling moments. Taking the estimated state of charge as an example of being 10% lower, under the NEDC operating cycle discharge, the corresponding correction results are as follows Figure 4 Show.

[0102] The method for predicting the state of charge of a lithium iron battery provided in an embodiment of the present invention collects and calculates the voltage estimation error and other values ​​of the target lithium iron battery at each sampling moment, and judges the charge and discharge progress of the lithium iron battery based on the sliding average of the voltage estimation error. It uses the two charge and discharge platform characteristics of the open circuit voltage to judge the timing of correcting the estimated state of charge, and corrects the state of charge to the set value, thereby improving the prediction accuracy of the state of charge. Further, by simultaneously judging the two conditions of the estimated state of charge being higher than the actual state of charge and the estimated state of charge being lower than the actual state of charge, it ensures that the state of charge prediction process realizes real-time monitoring of the above two conditions. The method of the present invention can also meet the needs of real-time online use, and does not require the amount of data required for monitoring, further expanding its scope of use.

[0103] Example 2

[0104] In order to solve the technical problems existing in the prior art, an embodiment of the present invention provides a method for predicting the state of charge of an iron-lithium battery.

[0105] Figure 5 FIG2 shows a schematic diagram of the structure of a battery state of charge prediction device according to an embodiment of the present invention; Figure 5 As shown, the battery state of charge prediction device according to the embodiment of the present invention includes a real-time estimated state of charge acquisition module, a real-time estimated terminal voltage acquisition module, an error calculation module and a correction judgment module.

[0106] The real-time estimated state of charge acquisition module is used to collect the real-time current value and real-time temperature value of the target iron-lithium battery, and calculate the real-time estimated state of charge of the target iron-lithium battery based on the real-time current value.

[0107] The real-time estimated terminal voltage acquisition module is used to obtain the real-time estimated terminal voltage of the target iron-lithium battery through a second-order RC equivalent circuit model based on the real-time current value, real-time temperature value and real-time estimated state of charge.

[0108] The error calculation module is used to collect the real-time terminal voltage of the target iron-lithium battery, and obtain the voltage estimation error mean, voltage estimation error standard deviation and voltage estimation error sliding average of the target iron-lithium battery at each sampling moment based on the real-time terminal voltage and the real-time estimated terminal voltage.

[0109] The correction judgment module is used to judge in turn whether the sliding average of the voltage estimation error at a single sampling moment meets the preset conditions. If so, the estimated state of charge of the target iron-lithium battery at the corresponding sampling moment is corrected; otherwise, the real-time estimated state of charge is used as the estimated state of charge of the target iron-lithium battery at the corresponding sampling moment.

[0110] The method of determining whether a sliding average of a voltage estimation error at a certain sampling moment meets a preset condition and, if so, correcting the estimated state of charge of the target iron-lithium battery at the sampling moment includes:

[0111] Determine whether the sliding average value of the voltage estimation error at the sampling moment is greater than the first condition value, and at the same time determine whether the sliding average value of the voltage estimation error at the current sampling moment is less than the second condition value,

[0112] If the sliding average of the voltage estimation error at the sampling moment is greater than the first condition value, the sampling moment is used as the correction moment, and the estimated state of charge of the target iron-lithium battery at the correction moment is corrected to the first correction value, and at the sampling moment after the correction moment, the real-time estimated state of charge at the subsequent sampling moments is calculated using the first correction value as the percentage of the starting capacity;

[0113] If the sliding average value of the voltage estimation error at the sampling moment is less than the second condition value, the sampling moment is taken as the standard moment, and the voltage estimation error mean corresponding to the standard moment is taken as the standard value. Then, it is judged in sequence whether the voltage estimation error sliding average values ​​corresponding to the sampling moments after the standard moment are equal to the calibration value. When it is judged that the voltage estimation error sliding average value corresponding to a certain sampling moment after the standard moment is equal to the calibration value, the sampling moment at which the voltage estimation error sliding average value is equal to the calibration value is taken as the correction moment, and the estimated state of charge of the target iron-lithium battery at the correction moment is corrected to the second correction value, and at the sampling moment after the correction moment, the real-time estimated state of charge at the subsequent sampling moments is calculated using the second correction value as the percentage of the starting capacity;

[0114] The first conditional value expression is μ(t)+n*σ(t), and the second conditional value expression is: μ(t)-n*σ(t), where μ(t) represents the mean value of the voltage estimation error, σ(t) represents the standard deviation of the voltage estimation error, 0<n<5, and t represents the sampling time.

