A battery health estimation method, apparatus and system

Abstract

The application discloses a battery health estimation method, device and system. The method uses a plurality of characteristic dimension curve weighting algorithms in combination with battery array management system (BAMS) core capacity correction to estimate the state of health (SoH) of a battery in real time online. The method improves the estimation accuracy of the state of health (SoH) of a power station, and the method estimates the state of health (SoH) of each single battery. The implementation of the application is beneficial to the sorting, selection and reuse of retired batteries, and saves the labor cost of testing each battery.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of energy storage battery, and particularly relates to a battery health estimation method, device and system. BACKGROUND

[0002] At present, people pay more and more attention to the protection of the environment and the effective and reasonable use of energy. Therefore, high efficiency, energy saving and environmental protection have become the development trend. Energy storage power stations use the electric energy stored in the battery as energy. Accurate measurement of the health state of the battery is particularly important for the implementation of the entire control strategy of the energy storage power station.

[0003] The SoH (State of Health) of the battery, that is, the health state of the battery, is a parameter reflecting the overall performance of the battery and the ability to release electric energy under certain conditions, that is, the ratio of the total discharge capacity of the battery under certain conditions to the available capacity of the new battery. With the use of the battery, especially the degree of cycle times, many non-recoverable physical and chemical factors will cause the battery to age, so that the health degree of the battery decreases. For a new battery, its SoH is often greater than or equal to 100%, and with the aging of the battery, the SoH gradually decreases.

[0004] The prior art mostly uses a single feature vector to estimate the SoH of the battery, resulting in a large error in the estimation of the SoH of the battery. The method of manual nuclear capacity estimation only estimates the SoH of one power station, and each energy storage power station is composed of tens of thousands of batteries. Due to the different positions of the battery packs on the battery rack and the different positions of the air outlets of the air conditioner and the fan, the service life of each battery pack is greatly different. And due to the different positions of the batteries in each battery pack, the health life of the batteries is also different. Such rough calculation of the SoH of the battery is not conducive to the operation of the power station, and is also not conducive to the sorting, selection and reuse of retired batteries.

[0005] It should be noted that the statements herein only provide background information related to the present application and do not necessarily constitute the prior art. SUMMARY

[0006] In view of the above problems, the present application proposes a battery health estimation method, device and system which overcomes the above problems or at least partially solves the above problems.

[0007] The embodiments of the present application adopt the following technical solutions:

[0008] In a first aspect, the embodiments of the present application provide a battery health estimation method for a battery PACK in a high-voltage energy storage system, the battery PACK comprising a plurality of batteries, wherein the method comprises: obtaining a plurality of characteristic data of the batteries in a preset scenario, the plurality of characteristic data comprising at least battery monomer voltage, battery temperature, battery internal resistance, battery cumulative charge and discharge capacity, and battery capacity increment; fitting the characteristic data according to different characteristic dimensions to obtain a plurality of battery health estimation curves, wherein each characteristic dimension corresponds to a battery health estimation curve; adjusting the fitting processing parameters of the battery health estimation curves to obtain a weight value parameter of the battery health estimation curve corresponding to each characteristic dimension; and optimizing and merging the plurality of battery health estimation curves according to the weight value parameter to obtain a battery health estimation merged curve.

[0009] Optionally, the optimizing and merging the plurality of battery health estimation curves according to the weight value parameter to obtain a battery health estimation merged curve comprises: offsetting estimation errors according to the relationship between the battery health estimation curves, and calculating according to the following formula:

[0010]

[0011] wherein α, β, γ is a weight value, and SoH1, SoH2, SoH3, SOH4 are battery health estimation curves of different dimensions; and SoH` is a battery health estimation merged curve.

[0012] Optionally, the optimizing and merging the plurality of battery health estimation curves according to the weight value parameter to obtain a battery health estimation merged curve further comprises: the high-voltage energy storage system performing capacity checking on the batteries to obtain a battery health estimation optimized curve; and substituting a plurality of characteristic dimension data collected during battery operation into the battery health estimation optimized curve, updating the battery health estimation optimized curve, and thus obtaining the health status of the batteries.

[0013] Optionally, the fitting the characteristic data according to different characteristic dimensions to obtain a plurality of battery health estimation curves comprises: fitting battery internal resistance characteristic data, battery cumulative charge and discharge capacity characteristic data, battery capacity increment characteristic data, and battery voltage characteristic data to obtain a battery internal resistance health estimation curve, a battery charge and discharge capacity health estimation curve, a battery capacity increment health estimation curve, and a battery voltage health estimation curve.

[0014] Optionally, the fitting process of the feature data includes: using at least one algorithm to fit the data of each feature dimension to obtain the battery health estimation curve corresponding to each feature dimension, wherein the algorithm includes a neural network algorithm, a least squares algorithm, and a simulated annealing algorithm.

[0015] Optionally, adjusting the fitting parameters of the battery health estimation curve to obtain the weight parameters of the battery health estimation curve corresponding to each feature dimension includes: adjusting the parameters of the fitting algorithm so that the expected battery health value is on the battery health estimation curve or evenly distributed on both sides of it; obtaining the average absolute error and the maximum absolute error between the estimated battery health value and the expected battery health value, and obtaining the absolute error of the battery health curve based on the average absolute error and the maximum absolute error; and assigning weight values ​​according to the magnitude of the absolute error of each battery health estimation curve so that the absolute error of each battery health curve has an equal impact on the merged battery health estimation curve.

