Flow battery health state evaluation method and system based on multi-dimensional data fusion

Abstract

The present application relates to the technical field of battery monitoring, and particularly relates to a flow battery health state evaluation method and system based on multi-dimensional data fusion, which comprises the following steps: setting an energy storage space of a flow battery, 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 the charging process curve and the 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 the charging health state or the power consumption health state according to the offset degree of each sub energy storage space; and using the method of setting a plurality of division marks, so that the energy storage state of the flow battery during a single charging / discharging process can be effectively marked, 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 during operation is effectively improved.

Description

Technical Field

[0001] This invention relates to the field of battery monitoring technology, and in particular to a method and system for assessing the health status of flow batteries based on multi-dimensional data fusion. Background Technology

[0002] As a type of battery with high discharge efficiency, flow batteries are currently experiencing rapid development. Because their electrolyte is in a real-time circulation state and has strong corrosiveness, the health status of flow batteries and flow battery equipment needs to be monitored in real time.

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

[0004] To address this, the aforementioned technologies need to overcome engineering challenges such as sensor durability and real-time data delivery.

[0005] Chinese Patent Publication No. CN117239186B discloses an online evaluation device and method for the State of Health (SOH) of a vanadium redox flow battery system, comprising the following steps: a battery management system collects data parameters; the electrolyte health is calculated based on the volume, concentration, and valence state of the positive and negative electrolytes; the working stack internal resistance is calculated based on the number of cells, voltage, and current of the working stack, the voltage and number of cells of the test stack; the stack health is calculated based on the working stack internal resistance and the initial internal resistance; and the SOH of the vanadium redox flow battery system is calculated based on the electrolyte health and the stack health. This invention can comprehensively calculate the SOH of the battery system based on both electrolyte health and stack health, and is applicable to real-time monitoring of the SOH of a vanadium redox flow battery system, which is of significant importance.

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

[0007] To address this, the present invention provides a method and system for assessing the health status of flow batteries based on multi-dimensional data fusion. This overcomes the problems in the prior art where it is impossible to effectively assess the health status of the flow battery's operating environment simply by collecting its parameters. Furthermore, it is difficult to avoid damage to the flow battery's metering equipment, which leads to a loss of accuracy in assessing the flow battery's operating status.

[0008] To achieve the above objectives, on the one hand, the present invention provides a method for assessing the health status of flow batteries based on multi-dimensional data fusion, comprising:

[0009] Step S1: Set the energy storage space of the flow battery and divide the energy storage space according to a preset ratio to form several initial division marks and several corresponding sub-energy storage spaces.

[0010] Step S2: Monitor the charging process curve and power consumption process curve of each sub-energy storage space;

[0011] Step S3: Obtain the charging process loss and / or the power consumption process loss;

[0012] Step S4: Determine a new sub-energy storage space based on the charging process loss and / or the power consumption process loss;

[0013] Step S5: Compare the sub-energy storage space with the new sub-energy storage space, and determine the corresponding charging health status or power consumption health status based on the degree of offset of each sub-energy storage space.

[0014] The degree of offset refers to the difference between the partition mark corresponding to the new sub-energy storage space and the initial partition mark.

[0015] Furthermore, in step S1, for a single charge / discharge process, the energy storage space of the flow battery is set to the energy stored by the flow battery during that charge.

[0016] The preset ratio is based on the energy storage space and divides the energy storage space into integer multiples of each other.

[0017] Each division marker is a segmentation marker corresponding to a preset ratio;

[0018] The intervals divided by adjacent dividing marks are the sub-energy storage spaces.

[0019] Further, in step S2, the step of plotting the charging process curve for a single charging process includes:

[0020] Using the maximum energy storage capacity 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;

[0021] or,

[0022] Set a maximum charging target and form a corresponding energy storage space and sub-energy storage space;

[0023] The energy charged into the flow battery is measured per unit time, and the corresponding energy change curve is recorded;

[0024] The energy change curves corresponding to the sub-energy storage spaces are divided to form the charging process curves of each sub-energy storage space.

[0025] Furthermore, the step of determining the new sub-energy storage space includes:

[0026] Step S3a: Determine the charging process loss based on the charging curves of each sub-energy storage space and the actual charging curve of the charging equipment.

[0027] Step S4a: In response to the charging process loss exceeding the charging loss threshold, adjust the positions of each division mark corresponding to the sub-energy storage space and form a new sub-energy storage space;

[0028] The charging loss threshold is the difference between the energy acquired by the flow battery and the actual stored energy, which is related to the location of the sub-energy storage space within the flow battery energy storage space and the power transmission equipment of the flow battery.

