Battery SOH estimation method, control device and storage medium

Abstract

The invention relates to the technical field of vehicle batteries, particularly provides a battery SOH estimation method, a control device and a storage medium, and aims to solve the problem that in the prior art, the use scene for detecting the SOH of a vehicle battery is limited. In order to achieve the purpose, the method comprises the steps that S100, in the charging process, an ICA curve of a battery is obtained; and S200, based on the first peak valley position of the ICA curve, obtaining a battery SOH estimation value. According to the method, the complete ICA curve can be complemented and obtained by only measuring a part of the values of the preset charging time, the accurate SOH value is finally obtained, the battery does not need to be fully charged, the application scene of SOH prediction is expanded, and a user is further away from mileage anxiety.

Description

Technical Field

[0001] The present application relates to the technical field of vehicle batteries, and specifically provides a method for estimating the state of health (SOH) of a battery, a control device, and a storage medium. Background Art

[0002] With the vigorous development and popularization of electric vehicles, users' requirements for the safety performance of batteries are also getting higher and higher. The power batteries currently used in electric vehicles are mainly divided into lithium iron phosphate system batteries (LFP) and ternary system batteries (NCM). Since LFP shows more excellent safety performance, even though its energy density is average, more and more electric vehicles using LFP have emerged in recent years.

[0003] The estimation of the state of health (SOH) of vehicle batteries is a core issue of the battery management system. Accurately estimating the battery SOH is beneficial for the vehicle to formulate appropriate control strategies, improve the accuracy of SOC calculation, extend the driving range, and reduce users' range anxiety. Existing methods for calculating SOH require the battery to be fully charged to obtain a complete ICA curve (the discharge curve of the battery), and then estimate the SOH. That is, if the user disconnects from charging without fully charging, the vehicle will not be able to accurately calculate the value of SOH at this time. Even in most cases, the vehicle usually disconnects the power after being fully charged, there are still many scenarios where the user cannot wait too long and terminates charging midway, unable to reach the full charge state, which limits the application scenarios of the vehicle in evaluating SOH.

[0004] The above limitations make it impossible to accurately estimate the SOH of LFP batteries when they are not fully charged. The problem of estimating the SOH of LFP batteries has always been a difficult problem in the industry.

[0005] Correspondingly, a new solution is needed in this field to solve the above problems. Summary of the Invention

[0006] The present application aims to solve or partially solve the above technical problems, that is, the problem that the SOH of LFP batteries cannot be accurately estimated when they are not fully charged.

[0007] In a first aspect, the present application proposes a method for estimating the SOH of a battery, the method including:

[0008] During the charging process, obtain the ICA curve of the battery;

[0009] Based on the first peak-valley position of the ICA curve, obtain the estimated value of the battery SOH.

[0010] In a preferred technical solution of the above method for estimating the SOH of a battery, the obtaining the estimated value of the battery SOH based on the peak-valley position of the ICA curve includes:

[0011] Obtain the maximum voltage value among the voltages corresponding to the peaks and valleys of the ICA curve;

[0012] Obtain the area under the ICA curve of the part of the ICA curve where the voltage value is greater than or equal to the maximum voltage value;

[0013] Based on the area under the curve and the charge capacity, obtain the estimated value of the battery SOH.

[0014] In a preferred technical solution of the above battery SOH estimation method, the battery includes a plurality of battery cells, and obtaining the estimated value of the battery SOH based on the position of the first peak and valley of the ICA curve includes:

[0015] Obtain the first peak and valley of the ICA curve of each battery cell;

[0016] For each battery cell, obtain the area under the ICA curve from the first peak and valley to the cut-off voltage, and based on the area under the curve and the charge capacity, obtain the estimated value of the SOH of this battery cell;

[0017] Take the minimum value of the SOH estimated values of the plurality of battery cells as the estimated value of the battery SOH.

[0018] In a preferred technical solution of the above battery SOH estimation method, after obtaining the ICA curve of the battery, it further includes:

[0019] Determine the position of the second peak and valley in the ICA curve;

[0020] Based on the position of the first peak and valley, the position of the second peak and valley, and the charge capacity from the second peak and valley to the cut-off voltage, obtain the estimated value of the battery SOH.

[0021] In a preferred technical solution of the above battery SOH estimation method, obtaining the ICA curve of the battery includes:

[0022] Obtain the ICA data of the battery;

[0023] In response to a supplement instruction, supplement the ICA data to obtain the supplemented ICA data;

[0024] Obtain the ICA curve according to the supplemented ICA data.

[0025] In a preferred technical solution of the above battery SOH estimation method, supplementing the ICA data includes:

[0026] Supplement the ICA data through a trained ICA data acquisition model.