[0016] Optionally, the absolute error of the battery health curve is obtained based on the average absolute error and the maximum absolute error, using the following formula:

[0017] E n =E max ×m+E ave ×(1-m)

[0018] Among them, E n For the battery health curve error, n ranges from 1 to 4; E max To estimate the maximum error between the battery health value and the expected value; E ave The absolute average error between the estimated and expected values ​​of battery health over the entire battery lifecycle; m ranges from 0 to 0.2.

[0019] Optionally, the weighting of each battery health estimation curve based on its absolute error, ensuring that the absolute error of each battery health curve has an equal impact on the merged battery health estimation curve, is achieved using the following formula:

[0020] 1 / E1:1 / E2:1 / E3:1 / E4 = a:b:c:d

[0021]

[0022]

[0023] Where E1-E4 represent the absolute error of the battery health curve; α, β, γ is the weighting value, which ranges from 0.2 to 0.3.

[0024] In a second aspect, the embodiments of the present application also provide a battery health estimation device, wherein the device comprises: a feature data acquisition unit configured to acquire a plurality of feature data of the battery in a preset scenario, the plurality of feature data comprising at least battery monomer voltage, battery temperature, battery internal resistance, battery cumulative charge and discharge capacity, and battery capacity increment; a battery health estimation curve acquisition unit configured to perform fitting processing on the feature data according to different feature dimensions to acquire a plurality of battery health estimation curves, wherein one feature dimension corresponds to one battery health estimation curve; a weight value parameter acquisition unit configured to adjust the fitting processing parameter of the battery health estimation curve to acquire a weight value parameter of the battery health estimation curve corresponding to each feature dimension; and a battery health estimation combined curve acquisition unit configured to optimize and combine the plurality of battery health estimation curves according to the weight value parameter to obtain a battery health estimation combined curve.

[0025] In a third aspect, the embodiments of the present application also provide a battery health estimation system, wherein the system comprises an energy management system EMS, a battery management system general control unit BAMS, a power storage converter PCS, a battery management system master control unit BCMU, a battery management system BMU, and a battery energy storage system; the battery energy storage system comprises a PACK composed of a single cell, a battery cluster composed of a plurality of battery PACKs, and a battery stack composed of a plurality of battery clusters; the battery management system general control unit BAMS, the energy management system EMS, the power storage converter PCS, and the battery management system master control unit BCMU perform data interaction through corresponding communication buses; the system further comprises a processor and a memory arranged to store computer executable instructions, which, when executed, cause the processor to perform the method of any of the first aspect.

[0026] The above at least one technical solution adopted by the embodiments of the present application can achieve the following beneficial effects:

[0027] The present application utilizes a plurality of feature dimension curve weighting algorithms combined with battery array management system BAMS core capacity correction to estimate the health state SoH of the battery in real time online. The method of the present application improves the estimation accuracy of the health state SoH of the power station, and at the same time, estimates the health state SoH of each monomer battery. The implementation of the present application is beneficial to the sorting, selection, and reuse of retired batteries, saving the labor cost of testing each battery.

[0028] From the above, the technical solution of the present application, the above description is only a summary of the technical solution of the present application, in order to be able to more clearly understand the technical means of the present application, which can be implemented according to the content of the specification, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the following specific embodiments of the present application are described. Attached Figure Description

[0029] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of this application. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0030] Figure 1 This is a flowchart of the battery health estimation method according to an embodiment of this application;

[0031] Figure 2 This is a schematic diagram of a battery health estimation device according to an embodiment of this application;

[0032] Figure 3 This is a framework diagram of a high-voltage energy storage system according to an embodiment of this application;

[0033] Figure 4 This is a flowchart illustrating the update process of SoH1 and SoH2 in embodiments of this application.

[0034] Figure 5 This is a flowchart illustrating the SoH3 update process in an embodiment of this application.

[0035] Figure 6 This is a flowchart illustrating the SoH4 update process in an embodiment of this application.

[0036] Figure 7 This is a flowchart illustrating the SoH modification process in an embodiment of this application.

[0037] Figure 8 This is a schematic diagram of the battery health estimation device in the embodiments of this application. Detailed Implementation

[0038] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0039] The concept of the present application is that there is less research on battery health life in the prior art, and generally the energy storage power station project uses a single feature vector to estimate the health state (State of Charge, Health, SoH) of the entire battery cluster, which leads to a large deviation between the estimated value and the expected value of the SoH of the battery. Some energy storage manufacturers rely on manual intervention to fully charge and discharge the power station to estimate the SoH of the power station, but the SoH of each cluster or each battery cell cannot be accurately estimated. The present application designs a battery health estimation method, which uses multiple feature dimension data in the battery operation process to fit the SoH curve related to the feature dimension, weights according to the characteristics of multiple feature dimension related curves, and combines the real-time online estimation of the SoH of the power station by the BAMS nuclear capacity correction method. It has high estimation accuracy, can not only estimate the cluster SoH, but also estimate the SoH of a single battery cell; it is beneficial to the sorting, selection and reuse of the Pack when the battery is retired.

[0040] The technical solutions provided by the embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0041] The present application provides a battery health estimation method, as shown in Figure 1 The method provided in the embodiments of the present application includes at least the following steps S110 to S140:

[0042] Step S110, obtaining a plurality of feature data of the battery in a preset scenario, wherein the plurality of feature data at least includes battery monomer voltage, battery temperature, battery internal resistance, battery cumulative charge and discharge capacity, and battery capacity increment.

