Method and system for evaluating state of health of flow battery based on multi-dimensional data fusion

Abstract

The invention relates to the technical field of battery monitoring, in particular to a flow battery health state evaluation method and system based on multi-dimensional data fusion, and the method comprises the steps: setting an energy storage space of a flow battery, and dividing the energy storage space according to a preset proportion to form a plurality of initial division marks and a plurality of corresponding sub energy storage spaces; monitoring a charging process curve and a power consumption process curve of each sub energy storage space; determining a new sub energy storage space; comparing the sub-energy storage space with the new sub-energy storage space, and determining a corresponding charging health state or power consumption health state according to the offset degree of each sub-energy storage space; according to the method, the energy storage state of the flow battery in the single charging / discharging process can be effectively marked by setting the plurality of division marks, so that external interference caused by the fact that the battery is not in a rated state is effectively avoided, and the accuracy of evaluating the environmental health state of the flow battery in the operation process is effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of battery monitoring, and in particular to a method and system for evaluating the health status of a flow battery based on multi-dimensional data fusion. Background Art

[0002] As a battery with high discharge efficiency, flow batteries have entered a period of rapid development. Since their electrolytes are in a real-time circulation state and are highly corrosive, the health status of flow batteries and flow battery equipment needs to be monitored in real time.

[0003] In existing technologies, multi-source sensor data such as electrochemical parameters (voltage, current, internal resistance), electrolyte state (ion concentration, pH value), temperature field distribution and mechanical vibration are mostly integrated, combined with dynamic time warping (DTW) or deep learning models (such as LSTM, CNN) for feature fusion and degradation modeling to achieve quantitative evaluation of core indicators such as capacity decay and energy efficiency.

[0004] To this end, the above-mentioned technologies need to address engineering challenges such as sensor durability and data real-time performance.

[0005] Chinese Patent Authorization Publication No. CN117239186B discloses an online evaluation device and method for the SOH of an all-vanadium redox flow battery system, comprising the following steps: a battery management system collects data parameters; calculates the electrolyte health based on the volume, concentration, and valence of the positive electrolyte and the volume, concentration, and valence of the negative electrolyte; calculates the working stack internal resistance based on the number of cells in the working stack, the voltage of the working stack, the current of the working stack, the voltage of the test stack, and the number of cells in the test stack; calculates the stack health based on the working stack internal resistance and the initial internal resistance of the working stack; and calculates the SOH of the all-vanadium redox flow battery system based on the electrolyte health and the stack health. The present invention can calculate the battery system SOH based on a comprehensive analysis of the electrolyte health and the stack health, and is suitable for real-time monitoring of the SOH of an all-vanadium redox flow battery system, thus possessing significant significance.

[0006] However, the above method has the following problems: it is impossible to effectively evaluate the health status of the flow battery operating environment by only collecting the parameters of the flow battery. At the same time, it is difficult to avoid damage to the metering equipment of the flow battery, which leads to the loss of accuracy in the evaluation of the operating status of the flow battery. Summary of the Invention

[0007] To this end, the present invention provides a method and system for evaluating the health status of a flow battery based on multi-dimensional data fusion, so as to overcome the problem in the prior art that it is impossible to effectively evaluate the health status of the flow battery operating environment by only collecting the parameters of the flow battery. At the same time, it is difficult to avoid damage to the metering equipment of the flow battery, which leads to a loss of accuracy in the evaluation of the operating status of the flow battery.

[0008] To achieve the above objectives, the present invention provides a method for evaluating the health status of a flow battery based on multidimensional data fusion, comprising: Step S1, setting the energy storage space of the flow battery and dividing the energy storage space according to a preset ratio to form a number of initial division marks and a number of corresponding sub-energy storage spaces; Step S2, monitoring the charging process curve and power consumption process curve of each sub-energy storage space; Step S3, obtaining the loss during the charging process and / or the loss during the power consumption process; Step S4, determining a new sub-energy storage space according to the charging process loss and / or the power consumption process loss; Step S5: comparing the sub-storage space with the new sub-storage space, and determining the corresponding charging health state or power consumption health state according to the offset degree of each sub-storage space; The offset degree is the difference between the division mark corresponding to the new sub-energy storage space and the initial division mark.

[0009] Furthermore, in the step S1, for a single charge / discharge process, the energy storage space of the flow battery is set to the energy stored in the flow battery during the charge; The preset ratio is based on the energy storage space and divides the energy storage space into equal proportions according to integer multiples; Each division mark is a division mark corresponding to a preset ratio; The interval divided by adjacent segmentation marks is the sub-energy storage space.