[0027] In the preferred technical solution of the above battery SOH estimation method, the ICA data includes a sequence of the first number of data within the first voltage range. When the first number is less than the preset number, the supplementary instruction is triggered.

[0028] In the preferred technical solution of the above battery SOH estimation method, the first data in the ICA data is the dQ / dV value corresponding to the lower limit voltage of the first voltage range. The ICA data is the first number of dQ / dV values, and the first number is greater than or equal to the second number. Supplementing the ICA data through the trained ICA data acquisition model includes:

[0029] Step A, obtaining the previous second number of data in the ICA data as the current data;

[0030] Step B, inputting the current data into the ICA data acquisition model to obtain the next data of the current data;

[0031] Step C, using the next data as the last data of the second number of data, and re-obtaining the second number of data as the current data;

[0032] Repeat Step B and Step C until the dQ / dV value corresponding to the upper limit voltage of the first voltage range is obtained.

[0033] In the preferred technical solution of the above battery SOH estimation method, obtaining the historical dQ / dV data sequence corresponding to the third voltage range obtained according to the actual sampling frequency during the battery charging process;

[0034] Based on the historical dQ / dV data sequence, according to the first voltage range and the voltage sampling step size, obtaining the sample dQ / dV data sequence through interpolation operation;

[0035] Training the ICA data acquisition model based on the sample dQ / dV data sequence;

[0036] Wherein, the lower limit voltage of the third voltage range is less than or equal to the lower limit voltage of the first voltage range, and the upper limit voltage of the third voltage range is greater than or equal to the upper limit voltage of the first voltage range.

[0037] In the preferred technical solution of the above battery SOH estimation method, the method further includes:

[0038] Constructing the ICA data acquisition model based on the improved Transformer network; wherein, the improved Transformer network includes a decoding layer constructed based on a fully connected layer.

[0039] In the preferred technical solution of the above battery SOH estimation method, the battery is a lithium iron phosphate battery, and the upper limit voltage of the first voltage range is the charging cut-off voltage of the lithium iron phosphate battery.

[0040] In a second aspect, the present application proposes a control device, including at least one processor and at least one storage device. The storage device is adapted to store multiple program codes, and the program codes are adapted to be loaded and run by the processor to execute the battery SOH estimation method described in any one of the above technical solutions.

[0041] In a third aspect, the present application proposes a storage medium, which is adapted to store multiple program codes, and the program codes are adapted to be loaded and run by a processor to execute the battery SOH estimation method described in any one of the above solutions.

[0042] The present application can finally complete and obtain a complete ICA curve by only measuring the values of partial preset charging times, and finally obtain an accurate SOH value, without waiting for the battery to be fully charged, which expands the application scenario of SOH prediction and enables users to further distance themselves from range anxiety.

[0043] Solution 1. A battery SOH estimation method, characterized in that the method includes:

[0044] During the charging process, obtain the ICA curve of the battery;

[0045] Based on the first peak-valley position of the ICA curve, obtain the battery SOH estimated value.

[0046] Solution 2. The battery SOH estimation method according to Solution 1, characterized in that the obtaining the battery SOH estimated value based on the peak-valley position of the ICA curve includes:

[0047] Obtain the maximum voltage value among the voltages corresponding to the peaks and valleys of the ICA curve;

[0048] Obtain the area under the ICA curve of the part of the ICA curve where the voltage value is greater than or equal to the maximum voltage value;

[0049] Based on the area under the curve and the charged capacity, obtain the battery SOH estimated value.

[0050] Solution 3. The battery SOH estimation method according to Solution 1, characterized in that the battery includes a plurality of battery cells, and the obtaining the battery SOH estimated value based on the first peak-valley position of the ICA curve includes:

[0051] Obtain the first peak and valley of the ICA curve of each battery cell;

[0052] For each battery cell, obtain the area under the ICA curve between the first peak-valley and the cut-off voltage, and based on the area under the curve and the charge capacity, obtain the SOH estimation value of this battery cell;

[0053] Take the minimum value of the SOH estimation values of the multiple battery cells as the SOH estimation value of the battery.

[0054] Solution 4. The battery SOH estimation method according to any one of Solutions 1 to 3, characterized in that after obtaining the ICA curve of the battery, it further includes:

[0055] Determine the position of the second peak-valley in the ICA curve;

[0056] Based on the position of the first peak-valley, the position of the second peak-valley, and the charge capacity from the second peak-valley to the cut-off voltage, obtain the SOH estimation value of the battery.

[0057] Solution 5. The battery SOH estimation method according to any one of Solutions 1 to 3, characterized in that obtaining the ICA curve of the battery includes:

[0058] Obtain the ICA data of the battery;

[0059] In response to the supplement instruction, supplement the ICA data to obtain the supplemented ICA data;

[0060] Obtain the ICA curve according to the supplemented ICA data.