[0115] The iron-lithium battery state of charge prediction device provided by the embodiment of the present invention collects and calculates the voltage estimation error and other values ​​of the target iron-lithium battery at each sampling moment, and judges the charge and discharge progress of the iron-lithium battery based on the sliding average of the voltage estimation error. It uses the two charge and discharge platform characteristics of the open circuit voltage to judge the timing of correcting the estimated state of charge, and corrects the state of charge to the set value, thereby improving the prediction accuracy of the state of charge. Further, by simultaneously judging the two conditions of the estimated state of charge being higher than the actual state of charge and the estimated state of charge being lower than the actual state of charge, it ensures that the state of charge prediction process realizes real-time monitoring of the above two conditions. The device of the present invention can also meet the needs of real-time online use, and does not require the amount of data required for monitoring, further expanding its scope of use.

[0116] Example 3

[0117] In order to solve the above technical problems existing in the prior art, an embodiment of the present invention further provides a storage medium storing a computer program, which can implement all steps in the iron-lithium battery state of charge prediction method when executed by a processor.

[0118] The specific steps of the method for predicting the state of charge of a lithium iron battery and the beneficial effects obtained by applying the readable storage medium provided in the embodiment of the present invention are the same as those in the first embodiment and will not be described in detail here.

[0119] It should be noted that the storage medium includes various media that can store program codes, such as ROM, RAM, magnetic disk or optical disk.

[0120] Example 4

[0121] In order to solve the above technical problems existing in the prior art, an embodiment of the present invention further provides a terminal.

[0122] Figure 6 The schematic diagram of the terminal structure of the fourth embodiment of the present invention is shown. Figure 6 In this embodiment, the terminal includes a processor and a memory connected to each other; the memory is used to store computer programs, and the processor is used to execute the computer programs stored in the memory, so that all steps in the iron-lithium battery charge state prediction method can be implemented when the terminal executes the computer programs.

[0123] The specific steps in the method for predicting the state of charge of a lithium iron battery and the beneficial effects obtained by applying the terminal provided by the embodiment of the present invention are the same as those in the first embodiment and will not be described in detail here.

[0124] It should be noted that the memory may include random access memory (RAM) and may also include non-volatile memory (non-volatile memory), such as at least one disk storage. Similarly, the processor may also be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0125] Although the embodiments disclosed herein are as described above, the contents described herein are merely embodiments for facilitating understanding of the present invention and are not intended to limit the present invention. Any person skilled in the art may make any modifications and variations in the form and details of the embodiments without departing from the spirit and scope of the present invention. However, the scope of protection of the present invention shall remain subject to the scope defined by the appended claims.

