System and method for calibrating state of charge and state of health of energy storage system

Abstract

The invention provides a system and a method for calibrating the electric quantity state and the health state of an energy storage system, the system comprises a main battery management system and a plurality of battery cabinets, when the main battery management system judges that one of the plurality of battery cabinets accords with an automatic calibration condition, the main battery management system is connected with the plurality of battery cabinets; the main battery management system obtains characteristic value data of each battery pack by using a battery characteristic value obtaining algorithm, and predicts the number of battery cycles of each of the plurality of battery packs through a battery aging correction model, thereby accurately calculating the state of charge (SOC) and state of health (SOH) of each of the plurality of battery packs. Therefore, the maintenance personnel can clearly master the state of each battery cabinet, and the power management efficiency is improved.

Description

Technical Field

[0001] The present invention relates to a calibration technology for an energy storage system, and in particular to a calibration system and method for the state of charge and health of an energy storage system. Background Art

[0002] In order to slow down global warming and achieve the goal of sustainable development, renewable energy has become the trend of energy development in the world. At the same time, driven by renewable energy development regulations in various countries around the world, more and more offshore wind power generation and solar power generation will be used in the future. However, considering that the unstable factors of renewable energy will impact the power grid and cause power outages and other accidents, various countries have successively promulgated renewable energy smoothing regulations to limit the power fluctuation rate of renewable energy. The government is also actively promoting the development of renewable energy and electricity trading markets. Therefore, energy storage systems (hereinafter referred to as energy storage systems) have received considerable attention.

[0003] The battery is an indispensable role in the energy storage system. In actual use, since the voltage and capacity of a single battery cell cannot meet the needs of the energy storage system, it needs to be presented in the form of multiple battery packs, which requires a large number of series and parallel operations on the batteries. When multiple batteries are connected in series and parallel to form a battery pack, the capacity difference and electrical characteristics of each battery cell will cause an imbalance, which will have a serious impact on the life of the battery pack. Therefore, it is necessary to monitor and control the capacity state of each cell. At this time, the calibration of the state of charge (SOC) and the state of health (SOH) becomes an important link.

[0004] Therefore, when using energy storage systems, a good mechanism is needed to estimate the state of charge (SOC) of each battery cell and to charge and discharge it appropriately to avoid errors in the battery's state of charge (SOC) and state of health (SOH). At the same time, calibration mechanisms and methods are used to ensure that the battery pack will not be damaged or dangerous due to overcharging or over-discharging.

[0005] The battery state of charge (SOC) and state of health (SOH) are indicators that designers and users are concerned about, and related calculation methods such as coulomb counting are one of the methods. Coulomb counting integrates the current flowing into or out of the battery cell to calculate the total charge flowing through the battery cell, and then estimates the relative state of charge (SOC) change within a given time. However, there are still errors (such as battery temperature and aging) in the state of charge (SOC) based on coulomb counting, which leads to inaccurate estimation of the initial value. Therefore, it is necessary to determine whether to calibrate the state of charge (SOC) of the coulomb counting regularly and irregularly to ensure the accuracy of the state of charge (SOC). There are many patents that have proposed improvement methods for the above-mentioned solutions that cause state of charge (SOC) errors, such as:

[0006] 1. To address the impact of battery temperature, such as choosing an appropriate rest time or heating and cooling the battery before reading the voltage, weighting and correcting the battery's state of charge (SOC) and state of health (SOH).

[0007] 2. To address the impact of battery aging, the battery state of charge (SOC) and state of health (SOH) can be weighted and corrected through the battery impedance database and the statistics of measured DC or AC impedance and number of uses.

[0008] Although the above method can improve the state of charge (SOC) error, the battery must be limited to the state of stop, full charge or reverse discharge, which still has many gaps in the need to provide accurate state of charge (SOC) and health state (SOH) in real time.

[0009] Therefore, how to provide a calibration technology for energy storage systems that can accurately assess and calibrate the battery's state of charge (SOC) and state of health (SOH) has become an issue that the industry needs to solve urgently. Summary of the invention

[0010] In order to solve the aforementioned known technical problems or provide related technical effects, the present invention provides a calibration system for the power state and health state of an energy storage system, which includes: a power regulation system, which provides power; multiple battery cabinets, which are electrically connected to the power regulation system to obtain the power, and each of the multiple battery cabinets includes multiple battery packs; and a main battery management system, which is communicatively connected to the multiple battery cabinets and has a battery aging correction model. When the main battery management system determines that one of the multiple battery cabinets meets the automatic calibration conditions, the main battery management system causes one of the multiple battery cabinets to communicate with the power regulation system. The battery management system is configured to disassemble the battery cabinets and obtain the battery voltage and battery current of each battery group in the multiple battery groups of one of the multiple battery cabinets, wherein the main battery management system executes a battery characteristic value acquisition algorithm to obtain the corresponding characteristic value data according to the battery voltage and battery current of each battery group in the multiple battery groups, so as to predict the number of battery cycles of each battery group in the multiple battery groups according to the characteristic value data of each battery group in the multiple battery groups through the battery aging correction model, thereby calculating the corresponding health status value according to the number of battery cycles of each battery group in the multiple battery groups, and then updating the multiple battery groups.