[0061] Solution 6. The battery SOH estimation method according to Solution 5, characterized in that supplementing the ICA data includes:

[0062] Supplement the ICA data through a trained ICA data acquisition model.

[0063] Solution 7. The battery SOH estimation method according to Solution 5, characterized in that the ICA data includes a sequence of the first number of data within the first voltage range, and when the first number is less than the preset number, the supplement instruction is triggered.

[0064] Solution 8. The battery SOH estimation method according to Solution 6, characterized in that the first data in the ICA data is the dQ / dV value corresponding to the lower limit voltage of the first voltage range, the ICA data is the first number of dQ / dV values, the first number is greater than or equal to the second number, and supplementing the ICA data through a trained ICA data acquisition model includes:

[0065] Step A, obtain the previous second number of data in the ICA data as the current data;

[0066] Step B: Input the current data into the ICA data acquisition model to obtain the next data of the current data.

[0067] Step C: Use the next data as the last data of the second quantity of data, and re-acquire the second quantity of data as the current data.

[0068] Repeat Step B and Step C until the dQ / dV value corresponding to the upper limit voltage of the first voltage range is obtained.

[0069] Solution 9. The battery SOH estimation method according to Solution 7, characterized in that a historical dQ / dV data sequence corresponding to a third voltage range obtained according to the actual sampling frequency during the battery charging process is acquired.

[0070] Based on the historical dQ / dV data sequence, according to the first voltage range and the voltage sampling step size, obtain a sample dQ / dV data sequence through interpolation operation.

[0071] Train the ICA data acquisition model based on the sample dQ / dV data sequence.

[0072] Wherein, the lower limit voltage of the third voltage range is less than or equal to the lower limit voltage of the first voltage range, and the upper limit voltage of the third voltage range is greater than or equal to the upper limit voltage of the first voltage range.

[0073] Solution 10. The battery SOH estimation method according to Solution 1, characterized in that the method further includes:

[0074] Construct the ICA data acquisition model based on an improved Transformer network; wherein,

[0075] The improved Transformer network includes a decoding layer constructed based on a fully connected layer.

[0076] Solution 11. The battery SOH estimation method according to Solution 7, characterized in that the battery is a lithium iron phosphate battery, and the upper limit voltage of the first voltage range is the charging cut-off voltage of the lithium iron phosphate battery.

[0077] Solution 12. A control device includes at least one processor and at least one storage device, the storage device is adapted to store multiple program codes, characterized in that the program codes are adapted to be loaded and run by the processor to execute the battery SOH estimation method according to any one of Solutions 1 to 11.

[0078] Solution 13. A storage medium adapted to store multiple program codes, characterized in that the program codes are adapted to be loaded and run by a processor to execute the battery SOH estimation method according to any one of Solutions 1 to 11. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] Referring to the drawings, the disclosure of the present application will become more understandable. It is easy for those skilled in the art to understand that these drawings are only for illustrative purposes and are not intended to limit the protection scope of the present application.

[0080] Figure 1 It is the main flowchart of the battery SOH estimation method according to an embodiment of the present application.

[0081] Figure 2 It is the specific flowchart of step S100 of the battery SOH estimation method according to an embodiment of the present application.

[0082] Figure 3 It is an implementation manner of the specific flowchart of step S200 of the battery SOH estimation method according to an embodiment of the present application.

[0083] Figure 4 It is another implementation manner of the specific flowchart of step S200 of the battery SOH estimation method according to an embodiment of the present application.

[0084] Figure 5 It is the composition diagram of the Transformer model of the ICA data acquisition model in step S200 of the battery SOH estimation method according to an embodiment of the present application.

[0085] Figure 6 It is the comparison diagram of the ICA curve obtained in step S200 of the battery SOH estimation method according to an embodiment of the present application and the actually measured ICA curve.

[0086] Figure 7 It is the schematic diagram of the method for obtaining the offline area part in step S200 of the battery SOH estimation method according to an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0087] To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the drawings. Obviously, the described embodiments are some, but not all, of the embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts fall within the protection scope of the present application.

[0088] It should be noted that the terms "first", "second", etc. in the description, claims and above-mentioned drawings of this application are used to distinguish similar objects, and do not necessarily describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of this application described here can be implemented in an order other than those illustrated or described here. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0089] Those skilled in the art should understand that these embodiments are only used to explain the technical principles of this application and are not intended to limit the protection scope of this application. Those skilled in the art can make adjustments according to needs to adapt to specific application scenarios.