Claims

1. A method for predicting the state of charge of a lithium iron battery, comprising: Collecting a real-time current value and a real-time temperature value of a target iron-lithium battery, and calculating a real-time estimated state of charge of the target iron-lithium battery based on the real-time current value; Based on the real-time current value, the real-time temperature value and the real-time estimated state of charge, obtaining the real-time estimated terminal voltage of the target iron-lithium battery through a second-order RC equivalent circuit model; Collecting the real-time terminal voltage of the target iron-lithium battery, and obtaining the voltage estimation error mean, voltage estimation error standard deviation, and voltage estimation error sliding average of the target iron-lithium battery at each sampling moment based on the real-time terminal voltage and the real-time estimated terminal voltage; determining in sequence whether the sliding average of the voltage estimation error at a single sampling moment meets a preset condition; if so, revising the estimated state of charge of the target iron-lithium battery at the corresponding sampling moment; otherwise, using the real-time estimated state of charge as the estimated state of charge of the target iron-lithium battery at the corresponding sampling moment; The step of determining whether the voltage estimation error sliding average at a single sampling moment meets a preset condition, and if so, correcting the estimated state of charge of the target iron-lithium battery at the sampling moment includes: Determine whether the voltage estimation error sliding average value at the sampling moment is greater than the first condition value, and at the same time determine whether the voltage estimation error sliding average value at the current sampling moment is less than the second condition value, If the voltage estimation error sliding average at the sampling moment is greater than the first condition value, the sampling moment is used as a correction moment, and the estimated state of charge of the target iron-lithium battery at the correction moment is corrected to a first correction value, and at the sampling moment after the correction moment, the real-time estimated state of charge at the subsequent sampling moment is calculated using the first correction value as a percentage of the starting capacity; If the voltage estimation error sliding average value at the sampling moment is less than the second condition value, the sampling moment is used as the standard moment, and the voltage estimation error mean value corresponding to the standard moment is used as the standard value. Then, it is judged in sequence whether the voltage estimation error sliding average values ​​corresponding to the sampling moments after the standard moment are equal to the standard value. When it is judged that the voltage estimation error sliding average value corresponding to a certain sampling moment after the standard moment is equal to the standard value, the sampling moment when the voltage estimation error sliding average value is equal to the standard value is used as the correction moment, and the estimated state of charge of the target iron-lithium battery at the correction moment is corrected to the second correction value, and the real-time estimated state of charge at the subsequent sampling moments is calculated using the second correction value as the percentage of the starting capacity at the sampling moments after the correction moment; The first conditional value expression is μ(t)+n*σ(t), and the second conditional value expression is: μ(t)-n*σ(t), where μ(t) represents the mean value of the voltage estimation error, σ(t) represents the standard deviation of the voltage estimation error, 0<n<5, and t represents the sampling time; The first correction value is obtained based on the state of charge of the target iron-lithium battery at the end inflection point of the first voltage platform, and the second correction value is obtained based on the state of charge of the target iron-lithium battery at the start inflection point of the second voltage platform.

2. The prediction method according to claim 1, characterized in that A-3%≤x1≤A+3%, where A is the state of charge of the target iron-lithium battery at the end inflection point of the first voltage platform, and x1 is the first correction value.

3. The prediction method according to claim 1, wherein: It is set that B-3%≤x2≤B+3%, wherein B is the charge state of the starting inflection point of the second voltage platform of the target iron-lithium battery, and x2 is the second correction value.

4. The prediction method according to claim 1, wherein: The real-time estimated state of charge of the target iron-lithium battery is calculated by the ampere-hour integration method.

5. The prediction method according to claim 4, characterized in that The calculation expression for calculating the real-time estimated state of charge at subsequent sampling moments using the first correction value as a percentage of the initial capacity is: Among them, SOC_AHI(t) represents the real-time estimated state of charge of the target iron-lithium battery, Cap_rated represents the rated capacity of the target iron-lithium battery, I_MEA(t) represents the real-time current value of the target iron-lithium battery, x1 is the first correction value, and t1 represents the correction time.

6. The prediction method according to claim 4, characterized in that The calculation expression for calculating the real-time estimated state of charge at subsequent sampling moments using the second correction value as a percentage of the initial capacity is: Among them, SOC_AHI(t) represents the real-time estimated state of charge of the target iron-lithium battery, Cap_rated represents the rated capacity of the target iron-lithium battery, I_MEA(t) represents the real-time current value of the target iron-lithium battery, x2 is the second correction value, and t1 represents the correction time.

7. The prediction method according to claim 1, wherein: The voltage estimation error mean expression is: Wherein, μ(t) represents the mean voltage estimation error, μ(t-Δt) represents the mean voltage estimation error at the previous sampling moment; Err(t) represents the voltage estimation error of the target iron-lithium battery; The voltage estimation error standard deviation expression is: Where, σ(t) represents the standard deviation of voltage estimation error, U MEA (t) represents the real-time terminal voltage of the target iron-lithium battery, and V(t-Δt) is the variance of the target iron-lithium battery voltage estimation error at the previous sampling moment; The voltage estimation error sliding average expression is: Where Err_SW(t) represents the sliding average of the voltage estimation error, t i =t-ΔT, ΔT is the time length of the sliding window, and ΔT is an integer multiple of Δt, and Δt is the sampling interval.