[0029] Furthermore, the step of determining the new sub-energy storage space includes:

[0030] Step S3b: Determine 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 electrical equipment.

[0031] Step S4b: In response to the power consumption process loss exceeding the power consumption loss threshold, adjust the position of each division mark corresponding to the sub-energy storage space and form a new sub-energy storage space;

[0032] The power consumption threshold is related to the energy released by the flow battery and the energy actually obtained by the electrical appliance, and it is related to the operating efficiency of the flow battery's circulation pump and the power transmission equipment of the electrical appliance.

[0033] Further, in step S5, the step of determining the degree of offset of the sub-energy storage space includes:

[0034] Determine the preceding charging process curve and preceding power consumption process curve of a single sub-energy storage space in the previous charging / discharging process, and determine the corresponding position of each division mark;

[0035] Based on the bending path of the preceding charging process curve, the offset of the charging process curve is compared with that of the preceding charging process curve to form a corresponding charging offset result.

[0036] Based on the bending path of the preceding power consumption process curve, the offset of the power consumption process curve is compared with that of the preceding power consumption process curve to form a corresponding power consumption offset result.

[0037] Furthermore, the offset comparison step includes:

[0038] Determine the geometric midpoint of each curve;

[0039] Compare the geometric midpoints of each curve. If the geometric midpoint is earlier, it is determined to be a downward shift; if the geometric midpoint is later, it is determined to be an upward shift.

[0040] Compare the spacing of the dividing marks corresponding to each curve. If the spacing is shorter, it is determined to be a shrinkage offset; if the spacing is longer, it is determined to be an expansion offset.

[0041] Furthermore, when determining the charging health state, the state of the flow battery is determined based on the charging offset result, wherein,

[0042] If the charging offset result is downward offset, it is determined that the power transmission equipment is abnormal;

[0043] If the charging offset result is upward, it is determined to be an abnormal electrolyte circulation;

[0044] If the charging offset result is a reduced offset, it is determined that the power transmission equipment is abnormal;

[0045] If the charging offset result is an expansion offset, it is determined to be an electrolyte circulation abnormality;

[0046] When determining the power consumption health state, the state of the flow battery is determined based on the power consumption offset result, wherein...

[0047] If the power consumption offset result is downward, it is determined to be an electrolyte abnormality;

[0048] If the power consumption offset result is upward, it is determined to be an abnormal electrolyte circulation.

[0049] On the other hand, the present invention provides a flow battery health status assessment system based on multi-dimensional data fusion, comprising:

[0050] The flow acquisition device installed on the circulating pump is used to collect the flow parameters of the electrolyte delivered by the circulating pump.

[0051] A storage meter, which is connected to the fuel cell stack of a flow battery, is used to collect the stored electrical energy of the flow battery.

[0052] A power meter, connected to the battery stack, is used to collect the discharge energy of the flow battery;

[0053] The standard battery installed on the flow battery is connected to the energy storage meter and the power supply meter to mark the actual energy storage capacity of the flow battery.

[0054] The central control unit is connected to the flow collector, the energy storage meter, the power supply meter, and the standard battery, and is used to determine the actual energy storage ratio of the flow battery based on the energy test of the standard battery.

[0055] Furthermore, the standard battery is a removable battery, and its electrolyte composition is the same as that of the flow battery.

[0056] Compared with the prior art, the beneficial effects of the present invention are that by setting up an energy storage space, the maximum energy storage of the flow battery is marked, and by setting up several division marks, the energy storage state of the flow battery during a single charge / discharge process can be effectively marked. The energy storage efficiency and discharge efficiency of the flow battery are determined according to the division marks, thereby determining the health state of the battery during operation. This effectively improves the timeliness of evaluating the health state of the flow battery during operation, while effectively avoiding external interference caused by the battery not being in its rated state, thus effectively improving the accuracy of evaluating the environmental health state of the flow battery during operation.

[0057] Furthermore, by dividing the energy storage space proportionally, the energy stored in a single charge by the flow battery is divided. At the same time, the divided energy storage space is used as the data collection target. This effectively reduces the data collection cycle and avoids the problem of the health assessment system being too sensitive or too sluggish due to the data collection cycle being too long or too short. This improves the timeliness of evaluating the environmental health status of the flow battery during operation.

[0058] Furthermore, by comparing the same energy storage environment in different collection cycles, the battery status during charging or discharging can be determined, which improves the detection of electrolyte abnormalities caused by entering the next charge / discharge cycle before the charging or discharging is completed. This allows for the early detection of operational faults in the electrolyte solution of the flow battery, thereby further improving the accuracy of evaluating the environmental health status of the flow battery during operation.