[0011] The present invention further provides a method for calibrating the charge state and health state of an energy storage system, which includes: when a main battery management system determines that one of a plurality of battery cabinets meets the automatic calibration conditions, the main battery management system disconnects one of the plurality of battery cabinets from a power regulation system to obtain the battery voltage and battery current of each of the plurality of battery groups of the one of the plurality of battery cabinets; the main battery management system executes a battery characteristic value acquisition algorithm to obtain corresponding characteristic value data of each of the plurality of battery groups according to the battery voltage and battery current; the main battery management system predicts the number of battery cycles of each of the plurality of battery groups according to the characteristic value data of each of the plurality of battery groups through a battery aging correction model; and the main battery management system calculates the corresponding health state value according to the number of battery cycles of each of the plurality of battery groups, thereby updating the plurality of battery groups.

[0012] In the aforementioned embodiment, a calibration device is further included, which provides a certain current, and after one of the multiple battery cabinets is disconnected from the power regulation system, the main battery management system enables one of the multiple battery cabinets to be electrically connected to the calibration device, so that one of the multiple battery cabinets obtains the constant current from the calibration device.

[0013] In the aforementioned embodiment, the characteristic value data of each battery pack in the plurality of battery packs includes a plurality of power state characteristic values ​​and a plurality of peak characteristic values.

[0014] In the aforementioned embodiment, the battery aging correction model predicts multiple candidate cycle numbers for each of the multiple battery groups based on multiple state of charge characteristic values ​​and multiple peak characteristic values ​​of each of the multiple battery groups, and then selects the battery cycle number for each of the multiple battery groups from the multiple candidate cycle numbers for each of the multiple battery groups.

[0015] In the aforementioned embodiment, the main battery management system calculates multiple peak charge status values ​​of each of the multiple battery groups based on the number of battery cycles of each of the multiple battery groups, and then calculates the health status value of each of the multiple battery groups based on the multiple peak charge status values ​​of each of the multiple battery groups.

[0016] In the aforementioned embodiment, the battery aging correction model is a neural network-like model.

[0017] As can be seen from the above, the calibration system and method for the state of charge and state of health of the energy storage system of the present invention can accurately obtain the characteristic value data of multiple battery packs through the battery characteristic value acquisition algorithm, and then accurately predict the corresponding battery cycle numbers of multiple battery packs through the battery aging correction model based on the neural network, so as to calculate the correct state of charge (SOC) and state of health (SOH) in real time, so that maintenance personnel can quickly and clearly grasp the status of each battery cabinet, thereby improving the efficiency of power management. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a schematic diagram of the architecture of the system for calibrating the state of charge and health of an energy storage system according to the present invention.

[0019] Figure 2 It is a flow chart of a method for calibrating the state of charge and state of health of an energy storage system according to the present invention.

[0020] Figure 3 It is a schematic diagram of the flow chart of the battery cabinet calibration method of the present invention.

[0021] Figure 4A is the battery voltage of a battery pack of the present invention.

[0022] Figure 4B is the battery current of a battery pack of the present invention.

[0023] Figure 5 It is the characteristic value data of a battery pack of the present invention.

[0024] Figure 6 This is a training data set for the battery aging correction model of the present invention.

[0025] Figure 7 It is a schematic diagram of a specific embodiment of the battery aging correction model of the present invention.

[0026] Description of reference numerals:

[0027] 1 Calibration system for state of charge and health of energy storage systems

[0028] 10 Main battery management system

[0029] 10a Battery aging correction model

[0030] 11 Battery Pack Control Module

[0031] 12 Current sensing module

[0032] 13a First switch

[0033] 13b Second switch

[0034] 20 Calibration Equipment

[0035] 30 Power Regulation System

[0036] 40 Energy Management System

[0037] C+ Positive

[0038] C- Negative electrode

[0039] D1~D n Battery Cabinet

[0040] PA1~PA n Battery Pack

[0041] Power+ positive voltage terminal

[0042] Power-Negative voltage terminal

[0043] Steps S21 to S24, S31 to S35. DETAILED DESCRIPTION

[0044] The following describes the implementation of the present invention through specific embodiments. Those skilled in the art can easily understand other advantages and technical effects of the present invention from the contents described in this specification.

[0045] It should be noted that the structures, proportions, sizes, etc. shown in the drawings of this specification are only used to match the contents described in the specification for the understanding and reading of those skilled in the art, and are not used to limit the limiting conditions under which the present invention can be implemented, so they have no substantial technical significance. Any structural modification, change in proportional relationship, or adjustment of size, without affecting the technical effects and purposes that can be achieved by the present invention, should still fall within the scope of the technical contents described in the present invention. At the same time, the terms such as "one", "first", "second", "upper" and "lower" quoted in this specification are only for the convenience of description, and are not used to limit the scope of the implementation of the present invention. The change or adjustment of their relative relationship should be regarded as the scope of the implementation of the present invention without substantially changing the technical contents.