[0010] Furthermore, in step S2, for a single charging process, the step of drawing the charging process curve includes: Taking the maximum energy storage of the flow battery as the energy storage space, forming a sub-energy storage space corresponding to the energy storage space, and setting a maximum charging target; or, Set the maximum charging target and form the corresponding energy storage space and sub-energy storage space; Measuring the energy charged into the flow battery per unit time and recording the corresponding energy change curve; The corresponding energy change curve is divided according to the sub-energy storage space to form a charging process curve of each sub-energy storage space.

[0011] Furthermore, the step of determining the new sub-energy storage space includes: Step S3a, determining the charging process loss based on the charging curve of each sub-energy storage space and the actual charging curve of the charging device; Step S4a, in response to the charging process loss exceeding the charging loss threshold, adjusting the positions of the division marks corresponding to the sub-energy storage space and forming a new sub-energy storage space; The charging loss threshold is the difference between the energy obtained by the liquid flow battery and the actual stored energy, which is related to the position of the sub-energy storage space in the liquid flow battery energy storage space and the power transmission equipment of the liquid flow battery.

[0012] Furthermore, the step of determining the new sub-energy storage space includes: Step S3b, determining the power consumption process loss based on the power consumption curve of each sub-energy storage space and the actual power acquisition curve of the power-consuming equipment; Step S4b, in response to the power consumption process loss exceeding the power consumption threshold, adjusting the position of each division mark corresponding to the sub-energy storage space and forming a new sub-energy storage space; The power loss threshold is related to the energy released by the flow battery and the energy actually obtained by the electrical appliance, which is related to the operating efficiency of the circulation pump of the flow battery and the power transmission equipment of the electrical appliance.

[0013] Furthermore, in step S5, the step of determining the offset degree of the sub-energy storage space includes: Determine the preceding charging process curve and preceding power consumption process curve corresponding to the previous charging / discharging process of a single sub-energy storage space, and determine the corresponding position of each division mark; According to the bending path of the preceding charging process curve, the charging process curve is offset and compared with the preceding charging process curve to form a corresponding charging offset result; According to the bending path of the preceding power consumption process curve, the power consumption process curve is offset and compared with the preceding power consumption process curve to form a corresponding power consumption offset result.

[0014] Furthermore, the step of offset comparison includes: Determine the geometric midpoint of each curve; Compare the geometric midpoints of each curve. If the geometric midpoint is closer to the front, it is determined to be a downward shift; if the geometric midpoint is closer to the back, it is determined to be an upward shift. The spacing between the division marks corresponding to each curve is compared. If the spacing is shorter, it is determined to be a contraction offset; if the spacing is longer, it is determined to be an expansion offset.

[0015] Furthermore, when determining the charging health state, the state of the flow battery is determined based on the charging offset result, wherein: If the charging offset result is a downward offset, it is determined that the power transmission equipment is abnormal; If the charge offset result is an upward offset, it is determined that the electrolyte circulation is abnormal; If the charging offset result is a reduction offset, it is determined that the power transmission equipment is abnormal; If the charge offset result is an expansion offset, it is determined to be an abnormal electrolyte circulation; When determining the power consumption health state, the state of the flow battery is determined according to the power consumption offset result, wherein, If the power consumption deviation result is a downward deviation, it is determined that the electrolyte is abnormal; If the power consumption deviation result is an upward deviation, it is determined that the electrolyte circulation is abnormal.

[0016] In another aspect, the present invention provides a flow battery health status assessment system based on multi-dimensional data fusion, comprising: A flow collector provided on the circulation pump, which is used to collect the flow parameters of the electrolyte delivered by the circulation pump; An electric storage meter connected to the flow battery stack to collect the stored electric energy of the flow battery; a power transmission meter connected to the battery stack for collecting discharged electrical energy from the flow battery; Among them, a standard battery is set on the liquid flow battery and is connected to the power storage meter and the power transmission meter to mark the actual power storage capacity of the liquid flow battery; A central console is connected to the flow collector, the electricity storage meter, the electricity transmission meter and the standard battery, and is used to determine the actual energy storage ratio of the flow battery according to the electric energy test of the standard battery.

[0017] Furthermore, the standard battery is a detachable battery, and the composition of its electrolyte is the same as that of the flow battery.

[0018] Compared with the prior art, the beneficial effect of the present invention lies in that the maximum energy storage of the liquid flow battery is marked by setting an energy storage space, and the energy storage state of the liquid flow battery during a single charge / discharge process can be effectively marked by setting a number of division marks, and the energy storage efficiency and discharge efficiency of the liquid flow battery are determined according to the division marks, thereby determining the health status of the battery during operation. While effectively improving the timeliness of evaluating the health status of the liquid flow battery during operation, it effectively avoids external interference caused by the battery not being in the rated state, thereby effectively improving the accuracy of evaluating the environmental health status of the liquid flow battery during operation.