[0090] Next, with reference to the drawings, a specific implementation manner of the battery SOH estimation method mentioned in this application will be described.

[0091] As Figure 1-7 shown, the battery SOH estimation method of this application includes:

[0092] S100. During the charging process, obtain the ICA curve of the battery;

[0093] S200. Based on the first peak-valley position of the ICA curve, obtain the battery SOH estimation value.

[0094] Next, in combination with the specific implementation manner, the specific method of obtaining the ICA curve of step S100 will be elaborated first.

[0095] As Figure 2 shown, in a possible implementation manner, the method of obtaining the ICA curve in step S100 further includes:

[0096] S110. Obtain the ICA data of the battery.

[0097] S120. In response to the supplement instruction, supplement the ICA data to obtain the supplemented ICA data.

[0098] S130. Obtain the ICA curve according to the supplemented ICA data.

[0099] Among them, supplementing the ICA data includes: supplementing the ICA data through a trained ICA data acquisition model;

[0100] Among them, the ICA data includes a sequence of the first number of data within the first voltage range. When the first number is less than the preset number, a supplementary instruction is triggered.

[0101] In a possible implementation manner, the first data in the ICA data is the dQ / dV value corresponding to the lower limit voltage of the first voltage range. The ICA data is the first number of dQ / dV values, and the first number is greater than or equal to the second number. Supplementing the ICA data through the trained ICA data acquisition model includes:

[0102] Step A, obtaining the previous second number of data in the ICA data as the current data;

[0103] Step B, inputting the current data into the ICA data acquisition model to obtain the next data of the current data;

[0104] Step C, using the next data as the last data of the second number of data, and re-obtaining the second number of data as the current data;

[0105] Repeat Step B and Step C until the dQ / dV value corresponding to the upper limit voltage of the first voltage range is obtained.

[0106] Taking a specific possible embodiment, the specific solution for obtaining the ICA curve in the above S100 is elaborated in detail:

[0107] In a possible implementation manner, the battery can be a lithium iron phosphate battery. The upper limit voltage of the first voltage range is the charging cut-off voltage of the lithium iron phosphate battery. Taking the first voltage range as [3.300, 3.650] and the voltage sampling step size as 0.005V as an example for illustration. Of course, the values can be adjusted according to actual needs. For example, 3.300V can be modified to 3.280V, etc. In the conventional solutions of the prior art, it is necessary to collect all sampling points with a step size of 0.005V between 3.300V and 3.650V (in this embodiment of the preset interval, there are 71 points, 3.300V, 3.305V, 3.310V... 3.650V), and obtain the dQ / dV values of all sampling points. Using the dQ / dV values as the ordinate and the voltage values as the abscissa, a complete battery ICA curve (the discharge curve of the battery) is plotted, so as to reasonably estimate the battery SOH, give an accurate judgment, and avoid the user's mileage anxiety caused by inaccurate trip estimation. However, if the user directly terminates the charging without fully charging, at this time, it is impossible to completely sample 71 points, which will cause incomplete sampling, the ICA curve cannot be plotted, and the SOH value cannot be calculated, so that the user cannot obtain the real SOH value in this application scenario and can only use the last SOH value. In this way, if the SOH value is not corrected for a long time without being fully charged, it will cause inaccurate vehicle mileage evaluation.

[0108] However, in the present application, it is not necessary to collect completely. In step S100, it is set that "the ICA data includes a sequence of the first number of data within the first voltage range". Taking the first number as 16 as an example here, that is, for the current 71 values, only the first 16 points need to be sampled as the values of the first dQ / dV data sequence for evaluating the SOH in the present application. At this time, the V values are 3.300V, 3.305V, 3.310V... 3.375V. That is, only by sampling 0.075V of vehicle charging, we can obtain the 16 values we need. Even if the battery is not fully charged to 3.65V (taking 0.35V of full charge as an example), the present application can accurately complete the remaining ICA curve and achieve accurate evaluation of the SOH. The first element in the first dQ / dV data sequence (i.e., 1 in 1 - 16) is the dQ / dV value corresponding to the lower limit voltage of the first voltage range (i.e., the dQ / dV value corresponding to 3.300V). The first number (i.e., 16) is greater than or equal to the window size of the sliding time window (the window size of the sliding time window is the second number. The first number is greater than or equal to the second number. In this embodiment, equality is used for illustration, that is, the second number is also 16).