[0043] As shown in Figure 3 The composition of the high-voltage energy storage system is relatively complex, generally tens of thousands of battery cells are needed to form a set of high-voltage energy storage system. Usually one or more monomer battery cells are connected in parallel to form a module, multiple modules are connected in series to form a Pack, multiple Packs are connected in series to form a battery cluster, multiple battery clusters form a phase, three phases and a power conversion system (PCS) form a set of high-voltage energy storage system, and a power storage power station is composed of multiple sets of high-voltage energy storage systems.

[0044] Considering the complexity of the high-voltage energy storage system architecture, the entire system's communication network adopts a layered architecture. The Battery Monitor Unit (BMU) collects information such as cell voltage, battery temperature, battery internal resistance, and battery balancing data for each battery pack. The BMU uploads this information to the Battery Control Management Unit (BCMU) via a CAN / RS485 bus. The BCMU then uploads battery information to the Battery Array Management System (BAMS). The BAMS communicates with the PCS and the Energy Management System (EMS). Each battery cluster consists of multiple BMUs and one BCMU unit, and each energy storage system comprises one or more BAMS, PCS, and EMS units.

[0045] This application verifies the charge-discharge lifespan of multiple battery cells through multiple charge-discharge cycles to obtain characteristic data of battery cells under various scenarios. For example, data includes battery cell voltage, battery temperature, battery internal resistance, cumulative charge-discharge capacity, and battery capacity increment. The acquisition methods include, but are not limited to, conducting 9 sets of experiments under scenarios where the room temperature is adjusted to 20℃, 25℃, and 30℃ (the operating environment temperature of the energy storage power station), and the charge-discharge currents are 0.5C, 1C, and 2C respectively. Each set of experiments uses 3-5 batteries, and each set undergoes 3001 charge-discharge cycles. At the 30N+1th experiment (where N is [0 100]), the battery cell voltage, battery temperature, battery internal resistance, cumulative charge-discharge capacity, and capacity increment data are recorded for the entire charge-discharge cycle.

[0046] Step S120: Fit the feature data according to different feature dimensions to obtain multiple battery health estimation curves, wherein each feature dimension corresponds to one battery health estimation curve.

[0047] The feature data obtained in step S110 is processed. For each feature dimension, one or more algorithms (neural network algorithm, least squares method, simulated annealing algorithm) are used to fuse the feature data from different groups, and then a feature dimension is fitted with SoH. n The relationship curve, where n represents different feature dimensions.

[0048] Step S130: Adjust the fitting processing parameters of the battery health estimation curve and obtain the weight value parameters of the battery health estimation curve corresponding to each feature dimension.

[0049] By adjusting the parameters in the corresponding fitting algorithm, the expected value of the data is made to lie on the fitting curve or be evenly distributed on both sides of it, minimizing the distance between the expected value and the straight line of the fitting curve. Finally, the average absolute error and the maximum absolute error between the estimated value and the expected value are obtained as the SoH. n The absolute value error. Weights are assigned based on the magnitude of the absolute value error to obtain each SoH. n The weights are assigned. Among them, SoH n The higher the accuracy of the estimation, the greater the weight, and SoH n Each battery health estimation curve has an equal impact on the overall estimation error.

[0050] Step S140: Based on the weight value parameters, optimize and merge the multiple battery health estimation curves to obtain the merged battery health estimation curve.

[0051] By employing multiple feature dimensions and jointly estimating the battery's SoH` in real-time online, based on the acquired feature dimension information such as battery voltage, battery internal resistance, cumulative charge / discharge capacity, ΔC / ΔV, and battery voltage, SoH` is calculated using the formula... The calculations yielded the following: SoH1 estimates the battery's state of health using its internal resistance; SoH2 estimates the battery's state of health using the current system's cumulative charge / discharge capacity; SoH3 estimates the battery's state of health by calculating the battery's ΔC / ΔV; SoH4 estimates the battery's state of health by monitoring the battery's voltage plateau; and SoH` is the combined curve of the battery health estimates. α, β, γ is the weight of SoH1, SoH2, SoH3, and SoH4; SoH1, SoH2, SoH3, SoH4, as well as α, β, γ is obtained by curve fitting of the algorithm after verifying the cycle life.

[0052] In some instances of this application, the step of optimizing and merging the multiple battery health estimation curves according to the weight value parameters to obtain a merged battery health estimation curve includes: offsetting estimation errors based on the relationship between the battery health estimation curves, and calculating using the following formula:

[0053]

[0054]

[0055] Among them, α, β, γ is the weight value; SoH1, SoH2, SoH3, and SoH4 are battery health estimation curves of different dimensions; SoH` is the merged battery health estimation curve. Since each dimension of the battery health estimation curve has an error, typically (-5, +5), this application utilizes the error relationship to merge an even number of battery health estimation curves. Therefore, some errors can cancel each other out. By using multiple suitable dimensions, the error of the merged battery health curve is greatly reduced.

[0056] In some instances of this application, the process of optimizing and merging the multiple battery health estimation curves according to the weighted parameters to obtain a merged battery health estimation curve further includes: the high-voltage energy storage system performing capacity verification on the battery to obtain an optimized battery health estimation curve. The optimized battery health estimation curve (SoH) is obtained using the following formula:

[0057] SoH=SoH`+Δ (1-3)

[0058] Where △ is the correction value of SoH after the battery array management system (BAMS) is approved; SoH` is the merged curve of battery health estimation.