[0059] Furthermore, by setting several offset comparison results and combining them with the energy storage division of the flow battery, abnormal points can be quickly located and possible abnormal states can be discovered, thereby enabling corresponding health assessments. This effectively improves the sensitivity of health assessments and enhances the timeliness of evaluating the environmental health status of the flow battery during operation.

[0060] Compared with the prior art, the beneficial effects of the present invention are that by setting up a flow collector, a storage meter, a power supply meter, a standard battery, and a central control console, the energy storage, discharge, and power of the flow battery are evaluated respectively. This effectively improves the timeliness of the analysis of the energy storage of the flow battery, while using a standard battery that can be separated from the flow battery as a marker, effectively improving the accuracy of the environmental health status of the flow battery during operation. Attached Figure Description

[0061] Figure 1 The flowchart below shows the flow battery health status assessment method and system based on multi-dimensional data fusion according to the present invention.

[0062] Figure 2 This is a flowchart illustrating the determination of sub-energy storage space in an embodiment of the present invention;

[0063] Figure 3 This is a schematic diagram of the structure of the flow battery health status assessment system based on multi-dimensional data fusion according to the present invention. Detailed Implementation

[0064] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0065] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0066] It should be noted that in the description of this invention, the terms "upper", "lower", "left", "right", "inner", "outer", etc., which indicate 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 is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0067] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0068] Please see Figure 1The flowchart shown is a presentation of the invention's method and system for assessing the health status of flow batteries based on multi-dimensional data fusion, including:

[0069] Step S1: Set the energy storage space of the flow battery and divide the energy storage space according to a preset ratio to form several initial division marks and several corresponding sub-energy storage spaces.

[0070] Step S2: Monitor the charging process curve and power consumption process curve of each sub-energy storage space;

[0071] Step S3: Obtain the charging process loss and / or the power consumption process loss;

[0072] Step S4: Determine a new sub-energy storage space based on the charging process loss and / or the power consumption process loss;

[0073] Step S5: Compare the sub-energy storage space with the new sub-energy storage space, and determine the corresponding charging health status or power consumption health status based on the degree of offset of each sub-energy storage space.

[0074] The degree of offset is the difference between the partition mark corresponding to the new sub-energy storage space and the initial partition mark.

[0075] By setting up energy storage space, the maximum energy storage capacity of the flow battery is marked. By setting several division marks, the energy storage state of the flow battery during a single charge / discharge cycle can be effectively marked. Based on the division marks, the energy storage efficiency and discharge efficiency of the flow battery are determined, thereby determining the health status of the battery during operation. This effectively improves the timeliness of evaluating the health status of the flow battery during operation, while effectively avoiding external interference caused by the battery not being in its rated state, thus effectively improving the accuracy of evaluating the environmental health status of the flow battery during operation.

[0076] In implementation, the following divisions can be made:

[0077] Example 1:

[0078] Charging health status assessment

[0079] Scenario: A vanadium redox flow battery system initially divides its total energy storage space (e.g., 100kWh) into 5 subspaces at a ratio of 20% (labeled as 0%~20%, 20%~40%...80%~100%).

[0080] Steps to be executed:

[0081] S1: The initial partitioning is marked as A0-A4, corresponding to a subspace of 20kWh / space.

[0082] S2: Monitoring revealed that the voltage rise rate was abnormally fast during charging of the third subspace (40%~60%).

[0083] S3: Analysis of charging loss shows that only 18kWh was actually stored in this range before the upper limit was triggered (the original design should have stored 20kWh).

[0084] S4: The new division markers are obtained by recalculation as [0%~18%, 18%~38%, 38%~54%, 54%~74%, 74%~100%].

[0085] S5: The third subspace (originally 40%~60%) is now marked as 38%~54%, showing a negative shift of 6% (design 40% → actual 38%). It is determined that the charging health status (SOH-C) in this range has decreased, which may be due to the aging of the ion exchange membrane leading to a decrease in charging efficiency.

[0086] Example 2:

[0087] Discharge health status assessment

[0088] Scenario: The zinc-bromine flow battery system divides the discharge range into 10 equal subspaces (each marked with a 10% mark).

[0089] Steps to be executed:

[0090] S1: Initial markings B0-B9, each 10% corresponds to a discharge capacity of 5kWh.

[0091] S2: An abnormally prolonged discharge time was detected in the marked B7 (70%~80%) region.

[0092] S3: Calculation of discharge loss revealed that the actual energy released in this range was only 4.2kWh (designed for 5kWh), resulting in a 16% decrease in energy efficiency.

[0093] S4: Generate new markers [...,70%~82%,82%~94%,...], and extend the B7 interval to the right by 2%.