[0109] The following is an example for steps A - C. Taking the size of the sliding time window as 16 as an example, a sequence of 16 data of the first number (values from 1 to 16) generates a first model input sequence, and then it is input into the trained ICA data acquisition model (such as a Transformer model) to obtain the calculated dQ / dV value corresponding to the next voltage sampling point of the first current voltage sampling point, that is, the value with a predicted index of 17. Add the calculated dQ / dV value (the value of 17) to the end of the first dQ / dV data sequence (values from 1 - 16, and the second number is also 16) to generate a new first dQ / dV data sequence (values from 1 - 17) composed of the first dQ / dV data sequence and the calculated dQ / dV value. Then slide the window once (the window value is still the second number 16), and the values with indexes from 2 to 17 are re - input into the Transformer model to predict the value with an index of 18, and so on, until the values with indexes from 55 to 70 predict the value with an index of 71. The 71 dQ / dV sampling points generated from the charging data of a battery pack on a certain day can have 55 training data of sliding time windows. These 55 training data are used to complete the initially measured 16 data, and finally obtain a 71 - data sequence for the complete first voltage range [3.300, 3.650], so that the ICA curve can be supplemented based on the incomplete data sequence.

[0110] To reduce the computational complexity and increase the accuracy, when the size of the sliding time window is still 16, but the actual sampled data exceeds 16, for example, when 20 data are actually sampled. At this time, if the prediction starts from 17, it will be a waste of calculation, and the actual accuracy will decrease because there is a measured value for 17, which is more accurate. To adapt to the actual sampled values, for example, if 20 groups of data are actually sampled, the last element is 20, then the last element of the first dQ / dV data sequence is also 20. The first dQ / dV data sequence is the values from 5 to 20. Then, only the value of 21 needs to be deduced from the values of 5 to 20, and so on, until the values with indices 55 to 70 are used to predict the value with index 71 (the dQ / dV value corresponding to the upper limit voltage of 3.650V in the first voltage range), reducing four deductions of 1 - 16, 2 - 17, 3 - 18, and 4 - 19, thereby reducing the computational complexity and improving the accuracy.

[0111] The present application also proposes a scheme for supplementing instructions. Only when the first quantity (16) is less than the preset quantity (71), the supplementary instruction is triggered to supplement the ICA curve. If the first quantity is equal to the preset quantity, there is no need to supplement because the real data of the ICA curve has been collected and no further deduction is required.

[0112] Due to different charging piles, different initial charging voltages of vehicles, different sampling frequencies, etc., it is usually impossible to directly obtain the 16 points of V values of 3.300V, 3.305V, 3.310V... 3.375V required by the present application. For example, when the initial voltage of the vehicle is 3.298V and the sampling frequency is 0.003V, the sampled values obtained are 3.298V, 3.301V, 3.304V, 3.307V, etc. It can be seen that 3.300V and 3.305V are not sampled. To solve this practical problem, the present application also proposes a further refinement scheme, including:

[0113] Obtain the original dQ / dV data sequence corresponding to the second voltage range obtained according to the actual sampling frequency during the battery charging process;

[0114] Based on the original dQ / dV data sequence, according to the first voltage range and the voltage sampling step, obtain the first dQ / dV data sequence through interpolation operation;

[0115] Wherein, the lower limit voltage of the second voltage range is less than or equal to the lower limit voltage of the first voltage range.

[0116] The original dQ / dV data sequence obtained in step S110, that is, corresponding to a possible sampling sequence mentioned in the present application: 3.298V, 3.301V, 3.304V, 3.307V... 3.376V, 3.379V, etc. The actual sampling frequency is 0.003V. At this time, the second voltage interval is [3.298, 3.376]. At this time, the sampling data of 16 points such as 3.300V, 3.305V, 3.310V... 3.375V actually required in the present application can be obtained through interpolation operations. The calculation method can be to calculate after connecting points with a straight line, or to obtain the numerical value after obtaining the fitting curve. There are various acquisition methods, and those skilled in the art can actually select according to needs. In order to ensure that the values of these 16 points in [3.300, 3.375] of the present application must be complete, the value range of the second voltage interval is specified. The lower limit voltage of the second voltage interval needs to be less than or equal to the lower limit voltage of the first voltage interval, that is, 3.298V < 3.300V, to ensure the integrity of the starting point. The end point is different due to the different selected first quantity (16 in the embodiment), so there is no need to limit it, and it only needs to be actually obtained later according to the situation.