[0059] Specifically, after the high-voltage energy storage system performs capacity verification on the battery, it will obtain a rough battery health status result with an error of about 10% to 15%. Using this rough battery health status result, the SoH` (battery health estimation merged curve) is corrected again in both positive and negative directions to obtain the battery health estimation optimized curve SoH.

[0060] The battery health status is obtained by substituting multiple feature dimension data collected during battery operation into the battery health estimation optimization curve and updating the battery health estimation optimization curve.

[0061] Specifically, such as Figure 4 As shown, during normal operation of the high-voltage energy storage system, the battery monitoring unit (BMU) collects data on individual cell voltage, temperature, battery internal resistance, and total voltage every 100ms and uploads it to the battery control and management unit (BCMU). The BCMU collects current data every 10ms.

[0062] The Battery Control and Management Unit (BCMU) inputs the collected data into the corresponding SoH1 estimation algorithm to update SoH1. By judging the current value, if it is greater than 0.3A, the cumulative charge and discharge capacity of the power station is updated, and then SoH2 is updated.

[0063] like Figure 5 As shown, if the power station is in a charging state, when the individual cell voltage value is in [V u1 V u2 When the range is met, and the charging time is greater than T.u At this time, the value of AC / AV is updated; and the SoH3 algorithm is substituted to update it.

[0064] If the power station is in the discharging state, when the single voltage value is in the range of [V d1 V d2 ], and the discharging duration is greater than T d , the value of AC / AV is updated, the SoH3 algorithm is substituted to update it; if the first two conditions are not met, the SoH3 is not updated.

[0065] As shown in Figure 6 , if the power station is in the charging state, when the current is less than I C , and the charging duration satisfies T c , the single voltage change is less than V D1 , the SoH4 algorithm is substituted to update it.

[0066] If the power station is in the discharging state, the current is greater than I D , and the charging duration satisfies T D , the single voltage change is less than V D2 , the SoH4 algorithm is substituted to update it; if the first two conditions are not met, the SoH4 is not updated.

[0067] As shown in Figure 7 , if the energy storage power station is capacity-tested, the battery array management system BAMS transmits the capacity-testing result to the battery control management unit BCMU, the battery control management unit BCMU updates the correction value Δ through the capacity-testing result, and the SoH value is updated according to the result of the above updating mode.

[0068] Wherein, V u1 is the minimum single voltage value in the charging state; V u2 is the maximum single voltage value in the charging state; T u is the charging duration; V d1 is the minimum single voltage value in the discharging state; V d2 is the maximum single voltage value in the discharging state; T d is the discharging duration; I C is the charging current, which is negative; T C is the charging duration; I D is the discharging current, which is positive; T D is the discharging duration; V D1 is the battery voltage increment in the charging state; V D2 is the battery voltage increment in the discharging state.

[0069] In some examples of the present application, the fitting processing of the feature data according to different feature dimensions to obtain a plurality of battery health estimation curves comprises: fitting processing of battery internal resistance feature data, battery cumulative charge and discharge capacity feature data, battery capacity increment feature data, and battery voltage feature data to obtain a battery internal resistance health estimation curve, a battery charge and discharge capacity health estimation curve, a battery capacity increment health estimation curve, and a battery voltage health estimation curve.

[0070] Specifically, the present application estimates the health status of the battery in real time online by adopting a plurality of feature dimensions in combination, and the battery operation information required to be obtained includes: battery voltage, battery temperature, battery internal resistance, cumulative charge and discharge capacity, △C / △V, battery voltage, etc., and then the obtained battery operation information is processed to obtain a battery internal resistance health estimation curve SoH1, a battery charge and discharge capacity health estimation curve SoH2, a battery capacity increment health estimation curve SoH3, and a battery voltage health estimation curve SoH4.

[0071] In some examples of the present application, the fitting processing of the feature data comprises: using at least one algorithm to perform fitting processing on each feature dimension data to obtain a battery health estimation curve corresponding to each feature dimension, wherein the algorithm includes a neural network algorithm, a least square algorithm, and a simulated annealing algorithm.

[0072] Specifically, the obtained feature dimension data is processed by using a convolutional neural network, or a least square algorithm, or a simulated annealing algorithm, and the obtained feature data of different dimensions is fitted into different feature curves for subsequent processing.

[0073] In some examples of the present application, the adjustment of the fitting processing parameters of the battery health estimation curve to obtain a weight value parameter of the battery health estimation curve corresponding to each feature dimension comprises: adjusting the parameters of the fitting processing algorithm so that the battery health expectation value is uniformly distributed on the battery health estimation curve or on both sides thereof; obtaining an absolute value average error and an absolute value maximum error between the battery health estimation value and the battery health expectation value, and obtaining an absolute value error of the battery health curve according to the absolute value average error and the absolute value maximum error; and according to the absolute value error of each battery health estimation curve, assigning a weight value so that the absolute value error of each battery health curve has an equal influence on the battery health estimation combined curve.

