Battery pack leakage detection method and system

Abstract

The application discloses a kind of battery pack liquid leakage detection method and system, the present application relates to the technical field of liquid leakage detection, based on the division of liquid leakage control path to determine multiple liquid leakage detection points;Multiple working parameters of battery pack are collected, and the current state of battery pack is determined according to multiple working parameters and the data detected by multiple liquid leakage detection points, to ensure the accuracy of the current state of battery pack.Therefore, if the current state of battery pack is liquid leakage state, determine the current position of liquid leakage based on abnormal data set and liquid leakage control path;Determine the liquid leakage point of battery pack based on the detection of battery pack, determine the corresponding liquid leakage early warning logic according to the liquid leakage point of battery pack, the current position of liquid leakage and the model of battery pack, trigger the early warning measure of battery pack itself and the bottom shell early warning measure of the position passed by liquid leakage, early warning control is carried out for the liquid leakage state of battery pack, to ensure the accuracy of liquid leakage early warning logic.

Description

Technical Field

[0001] This invention relates to the technical field of leakage detection, and more particularly to a method and system for detecting leakage in a battery pack. Background Technology

[0002] With the development of technology, battery packs are used in electric vehicles. As the power supply component of electric vehicles, the battery pack is installed on the bottom shell and supplies power to external functional modules. The battery pack is in direct contact with the bottom shell. In the existing technology, the battery pack will be subjected to a certain impact during operation due to collisions with electric vehicles. Under this impact, the battery pack may leak. However, existing battery packs generally predict the leakage status of the battery pack through multiple operating parameters, making predictions along a single dimension, resulting in low accuracy of the leakage status and no control over the leakage status of the battery pack. Summary of the Invention

[0003] The purpose of this invention is to overcome the shortcomings of the prior art. This invention provides a method and system for detecting leakage in a battery pack.

[0004] This invention provides a method for detecting leakage in a battery pack, comprising:

[0005] When the battery pack is installed on the bottom shell, the leakage detection area of ​​the battery pack is determined based on the position of the battery pack and the coordinate system of the bottom shell.

[0006] The leakage control path is determined based on the leakage detection area and the shape of the battery pack, and multiple leakage detection points are determined based on the division of the leakage control path.

[0007] Collect multiple operating parameters of the battery pack, and determine the current state of the battery pack based on the data detected by multiple operating parameters and multiple leakage detection points. The current state is either a leakage state or a non-leakage state.

[0008] If the current state of the battery pack is leakage, an abnormal data set is determined based on multiple battery parameters and data detected by multiple leakage detection points. The current location of the leakage is determined based on the abnormal data set and the leakage control path.

[0009] Based on the detection of the battery pack, the leakage point of the battery pack is determined. According to the leakage point, the current location of the leakage and the model of the battery pack, the corresponding leakage warning logic is determined, which triggers the battery pack's own warning measures and the bottom shell warning measures of the location through which the leakage has passed.

[0010] This invention provides a battery pack leakage detection system, which is applied to the above-described battery pack leakage detection method. The battery pack leakage detection system includes:

[0011] The leakage detection area module is used to determine the leakage detection area of ​​the battery pack based on the position of the battery pack and the coordinate system of the bottom shell when the battery pack is installed on the bottom shell.

[0012] The leakage detection point module is used to determine the leakage control path based on the leakage detection area and the shape of the battery pack, and to determine multiple leakage detection points based on the division of the leakage control path.

[0013] The status module is used to collect multiple operating parameters of the battery pack and determine the current status of the battery pack based on the data detected by multiple operating parameters and multiple leakage detection points. The current status is either a leakage state or a non-leakage state.

[0014] The leakage location module is used to determine the abnormal data set based on multiple battery parameters and data detected by multiple leakage detection points if the current state of the battery pack is a leakage state, and to determine the current location of the leakage based on the abnormal data set and the leakage control path.

[0015] The leakage warning module is used to determine the leakage point of the battery pack based on the detection of the battery pack. According to the leakage point, the current location of the leakage and the model of the battery pack, the corresponding leakage warning logic is determined, which triggers the battery pack's own warning measures and the bottom shell warning measures of the location through which the leakage has passed.