[0094] S5: The new B7 label corresponds to 70%~82% vs the original 70%~80%, showing a 2% positive shift, indicating that the discharge health status (SOH-D) in this range has deteriorated, possibly due to a decrease in the bromine diffusion rate leading to a decrease in discharge capacity.

[0095] Specifically, in step S1, for a single charge / discharge process, the energy storage space of the flow battery is set to the energy stored by the flow battery during that charge.

[0096] The preset ratio is based on the energy storage space, and the energy storage space is divided into equal parts in integer multiples.

[0097] Each division marker is a segmentation marker corresponding to a preset ratio;

[0098] The intervals divided by adjacent dividing marks are sub-energy storage spaces.

[0099] Specifically, in step S2, the step of plotting the charging process curve for a single charging process includes:

[0100] Using the maximum energy storage capacity of the flow battery as the energy storage space, sub-energy storage spaces corresponding to the energy storage space are formed, and a maximum charging target is set;

[0101] or,

[0102] Set a maximum charging target and form a corresponding energy storage space and sub-energy storage space;

[0103] The energy charged into the flow battery is measured per unit time, and the corresponding energy change curve is recorded;

[0104] The energy change curves are divided according to the sub-energy storage space to form the charging process curves of each sub-energy storage space.

[0105] The set change in flow battery energy per unit time is no higher than 1%.

[0106] By dividing the energy storage space proportionally, the energy stored in a single flow battery is divided. At the same time, the divided energy storage space is used as the data acquisition target. This effectively reduces the data acquisition cycle and avoids the problem of the health assessment system being too sensitive or too sluggish due to the data acquisition cycle being too long or too short. This improves the timeliness of evaluating the environmental health status of the flow battery during operation. Example 3

[0107] Rated maximum energy storage of flow battery: 200 kWh

[0108] Preset ratio: 5 equal parts (divided into integer multiples, each 20% is a subspace)

[0109] Initial partition markers: A0 (0%), A1 (40kWh), A2 (80kWh), A3 (120kWh), A4 (160kWh), A5 (200kWh)

[0110] Sampling per unit time: Energy is measured every minute, with a change of ≤1% (≤2kWh / minute).

[0111] Steps to be executed:

[0112] S1: Based on a maximum energy storage of 200 kWh, 5 subspaces (40 kWh / interval) are generated.

[0113] S2: Set the charging target for this charge to 180 kWh (not yet at maximum).

[0114] Real-time recording of the energy change curve revealed that when charging to A3 (120kWh), the actual charging time was 15 minutes longer than designed (expected 60 minutes → actual measurement 75 minutes).

[0115] S3: Calculate charging loss:

[0116] Theoretically, it should be charged with 40kWh (80kWh→120kWh), but in reality, only 38.4kWh was charged (efficiency 96%).

[0117] S4: Dynamically adjust subspace:

[0118] The new label A3' has been adjusted to 118.4kWh (originally 120kWh), because the actual storage capacity in the 80~118.4kWh range is 38.4kWh.

[0119] S5: Health Status Assessment

[0120] Offset between A2 and A3: (118.4-120) / 200 = -0.8%

[0121] Conclusion: The decrease in State of Health at Charge (SOH-C) may be due to the reduction in the charging rate in this range caused by the decay of electrolyte pump power. Example 4

[0122] System parameters:

[0123] The target discharge for this project is 150 kWh (as a benchmark for energy storage space).

[0124] Preset ratio: 10 equal parts (one subspace per 15kWh)

[0125] Initial partition markers: B0 (0 kWh), B1 (15 kWh)...B10 (150 kWh)

[0126] Sampling per unit time: Energy is measured every 10 seconds, with a change of ≤0.5% (≤0.75kWh / 10 seconds).

[0127] Steps to be executed:

[0128] S1: Based on a discharge target of 150 kWh, generate 10 subspaces (15 kWh / interval).

[0129] S2:

[0130] Monitoring the discharge curve revealed a sudden voltage drop in the B6-B7 range (90~105kWh), with only 13.8kWh released before triggering the lower limit.

[0131] S3: Calculate discharge loss:

[0132] Theoretically, it should release 15kWh, but in reality, it released 13.8kWh (efficiency 92%).

[0133] S4: Dynamically adjust subspace:

[0134] The new label B7' has been adjusted to 103.8kWh (originally 105kWh), because the actual release in the 90~103.8kWh range is 13.8kWh.

[0135] S5: Health Status Assessment

[0136] Offset in the B6-B7 interval: (103.8-105) / 150 = -0.8%

[0137] Conclusion: The deterioration of the state of discharge health (SOH-D) may be due to the instability of energy output in this region caused by local blockage of the fuel cell stack.