[0046] Figure 1 The schematic diagram of the structure of the calibration system 1 for the state of charge and health status of the energy storage system of the present invention comprises: a main battery management system (MBMS) 10 with a battery aging correction model 10a and a plurality of battery cabinets (RACK) D1-D n , wherein the plurality of battery cabinets D1~D n Each battery cabinet D1~D n The present invention comprises a battery pack control module 11, a current sensing module 12, a first switch 13a, a second switch 13b and a plurality of battery packs PA1-PA n In addition, the calibration system 1 for the state of charge and health status of the energy storage system further comprises: a calibration device 20 , a power regulation system 30 and an energy management system 40 .

[0047] Specifically, the calibration device 20 and the power regulation system 30 can be power supply devices, and the main battery management system 10 and the energy management system 40 can be established in back-end electronic devices with appropriate mechanisms such as servers (such as general-purpose servers, file servers, storage unit servers, etc.) and computers, wherein each module or model in the main battery management system 10 and the energy management system 40 can be software, hardware or firmware; if it is hardware, it can be a processing unit, processor, computer or server with data processing and computing capabilities; if it is software or firmware, it can include instructions executable by the processing unit, processor, computer or server, and can be installed on the same hardware device or distributed on multiple different hardware devices.

[0048] In one embodiment, the main functions of the power conversion system (PCS) 30 are as follows: (1) energy conversion and regulation, converting DC to AC for use in the power grid or load; (2) current and voltage stabilization, monitoring and controlling the current and voltage of the energy storage system to ensure that they are within a safe and stable range, thereby preventing abnormal conditions such as excessively high or low voltage; (3) stabilizing the power grid frequency; and (4) improving energy storage efficiency.

[0049] In one embodiment, the main functions of the energy management system (EMS) 40 are as follows: (1) energy power control and monitoring; (2) energy power optimization; (3) energy storage system management; (4) fault detection. The energy management system can intelligently monitor and control the flow of energy, help achieve efficient utilization and comprehensive management of energy, so as to reduce energy waste and ensure the stability of energy supply.

[0050] Furthermore, in the plurality of battery cabinets D1 to D n In the embodiment, the current sensing module 12 may be an ammeter, the first switch 13a and the second switch 13b may be relays, and the plurality of battery packs PA1-PA n Each battery pack PA1~PA n The battery packs PA1-PA2 include multiple batteries, and the multiple batteries may be lithium iron phosphate batteries (LFP), nickel cadmium batteries (Ni-Cd), nickel metal hydride batteries (Ni-MH) or lithium ion batteries (Li-Ion), etc., and are not limited to the multiple battery packs PA1-PA2. n The battery types and battery combination methods in the battery pack control module 11 can be software, hardware or firmware; if it is hardware, it can be a processing unit with data processing and computing capabilities; if it is software or firmware, it can include instructions executable by a processing unit, a processor, a computer or a server, and can be installed in the same hardware device or distributed in different multiple hardware devices.

[0051] In this embodiment, if Figure 1 As shown, the main battery management system 10 is connected to the multiple battery cabinets D1 to D n The battery pack control module 11, the calibration device 20 and the energy management system 40; the energy management system 40 is connected to the power regulation system 30 in communication; the multiple battery cabinets D1~D n The multiple battery cabinets D1 to D n Electrically connected to the power regulation system 30 .

[0052] Furthermore, in the plurality of battery cabinets D1 to D n Each battery cabinet D1~D nIn the embodiment, the battery pack control module 11 is connected to the current sensing module 12, the first switch 13a, the second switch 13b and the plurality of battery packs PA1-PA n , and the plurality of battery packs PA1~PA n The first switch 13a is electrically connected to the positive voltage terminal Power+ in the power conditioning system 30, and the second switch 13b is electrically connected to the negative voltage terminal Power- in the power conditioning system 30, so that the plurality of battery cabinets D1-D n The power source is obtained from the power regulation system 30, wherein the first switch 13a is connected in series with the current sensing module 12, and the plurality of battery packs PA1-PA2 are connected in series through the current sensing module 12. n The positive electrode of one of the plurality of battery packs PA1 to PA n The negative electrode of the other one is connected in series back to the second switch 13b, thereby forming a charge and discharge loop.

[0053] In addition, another contact of the first switch 13a is electrically connected to the positive electrode C+ of the calibration device 20, and another contact of the second switch 13b is electrically connected to the negative electrode C- of the calibration device 20, so as to n When performing calibration, a constant current is obtained from the calibration device 20 .