[0019] Furthermore, by dividing the energy storage space in equal proportion, the energy stored in the flow battery at a single time is divided. At the same time, the divided energy storage space is used as the collection target. While effectively reducing the collection cycle, it can avoid the problem of the health assessment system being too sensitive or too slow due to the collection cycle being too long or too short, thereby improving the timeliness of evaluating the environmental health status of the flow battery during operation.

[0020] Furthermore, by comparing the same storage environment in different collection cycles, the battery status during charging or discharging is determined, which improves the detection of electrolyte anomalies caused by entering the next charge and discharge cycle without completing charging or discharging, and thus can detect operational failures of the liquid flow battery electrolyte solution in advance, thereby further improving the accuracy of evaluating the environmental health status of the liquid flow battery during operation.

[0021] Furthermore, by setting several offset comparison results and combining them with the division of the energy storage of the flow battery, it is possible to quickly locate abnormal points and discover possible abnormal conditions, so as to perform corresponding health assessments. While effectively improving the sensitivity of the health assessment, it also improves the timeliness of evaluating the environmental health status of the flow battery during operation.

[0022] Compared with the existing technology, the beneficial effect of the present invention is that, by setting up a flow collector, a storage meter, a transmission meter, a standard battery and a central console, the energy storage, discharge and power of the liquid flow battery are respectively evaluated accordingly. While effectively improving the timeliness of the analysis of the liquid flow battery energy storage, the standard battery that can be separated from the liquid flow battery is used as a marker to effectively improve the accuracy of the environmental health status of the liquid flow battery during operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is a flow chart of a method and system for evaluating the health status of a flow battery based on multidimensional data fusion according to the present invention; Figure 2 A flowchart for determining a sub-energy storage space according to an embodiment of the present invention; Figure 3 The figure is a structural diagram of the flow battery health status assessment system based on multi-dimensional data fusion of the present invention. DETAILED DESCRIPTION

[0024] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are merely used to explain the present invention and are not intended to limit the present invention.

[0025] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0026] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside", and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0027] Furthermore, it should be noted that, in the description of the present invention, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed connections, detachable connections, or integral connections; mechanical connections or electrical connections; direct connections or indirect connections through an intermediate medium; and internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0028] See also Figure 1 As shown, it is a flow chart of the invention's method and system for evaluating the health status of a flow battery based on multi-dimensional data fusion, including: Step S1, setting the energy storage space of the flow battery and dividing the energy storage space according to a preset ratio to form a number of initial division marks and a number of corresponding sub-energy storage spaces; Step S2, monitoring the charging process curve and power consumption process curve of each sub-energy storage space; Step S3, obtaining the loss during the charging process and / or the loss during the power consumption process; Step S4, determining a new sub-energy storage space according to the loss during the charging process and / or the loss during the power consumption process; Step S5: comparing the sub-storage space with the new sub-storage space, and determining the corresponding charging health state or power consumption health state according to the offset degree of each sub-storage space; The offset degree is the difference between the division mark corresponding to the new sub-energy storage space and the initial division mark.

[0029] By setting up energy storage space, the maximum energy storage of the flow battery is marked, and by setting up a number of division marks, the energy storage state of the flow battery during a single charge / discharge process can be effectively marked, and the energy storage efficiency and discharge efficiency of the flow battery are determined according to the division marks, thereby determining the health status of the battery during operation. While effectively improving the timeliness of evaluating the health status of the flow battery during operation, it effectively avoids external interference caused by the battery not being in the rated state, thereby effectively improving the accuracy of evaluating the environmental health status of the flow battery during operation.

[0030] In practice, the following divisions can be made: Example 1: Charging health assessment Scenario: A vanadium flow battery system initially divides the total energy storage space (e.g., 100 kWh) into five subspaces (marked 0% to 20%, 20% to 40%, and 80% to 100%) at a 20% ratio.

[0031] Steps to execute: S1: The initial division is marked as A0-A4, corresponding to 20kWh / subspace.

[0032] S2: Monitoring found that the voltage rise rate in the third subspace (40%~60%) was abnormally accelerated during charging.

[0033] S3: Analysis of charging loss shows that this range actually only stores 18kWh, triggering the upper limit (the original design should store 20kWh).

[0034] S4: Recalculate and obtain the new partition marks as [0%~18%, 18%~38%, 38%~54%, 54%~74%, 74%~100%].

[0035] S5: The third subspace (originally 40%-60%) is now marked as 38%-54%, a 6% negative shift (designed 40% → actual 38%). This indicates a decrease in the state of charge (SOH-C) in this range, likely due to reduced charging efficiency caused by aging of the ion exchange membrane.

[0036] Example 2: Discharge health status assessment Scenario: The zinc-bromine flow battery system divides the discharge interval into 10 equal subspaces (one marker for every 10%).

[0037] Steps to execute: S1: Initial markings B0-B9, each 10% corresponds to a discharge capacity of 5kWh.