[0016] Compared with the prior art, the beneficial effects of the present invention are:

[0017] In this embodiment of the invention, when the battery pack is installed on the bottom shell, the leakage detection area of ​​the battery pack is determined based on the position of the battery pack and the coordinate system of the bottom shell; the leakage control path is determined according to the leakage detection area and the shape of the battery pack, and multiple leakage detection points are determined based on the division of the leakage control path; multiple operating parameters of the battery pack are collected, and the current state of the battery pack is determined based on the data detected by the multiple operating parameters and the multiple leakage detection points. This overall consideration of multiple operating parameters and the data detected by the multiple leakage detection points ensures the accuracy of the current state of the battery pack, which is a leakage state and a non-leakage state.

[0018] Therefore, if the battery pack is currently in a leaking state, an abnormal data set is determined based on data detected by multiple battery parameters and multiple leak detection points. The current location of the leak is determined based on the abnormal data set and the leak control path. The leak point of the battery pack is determined based on the battery pack detection. The corresponding leak warning logic is determined based on the leak point, the current location of the leak, and the battery pack model. This triggers the battery pack's own warning measures and the bottom shell warning measures at the location the leak has passed through. Warning control is implemented for the battery pack's leaking state. The leak point and the current location of the leak are introduced to ensure the accuracy of the leak warning logic. Synchronous control is also implemented for the battery pack's own warning measures and the bottom shell warning measures at the location the leak has passed through. Attached Figure Description

[0019] Figure 1 This is a schematic flowchart of the battery pack leakage detection method in an embodiment of the present invention;

[0020] Figure 2 This is a flowchart illustrating step S11 of the battery pack leakage detection method in this embodiment of the invention.

[0021] Figure 3 This is a flowchart illustrating step S12 of the battery pack leakage detection method in this embodiment of the invention.

[0022] Figure 4 This is a flowchart illustrating step S13 of the battery pack leakage detection method in this embodiment of the invention.

[0023] Figure 5 This is a flowchart illustrating step S14 of the battery pack leakage detection method in this embodiment of the invention.

[0024] Figure 6 This is a flowchart illustrating step S15 of the battery pack leakage detection method in this embodiment of the invention.

[0025] Figure 7 This is a schematic diagram of the structure of the battery pack leakage detection system in an embodiment of the present invention. Detailed Implementation

[0026] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0027] Please see Figures 1 to 7 A method for detecting leakage in a battery pack, applied to scenarios involving leakage detection of battery packs; the method includes:

[0028] Step S11: When the battery pack is installed on the bottom shell, determine the leakage detection area of ​​the battery pack based on the position of the battery pack and the coordinate system of the bottom shell;

[0029] Step S12: Determine the leakage control path based on the leakage detection area and the shape of the battery pack, and determine multiple leakage detection points based on the division of the leakage control path;

[0030] Step S13: Collect multiple operating parameters of the battery pack, and determine the current state of the battery pack based on the data detected by multiple operating parameters and multiple leakage detection points. The current state is a leakage state and a non-leakage state.

[0031] Step S14: If the current state of the battery pack is leakage, determine the abnormal data set based on the data detected by multiple battery parameters and multiple leakage detection points, and determine the current location of the leakage based on the abnormal data set and the leakage control path.

[0032] Step S15: Determine the leakage point of the battery pack based on the detection of the battery pack, determine the corresponding leakage warning logic according to the leakage point of the battery pack, the current location of the leakage and the model of the battery pack, and trigger the battery pack's own warning measures and the bottom shell warning measures of the location through which the leakage has passed.

[0033] refer to Figure 2 In step S11, when the battery pack is installed on the bottom shell, the leakage detection area of ​​the battery pack is determined based on the position of the battery pack and the coordinate system of the bottom shell.

[0034] In the specific implementation of this invention, the specific steps are as follows:

[0035] S111: The battery pack is installed on the bottom shell, and the lower side wall of the battery pack is in direct contact with the upper side wall of the bottom shell. At this time, a coordinate system of the bottom shell is constructed based on the shape of the bottom shell, and the contact position between the lower side wall of the battery pack and the upper side wall of the bottom shell is defined as the zero point position of the coordinate system.