[0117] Above, in combination with a specific solution, the acquisition method of the ICA curve has been expanded. Among them, the ICA data acquisition model is applied. Next, the training method of the ICA data acquisition model will be further expanded:

[0118] The ICA data acquisition model mentioned in the present application can, based on a preset first dQ / dV data sequence (for example, 16), derive a complete dQ / dV data sequence (for example, 71), and then obtain a complemented ICA model (the first 16 are actually obtained, and the last 55 are derived). Finally, the ICA curve corresponding to the first voltage interval is obtained. There can be various ICA data acquisition models used for the derivation, such as the Transformer model, the recurrent network RNN, GRU, etc. Among them, the prediction is carried out through the trained ICA data acquisition model. A specific implementation manner proposed in the present application is as Figure 5As shown, the prediction is implemented using a Transformer model. In particular, in this application, the ICA data acquisition model is constructed based on an improved Transformer network. The improved Transformer network includes an embedding layer, a position encoding layer, an encoding layer constructed based on a multi-head attention layer and a feedforward layer, and a decoding layer constructed based on a fully connected layer. The inventive point of the Transformer model in this application that differentiates it from the traditional Transformer model is that the decoder layer of the traditional Transformer model is too redundant and cannot adapt to the calculations required in this application. Therefore, based on the traditional Transformer model, this application replaces the traditional decoder layer with a fully connected layer, making the overall algorithm more concise and improving the accuracy, making it more suitable for this application to predict the ICA curve. As Figure 6 shown, the actual data of the same battery pack is compared with the predicted data, and their coincidence degree is very high, making the average error of SOH within 0.5%, fully meeting the accuracy requirements for SOH estimation.

[0119] Of course, the prediction model can also be a recurrent model such as RNN.

[0120] The improved Transformer model also needs to be trained. The training method for the model in this application is as follows:

[0121] Obtain the historical dQ / dV data sequence corresponding to the third voltage interval obtained according to the actual sampling frequency during the battery charging process;

[0122] Based on the historical dQ / dV data sequence, according to the first voltage interval and the voltage sampling step, obtain the sample dQ / dV data sequence through interpolation operations;

[0123] Train the ICA data acquisition model based on the sample dQ / dV data sequence;

[0124] Among them, the lower limit voltage of the third voltage interval is less than or equal to the lower limit voltage of the first voltage interval, and the upper limit voltage of the third voltage interval is greater than or equal to the upper limit voltage of the first voltage interval. With such a setting, the third voltage interval can completely enclose the first voltage interval, so that the sampling data of the first voltage interval is complete, ensuring that the prediction of the Transformer model in the first voltage interval will not be distorted at the edges, and making the model trained by the samples more accurate.

[0125] So far, the introduction of obtaining the ICA curve in step S100 is completed. In step S100, we obtained the ICA curve by combining the measured values and the predicted values. Next, for the solution of "obtaining the battery SOH estimation value based on the first peak-valley position of the ICA curve" in step S200, the further specific implementation will be elaborated:

[0126] As Figure 3 shown, in a possible implementation manner, step S200 further includes:

[0127] S211. Obtain the maximum voltage value among the voltages corresponding to the peaks and valleys of the ICA curve;

[0128] S212. Obtain the area under the ICA curve of the part of the ICA curve where the voltage value is greater than or equal to the maximum voltage value;

[0129] S213. Based on the area under the curve and the charge capacity, obtain the battery SOH estimation value.

[0130] The ICA curve forms of different batteries are different. Taking the lithium iron phosphate system battery (LFP) as an example (of course, it is not limited to lithium iron phosphate batteries, and the ICA curves of similar batteries that may appear later with two plateau periods, that is, batteries with at least two peaks, are applicable to the SOH estimation method of this application). As Figure 7 shown, Figure 7 there are two peaks and two valleys. The first valley is before 3.30V, and the figure does not show it completely. The last valley, that is, the maximum voltage value among the voltages corresponding to the peaks and valleys, this voltage value is 3.35V. Obtain the area S under the ICA curve of the part of the ICA curve where the voltage value is greater than or equal to the maximum voltage value (3.35V) corresponding to the valley. Then, based on the sum of the area S under the curve and the charge capacity, obtain the final SOH value. Since the area S under the curve will become smaller and smaller with attenuation, the SOH value can reflect the attenuation degree of the battery. Among them, the definition of the charge capacity is the capacity charged by the battery from the discharge cut-off voltage to the maximum voltage value among the voltages corresponding to the peaks and valleys (3.35V in this embodiment). This value is a standard value and is fixed for each battery. Those skilled in the art can obtain it through preliminary tests.

[0131] In this way, finally, by only measuring the values of some preset charging times (such as 16, 17, 18, etc.), the complete ICA curve can be obtained, and finally the accurate SOH value can be obtained without waiting for the battery to be fully charged, which expands the application scenario of SOH prediction and enables users to further stay away from range anxiety.