[0054] In one embodiment, the master battery management system 10 directly sends the battery cabinets D1 to D n The main battery management system 10 obtains the battery cabinet data through the multiple battery cabinets D1 to D n One of the plurality of battery cabinets D1 to D n The battery cabinet data of other battery cabinets in the battery cabinet data includes: n The voltage sensing module (not shown) in the figure measures the voltage of the multiple battery packs PA1 to PA n The battery voltage of the multiple battery cabinets D1 to D n The battery cabinet current measured by the current sensing module 12 in the battery cabinet D1 to D n The temperature of the multiple battery packs PA1 to PA2 is measured by the temperature sensing module (not shown) in the figure. n The battery temperature is, but not limited to,

[0055] In one embodiment, the main battery management system 10 is based on the plurality of battery cabinets D1 to D n The battery cabinet data is used to calculate the value of each battery group PA1~PA n The state of charge (SOC) of each battery in the main battery management system 10 can be based on the multiple battery cabinets D1-D using methods such as Euclidean distance prediction method and Kalman filter prediction. nThe battery cabinet data is used to calculate each battery group PA1~PA n The state of charge (SOC) of each battery in the .

[0056] Figure 2 FIG. 1 is a flow chart of a method for calibrating the state of charge and health status of an energy storage system according to the present invention, and is also referred to in conjunction with FIG. Figure 1 illustrate.

[0057] In step S21, a master battery management system 10 checks a plurality of battery cabinets D1 to D n Whether the automatic calibration conditions are met, and when the multiple battery cabinets D1~D n When at least one (such as one or more) of the conditions meets the automatic calibration condition, the decoupling strategy procedure is executed.

[0058] In one embodiment, the automatic calibration conditions include but are not limited to the battery cabinet current threshold, the battery state of charge (SOC) threshold, the total power demand, etc., and when the plurality of battery cabinets D1-D n When the automatic calibration condition is not met, the main battery management system 10 executes the normal working mode program, such as evaluating the multiple battery cabinets D1-D n The state of charge (SOC).

[0059] In step S22, when the main battery management system 10 determines that the plurality of battery cabinets D1 to D n When one of the battery cabinets D1 to D2 meets the automatic calibration condition, the main battery management system 10 performs the automatic calibration through the multiple battery cabinets D1 to D2. n The battery control module 11 of one of the battery cabinets D1 to D1 activates the first switch 13a and the second switch 13b to disconnect the electrical connection between the positive voltage terminal Power+ and the negative voltage terminal Power- of a power regulation system 30, that is, to make the plurality of battery cabinets D1 to D1 n One of them is disconnected from the power conditioning system 30.

[0060] In step S23, the main battery management system 10 uses the multiple battery cabinets D1 to D n The battery pack control module 11 of one of the battery packs 11 activates the first switch 13a and the second switch 13b to conduct the electrical connection between the positive electrode C+ and the negative electrode C- of a calibration device 20, that is, the calibration device 20 is electrically connected to the plurality of battery cabinets D1 to D n The calibration device 20 provides a constant current to the plurality of battery cabinets D1 to D n One of them, and then calibrate the multiple battery cabinets D1~D n one of them.

[0061] In step S24, when the plurality of battery cabinets D1 to D nAfter one of the battery cabinets D1 to D2 is calibrated, the master battery management system 10 executes the parallel strategy program. n The battery control module 11 of one of the battery cabinets D1 to D2 activates the first switch 13a and the second switch 13b to disconnect the calibration device 20 and turn on the power regulation system 30, so that the plurality of battery cabinets D1 to D2 n One of them is connected in parallel with the power conditioning system 30.

[0062] Figure 3 The present invention is a flow chart of the battery cabinet calibration method described above, and can also be referred to in conjunction with Figure 1 and Figure 2 illustrate.

[0063] In step S31, a master battery management system 10 instructs a calibration device 20 to provide a certain current (such as a charge / discharge rate of 0.1C or 0.2C) to the plurality of battery cabinets D1 to D n Then the main battery management system 10 controls the plurality of battery cabinets D1 to D n One of the battery cabinets D1 to D2 is discharged at a constant current (such as a charge and discharge rate of 0.1C or 0.2C) to measure the voltage of the battery cabinets D1 to D2 through a voltage sensing module and a current sensing module 12. n Each battery pack PA1-PA n The battery voltage (such as Figure 4A The battery voltage of a battery pack shown in the figure) and the battery current (or battery cabinet current) (such as Figure 4B The battery current of one battery pack is shown).

[0064] In step S32, the main battery management system 10 generates a battery pack according to the plurality of battery cabinets D1 to D n The plurality of battery packs PA1-PA n Each battery pack PA1~PA n The battery voltage and battery current are used to execute a battery characteristic value acquisition algorithm to obtain the battery characteristics of the plurality of battery groups PA1-PA n Each battery pack PA1~PA n The characteristic value data of Figure 5 As shown, the characteristic value data of a battery pack includes multiple power state characteristic values ​​F A ,F B ,F C ,F D ,F E and multiple peak eigenvalues ​​F P1 ,F P2 .