[0038] S2: The discharge time in the interval marked B7 (70%~80%) was detected to be abnormally prolonged.

[0039] S3: Calculation of discharge loss revealed that the actual energy released in this interval was only 4.2 kWh (designed 5 kWh), and the energy efficiency ratio decreased by 16%.

[0040] S4: Generate new marks [...,70%~82%,82%~94%,...], and expand the B7 interval to the right by 2%.

[0041] S5: The new B7 mark corresponds to 70%~82% vs. the original 70%~80%, showing a 2% positive shift, indicating that the discharge health state (SOH-D) in this range has deteriorated, possibly due to the decrease in bromine diffusion rate, which leads to discharge capacity decay.

[0042] Specifically, in step S1, for a single charge / discharge process, the energy storage space of the flow battery is set to the energy stored in the flow battery during that charge; The preset ratio is based on the energy storage space, and the energy storage space is divided into integer multiples of equal proportions; Each division mark is a division mark corresponding to a preset ratio; The interval divided by adjacent segmentation marks is the sub-energy storage space.

[0043] Specifically, in step S2, for a single charging process, the step of drawing a charging process curve includes: Taking the maximum energy storage of the flow battery as the energy storage space, a sub-energy storage space corresponding to the energy storage space is formed, and a maximum charging target is set; or, Set the maximum charging target and form the corresponding energy storage space and sub-energy storage space; Measure the energy charged into the flow battery per unit time and record the corresponding energy change curve; The corresponding energy change curve is divided according to the sub-energy storage space to form the charging process curve of each sub-energy storage space.

[0044] Among them, the energy change of the flow battery corresponding to the set unit time is no more than 1%.

[0045] By dividing the energy storage space in proportion, the energy stored in a single flow battery is divided. At the same time, the divided energy storage space is used as the collection target. While effectively reducing the collection cycle, it can avoid the problem of the health assessment system being too sensitive or too slow due to the collection cycle being too long or too short, thereby improving the timeliness of evaluating the environmental health status of the flow battery during operation. Example 3

[0046] Flow battery rated maximum energy storage: 200 kWh Default ratio: 5 equal divisions (integer multiple division, each 20% is a subspace) Initial classification mark: A0 (0%), A1 (40kWh), A2 (80kWh), A3 (120kWh), A4 (160kWh), A5 (200kWh) Unit time sampling: Energy is measured every minute, with a change of ≤1% (≤2kWh / minute) Steps to execute: S1: Based on the maximum energy storage of 200 kWh, five subspaces are generated (40 kWh / interval).

[0047] S2: Set the charging target to 180 kWh (below the maximum value).

[0048] By recording the energy change curve in real time, it was found that when charging to A3 (120kWh), the actual time consumed was 15 minutes longer than the design (expected 60 minutes → measured 75 minutes).

[0049] S3: Calculate charging loss: Theoretically, 40kWh should be charged (80kWh→120kWh), but in reality only 38.4kWh was charged (efficiency 96%).

[0050] S4: Dynamically adjust subspace: The new mark A3' is adjusted to 118.4kWh (originally 120kWh) because the actual storage range between 80 and 118.4kWh is 38.4kWh.

[0051] S5: Health status determination: A2-A3 interval offset: (118.4-120) / 200 = -0.8% Conclusion: The decrease in the state of charge health (SOH-C) may be due to the power attenuation of the electrolyte pump, which leads to a decrease in the charging rate in this range. Example 4

[0052] System parameters: Discharge target for this time: 150 kWh (as a benchmark for energy storage space) Preset ratio: 10 equal parts (one subspace for every 15kWh) Initial classification mark: B0 (0kWh), B1 (15kWh)...B10 (150kWh) Unit time sampling: Energy is measured every 10 seconds, and the change is ≤0.5% (≤0.75kWh / 10 seconds) Steps to execute: S1: Based on the discharge target of 150 kWh, 10 subspaces (15 kWh / interval) are generated.

[0053] S2: By monitoring the discharge curve, it was found that the voltage dropped sharply in the B6-B7 range (90~105kWh), and the lower limit was triggered when only 13.8kWh was released.

[0054] S3: Calculate discharge loss: Theoretically, 15kWh should be released, but 13.8kWh was actually released (92% efficiency).

[0055] S4: Dynamically adjust subspace: The new mark B7' is adjusted to 103.8kWh (originally 105kWh) because the actual release range of 90~103.8kWh is 13.8kWh.

[0056] S5: Health status determination: B6-B7 interval offset: (103.8-105) / 150 = -0.8% Conclusion: The deterioration of the state of health of discharge (SOH-D) may be caused by local blockage of the fuel cell stack, resulting in unstable energy output in this range.