[0132] As Figure 4As shown, in another possible implementation, since the battery includes multiple battery cells, it is also possible to calculate for each battery cell:

[0133] S221. Obtain the first peak and valley of the ICA curve of each battery cell (i.e., corresponding to the maximum voltage value among the voltages corresponding to the peaks and valleys of the Figure 3 scheme);

[0134] S222. For each battery cell, obtain the area under the ICA curve between the first peak and valley and the cut-off voltage, and based on the area under the curve and the charged capacity, obtain the SOH estimated value of this battery cell;

[0135] S223. Take the minimum value of the SOH estimated values of the multiple battery cells as the SOH estimated value of the battery.

[0136] Furthermore, when the battery is in normal use, the above scheme can already accurately estimate the SOH value. However, when the battery is over-discharged, Figure 7 the first peak and valley in

[0137] will be very obvious. If it is ignored at this time, it will have a certain impact on the accuracy of SOH estimation. Therefore, after obtaining the ICA curve, the estimation method further includes:

[0138] Determine the position of the second peak and valley (i.e., the first peak and valley, not the peak and valley with the largest voltage value) in the ICA curve;

[0139] By also calculating the peak and valley that is not the one with the largest voltage value and fine-tuning the SOH value through the position between the two peak and valleys, it is possible to estimate the SOH more accurately under the condition of over-discharged battery.

[0140] Furthermore, the present application also provides a battery SOH estimation system.

[0141] The battery SOH estimation system in the embodiments of the present application mainly includes:

[0142] An ICA curve acquisition module, configured to acquire the ICA curve of the battery during the charging process;

[0143] A battery SOH estimation module, configured to obtain the battery SOH estimated value based on the position of the first peak and valley of the ICA curve.

[0144] In some embodiments, one or more of the ICA curve acquisition module and the battery SOH estimation module can be combined into one module.

[0145] It should be noted that the ordinal numbers such as "first", "second", etc. in the description, claims and the above-mentioned drawings of this application are only used to distinguish similar objects, rather than to describe or represent a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of this application described here can be implemented in an order other than those illustrated or described here.

[0146] Furthermore, this application also provides a control device. In an embodiment of the control device according to this application, the control device includes a processor and a storage device. The storage device can be configured to store a program for executing the battery SOH estimation method in the above method embodiment. The processor can be configured to execute the program in the storage device, and the program includes, but is not limited to, a program for executing the battery SOH estimation method in the above method embodiment. For the sake of convenience of description, only the parts related to the embodiments of this application are shown. For the specific technical details not disclosed, please refer to the method part of the embodiments of this application. The control device can be a control device formed by various electronic devices.

[0147] In the embodiments of this application, the control device can be a control device formed by various electronic devices. In some possible implementation manners, the control device can include multiple storage devices and multiple processors. The program for executing the battery SOH estimation method in the above method embodiment can be divided into multiple sub-programs, and each sub-program can be loaded and run by a processor respectively to execute different steps of the battery SOH estimation method in the above method embodiment. Specifically, each sub-program can be stored in a different storage device respectively, and each processor can be configured to execute the program in one or more storage devices to jointly implement the battery SOH estimation method in the above method embodiment, that is, each processor respectively executes different steps of the battery SOH estimation method in the above method embodiment to jointly implement the battery SOH estimation method in the above method embodiment.

[0148] The above-mentioned multiple processors can be processors deployed on the same device. For example, the above-mentioned control device can be a high-performance device composed of multiple processors, and the above-mentioned multiple processors can be the processors configured on the high-performance device. In addition, the above-mentioned multiple processors can also be processors deployed on different devices. For example, the above-mentioned control device can be a server cluster, and the above-mentioned multiple processors can be the processors on different servers in the server cluster.

[0149] Further, the present application also provides a computer-readable storage medium. In an embodiment of the computer-readable storage medium according to the present application, the computer-readable storage medium may be configured to store a program for executing the battery SOH estimation method in the above method embodiment. This program can be loaded and run by a processor to implement the above battery SOH estimation method. For ease of description, only parts related to the embodiments of the present application are shown. For specific technical details not disclosed, please refer to the method part of the embodiments of the present application. The computer-readable storage medium may be a storage device formed by various electronic devices. Optionally, the computer-readable storage medium in the embodiments of the present application is a non-transitory computer-readable storage medium.

[0150] Furthermore, it should be understood that since the setting of each module is only for explaining the functional units of the device of the present application, the corresponding physical devices of these modules may be the processor itself, or a part of the software in the processor, a part of the hardware, or a part of the combination of software and hardware. Therefore, the number of each module in the figure is only illustrative.

[0151] Those skilled in the art can understand that the various modules in the device can be adaptively split or combined. Such splitting or combination of specific modules will not cause the technical solution to deviate from the principle of the present application. Therefore, the technical solutions after splitting or combination will all fall within the protection scope of the present application.