[0065] In one embodiment, the battery characteristic value acquisition algorithm may be a differential method, so as to obtain the plurality of battery groups PA1-PA n Each battery pack PA1~PA n The characteristic value data of the differential method includes the operating voltage range, the order (n) and the capacitance (Q), and the formulas (1) and (2) of the differential method are as follows:

[0066] Differential capacitance

[0067] Differential Voltage

[0068] For example, if a typical lithium iron phosphate battery is taken as an example, its operating voltage range can be: 2.6 to 3.45V; if a lithium ternary battery is taken as an example, its operating voltage range can be: 2.4 to 4.2V; the order (n) is calculated as a positive integer, which is related to the sampling time. Taking into account the calculation efficiency and time, when the sampling time is 1 second, the charge / discharge is 0.1 to 0.5C, and the error is 1%, the order n range can be: 72 to 360, and the order (n) can be adjusted according to the above principles; if a typical lithium iron phosphate battery is taken as an example, the capacity (Q) is 280Ah, and it is calculated as the Q change generated during charge / discharge, so the capacity range can be 1A / sec (ampere-second) to 1008000A / sec (280×3600) to calculate the characteristic value, where dQ is the denominator value and dQ is not equal to 0.

[0069] In step S33, the master battery management system 10 generates a battery pack according to the plurality of battery packs PA1-PA n Each battery pack PA1~PA n The characteristic value data (i.e., the multiple power state characteristic values ​​F A ~F E and the multiple peak characteristic values ​​F P1 ,F P2 ), using a battery aging correction model 10a, and predicting the multiple battery groups PA1-PA n Each battery pack PA1~PA n The number of cycles (Cycle).

[0070] In one embodiment, the training data set of the battery aging correction model 10a uses 1 to 4800 battery full cycle data and includes the actual voltage value characteristics at each time point (such as Figure 6 shown), where Figure 6A to L shown in the figure refer to the number of complete cycles of the battery, and B>A, C>B, and so on; and the battery aging correction model 10a can adopt one of the machine learning models, support vector machine (SVM), XGBoost, convolutional neural network (CNN) or deep neural network (DNN).

[0071] In one embodiment, if Figure 7 As shown, the battery aging correction model 10a is a model based on a deep neural network (DNN), and includes a plurality of group prediction models M1-M7 and an output layer (vote vector) L connecting the plurality of group prediction models M1-M7, wherein the plurality of battery full cycle data in the training data set (such as Figure 6 As shown), a plurality of corresponding training characteristic value data are obtained through the battery characteristic value acquisition algorithm, and the plurality of training characteristic value data also includes a plurality of power state characteristic values ​​F A ~F E and multiple peak eigenvalues ​​F P1 ,F P2 , so that the battery aging correction model 10a performs deep learning based on the multiple training feature value data, so that the trained battery aging correction model 10a can accurately predict the multiple battery groups PA1~PA n Each battery pack PA1~PA n The number of battery cycles (Cycle).

[0072] For example, Figure 7 As shown, the multiple training feature value data include 5 power state feature values ​​F A ~F E and 2 peak eigenvalues ​​F P1 ,F P2 , a total of 7 characteristic values, and the 7 groups of sub-prediction models M1-M7 in the battery aging correction model 10a correspond to the 7 characteristic values, so as to train the corresponding sub-prediction models M1-M7 according to the characteristic values, wherein the multiple sub-prediction models M1-M7 will respectively input 5 power state characteristic values ​​F A ~F E , 2 peak eigenvalues ​​F P1 ,F P2 , and the square, cube, natural logarithm, and reciprocal exponential functions of these feature values ​​for training. For example, a sub-prediction model M1 will take a power state feature value F AThe square, cube, natural logarithm, and reciprocal exponential functions of are used as inputs for training. Therefore, the sub-prediction models M1 to M7 will all predict the candidate cycle times through their corresponding feature values.

[0073] Next, the output layer L is trained to perform a voting mechanism, so that the output layer L uses a regression method to obtain a battery cycle number from a plurality of candidate cycle numbers predicted by the plurality of prediction models M1 - M7 .

[0074] Specifically, the battery aging correction model 10a based on the deep neural network (DNN) has a DNN algorithm formula (3) as follows:

[0075] h(x ij ,w ij ,b ij )=σ(x ij ,w ij ,b ij )

[0076] Regression analysis F = MSELoss(y ij ,y r ) (3)

[0077] Among them, h is the hidden layer; x ij is the input value; ij is an estimated value; r is the true value; w ij is the weight matrix in the DNN algorithm; b ij is the error term in the DNN algorithm; σ is the nonlinear activation function (Activation Function; also called activation function), ReLU (Rectified Linear Unit) is used as the activation function here, and the sigmoid function is used in the last output layer; F is the loss function in the DNN algorithm, and the regression analysis method uses the mean square error MSELoss(x) for calculation, and the classification method uses the cross entropy for calculation; n is the number of samples; and m is the number of categories.