[0057] See also Figure 2 As shown, it is a flow chart of determining a sub-energy storage space according to an embodiment of the present invention, including: Step S3a, determining the charging process loss based on the charging curve of each sub-energy storage space and the actual charging curve of the charging device; Step S4a, in response to the charging process loss exceeding the charging loss threshold, adjusting the positions of the division marks corresponding to the sub-energy storage spaces, and forming a new sub-energy storage space; Among them, the charging loss threshold is the difference between the energy obtained by the liquid flow battery and the actual stored energy, which is related to the position of the sub-energy storage space in the liquid flow battery energy storage space and the liquid flow battery's transmission equipment.

[0058] Specifically, the steps for determining a new sub-storage space include: Step S3b, determining the power consumption process loss based on the power consumption curve of each sub-energy storage space and the actual power acquisition curve of the power-consuming equipment; Step S4b, in response to the power consumption process loss exceeding the power consumption loss threshold, adjusting the position of each division mark corresponding to the sub-energy storage space and forming a new sub-energy storage space; Among them, the power loss threshold is related to the energy released by the liquid flow battery and the energy actually obtained by the electrical appliance, which is related to the operating efficiency of the circulation pump of the liquid flow battery and the power transmission equipment of the electrical appliance.

[0059] Specifically, in step S5, the step of determining the offset degree of the sub-energy storage space includes: Determine the preceding charging process curve and preceding power consumption process curve corresponding to the previous charging / discharging process of a single sub-energy storage space, and determine the corresponding position of each division mark; According to the bending path of the previous charging process curve, the charging process curve is offset and compared with the previous charging process curve to form a corresponding charging offset result; According to the bending path of the previous power consumption process curve, the power consumption process curve is offset and compared with the previous power consumption process curve to form a corresponding power consumption offset result.

[0060] By comparing the same storage environment in different collection cycles, the battery status during charging or discharging is determined, which improves the detection of electrolyte anomalies caused by entering the next charge and discharge cycle without completing charging or discharging. This can then enable early detection of operational failures in the electrolyte solution of the flow battery, further improving the accuracy of evaluating the environmental health status of the flow battery during operation.

[0061] Example 5 Charging process loss assessment and dynamic adjustment (steps S3a→S4a→S5) Scenario: During a single charge of a vanadium flow battery, the maximum energy storage capacity is 100 kWh, which is divided into five equal parts (one sub-space for every 20 kWh).

[0062] Step Execution S1: The initial division is marked as C0 (0kWh), C1 (20kWh), C2 (40kWh), C3 (60kWh), C4 (80kWh), and C5 (100kWh).

[0063] S2: Real-time monitoring of the charging curve reveals abnormal charging efficiency in the C2-C3 range (40-60 kWh): Designed charging curve: Linear charging of 20kWh takes 30 minutes (power 40kW).

[0064] Actual charging curve: It takes 36 minutes to charge 20kWh (power 33.3kW), and the voltage fluctuates significantly.

[0065] S3a: Calculate the loss during charging: Theoretical input energy: 40kW × 0.5h = 20kWh.

[0066] Actual stored energy: Only 18.5kWh (92.5% efficiency) via SOC calibration.

[0067] Charging loss threshold: set to 5% (i.e. loss ≤ 1kWh / subspace), actual loss 1.5kWh (exceeding the threshold).

[0068] S4a: Dynamically adjust the partition mark: Due to excessive losses in the C2-C3 interval, the C3 mark was adjusted from 60kWh to 58.5kWh (originally 60kWh - 1.5kWh loss).

[0069] S5: Offset comparison and health diagnosis: Previous charging curve (historical data): The C2-C3 interval is linear and has no fluctuations.

[0070] Current charging curve: Voltage fluctuations occur and the charged energy decreases.

[0071] Charging offset result: The marker position is negatively offset by 1.5 kWh (-1.5% of the total capacity), indicating that the ion exchange membrane in this area may be clogged.

[0072] Example 6 Discharge process loss assessment and dynamic adjustment (steps S3b→S4b→S5) Scenario: Single discharge process of zinc-bromine flow battery, with a discharge target of 80kWh, and preset 4 equal divisions (one subspace for every 20kWh).

[0073] Step Execution S1: The initial divisions are marked as D0 (0 kWh), D1 (20 kWh), D2 (40 kWh), D3 (60 kWh), and D4 (80 kWh).

[0074] S2: Monitoring the discharge curve, it is found that the voltage drops sharply in the D1-D2 interval (20-40kWh): Designed discharge curve: 20kWh is released at a constant current for 40 minutes (power 30kW).

[0075] Actual discharge curve: low voltage protection is triggered after only 18kWh is released (power 27kW, efficiency 90%).

[0076] S3b: Calculate the power consumption process loss: Theoretical energy release: 20kWh.

[0077] Actual power consumption: The electrical appliances received 18kWh (transmission loss + pump efficiency loss = 2kWh).