[0152] For the relevant user personal information that may be involved in the embodiments of the present application, all are strictly in accordance with the requirements of laws and regulations, following the principles of legality, legitimacy, and necessity, and for reasonable purposes based on business scenarios, to process the personal information actively provided by users during the use of products / services or generated due to the use of products / services, as well as the personal information obtained with user authorization.

[0153] The user personal information processed by the present application will vary according to the specific product / service scenario. It is subject to the specific scenario of the user's use of the product / service and may involve the user's account information, device information, driving information, vehicle information, or other relevant information. The applicant will treat the user's personal information and its processing with a high degree of diligence.

[0154] The present application attaches great importance to the security of user personal information and has taken security protection measures that meet industry standards and are reasonable and feasible to protect user information and prevent personal information from being accessed, publicly disclosed, used, modified, damaged, or lost without authorization.

[0155] So far, the technical solutions of the present application have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easily understood by those skilled in the art that the protection scope of the present application is obviously not limited to these specific embodiments. Without departing from the principle of the present application, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the protection scope of the present application.

Claims

1. A method for estimating the state of health (SOH) of a battery, characterized in that, The method includes: During the charging process, obtaining the ICA curve of the battery; Based on the first peak-valley position of the ICA curve, obtaining the battery SOH estimation value.

2. The battery SOH estimation method according to claim 1, characterized in that, The obtaining the battery SOH estimation value based on the peak-valley position of the ICA curve includes: Obtaining the maximum voltage value among the voltages corresponding to the peaks and valleys of the ICA curve; Obtaining the area under the ICA curve of the part of the ICA curve where the voltage value is greater than or equal to the maximum voltage value; Based on the area under the curve and the charged capacity, obtaining the battery SOH estimation value.

3. The battery SOH estimation method according to claim 1, wherein The battery includes multiple battery cells. The obtaining the battery SOH estimation value based on the first peak-valley position of the ICA curve includes: Obtaining the first peak and valley of the ICA curve of each battery cell; For each battery cell, obtaining the area under the ICA curve from the first peak and valley to the cut-off voltage, and based on the area under the curve and the charged capacity, obtaining the SOH estimation value of this battery cell; Taking the minimum value of the SOH estimation values of the multiple battery cells as the SOH estimation value of the battery.

4. The battery SOH estimation method according to any one of claims 1 to 3, characterized in that, After obtaining the ICA curve of the battery, it further includes: Determining the position of the second peak and valley in the ICA curve; Based on the position of the first peak and valley, the position of the second peak and valley, and the charged capacity from the second peak and valley to the cut-off voltage, obtaining the SOH estimation value of the battery.

5. The battery SOH estimation method according to any one of claims 1 to 3, characterized in that The obtaining the ICA curve of the battery includes: Obtaining the ICA data of the battery; In response to the supplement instruction, supplementing the ICA data to obtain the supplemented ICA data; Obtaining the ICA curve according to the supplemented ICA data.

6. The battery SOH estimation method according to claim 5, wherein The supplementing the ICA data includes: Supplementing the ICA data through a trained ICA data acquisition model.

7. The battery SOH estimation method according to claim 5, characterized in that The ICA data includes a sequence of the first number of data within the first voltage range. When the first number is less than the preset number, triggering the supplement instruction.

8. The battery SOH estimation method according to claim 6, wherein, The first data in the ICA data is the dQ / dV value corresponding to the lower limit voltage of the first voltage range. The ICA data is the first number of dQ / dV values, and the first number is greater than or equal to the second number. The supplementing the ICA data through a trained ICA data acquisition model includes: Step A, obtaining the previous second number of data in the ICA data as the current data; Step B, inputting the current data into the ICA data acquisition model to obtain the next data of the current data; Step C, taking the next data as the last data of the second number of data, and re-obtaining the second number of data as the current data; Repeating Step B and Step C until obtaining the dQ / dV value corresponding to the upper limit voltage of the first voltage range.

9. The battery SOH estimation method according to claim 7, wherein Obtaining the historical dQ / dV data sequence corresponding to the third voltage range obtained according to the actual sampling frequency during the battery charging process; Based on the historical dQ / dV data sequence, according to the first voltage range and the voltage sampling step size, obtaining the sample dQ / dV data sequence through interpolation operation; Training the ICA data acquisition model based on the sample dQ / dV data sequence; Among them, the lower limit voltage of the third voltage range is less than or equal to the lower limit voltage of the first voltage range, and the upper limit voltage of the third voltage range is greater than or equal to the upper limit voltage of the first voltage range.

10. The battery SOH estimation method according to claim 1, characterized in that, The method further includes: Constructing the ICA data acquisition model based on the improved Transformer network; among them, The improved Transformer network includes a decoding layer constructed based on a fully connected layer.

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