[0078] Furthermore, when the loss function deviation is too large, the Adam optimization algorithm (Adaptive Moment Estimation Algorithm) is used for parameter optimization, wherein the Adam optimization algorithm maintains two momentum variables, namely the first-order moment estimate (mean, m) and the second-order moment estimate (variance, v), which respectively estimate the first-order moment and the second-order moment of the gradient, for adaptively adjusting the learning rate. Moreover, the learning rate (learning rate) α is initialized to calculate the gradient and update the moment estimate in each iteration step, and the deviation is corrected when the moment estimate exceeds the threshold, that is, the first-order moment estimate m and the second-order moment estimate v are divided by the correction term, so as to update the parameter value according to the first-order moment estimate m, the second-order moment estimate v and the learning rate α, and converge the model parameters.

[0079] In step S34, the main battery management system 10 performs a control operation according to each of the plurality of battery packs PA1-PA n Substitute the battery cycle number into a first state of charge regression equation (4) and a second state of charge regression equation (5) (described in detail below) to calculate the multiple battery groups PA1~PA n Each battery pack PA1~PA n The first peak state of charge value SOC p1 and the second peak state of charge value SOC p2 .

[0080] In one embodiment, the first state of charge regression equation (4) and the second state of charge regression equation (5) are as follows:

[0081] SOC p1 (C)=P1C 6 +P2C 5 +P3C 4 +P4C 3 +P5C 2 +P6C+P7 (4)

[0082] SOC p2 (C)=P8C 6 +P9C 5 +P 10 C 4 +P 11 C 3 +P 12 C 2 +P 13 C+P 14 (5)

[0083] Wherein, P1-P7 are coefficients of the first state of charge regression equation; and P8-P 14 are coefficients of the second state of charge regression equation, wherein the regression parameters of the sixth-order polynomial equation (ie, the first and second state of charge regression equations) are obtained by performing parameter iteration using the least squares method to calculate a curve with the smallest gap.

[0084] In step S35, the master battery management system 10 generates a battery pack according to the plurality of battery packs PA1-PA n Each battery pack PA1~PA n The first peak state of charge value SOC p1 and the second peak state of charge value SOC p2 , and calculate the battery health status of the plurality of battery groups PA1-PA using a battery health status calibration formula (6) (described in detail below). n Each battery pack PA1~PA n The health status (SOH) of the battery packs PA1 to PA2 is updated. n , thereby completing the multiple battery cabinets D1~D n Calibration of one of them.

[0085] In one embodiment, the battery health status calibration formula (6) is as follows:

[0086]

[0087] Where, Q0(C) is the total cycle capacity of the tested battery (cycle N); Q O (1) is the initial total charge of the tested battery (cycle 1); F p1 is the characteristic value of the first peak; F p2 is the characteristic value of the second wave peak; I is the battery cabinet current; t is the time; SOC p1 is the first peak power state value; and SOC p2 It is the second wave peak power status value.

[0088] The following example is a specific embodiment of the calibration system 1 for the state of charge and health status of the energy storage system of the present invention, and is also referred to in conjunction with Figures 1 to 3 In addition, the same parts of this embodiment as the above embodiment will not be described in detail.

[0089] In this embodiment, a master battery management system 10 obtains the plurality of battery cabinets D1-D n The working status of the first battery cabinet D1, the second battery cabinet D2, the third battery cabinet D3 and the fourth battery cabinet D4 is shown in Table 1 below:

[0090] Table 1: Battery cabinet working status

[0091]

[0092] The main battery management system 10 then checks whether the first battery cabinet D1, the second battery cabinet D2, the third battery cabinet D3 and the fourth battery cabinet D4 meet the automatic calibration conditions. For example, the automatic calibration conditions include that the battery cabinet current is less than the current threshold I th The SOC difference between the battery in the battery cabinet and the battery is greater than the charge difference threshold ΔSOC th , wherein the SOC difference of the battery in the battery cabinet refers to the difference between the battery (or battery cell) with the largest state of charge (SOC) and the battery (or battery cell) with the smallest state of charge (SOC) in a battery cabinet, wherein the current threshold I th and the charge difference threshold ΔSOC th A specific threshold value may be set as required to activate the automatic calibration condition.

[0093] Therefore, the main battery management system 10 detects that the battery cabinet current I2 of the second battery cabinet D2 is less than the current threshold I th , and the second power difference ΔSOC2 of the second battery cabinet D2 is greater than the power difference threshold ΔSOC th Therefore, the main battery management system 10 detects that the second battery cabinet D2 meets the automatic calibration condition, and performs a disconnection strategy procedure on the second battery cabinet D2 to disconnect the second battery cabinet D2 from a power conditioning system 30 .