[0138] Please see Figure 2 As shown, it is a flowchart for determining the sub-energy storage space according to an embodiment of the present invention, including:

[0139] Step S3a: Determine the charging process loss based on the charging curves of each sub-energy storage space and the actual charging curve of the charging equipment.

[0140] Step S4a: In response to the charging process loss exceeding the charging loss threshold, adjust the positions of the division marks corresponding to the sub-energy storage space and form a new sub-energy storage space.

[0141] The charging loss threshold is the difference between the energy acquired by the flow battery and the actual stored energy. It is related to the location of the sub-energy storage space within the flow battery's energy storage space and the flow battery's power transmission equipment.

[0142] Specifically, the steps to determine the new sub-storage space include:

[0143] Step S3b: Determine 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 electrical equipment.

[0144] Step S4b: In response to the power consumption process loss exceeding the power consumption loss threshold, adjust the positions of the division marks corresponding to the sub-energy storage space and form a new sub-energy storage space;

[0145] Among them, the power consumption loss threshold is related to the energy released by the flow battery and the energy actually obtained by the electrical appliance. It is related to the operating efficiency of the flow battery's circulation pump and the power transmission equipment of the electrical appliance.

[0146] Specifically, in step S5, the step of determining the degree of offset of the sub-energy storage space includes:

[0147] Determine the preceding charging process curve and preceding power consumption process curve of a single sub-energy storage space in the previous charging / discharging process, and determine the corresponding position of each division mark;

[0148] Based on the bending path of the previous charging process curve, the offset of the charging process curve is compared with that of the previous charging process curve to form the corresponding charging offset result.

[0149] Based on the bending path of the previous power consumption process curve, the offset of the power consumption process curve is compared with that of the previous power consumption process curve to form the corresponding power consumption offset result.

[0150] By comparing the same energy storage environment in different collection cycles, the battery status during charging or discharging can be determined, which improves the detection of electrolyte abnormalities caused by entering the next charge / discharge cycle before the charging or discharging is completed. This allows for the early detection of operational faults in the electrolyte solution of the flow battery, thereby further improving the accuracy of evaluating the environmental health status of the flow battery during operation.

[0151] Example 5

[0152] Charging process loss assessment and dynamic adjustment (steps S3a→S4a→S5)

[0153] Scenario: A vanadium redox flow battery has a maximum energy storage capacity of 100kWh during a single charge, and is pre-divided into 5 equal parts (each sub-space is 20kWh).

[0154] Step Execution

[0155] S1: The initial partitioning is marked as C0 (0kWh), C1 (20kWh), C2 (40kWh), C3 (60kWh), C4 (80kWh), and C5 (100kWh).

[0156] S2: Real-time monitoring of the charging curve revealed abnormal charging efficiency in the C2-C3 range (40~60kWh):

[0157] Designed charging curve: Linear charging of 20kWh takes 30 minutes (power 40kW).

[0158] Actual charging curve: It took 36 minutes to charge 20kWh (power 33.3kW), and the voltage fluctuated significantly.

[0159] S3a: Calculate the charging process loss:

[0160] Theoretical input energy: 40kW × 0.5h = 20kWh.

[0161] Actual storage energy: only 18.5 kWh (92.5% efficiency) after SOC calibration.

[0162] Charging loss threshold: set at 5% (i.e., loss ≤ 1kWh / subspace), actual loss 1.5kWh (exceeding the threshold).

[0163] S4a: Dynamically adjust partition markers:

[0164] Due to excessive losses in the C2-C3 range, the C3 rating has been adjusted from 60kWh to 58.5kWh (originally 60kWh - 1.5kWh loss).

[0165] S5: Offset Comparison and Health Diagnosis:

[0166] Pre-charge curve (historical data): The C2-C3 interval is linear with no fluctuations.

[0167] Current charging curve: Voltage fluctuations and reduced charging energy are observed.

[0168] Charging offset results: The marked position is offset negatively by 1.5 kWh (-1.5% of total capacity), indicating that the ion exchange membrane in this section may be clogged.

[0169] Example 6

[0170] Discharge process loss assessment and dynamic adjustment (steps S3b→S4b→S5)

[0171] Scenario: A single discharge process of a zinc-bromine flow battery, with a discharge target of 80kWh, pre-defined as 4 equal parts (each 20kWh subspace).

[0172] Step Execution

[0173] S1: The initial partitioning is marked as D0 (0kWh), D1 (20kWh), D2 (40kWh), D3 (60kWh), and D4 (80kWh).

[0174] S2: Monitoring the discharge curve revealed a sudden voltage drop in the D1-D2 range (20~40kWh):

[0175] Designed discharge curve: Constant current discharge of 20kWh takes 40 minutes (power 30kW).