[0074] Specifically, the data of the single cell voltage, battery temperature, battery internal resistance, cumulative charge and discharge capacity, capacity increment and other characteristic dimensions during the entire charge and discharge cycle are recorded. The obtained characteristic dimension data is processed, one or more algorithms (neural network, least square method, simulated annealing algorithm) are used for each characteristic dimension data to fuse the data, and then the fused data is processed and fitted into a characteristic dimension-SoH n relationship curve. By adjusting the parameters in the corresponding algorithm, the expected value is uniformly distributed on the fitting curve or on both sides thereof, so as to make the expected value as close as possible to the fitting curve. Finally, the absolute value average error and the absolute value maximum error between the estimated value and the expected value are obtained as the absolute value error of SoH n , and the calculation formula is as follows:

[0075] E n = E max × m + E ave × (1-m) (1-4)

[0076] Wherein, E n is the error of SoH n ; n takes a value in the range of 1-4; E max is the maximum error between the estimated SoH n and the expected value, which aims to consider the influence of error extreme value on the estimation accuracy of SoH n ; E ave is the absolute value average error between the estimated value and the expected value of SoH n in the entire battery life cycle; and m takes a value in the range of 0-0.2.

[0077] Thus, the absolute value errors E1-E4 of SoH1-SoH4 in the entire life cycle can be obtained, and the weights are allocated according to the absolute value errors of SoH1-SoH4. According to formulas (1-2), (1-5)-(1-6), the allocation weights α, β, γ (taking a value in the range of 0.2-0.3) of each SoH n are obtained. According to formulas (1-5) and (1-6), the higher the estimation accuracy of SoH n is, the greater the weight is. According to formulas (1-1)-(1-6), the estimation error influence of SoH1-SoH4 on SoH` is equal.

[0078] 1 / E1:1 / E2:1 / E3:1 / E4=a:b:c:d (1-5)

[0079]

[0080]

[0081] By assigning weights to SoH1-SoH4, the errors of SoH1-SoH4 have equal influence on SoH` (battery health estimation combined curve). The errors of SoH (battery health estimation optimized curve) can be obtained by formulas (1-8)-(1-12).

[0082]

[0083]

[0084]

[0085] E(SoH) = E(SoH` +△) (1-11)

[0086] E(SoH) = E(SoH`) (1-12)

[0087] For the technical effects of the above battery health estimation method, the following methods can be used to verify:

[0088] It is known that the estimated values of SoH1-SoH4 are larger or smaller than the expected values, i.e. the errors are positive or negative. It is assumed that the probability of positive or negative error is equal. The battery health life is calculated using the formula (1-3) method, and according to the estimation error of SoH1-SoH4, E(SoH) has three cases, as shown in formulas (1-13)-(1-15). The three possible cases of the estimation error of SoH can be obtained by formulas (1-16)-(1-18), as follows:

[0089]

[0090] E(SoH) mid = |2αE1| (1-14)

[0091] E(SoH) min = 0 (1-15)

[0092]

[0093]

[0094]

[0095] If only one of SoH1, SoH2, SoH3, SoH4 is used to estimate the battery health life, the expected estimation error is shown in formulas (1-19)-(1-22); if formula (1-3) is used to estimate the battery health life, the mathematical expectation value is derived according to formulas (1-13)-(1-18) - as shown in formula (1-19), and formula (1-20) is derived from formula (1-7). α, β, γ is in the range of 0.2-0.3. If SoH1 is used to estimate the battery health life, assuming that a = 0.3, ξ SoH = 1.5 * 0.3 * |E1| = 0.45 * |E1|, the mathematical expectation of SoH1 is as shown in formula (1-19). The error of the estimated value of the battery health life estimated by using formula (1-3) is reduced by 55%. SoH2, SoH3, SoH4 are derived in the same way as SoH1.

[0096] ξ SoH1 = |E1| (1-19)

[0097] ξ SoH2 = |E2| (1-20)

[0098] ξ SoH3 = |E3| (1-21)

[0099] ξ SoH4 = |E4| (1-22)

[0100]

[0101]

[0102] In summary, the estimation error of the SoH of the battery estimated by the method of jointly assigning weights by the four methods is reduced by more than 55%, which shows that the advantage of the method is much greater than that of using one method to estimate SoH.

[0103] The embodiment of the application further provides a battery health estimation device 200, as shown in Figure 2 The structure schematic diagram in the embodiment of the application is provided, and the device 200 at least includes: a feature data acquisition unit 210, a battery health estimation curve acquisition unit 220, a weight value parameter acquisition unit 230, and a battery health estimation combined curve acquisition unit 240, wherein:

[0104] In an embodiment of the application, the feature data acquisition unit 210 is specifically configured to: acquire a plurality of feature data of the battery in a preset scene, and the plurality of feature data at least includes battery monomer voltage, battery temperature, battery internal resistance, battery cumulative charge and discharge capacity, and battery capacity increment.

[0105] The application obtains characteristic data of battery monomers in multiple scenes through charging and discharging life cycle experiments on multiple groups of battery monomers. For example, battery monomer voltage, battery temperature, battery internal resistance, battery cumulative charging and discharging capacity, and battery capacity increment data. The acquisition methods include but are not limited to adjusting the room temperature to 20℃, 25℃, and 30℃ (operation environment temperature of an energy storage power station) respectively, adjusting the charging and discharging current to 0.5C, 1C, and 2C respectively, performing 9 groups of experiments, using 3-5 batteries for each group of experiments, and performing 3001 charging and discharging cycles for each group; recording the monomer voltage, battery temperature, battery internal resistance, cumulative charging and discharging capacity, and capacity increment data during the entire charging and discharging cycle at the 30N+1th experiment (N is a value in [0 100]).

[0106] In an embodiment of the application, the battery health estimation curve acquisition unit 220 is specifically configured to: perform fitting processing on the characteristic data according to different characteristic dimensions, and obtain multiple battery health estimation curves, wherein one characteristic dimension corresponds to one battery health estimation curve.