[0036] S112: Determine the battery pack control area based on the coordinate system of the bottom shell, the zero point of the coordinate system, and the position of the battery pack;

[0037] S113: Mark the liquid accumulation location on the bottom shell, and determine the leakage detection area of ​​the battery pack based on the liquid accumulation location on the bottom shell and the battery pack control area.

[0038] In the embodiments of this application, the battery pack is installed on the bottom shell, and the lower sidewall of the battery pack is in direct contact with the upper sidewall of the bottom shell. At this time, a coordinate system of the bottom shell is constructed based on the shape of the bottom shell, and the contact position between the lower sidewall of the battery pack and the upper sidewall of the bottom shell is defined as the zero point position of the coordinate system.

[0039] At this point, the battery pack is correctly installed onto the base shell. In practice, this usually involves placing the battery pack into the base shell in the predetermined direction and position, ensuring that the lower sidewall of the battery pack is in close contact with the upper sidewall of the base shell without any gaps. The installation process requires the use of specific tools or equipment to assist in positioning and securing the battery pack.

[0040] The purpose of constructing the bottom shell coordinate system is to provide a unified reference framework for describing the relative positions of the battery pack and the bottom shell, as well as subsequent leakage detection points. The construction of the coordinate system should be based on the actual shape of the bottom shell, ensuring that the axes of the coordinate system correspond to the main structural features of the bottom shell (such as the long side, wide side, height, etc.). Optionally, assuming the bottom shell is a rectangular box, the lower left corner of the bottom shell is chosen as the origin of the coordinate system (0,0,0), the long side direction of the bottom shell is taken as the X-axis, the wide side direction as the Y-axis, and the direction perpendicular to the plane of the bottom shell as the Z-axis. In this way, any point inside the bottom shell is represented by a three-dimensional coordinate (X,Y,Z).

[0041] The zero-point position is a reference point on the contact surface between the battery pack and the bottom shell, used to accurately locate the battery pack in the coordinate system. In actual operation, the zero-point position is usually selected as the center point or a feature point of the contact surface between the lower sidewall of the battery pack and the upper sidewall of the bottom shell. After determining the zero-point position, the position and shape of the battery pack are described based on the coordinates of this point in the coordinate system. Optionally, it is assumed that the battery pack is a cuboid with its lower sidewall in complete contact with the upper sidewall of the bottom shell. The center point of the lower sidewall of the battery pack is selected as the zero-point position. In the constructed bottom shell coordinate system, it is assumed that the coordinates of this point are (X0, Y0, 0), where X0 and Y0 are the projected distances of this point on the X-axis and Y-axis, respectively, and the Z-axis direction of 0 indicates that the point is located on the bottom shell plane.

[0042] Furthermore, the battery pack control area is determined based on the coordinate system of the bottom shell, the zero point of the coordinate system, and the position of the battery pack. This comprehensive consideration of the coordinate system of the bottom shell, the zero point of the coordinate system, and the position of the battery pack ensures the accuracy of the battery pack control area.

[0043] At this point, we review the previously established bottom shell coordinate system and the determined zero-point position. The bottom shell coordinate system provides a unified reference frame for describing the relative position of the battery pack and the bottom shell. The zero-point position is a reference point on the contact surface between the battery pack and the bottom shell, used to accurately position the battery pack. At the same time, based on the bottom shell coordinate system and the zero-point position, it is necessary to determine the specific position of the battery pack in the bottom shell. This usually involves measuring the coordinates of each side or corner of the battery pack in the bottom shell coordinate system, or calculating based on the design dimensions and installation position of the battery pack.

[0044] Based on the determined location of the battery pack, a certain safety margin needs to be extended outward to form a control area slightly larger than the battery pack's outer shape. This safety margin should take into account the slight displacement of the battery pack due to factors such as vibration and temperature changes, as well as the range of leakage diffusion. The control area will be the focus of subsequent leakage detection. Finally, the extent of the control area needs to be marked on the bottom shell, which can be achieved by drawing lines, affixing labels, or using other visualization methods. Marking the control area helps to quickly locate the sensor during installation and leakage detection.

[0045] Therefore, marking the liquid convergence point on the bottom shell and determining the leakage detection area of ​​the battery pack based on the liquid convergence point on the bottom shell and the battery pack control area takes into account the overall consideration of the liquid convergence point on the bottom shell and the battery pack control area, ensuring the accuracy of the leakage detection area of ​​the battery pack.