[0176] Actual discharge curve: Low voltage protection is triggered after only 18kWh is released (power 27kW, efficiency 90%).

[0177] S3b: Calculate power consumption process losses:

[0178] Theoretical energy release: 20kWh.

[0179] Actual received electrical energy: 18kWh (transmission loss + pump efficiency loss = 2kWh) was measured by the appliance.

[0180] Power consumption loss threshold: set at 3% (i.e., loss ≤ 0.6kWh / subspace), actual loss is 2kWh (exceeding the threshold).

[0181] S4b: Dynamically adjust partition markers:

[0182] The D2 label was adjusted from 40kWh to 38kWh (originally 40kWh - 2kWh loss).

[0183] S5: Offset Comparison and Health Diagnosis:

[0184] Previous power consumption curve (historical data): 20kWh is released steadily in the D1-D2 range.

[0185] Current power consumption curve: Low voltage protection is triggered prematurely.

[0186] Power consumption offset results: The marker position shifted negatively by 2 kWh (-2.5% of total capacity), indicating a decrease in circulation pump efficiency or uneven bromine electrolyte concentration.

[0187] Specifically, the offset alignment steps include:

[0188] Determine the geometric midpoint of each curve;

[0189] Compare the geometric midpoints of each curve. If the geometric midpoint is earlier, it is determined to be a downward shift; if the geometric midpoint is later, it is determined to be an upward shift.

[0190] Compare the spacing of the dividing marks corresponding to each curve. If the spacing is shorter, it is determined to be a shrinkage offset; if the spacing is longer, it is determined to be an expansion offset.

[0191] Specifically, when determining the charging health status, the state of the flow battery is determined based on the charging offset results, wherein...

[0192] If the charging offset result is downward offset, it is determined that the power transmission equipment is abnormal;

[0193] If the charging offset result is upward, it is determined to be an abnormal electrolyte circulation;

[0194] If the charging offset result is a reduced offset, it is determined that the power transmission equipment is abnormal;

[0195] If the charging offset result is an expansion offset, it is determined to be an electrolyte circulation abnormality;

[0196] When determining the power consumption health status, the state of the flow battery is determined based on the power consumption offset results, whereby...

[0197] If the power consumption offset result is downward, it is determined to be an electrolyte abnormality;

[0198] If the power consumption offset result is upward, it is determined to be an abnormal electrolyte circulation.

[0199] By setting several offset comparison results and combining them with the energy storage division of the flow battery, abnormal points can be quickly located and potential abnormal states can be detected, thereby enabling corresponding health assessments. This effectively improves the sensitivity of health assessments and enhances the timeliness of evaluating the environmental health status of the flow battery during operation.

[0200] Example 7: Charging process offset comparison and health diagnosis

[0201] Scenario: Vanadium redox flow battery is charged to 80kWh (preset to be divided into 4 equal parts, each 20kWh subspace).

[0202] Step Execution

[0203] Curve geometric midpoint comparison (taking a subspace of 40~60kWh as an example):

[0204] Pre-charge curve: Midpoint is at 50kWh (design value).

[0205] Current charging curve: The midpoint has shifted forward to 48kWh (due to decreased charging efficiency and a delay in actual energy storage).

[0206] Judgment: Downward offset (geometric midpoint forward).

[0207] Comparison of marker spacing:

[0208] Preceding marker spacing: C2(40kWh) → C3(60kWh) = 20kWh.

[0209] Current marker spacing: C2 (40kWh) → C3' (58kWh) = 18kWh (adjusted for losses).

[0210] Decision: Reduce offset (spacing shortened by 2kWh).

[0211] Health status conclusion:

[0212] Downward offset + reduced offset → Power transmission equipment malfunction (e.g., insufficient energy input due to aging of the charger power module).

[0213] Example 8: Discharge Process Offset Comparison and Health Diagnosis

[0214] Scenario: Zinc-bromine flow battery discharges 60kWh (preset to be divided into 3 equal parts, each 20kWh subspace).

[0215] Step Execution

[0216] Curve geometric midpoint comparison (taking a subspace of 20~40kWh as an example):

[0217] Pre-discharge curve: Midpoint is at 30kWh (design value).

[0218] Current discharge curve: The midpoint has shifted to 32kWh (due to delayed electrolyte circulation and lagging energy release).

[0219] Judgment: Upward offset (geometric midpoint further back).

[0220] Comparison of marker spacing:

[0221] Preceding marker spacing: D1(20kWh) → D2(40kWh) = 20kWh.