[0078] Power loss threshold: set to 3% (i.e. loss ≤ 0.6kWh / subspace), actual loss 2kWh (exceeding the threshold).

[0079] S4b: Dynamically adjust the partition mark: Adjusted the D2 marking from 40kWh to 38kWh (original 40kWh - 2kWh loss).

[0080] S5: Offset comparison and health diagnosis: Previous power consumption curve (historical data): 20kWh is released steadily in the D1-D2 interval.

[0081] Current power consumption curve: low voltage protection is triggered in advance.

[0082] Power consumption offset result: The mark position is offset negatively by 2 kWh (-2.5% of the total capacity), indicating that the circulation pump efficiency is reduced or the bromine electrolyte concentration is uneven.

[0083] Specifically, the steps of offset alignment include: Determine the geometric midpoint of each curve; Compare the geometric midpoints of each curve. If the geometric midpoint is closer to the front, it is determined to be a downward shift; if the geometric midpoint is closer to the back, it is determined to be an upward shift. The spacing between the division marks corresponding to each curve is compared. If the spacing is shorter, it is determined to be a contraction offset; if the spacing is longer, it is determined to be an expansion offset.

[0084] Specifically, when determining the state of charge health, the state of the flow battery is determined based on the charge offset result, where: If the charging offset result is a downward offset, it is determined that the power transmission equipment is abnormal; If the charge offset result is an upward offset, it is determined that the electrolyte circulation is abnormal; If the charging offset result is a reduction offset, it is determined that the power transmission equipment is abnormal; If the charge offset result is an expansion offset, it is determined to be an abnormal electrolyte circulation; When determining the power consumption health state, the state of the flow battery is determined based on the power consumption offset result, where: If the power consumption deviation result is a downward deviation, it is determined that the electrolyte is abnormal; If the power consumption deviation result is an upward deviation, it is determined that the electrolyte circulation is abnormal.

[0085] By setting several offset comparison results and combining them with the division of the flow battery's energy storage, it is possible to quickly locate abnormal points and discover possible abnormal conditions, thereby performing corresponding health assessments. This effectively improves the sensitivity of the health assessment while also improving the timeliness of evaluating the environmental health status of the flow battery during operation.

[0086] Example 7: Charging process offset comparison and health diagnosis Scenario: The vanadium flow battery is charged to 80kWh (preset to be divided into 4 equal parts, with each subspace being 20kWh).

[0087] Step Execution Comparison of geometric midpoints of the curve (taking the subspace 40~60kWh as an example): Pre-charging curve: the midpoint is at 50kWh (design value).

[0088] Current charging curve: The midpoint moves forward to 48kWh (due to decreased charging efficiency, actual energy storage is delayed).

[0089] Judgment: Downward offset (geometric midpoint is forward).

[0090] Comparison of the spacing between partition marks: Preamble mark spacing: C2(40kWh) → C3(60kWh) = 20kWh.

[0091] Current marking distance: C2(40kWh) → C3'(58kWh) = 18kWh (adjusted for losses).

[0092] Judgment: Reduce the offset (shorten the interval by 2kWh).

[0093] Health status conclusion: Downward offset + Reduction offset → Abnormality of power transmission equipment (such as aging of the charger power module resulting in insufficient energy input).

[0094] Example 8: Discharge process offset comparison and health diagnosis Scenario: The zinc-bromine flow battery discharges 60kWh (preset to be divided into 3 equal parts, with each subspace being 20kWh).

[0095] Step Execution Comparison of geometric midpoints of the curve (taking the subspace 20~40kWh as an example): Pre-discharge curve: the midpoint is at 30kWh (design value).

[0096] Current discharge curve: the midpoint moves back to 32kWh (due to delayed electrolyte circulation and delayed energy release).

[0097] Judgment: Upward offset (the geometric midpoint is at the back).

[0098] Comparison of the spacing between partition marks: Preamble mark spacing: D1(20kWh) → D2(40kWh) = 20kWh.

[0099] Current marking distance: D1(20kWh) → D2'(42kWh) = 22kWh (adjusted for efficiency fluctuations).

[0100] Judgment: Expansion offset (interval extended by 2kWh).

[0101] Health status conclusion: Upward deviation → Abnormal electrolyte circulation (e.g. insufficient circulation pump speed resulting in uneven distribution of reactants).

[0102] See also Figure 3As shown in FIG, it is a structural diagram of a flow battery health status assessment system based on multi-dimensional data fusion according to the present invention, comprising: A flow collector provided on the circulation pump, which is used to collect the flow parameters of the electrolyte delivered by the circulation pump; An electric storage meter connected to the flow battery stack to collect the stored electric energy of the flow battery; A power meter connected to the battery stack to collect the discharged energy of the flow battery; A standard battery installed on the flow battery and connected to the storage meter and the power transmission meter to mark the actual storage capacity of the flow battery; The central control console is connected to the flow collector, the electricity storage meter, the electricity transmission meter and the standard battery, and is used to determine the actual energy storage ratio of the flow battery based on the power test of the standard battery.