[0094] Next, the main battery management system 10 activates the first switch 13a and the second switch 13b through the battery group control module 11 in the second battery cabinet D2, so that a calibration device 20 is connected in parallel with the second battery cabinet D2, and the main battery management system 10 instructs the calibration device 20 to provide a constant current with a charge and discharge rate of 0.1C to the second battery cabinet D2, and then the main battery management system 10 instructs the second battery cabinet D2 to discharge with a constant current at a charge and discharge rate of 0.1C, thereby measuring each battery group PA1~PA in the second battery cabinet D2 n The battery voltage and battery current (or battery cabinet current).

[0095] The main battery management system 10 is based on each battery group PA1-PA in the second battery cabinet D2. n The battery voltage and battery current of the plurality of battery groups PA1-PA are calculated by using a battery characteristic value acquisition algorithm. n Each battery pack PA1~PA n The characteristic value data (such as Figure 5 shown).

[0096] Afterwards, the master battery management system 10 connects the plurality of battery packs PA1 to PA n Each battery pack PA1~PAn Multiple power state characteristic values ​​F in the characteristic value data A ,F B ,F C ,F D ,F E and multiple peak eigenvalues ​​F P1 ,F P2 Substitute into a battery aging correction model 10a to predict the multiple battery groups PA1-PA n Each battery pack PA1~PA n The battery cycle number (Cycle), and the plurality of battery packs PA1~PA n Substitute the number of battery cycles of each into a first state of charge regression equation (4) and a second state of charge regression equation (5) to obtain the multiple battery groups PA1-PA n The first peak state of charge SOC of each p1 and the second peak state of charge value SOC p2 .

[0097] Furthermore, the master battery management system 10 connects the plurality of battery packs PA1 to PA n Each battery pack PA1~PA n The first peak state of charge value SOC p1 and a second peak state of charge value SOC p2 Substitute into a battery health status calibration formula (6) to calculate the multiple battery groups PA1~PA n The health status (SOH) of each of the multiple battery packs PA1-PA n , and complete the calibration of the second battery cabinet D2, and then connect the second battery cabinet D2 in parallel with the power regulation system.

[0098] For example, Figure 7 As shown, taking the battery pack PA1 in the second battery cabinet D2 as an example, the main battery management system 10 substitutes the characteristic value data of the battery pack PA1 into the battery aging correction model 10a, and predicts the battery cycle number of the battery pack PA1 (Cycle=1495), and substitutes the battery cycle number of the battery pack PA1 into the first state of charge regression equation (4) and the second state of charge regression equation (5) to obtain the first peak state of charge value SOC of the battery pack PA1 p1 (C=1495)=67.9% and the second wave peak state of charge value SOC p2 (C = 1495) = 16.8%, wherein the first state of charge regression equation (4) is SOC p1 (C) = 0.8067C 6 -3.0139C 5+2.4006C 4 +1.7541C 3 -1.9749C 2 +2.9155C+67.9688, and the second state of charge regression equation (5) is SOC p2 (C) = 0.1692C 6 -0.2908C 5 -0.6857C 4 +1.4107C 3 -0.67C 2 +

[0099] 1.4081C+16.8188.

[0100] Furthermore, the first peak state of charge value SOC p1 =67.9% and the second peak state of charge SOC p2 =16.8% Substitute into the battery health state calibration formula (6) to obtain the health state The battery pack PA1 is then updated.

[0101] In summary, the calibration system and method for the power state and health state of an energy storage system of the present invention mainly use a main battery management system to determine whether multiple battery cabinets meet the automatic calibration conditions, and when one of the multiple battery cabinets meets the conditions, the main battery management system disconnects one of the multiple battery cabinets from the power supply, and uses a battery characteristic value acquisition algorithm to obtain characteristic value data of each battery group, and then predicts the number of battery cycles of each battery group in the multiple battery groups through a battery aging correction model, so as to calculate the corresponding health state according to the number of battery cycles of each battery group in the multiple battery groups, so as to update the multiple battery groups of one of the multiple battery cabinets.

[0102] Therefore, compared with the prior art that uses traditional methods such as Coulomb calculation to calibrate the battery's state of charge (SOC), which causes errors in the calibration results, or in order to improve the detection accuracy, the battery needs to be evaluated in a stable state. In contrast, the present invention can accurately obtain the characteristic value data (including the state of charge characteristic value and the peak characteristic value) of each battery pack through the battery characteristic value acquisition algorithm, and further adopts artificial intelligence methods to accurately predict the number of battery cycles of each battery pack in the multiple battery packs through a battery aging correction model based on a neural network. Therefore, the present invention can accurately and quickly calculate the state of charge (SOC) and health state (SOH), and the battery does not need to be in a stable state, so that maintenance personnel can clearly grasp the situation of each battery cabinet, thereby improving the efficiency of power management.