[0107] The characteristic data obtained by the characteristic data acquisition unit is processed, one or more algorithms (neural network algorithm, least square method, simulated annealing algorithm) are used for each characteristic dimension, different groups of characteristic data are fused, and then a relationship curve of a characteristic quantity and SoH n is fitted.

[0108] In an embodiment of the application, the weight value parameter acquisition unit 230 is specifically configured to: adjust the fitting processing parameters of the battery health estimation curve, and obtain the weight value parameters of the battery health estimation curve corresponding to each characteristic dimension.

[0109] By adjusting the parameters in the corresponding fitting algorithm, the expected value of the data is uniformly distributed on the fitting curve or on both sides thereof, and the expected value distance from the fitting curve is as small as possible. Finally, the absolute value average error and the absolute value maximum error between the estimated value and the expected value are obtained as the absolute value error of SoH n . According to the absolute value error size, the distribution weight of each SoH n is obtained. The higher the accuracy of SoH n estimation is, the greater the weight is, and the estimation error of each battery health estimation curve on the whole is equal. n

[0110] In an embodiment of the application, the battery health estimation curve acquisition unit 240 is specifically configured to: according to the weight value parameters, the multiple battery health estimation curves are optimized and combined to obtain a battery health estimation combined curve.

[0111] ​By adopting multiple feature dimensions, the SoH of the battery is estimated in real time and online. According to the obtained feature dimension information: battery voltage, battery temperature, battery internal resistance, cumulative charge and discharge capacity, △C / △V, voltage platform, etc. SoH is calculated by formula . Wherein, SoH1 is the battery health state estimated by the battery internal resistance; SoH2 is the battery health state estimated by the cumulative charge and discharge capacity of the current system; SoH3 is the battery health state estimated by calculating the battery's △C / △V; SOH4 is the battery health state estimated by monitoring the battery's voltage platform. α, β, γ are the weights of SoH1, SoH2, SoH3, SOH4. Wherein, SoH1, SoH2, SoH3, SOH4 and α, β, γ are obtained after the algorithm curve fitting after the cycle life verification.

[0112] It can be understood that the above battery health estimation device can realize each step of the battery health estimation method provided in the foregoing embodiments, and the related explanations about the battery health estimation method are all applicable to the battery health estimation device, which will not be repeated here.

[0113] The application also provides a battery health estimation system, which comprises an energy management system EMS, a battery management system general control unit BAMS, a power storage converter PCS, a battery management system master control unit BCMU, a battery management system BMU and a battery energy storage system. The battery energy storage system comprises a PACK composed of a single cell, a battery cluster composed of multiple battery PACKs, and a battery stack composed of multiple battery clusters. The battery management system general control unit BAMS, the energy management system EMS, the power storage converter PCS and the battery management system master control unit BCMU exchange data through corresponding communication buses. The system further comprises a processor and a memory arranged to store computer executable instructions, which, when executed, cause the processor to execute the battery health estimation method.

[0114] Figure 8 is a structural schematic diagram of an electronic device of an embodiment of the application. Please refer to Figure 8 At the hardware level, the electronic device comprises a processor, and optionally further comprises an internal bus, a network interface and a memory. The memory may contain a memory, such as a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk memory. Of course, the electronic device may also include other hardware required by the business.

[0115] The processor, the network interface and the memory can be connected with each other through an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 8 Only one bidirectional arrow is used to represent the bus, but it does not mean that there is only one bus or only one type of bus.

[0116] The memory is used to store programs. Specifically, the program can include program code including computer operation instructions. The memory can include an internal memory and a non-volatile memory, and provide instructions and data for the processor.

[0117] The processor reads the corresponding computer program from the non-volatile memory into the internal memory and then runs, and forms the battery health estimation device at a logical level. The processor executes the program stored in the memory, and is specifically used for executing the following operations:

[0118] Obtain a plurality of characteristic data of the battery in a preset scene, the plurality of characteristic data at least including battery monomer voltage, battery temperature, battery internal resistance, battery cumulative charge and discharge capacity, and battery capacity increment; fitting processing is performed on the characteristic data according to different characteristic dimensions, a plurality of battery health estimation curves are obtained, wherein each characteristic dimension corresponds to a battery health estimation curve; the fitting processing parameter of the battery health estimation curve is adjusted to obtain a weight value parameter of the battery health estimation curve corresponding to each characteristic dimension; the plurality of battery health estimation curves are optimized and combined according to the weight value parameter to obtain a battery health estimation combined curve.

[0119] The above as described in the present application Figure 1The method performed by the battery health estimation apparatus disclosed in the embodiments can be applied in a processor or implemented by the processor. The processor can be an integrated circuit chip with processing capability. In implementation process, each step of the above method can be completed by hardware integrated logic circuit or software form of instruction in the processor. The processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; or can be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware component. Each method, step and logic block diagram disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor. The steps of the method disclosed in combination with the embodiments of the present application can be directly embodied as a hardware code processor to execute, or be executed by a combination of hardware and software modules in the code processor. The software module can be located in a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium in the memory is read by the processor, and the hardware thereof is combined to complete the steps of the above method.

[0120] The electronic device can further execute Figure 1 the method performed by the battery health estimation apparatus, and implement the functions of the battery health estimation apparatus in Figure 1 the embodiments, which will not be described herein again.