[0103] Specifically, the standard battery is a removable battery, and the composition of its electrolyte is the same as that of the flow battery.

[0104] By setting up flow collectors, storage meters, transmission meters, standard batteries and central consoles, the energy storage, discharge and power of the flow battery are evaluated accordingly. While effectively improving the timeliness of the analysis of the flow battery energy storage, the standard battery that can be separated from the flow battery is used as a marker to effectively improve the accuracy of the environmental health status of the flow battery during operation.

[0105] In practice, the following configuration can be performed: Flow collector: installed at the outlet of the circulation pump to monitor the electrolyte flow in real time (unit: L / min).

[0106] Energy storage meter: connected to the charging end of the flow battery stack to record the charging energy (unit: kWh).

[0107] Power supply meter: connected to the discharge end of the battery stack and records the discharge energy (unit: kWh).

[0108] Standard battery: A removable auxiliary battery with the same electrolyte composition as the main battery, used to calibrate the actual energy storage capacity.

[0109] Center console: Integrates all sensor data and dynamically calculates health status.

[0110] Scenario: abnormality diagnosis during charging process Data collection: Flow collector: Detected that the circulation pump flow rate dropped from the design value of 10 L / min to 8 L / min (↓20%).

[0111] Energy storage meter: shows that the charged energy is 75kWh (the design value should be 80kWh, efficiency 93.75%).

[0112] Standard battery: Synchronous testing showed an actual energy storage ratio of 95% (indicating efficiency loss in the main battery).

[0113] Center console analysis: Charge offset determination: Geometric midpoint: The midpoint of the current charging curve is 5% ahead (downward) compared to the historical data.

[0114] Marking spacing: The spacing between the subspaces 30-50 kWh has been shortened from 20 kWh to 18 kWh (offset reduction).

[0115] Health diagnosis: Downward offset + Reduction offset → Abnormality of power transmission equipment (such as aging of charger power module).

[0116] The decrease in flow rate further verified that the circulation pump was inefficient, resulting in delayed electrolyte delivery.

[0117] Dynamic Adjustment: Recalibrate the sub-storage space: adjust the 50kWh mark to 48kWh (compensate for the 2kWh loss).

[0118] Scenario: Discharge process abnormality diagnosis Data collection: Flow collector: Flow rate fluctuation (8~12 L / min, design value 10 L / min).

[0119] Power transmission meter: released energy is 65kWh (design value 70kWh, efficiency 92.8%).

[0120] Standard battery: The actual energy storage ratio is 98% (indicating that energy loss mainly occurs during discharge).

[0121] Center console analysis: Discharge offset determination: Geometric midpoint: The midpoint of the current discharge curve is 3% later than the historical data (shifted upward).

[0122] Marking spacing: The spacing between subspaces 20-40 kWh has been extended from 20 kWh to 22 kWh (expansion offset).

[0123] Health diagnosis: Upward deviation + expansion deviation → abnormal electrolyte circulation (such as partial blockage of the pipeline).

[0124] Dynamic Adjustment: Recalibrate the sub-storage space: adjust the 40kWh mark to 42kWh (compensate for the 2kWh release delay).

[0125] Thus far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art may make equivalent changes or substitutions to the relevant technical features, and the technical solutions after such changes or substitutions will fall within the scope of protection of the present invention.

[0126] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that the present invention is susceptible to various modifications and variations. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A flow battery health status assessment method based on multidimensional data fusion, characterized in that: include: Step S1, setting the energy storage space of the flow battery and dividing the energy storage space according to a preset ratio to form a number of initial division marks and a number of corresponding sub-energy storage spaces; Step S2, monitoring the charging process curve and power consumption process curve of each sub-energy storage space; Step S3, obtaining the loss during the charging process and / or the loss during the power consumption process; Step S4, determining a new sub-energy storage space according to the charging process loss and / or the power consumption process loss; Step S5: comparing the sub-storage space with the new sub-storage space, and determining the corresponding charging health state or power consumption health state according to the offset degree of each sub-storage space; The offset degree is the difference between the division mark corresponding to the new sub-energy storage space and the initial division mark.

2. The method for evaluating the health status of a flow battery based on multidimensional data fusion according to claim 1, characterized in that: In the step S1, for a single charge / discharge process, the energy storage space of the flow battery is set to the energy stored in the flow battery during the charge; The preset ratio is based on the energy storage space and divides the energy storage space into equal proportions according to integer multiples; Each division mark is a division mark corresponding to a preset ratio; The interval divided by adjacent segmentation marks is the sub-energy storage space.