[0103] The above embodiments are only illustrative of the principles and technical effects of the present invention, and are not intended to limit the present invention. Any person skilled in the art may modify and change the above embodiments without violating the concept and scope of the present invention. Therefore, the scope of protection of the present invention should be as set forth in the claims.

Claims

1. A calibration system for the state of charge and health status of an energy storage system, comprising: a power conditioning system that provides a power source; A plurality of battery cabinets electrically connected to the power regulation system to obtain the power supply, and each of the plurality of battery cabinets includes a plurality of battery packs; and a main battery management system, which is communicatively connected to the plurality of battery cabinets and has a battery aging correction model, so that when the main battery management system determines that one of the plurality of battery cabinets meets the automatic calibration condition, the main battery management system disconnects the one of the plurality of battery cabinets from the power regulation system and obtains the battery voltage and battery current of each of the plurality of battery groups of the one of the plurality of battery cabinets, Among them, the main battery management system executes a battery characteristic value acquisition algorithm to obtain the corresponding characteristic value data of each battery group in the multiple battery groups according to the battery voltage and battery current, so as to predict the number of battery cycles of each battery group in the multiple battery groups according to the characteristic value data of each battery group in the multiple battery groups through the battery aging correction model, thereby calculating the corresponding health status value according to the number of battery cycles of each battery group in the multiple battery groups, and then updating the multiple battery groups.

2. According to claim 1, the calibration system for the charge state and health state of the energy storage system further includes a calibration device, which provides a certain current, and after one of the multiple battery cabinets is disconnected from the power regulation system, the main battery management system electrically connects one of the multiple battery cabinets to the calibration device, so that one of the multiple battery cabinets obtains the constant current from the calibration device.

3. The calibration system for the state of charge and health status of an energy storage system according to claim 1, wherein: The characteristic value data of each battery pack in the plurality of battery packs includes a plurality of power state characteristic values ​​and a plurality of peak characteristic values.

4. The calibration system for the state of charge and health status of an energy storage system according to claim 3, wherein: The battery aging correction model predicts multiple candidate cycle numbers for each of the multiple battery groups based on multiple power state characteristic values ​​and multiple peak characteristic values ​​of each of the multiple battery groups, and then selects the battery cycle number for each of the multiple battery groups from the multiple candidate cycle numbers for each of the multiple battery groups.

5. The calibration system for the state of charge and health status of an energy storage system according to claim 1, wherein: The main battery management system calculates multiple peak charge state values ​​of each of the multiple battery groups based on the number of battery cycles of each of the multiple battery groups, and further calculates the health status value of each of the multiple battery groups based on the multiple peak charge state values ​​of each of the multiple battery groups.

6. The calibration system for the state of charge and health status of an energy storage system according to claim 1, wherein: The battery aging correction model is a neural network-like model.

7. A method for calibrating the state of charge and health status of an energy storage system, comprising: When a main battery management system determines that one of the plurality of battery cabinets meets the automatic calibration condition, the main battery management system disconnects the one of the plurality of battery cabinets from a power regulation system to obtain a battery voltage and a battery current of each of the plurality of battery groups of the one of the plurality of battery cabinets; The main battery management system executes a battery characteristic value acquisition algorithm to obtain corresponding characteristic value data of each battery group in the plurality of battery groups according to the battery voltage and battery current; The main battery management system predicts the number of battery cycles of each of the plurality of battery groups according to the characteristic value data of each of the plurality of battery groups through a battery aging correction model; as well as The main battery management system calculates a corresponding health status value according to the battery cycle count of each battery pack in the multiple battery packs, and then updates the multiple battery packs.

8. The method for calibrating the charge state and health state of an energy storage system according to claim 7 further comprises, after one of the multiple battery cabinets is disconnected from the power regulation system, the main battery management system electrically connects one of the multiple battery cabinets to a calibration device so that one of the multiple battery cabinets obtains a certain current from the calibration device.

9. The method for calibrating the state of charge and health status of an energy storage system according to claim 7, wherein: The characteristic value data of each battery pack in the plurality of battery packs includes a plurality of power state characteristic values ​​and a plurality of peak characteristic values.

10. The method for calibrating the state of charge and health status of an energy storage system according to claim 9, further comprising using the battery aging correction model to predict multiple candidate cycle numbers for each of the multiple battery groups based on multiple state of charge characteristic values ​​and multiple peak characteristic values ​​of each of the multiple battery groups, and then selecting the battery cycle number for each of the multiple battery groups from the multiple candidate cycle numbers for each of the multiple battery groups.

11. The method for calibrating the state of charge and state of health of an energy storage system according to claim 7, further comprising the main battery management system calculating a plurality of peak state of charge values ​​of each of the plurality of battery groups according to the number of battery cycles of each of the plurality of battery groups, and then calculating a state of health value of each of the plurality of battery groups according to the plurality of peak state of charge values ​​of each of the plurality of battery groups.

12. The method for calibrating the state of charge and health status of an energy storage system according to claim 7, wherein: The battery aging correction model is a neural network-like model.

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