[0121] The embodiments of the present application further provide a computer readable storage medium storing one or more programs, the one or more programs including instructions, which when executed by an electronic device including a plurality of applications, can cause the electronic device to execute Figure 1 the method performed by the battery health estimation apparatus in the embodiments, and specifically execute

[0122] Obtain a plurality of characteristic data of the battery under a preset scene, the plurality of characteristic data at least including battery monomer voltage, battery temperature, battery internal resistance, battery cumulative charge and discharge capacity, battery capacity increment; fitting processing is carried out to the characteristic data according to different characteristic dimensions, a plurality of battery health estimation curves are obtained, wherein each characteristic dimension corresponds to a battery health estimation curve; the fitting processing parameter of the battery health estimation curve is adjusted, and the weight value parameter corresponding to the battery health estimation curve of each characteristic dimension is obtained; according to the weight value parameter, the plurality of battery health estimation curves are optimized and combined to obtain a battery health estimation combined curve.

[0123] Those skilled in the art will appreciate that embodiments of the application can be supplied as methods, systems, or computer program products. Accordingly, the application can be embodied in the form of complete hardware embodiments, complete software embodiments, or embodiments combining software and hardware aspects. Furthermore, the application can be embodied in the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage, etc.) having computer usable program code embodied thereon.

[0124] The present application is described in reference to flowcharts and / or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, as well as combinations of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer, a special purpose computer, an embedded processor, or other programmable data processing apparatus to produce a machine, so that the instructions, which are executed via the processor of the computer or other programmable data processing apparatus, generate a means for implementing the functions specified in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 an apparatus with the means for performing the functions specified in one or more flows and / or blocks.

[0125] These computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing apparatus to work in a specific manner, so that the instructions stored in the computer readable memory produce a manufactured product including instruction apparatus, which implements the functions specified in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks ​ an apparatus with the means for performing the functions specified in one or more flows and / or blocks.

[0126] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus, so that a series of operation steps are performed on the computer or other programmable data processing apparatus to produce a computer implemented process, so that the instructions executed on the computer or other programmable data processing apparatus provide a means for implementing the functions specified in the flowcharts and / or block diagrams. ​ one or more flows and / or blocks​ the steps recited in any combination of one or more blocks.

[0127] In one typical arrangement, the computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0128] The memory can include non-persistent memory and / or volatile memory, such as random access memory (RAM) and / or cache memory, non-volatile memory, such as read-only memory (ROM), EPROM, and / or flash memory, etc. The memory is an example of computer readable media.

[0129] Computer readable media includes permanent and non-permanent, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile discs (DVDs) or other optical storage, magnetic cassettes, magnetic tapes, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible to a computing device. According to the definition herein, computer readable media does not include transitory media, such as modulated data signals and carrier waves.

[0130] It should also be noted that the terms "comprising," "including," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements recited, but can also include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without limitation, an element preceded by "comprises... a" does not, without more constraints, foreclose the existence of additional identical elements in the process, method, article, or apparatus that comprises the recited element.

[0131] Those skilled in the art will appreciate that embodiments of the present application can be devised for a method, a system, or a computer program product. Accordingly, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application can take the form of a computer program product on one or more computer readable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage devices, etc.) embodying computer readable program code thereon.

[0132] The above merely provides an example of the present application, but is not intended to limit the present application. The present application can have various modifications and changes for those skilled in the art. Any modification, equivalent replacement, improvement, etc. within the spirit and principle of the present application shall be included in the scope of claims of the present application.

Claims

1. A battery health estimation method for a battery pack in a high-voltage energy storage system, wherein the battery pack comprises multiple batteries, The method includes: Acquire multiple characteristic data of the battery under a preset scenario, wherein the multiple characteristic data include at least the battery cell voltage, battery temperature, battery internal resistance, battery cumulative charge and discharge capacity, and battery capacity increment; By performing charge-discharge life cycle verification on multiple groups of battery cells, the characteristic data of battery cells under multiple scenarios, including battery cell voltage, battery temperature, battery internal resistance, battery cumulative charge-discharge capacity, and battery capacity increment, are obtained. The feature data is fitted according to different feature dimensions to obtain multiple battery health estimation curves, wherein each feature dimension corresponds to one battery health estimation curve. Adjust the fitting parameters of the battery health estimation curve and obtain the weight values ​​of the battery health estimation curve corresponding to each feature dimension. Based on the weight value parameters, the multiple battery health estimation curves are optimized and merged to obtain the merged battery health estimation curve; The high-voltage energy storage system performs capacity verification on the battery to obtain a battery health estimation optimization curve. The battery health estimation optimization curve (SoH) is obtained using the following formula: SoH = SoH` + Δ Where △ is the correction value of SoH after the battery array management system (BAMS) is approved; SoH` is the merged curve of battery health estimation. Battery health estimation merge curve SoH` through formula Calculations show that SoH1 is used to estimate the battery health status by measuring the battery's internal resistance. SoH2 estimates the battery's health status based on the current system's cumulative charge and discharge capacity. SoH3 estimates the battery's health status by calculating the battery's ΔC / ΔV. SOH4 is used to estimate the health status of a battery by monitoring its voltage plateau. α, β, γ is the weight of SoH1, SoH2, SoH3, and SoH4; Weights are assigned based on the magnitude of the absolute error of each battery health estimation curve, ensuring that the absolute error of each curve has an equal impact on the merged battery health estimation curve. This is achieved using the following formula: 1 / E1:1 / E2:1 / E3:1 / E4 = a:b:c:d Where E1-E4 represent the absolute error of the battery health curve; α, β, γ is the weighting value, ranging from 0.2 to 0.3; SoH1, SoH2, SoH3, SoH4, and α, β, γ is obtained by curve fitting of the algorithm after verifying the cycle life.