8. A battery state of charge prediction device, characterized in that: It includes a real-time estimated state of charge acquisition module, a real-time estimated terminal voltage acquisition module, an error calculation module and a correction judgment module; The real-time estimated state of charge acquisition module is used to collect the real-time current value and real-time temperature value of the target iron-lithium battery, and calculate the real-time estimated state of charge of the target iron-lithium battery based on the real-time current value; The real-time estimated terminal voltage acquisition module is used to obtain the real-time estimated terminal voltage of the target iron-lithium battery through a second-order RC equivalent circuit model based on the real-time current value, the real-time temperature value and the real-time estimated state of charge; The error calculation module is used to collect the real-time terminal voltage of the target iron-lithium battery, and obtain the voltage estimation error mean, voltage estimation error standard deviation and voltage estimation error sliding average of the target iron-lithium battery at each sampling moment based on the real-time terminal voltage and the real-time estimated terminal voltage; The correction judgment module is used to sequentially judge whether the sliding average of the voltage estimation error at a single sampling moment meets a preset condition, and if so, to correct the estimated state of charge of the target iron-lithium battery at the corresponding sampling moment; otherwise, the real-time estimated state of charge is used as the estimated state of charge of the target iron-lithium battery at the corresponding sampling moment; The step of determining whether the voltage estimation error sliding average value at a certain sampling moment meets a preset condition, and if so, correcting the estimated state of charge of the target iron-lithium battery at the sampling moment includes: Determine whether the voltage estimation error sliding average value at the sampling moment is greater than the first condition value, and at the same time determine whether the voltage estimation error sliding average value at the current sampling moment is less than the second condition value, If the voltage estimation error sliding average at the sampling moment is greater than the first condition value, the sampling moment is used as a correction moment, and the estimated state of charge of the target iron-lithium battery at the correction moment is corrected to a first correction value, and at the sampling moment after the correction moment, the real-time estimated state of charge at the subsequent sampling moment is calculated using the first correction value as a percentage of the starting capacity; If the voltage estimation error sliding average value at the sampling moment is less than the second condition value, the sampling moment is used as the standard moment, and the voltage estimation error mean value corresponding to the standard moment is used as the standard value. Then, it is judged in sequence whether the voltage estimation error sliding average values ​​corresponding to the sampling moments after the standard moment are equal to the standard value. When it is judged that the voltage estimation error sliding average value corresponding to a certain sampling moment after the standard moment is equal to the standard value, the sampling moment at which the voltage estimation error sliding average value is equal to the standard value is used as the correction moment, and the estimated state of charge of the target iron-lithium battery at the correction moment is corrected to the second correction value, and the real-time estimated state of charge at the subsequent sampling moments is calculated using the second correction value as the percentage of the starting capacity at the sampling moments after the correction moment; The first conditional value expression is μ(t)+n*σ(t), and the second conditional value expression is: μ(t)-n*σ(t), where μ(t) represents the mean value of the voltage estimation error, σ(t) represents the standard deviation of the voltage estimation error, 0<n<5, and t represents the sampling time; The first correction value is obtained based on the state of charge of the target iron-lithium battery at the end inflection point of the first voltage platform, and the second correction value is obtained based on the state of charge of the target iron-lithium battery at the start inflection point of the second voltage platform.

9. A computer storage medium storing a computer program, wherein: The computer program is executed by a processor to implement the method for predicting the state of charge of a lithium iron battery according to any one of claims 1 to 7.

10. A terminal, characterized in that: include: processor and memory; The memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory, so that the terminal executes the method for predicting the state of charge of a lithium iron battery as claimed in any one of claims 1 to 7.

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