[0222] Current marker spacing: D1(20kWh) → D2'(42kWh) = 22kWh (adjusted due to efficiency fluctuations).

[0223] Judgment: Expansion offset (spacing extended by 2kWh).

[0224] Health status conclusion:

[0225] Upward shift → Abnormal electrolyte circulation (such as insufficient circulation pump speed leading to uneven distribution of reactants).

[0226] Please see Figure 3 As shown, it is a schematic diagram of the structure of the flow battery health status assessment system based on multi-dimensional data fusion of the present invention, including:

[0227] The flow acquisition device installed on the circulating pump is used to collect the flow parameters of the electrolyte delivered by the circulating pump.

[0228] A storage meter, which is connected to the fuel cell stack of a flow battery, is used to collect the stored electrical energy of the flow battery.

[0229] A power meter, connected to the battery stack, is used to collect the discharge energy of the flow battery;

[0230] A standard battery is installed on the flow battery and connected to a storage meter and a power supply meter to mark the actual energy storage capacity of the flow battery.

[0231] The central control unit is connected to the flow collector, energy storage meter, power supply meter, and standard battery, and is used to determine the actual energy storage ratio of the flow battery based on the energy test of the standard battery.

[0232] Specifically, the standard battery is a removable battery, and its electrolyte composition is the same as that of the flow battery.

[0233] By setting up a flow collector, energy storage meter, power supply meter, standard battery, and central control console, the energy storage, discharge, and power of the flow battery are evaluated respectively. This effectively improves the timeliness of the analysis of the flow battery's energy storage, while using a standard battery that can be separated from the flow battery as a marker effectively improves the accuracy of the environmental health status of the flow battery during operation.

[0234] In implementation, the following configurations can be made:

[0235] Flow meter: Installed at the outlet of the circulating pump to monitor the electrolyte flow rate (unit: L / min) in real time.

[0236] Energy storage meter: Connected to the charging terminal of the flow battery stack to record the charging energy (unit: kWh).

[0237] Power meter: Connected to the discharge end of the fuel cell stack to record the discharge energy (unit: kWh).

[0238] Standard battery: A removable auxiliary battery with the same electrolyte composition as the main battery, used to calibrate actual energy storage capacity.

[0239] Central control panel: Integrates data from all sensors to dynamically calculate health status.

[0240] Scenario: Diagnosing abnormalities during the charging process

[0241] Data collection:

[0242] Flow meter: The circulation pump flow rate was detected to have decreased from the design value of 10 L / min to 8 L / min (↓20%).

[0243] Energy storage meter: Displays charged energy of 75kWh (design value should be 80kWh, efficiency 93.75%).

[0244] Standard battery: Synchronous testing showed that the actual energy storage ratio was 95% (indicating that there is an efficiency loss in the main battery).

[0245] Center console analysis:

[0246] Charging offset determination:

[0247] Geometric midpoint: The current charging curve midpoint is shifted forward by 5% (downward shift) compared to historical data.

[0248] Marker spacing: The 30~50kWh subspace spacing is reduced from 20kWh to 18kWh (reduced offset).

[0249] Health diagnosis:

[0250] Downward offset + reduced offset → Power transmission equipment malfunction (e.g., aging of charger power module).

[0251] The decrease in flow rate further confirms that the circulation pump is inefficient, leading to a delay in electrolyte delivery.

[0252] Dynamic adjustment:

[0253] Recalibrate the sub-storage space: adjust the 50kWh label to 48kWh (to compensate for 2kWh loss).

[0254] Scenario: Diagnosis of abnormal discharge processes

[0255] Data collection:

[0256] Flow collector: Flow fluctuation (8~12 L / min, design value 10 L / min).

[0257] Power meter: Releases 65kWh of energy (design value 70kWh, efficiency 92.8%).

[0258] Standard battery: The actual energy storage ratio is 98% (indicating that energy loss mainly occurs during the discharge stage).

[0259] Center console analysis:

[0260] Discharge offset determination:

[0261] Geometric midpoint: The midpoint of the current discharge curve has shifted 3% backward (upward) compared to historical data.

[0262] Marker spacing: The spacing between subspaces from 20kWh to 40kWh is extended from 20kWh to 22kWh (expansion offset).

[0263] Health diagnosis:

[0264] Upward offset + expansion offset → Abnormal electrolyte circulation (such as partial blockage in the pipeline).

[0265] Dynamic adjustment:

[0266] Recalibrate the sub-storage space: adjust the 40kWh label to 42kWh (to compensate for the 2kWh release delay).