[0046] At this point, identify and mark the locations where liquid accumulates on the bottom casing. These locations are typically the lowest point of the bottom casing, gaps, interfaces, or other areas where liquid can easily accumulate. Marking these locations helps to understand where the liquid flows and accumulates when the battery pack leaks. Optionally, the liquid accumulation locations can be determined through visual inspection, liquid flow simulation, or by referring to the bottom casing design drawings. Once determined, mark them on the bottom casing using lines, stickers, or other visual means.

[0047] The analysis examines the relationship between the battery pack control area and the marked liquid convergence points on the bottom shell. This includes determining which liquid convergence points are located within or near the battery pack control area, and the relative positions of these points to the battery pack leakage points. This analysis is also performed by comparing the coordinates of the battery pack control area boundary and the liquid convergence points. Furthermore, more precise geometric analysis is conducted using CAD software or other analysis tools.

[0048] Based on the analysis results of the battery pack control area and liquid convergence location, a leak detection area is determined that includes all potential leak points and liquid convergence locations. This area will be the focus for subsequent sensor installation and leak detection. At this time, the leak detection area is a rectangular, circular, or other shaped area surrounding the battery pack control area and key liquid convergence locations. When determining the area, the convenience of sensor placement and detection efficiency should be considered.

[0049] Finally, the extent of the leak detection area needs to be marked on the bottom shell. This helps to quickly locate critical areas when installing sensors and performing leak detection. Optionally, the leak detection area can be marked using scribing, stickers, laser engraving, or other visual means. The markings should be clear, durable, and easy to identify.

[0050] Specifically, suppose there is a rectangular bottom shell and a cuboid battery pack. The bottom shell is 400mm long, 300mm wide, and 150mm high; the battery pack is 380mm long, 280mm wide, and 120mm high. The battery pack control area has been determined, and the liquid accumulation points on the bottom shell have been marked. Through visual inspection and reference to the bottom shell design drawings, it was found that the lowest point of the bottom shell is located at the lower right corner, with coordinates (400, 300, 0), and the side gaps of the bottom shell also become liquid accumulation points. These locations were marked on the bottom shell with stickers.

[0051] The lowest point of the bottom shell is located near the lower right corner of the battery pack control area, while the side gaps are partially located within the control area. This means that if the battery pack leaks, the liquid will flow to these locations and accumulate, thus defining a rectangular leak detection area that includes the battery pack control area and the key liquid accumulation point. The length and width of this area are 20mm larger than the battery pack control area (considering safety margins and the nature of liquid diffusion), while the height is the same as the bottom shell. At the same time, a line is drawn along the boundary of the leak detection area on the bottom shell using a scribing tool, and a "Leak Detection Area" label is affixed to the boundary using a sticker. This allows for quick location of this critical area when installing sensors and performing leak detection.

[0052] In some embodiments of this application, a liquid convergence location matching table is collected, as shown in Table 1:

[0053] Table 1. Matching Table of Liquid Convergence Locations

[0054] Serial Number Liquid convergence point coordinate Relationship with battery pack control area 1 Lowest point of the bottom shell (400,300,0) Adjacent to the lower right corner of the controlled area 2 Side gap A (Along the X-axis, Y = 50) Some are located within the controlled area 3 Side gap B (Along the X-axis, Y = 250) Adjacent to the edge of the controlled area 4 Interface C (200,150,Z) Located within the controlled area

[0055] Based on the liquid convergence location matching table, determine a leak detection area that includes all potential leak points and liquid convergence locations. This area is a rectangular, circular, or other shaped area surrounding the battery pack control area and key liquid convergence locations. Note from the matching table that most liquid convergence locations are located at or adjacent to the battery pack control area, so the leak detection area is set to be slightly larger than the battery pack control area to ensure coverage of all potential leak points.

[0056] refer to Figure 3 In step S12, a leakage control path is determined based on the leakage detection area and the shape of the battery pack, and multiple leakage detection points are determined based on the division of the leakage control path.