3. The method for evaluating the health status of a flow battery based on multidimensional data fusion according to claim 2, characterized in that: In step S2, for a single charging process, the step of drawing the charging process curve includes: Taking the maximum energy storage of the flow battery as the energy storage space, forming a sub-energy storage space corresponding to the energy storage space, and setting a maximum charging target; or, Set the maximum charging target and form the corresponding energy storage space and sub-energy storage space; Measuring the energy charged into the flow battery per unit time and recording the corresponding energy change curve; The corresponding energy change curve is divided according to the sub-energy storage space to form a charging process curve of each sub-energy storage space.

4. The method for evaluating the health status of a flow battery based on multidimensional data fusion according to claim 3, characterized in that: The step of determining the new sub-energy storage space includes: Step S3a, determining the charging process loss based on the charging curve of each sub-energy storage space and the actual charging curve of the charging device; Step S4a, in response to the charging process loss exceeding the charging loss threshold, adjusting the positions of the division marks corresponding to the sub-energy storage space and forming a new sub-energy storage space; The charging loss threshold is the difference between the energy obtained by the liquid flow battery and the actual stored energy, which is related to the position of the sub-energy storage space in the liquid flow battery energy storage space and the power transmission equipment of the liquid flow battery.

5. The method for evaluating the health status of a flow battery based on multidimensional data fusion according to claim 3, characterized in that: The step of determining the new sub-energy storage space includes: Step S3b, determining the power consumption process loss based on the power consumption curve of each sub-energy storage space and the actual power acquisition curve of the power-consuming equipment; Step S4b, in response to the power consumption process loss exceeding the power consumption threshold, adjusting the position of each division mark corresponding to the sub-energy storage space and forming a new sub-energy storage space; The power loss threshold is related to the energy released by the flow battery and the energy actually obtained by the electrical appliance, which is related to the operating efficiency of the circulation pump of the flow battery and the power transmission equipment of the electrical appliance.

6. The method for evaluating the health status of a flow battery based on multidimensional data fusion according to claim 4 or 5, characterized in that: In step S5, the step of determining the offset degree of the sub-energy storage space includes: Determine the preceding charging process curve and preceding power consumption process curve corresponding to the previous charging / discharging process of a single sub-energy storage space, and determine the corresponding position of each division mark; According to the bending path of the preceding charging process curve, the charging process curve is offset and compared with the preceding charging process curve to form a corresponding charging offset result; According to the bending path of the preceding power consumption process curve, the power consumption process curve is offset and compared with the preceding power consumption process curve to form a corresponding power consumption offset result.

7. The method for evaluating the health status of a flow battery based on multidimensional data fusion according to claim 6, characterized in that: The step of offset comparison includes: Determine the geometric midpoint of each curve; Compare the geometric midpoints of each curve. If the geometric midpoint is closer to the front, it is determined to be a downward shift; if the geometric midpoint is closer to the back, it is determined to be an upward shift. The spacing between the division marks corresponding to each curve is compared. If the spacing is shorter, it is determined to be a contraction offset; if the spacing is longer, it is determined to be an expansion offset.

8. The method for evaluating the health status of a flow battery based on multidimensional data fusion according to claim 1, characterized in that: In determining the state of health of the charge, the state of the flow battery is determined based on the charge offset result, wherein: If the charging offset result is a downward offset, it is determined that the power transmission equipment is abnormal; If the charge offset result is an upward offset, it is determined that the electrolyte circulation is abnormal; If the charging offset result is a reduction offset, it is determined that the power transmission equipment is abnormal; If the charge offset result is an expansion offset, it is determined to be an abnormal electrolyte circulation; When determining the power consumption health state, the state of the flow battery is determined according to the power consumption offset result, wherein, If the power consumption deviation result is a downward deviation, it is determined that the electrolyte is abnormal; If the power consumption deviation result is an upward deviation, it is determined that the electrolyte circulation is abnormal.

9. A flow battery health status assessment system based on multidimensional data fusion, comprising: A flow collector provided on the circulation pump, which is used to collect the flow parameters of the electrolyte delivered by the circulation pump; An electric storage meter connected to the flow battery stack to collect the stored electric energy of the flow battery; a power transmission meter connected to the battery stack for collecting discharged electrical energy from the flow battery; It is characterized by further comprising: A standard battery is provided on the flow battery and is connected to the electricity storage meter and the electricity transmission meter to mark the actual electricity storage capacity of the flow battery; A central console is connected to the flow collector, the electricity storage meter, the electricity transmission meter and the standard battery, and is used to determine the actual energy storage ratio of the flow battery according to the electric energy test of the standard battery.

10. The flow battery health status assessment system based on multi-dimensional data fusion according to claim 9, characterized in that: The standard battery is a detachable battery, and the composition of its electrolyte is the same as that of the flow battery.

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