[0267] The technical solution of the present invention has been described above with reference to 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 can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

[0268] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for assessing the health status of flow batteries based on multi-dimensional data fusion, characterized in that, include: Step S1: Set the energy storage space of the flow battery and divide the energy storage space according to a preset ratio to form several initial division marks and several corresponding sub-energy storage spaces. Step S2: Monitor the charging process curve and power consumption process curve of each sub-energy storage space; Step S3: Obtain the charging process loss and / or the power consumption process loss; Step S3a: Determine the charging process loss based on the charging curves of each sub-energy storage space and the actual charging curve of the charging equipment. Step S3b: Determine 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 electrical equipment. Step S4: Determine a new sub-energy storage space based on the charging process loss and / or the power consumption process loss; Step S5: Compare the sub-energy storage space with the new sub-energy storage space, and determine the corresponding charging health status or power consumption health status based on the degree of offset of each sub-energy storage space. Wherein, the degree of offset is the difference between the partition mark corresponding to the new sub-energy storage space and the initial partition mark; for a single charge / discharge process, the energy storage space of the flow battery is set to the energy stored by the flow battery during that charge; The preset ratio is to divide the energy storage space proportionally based on the energy storage space. Each division marker is a segmentation marker corresponding to a preset ratio; The intervals divided by adjacent dividing marks are the sub-energy storage spaces.

2. The method for assessing the health status of a flow battery based on multi-dimensional data fusion according to claim 1, characterized in that, In step S2, the step of plotting the charging process curve for a single charging process includes: Using the maximum energy storage capacity 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 a maximum charging target and form a corresponding energy storage space and sub-energy storage space; The energy charged into the flow battery is measured per unit time, and the corresponding energy change curve is recorded; The energy change curves corresponding to the sub-energy storage spaces are divided to form the charging process curves of each sub-energy storage space.

3. The method for assessing the health status of a flow battery based on multi-dimensional data fusion according to claim 2, characterized in that, The steps for determining the new sub-energy storage space include: Step S4a: In response to the charging process loss exceeding the charging loss threshold, adjust the positions of each division mark corresponding to the sub-energy storage space and form a new sub-energy storage space; The charging loss threshold is the difference between the energy acquired by the flow battery and the actual stored energy, which is related to the location of the sub-energy storage space within the flow battery energy storage space and the power transmission equipment of the flow battery.

4. The method for assessing the health status of a flow battery based on multi-dimensional data fusion according to claim 2, characterized in that, The steps for determining the new sub-energy storage space include: Step S4b: In response to the power consumption process loss exceeding the power consumption loss threshold, adjust the position of each division mark corresponding to the sub-energy storage space and form a new sub-energy storage space; The power consumption threshold is related to the energy released by the flow battery and the energy actually obtained by the electrical appliance, and it is related to the operating efficiency of the flow battery's circulation pump and the power transmission equipment of the electrical appliance.

5. The method for assessing the health status of a flow battery based on multidimensional data fusion according to claim 3 or 4, characterized in that, In step S5, the step of determining the degree of offset of the sub-energy storage space includes: Determine the preceding charging process curve and preceding power consumption process curve of a single sub-energy storage space in the previous charging / discharging process, and determine the corresponding position of each division mark; Based on the bending path of the preceding charging process curve, the offset of the charging process curve is compared with that of the preceding charging process curve to form a corresponding charging offset result. Based on the bending path of the preceding power consumption process curve, the offset of the power consumption process curve is compared with that of the preceding power consumption process curve to form a corresponding power consumption offset result.

6. The method for assessing the health status of a flow battery based on multi-dimensional data fusion according to claim 5, characterized in that, The offset comparison step includes: Determine the geometric midpoint of each curve; Compare the geometric midpoints of each curve. If the geometric midpoint is earlier, it is determined to be a downward shift; if the geometric midpoint is later, it is determined to be an upward shift. Compare the spacing of the dividing marks corresponding to each curve. If the spacing is shorter, it is determined to be a shrinkage offset; if the spacing is longer, it is determined to be an expansion offset.

7. The method for assessing the health status of a flow battery based on multi-dimensional data fusion according to claim 1, characterized in that, When determining the charging health status, the state of the flow battery is determined based on the charging offset result, wherein... If the charging offset result is downward offset, it is determined that the power transmission equipment is abnormal; If the charging offset result is upward, it is determined to be an abnormal electrolyte circulation; If the charging offset result is a reduced offset, it is determined that the power transmission equipment is abnormal; If the charging offset result is an expansion offset, it is determined to be an electrolyte circulation abnormality; When determining the power consumption health state, the state of the flow battery is determined based on the power consumption offset result, wherein... If the power consumption offset result is downward, it is determined to be an electrolyte abnormality; If the power consumption offset result is upward, it is determined to be an abnormal electrolyte circulation.

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