2. The method as described in claim 1, wherein, The process of optimizing and merging the multiple battery health estimation curves based on the weight value parameters to obtain a merged battery health estimation curve further includes: The battery health status is obtained by substituting multiple feature dimension data collected during battery operation into the battery health estimation optimization curve and updating the battery health estimation optimization curve.

3. The method as described in claim 1, wherein, The process of fitting the feature data according to different feature dimensions to obtain multiple battery health estimation curves includes: By fitting data of battery internal resistance characteristics, battery cumulative charge and discharge capacity characteristics, battery capacity increment characteristics, and battery voltage characteristics, the following curves are obtained: battery internal resistance health estimation curve, battery charge and discharge capacity health estimation curve, battery capacity increment health estimation curve, and battery voltage health estimation curve.

4. The method of claim 3, wherein, The fitting process for the feature data includes: The data for each feature dimension is fitted to obtain the battery health estimation curve corresponding to each feature dimension. The fitting process includes neural network algorithm, least squares algorithm, and simulated annealing algorithm.

5. The method of claim 1, wherein, The adjustment of the fitting parameters for the battery health estimation curve, and the acquisition of the weight values ​​for the battery health estimation curve corresponding to each feature dimension, include: Adjust the parameters of the fitting algorithm so that the expected battery health value is on the battery health estimation curve or evenly distributed on both sides of it. Obtain the average absolute error and the maximum absolute error between the estimated battery health value and the expected battery health value, and obtain the absolute error of the battery health curve based on the average absolute error and the maximum absolute error. Weights are assigned based on the magnitude of the absolute error of each battery health estimation curve, so that the absolute error of each battery health curve has an equal impact on the merged battery health estimation curve.

6. The method of claim 5, wherein, The absolute error of the battery health curve is obtained based on the average absolute error and the maximum absolute error, using the following formula: AND n =And max ×m+E ave ×(1-m) Among them, E n For the pool health curve error, n ranges from 1 to 4; E max To calculate the maximum error between the battery health value and the expected value; E ave The absolute average error between the estimated and expected values ​​of battery health over the entire battery lifecycle; m ranges from 0 to 0.

2.

7. A battery health estimation device, wherein, The device includes: The feature data acquisition unit is used to acquire multiple feature data of the battery under a preset scenario. The multiple feature data include at least the battery cell voltage, battery temperature, battery internal resistance, battery cumulative charge and discharge capacity, and battery capacity increment. By performing charge-discharge life cycle verification on multiple groups of battery cells, the characteristic data of battery cells under multiple scenarios, including battery cell voltage, battery temperature, battery internal resistance, battery cumulative charge-discharge capacity, and battery capacity increment, are obtained. The battery health estimation curve acquisition unit is used to fit the feature data according to different feature dimensions to obtain multiple battery health estimation curves, where one feature dimension corresponds to one battery health estimation curve. The weight value parameter acquisition unit is used to adjust the fitting processing parameters of the battery health estimation curve and obtain the weight value parameters of the battery health estimation curve corresponding to each feature dimension. The battery health estimation merging curve acquisition unit is used to optimize and merge the multiple battery health estimation curves according to the weight value parameters to obtain a battery health estimation merging curve; after optimizing and merging the multiple battery health estimation curves to obtain the battery health estimation merging curve, it further includes: The high-voltage energy storage system performs capacity verification on the battery to obtain a battery health estimation optimization curve. The battery health estimation optimization curve (SoH) is obtained using the following formula: SoH = SoH` + Δ Where △ is the correction value of SoH after the battery array management system (BAMS) is approved; SoH` is the merged curve of battery health estimation. Battery health estimation merge curve SoH` through formula Calculations show that SoH1 is used to estimate the battery health status by measuring the battery's internal resistance. SoH2 estimates the battery's health status based on the current system's cumulative charge and discharge capacity. SoH3 estimates the battery's health status by calculating the battery's ΔC / ΔV. SOH4 is used to estimate the health status of a battery by monitoring its voltage plateau. α, β, γ is the weight of SoH1, SoH2, SoH3, and SoH4; Weights are assigned based on the magnitude of the absolute error of each battery health estimation curve, ensuring that the absolute error of each curve has an equal impact on the merged battery health estimation curve. This is achieved using the following formula: 1 / E1:1 / E2:1 / E3:1 / E4 = a:b:c:d Where E1-E4 represent the absolute error of the battery health curve; α, β, γ is the weighting value, ranging from 0.2 to 0.3; SoH1, SoH2, SoH3, SoH4, and α, β, γ is obtained by curve fitting of the algorithm after verifying the cycle life.

8. A battery health estimation system, wherein, The system includes an energy management system (EMS), a battery management system control unit (BAMS), an energy storage converter (PCS), a battery management system main control unit (BCMU), a battery management system (BMU), and a battery energy storage system. The battery energy storage system includes: a PACK consisting of a single cell, a battery cluster consisting of multiple battery PACKs, and a battery stack consisting of multiple battery clusters. The Battery Management System (BAMS), Energy Management System (EMS), Energy Storage Converter (PCS), and Battery Management System Main Control Unit (BCMU) interact with each other via corresponding communication buses. The system further includes a processor; and a memory arranged to store computer-executable instructions, which, when executed, cause the processor to perform the method of any one of claims 1-6.

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