[0057] In the specific implementation of this invention, the specific steps are as follows:

[0058] S121: Collect the leakage detection area and determine the first leakage path based on the area shape and location of the leakage detection area;

[0059] S122: Determine the second leakage path based on the shape and internal distribution diagram of the battery pack, and determine the leakage control path based on the combination of the first leakage path and the second leakage path;

[0060] S123: In the leakage control path, the leakage control path is divided into multiple leakage control segments. Based on each leakage control segment, the corresponding leakage detection point is marked to determine multiple leakage detection points.

[0061] In the embodiments of this application, a leakage detection area is collected, and a first leakage path is determined based on the area shape and location of the leakage detection area. This takes into account the overall consideration of the area shape and location of the leakage detection area, ensuring the accuracy of the first leakage path.

[0062] At this point, based on the design, installation location, and structural characteristics of the battery pack and bottom shell, an area where leakage accumulates is identified as the leakage detection area. This area is usually located below or around the battery pack and is easy to observe and access. At this point, the boundaries, shape, size, and positional relationship of the leakage detection area relative to the battery pack and bottom shell are recorded in detail. This information is recorded and saved in the form of drawings, photographs, or digital models.

[0063] Assess the regularity of the shape of the leak detection area and whether there are any factors that may affect liquid flow, such as protrusions, depressions, or gaps; determine the specific position of the leak detection area relative to the battery pack and bottom shell, including its height, tilt angle, and contact method with the battery pack and bottom shell; based on the design and internal distribution of the battery pack, identify the leak sources, such as gaps between battery cells, battery pack inlets and outlets, connectors, etc.

[0064] Determine the first leakage path: Based on the analysis of the area's morphology and location, predict how the liquid will flow to the leakage detection area if a leak occurs in the battery pack; consider the effects of gravity on the liquid, the surface characteristics of the bottom shell (such as smoothness, slope), and potential obstacles (such as other components, supports, etc.); based on the predicted liquid flow, determine one or more main paths from potential leakage sources to the leakage detection area. These paths typically extend along the lowest point of the bottom shell, gaps, or areas prone to liquid accumulation; record in detail the start point, end point, path length, path width, and any obstacles or features that affect the liquid flow of the first leakage path.

[0065] Furthermore, a second leakage path is determined based on the shape and internal distribution diagram of the battery pack. A leakage control path is then determined by combining the first and second leakage paths, taking into account the overall consideration of combining the first and second leakage paths, thus ensuring the accuracy of the leakage control path.

[0066] At this point, the morphology of the battery pack is analyzed. External morphology assessment: Examine the external shape and size of the battery pack, as well as any protruding or recessed areas that may affect fluid flow; Connection and interface inspection: Inspect all connectors and interfaces on the battery pack, including the cooling system, battery management system (BMS) harness, charging ports, etc., to determine if they are potential leakage points.

[0067] Based on the internal layout diagram of the battery pack, understand the layout and arrangement of components such as battery cells, electrolyte pipes, cooling plates, and heat insulation materials; identify components or areas inside the battery pack that are leaking, such as gaps between battery cells, interfaces of electrolyte pipes, and leak points on the cooling plates.

[0068] Determine the second leakage path: Based on the internal layout of the battery pack and potential leakage sources, predict how the liquid will flow if leakage occurs inside the battery pack; consider the effects of gravity on the liquid, obstructions from the internal structure of the battery pack, and the liquid channels formed; based on the flow prediction, define the main paths from potential internal leakage sources to the outside of the battery pack or the first leakage path, which extend along the gaps between battery cells, electrolyte pipes, or the edges of cooling plates.

[0069] The first and second leakage paths are combined to form a complete leakage control path, which should cover all potential leakage points from the inside to the outside of the battery pack. The leakage control path is optimized according to actual needs and monitoring requirements, including adding monitoring points, adjusting the coverage of the path, or refining the resolution of the path.

[0070] Specifically, in order to determine the second leakage path and the synthetic leakage control path, the external shape of the battery pack was observed to be cuboid with a flat bottom and a raised BMS module on the top; all connectors and interfaces on the battery pack were inspected, and the charging port and BMS harness were identified as potential external leakage points.

[0071] Based on the internal distribution diagram, it was learned that the battery pack is composed of multiple battery cells, which are interconnected through electrolyte pipes and cooling plates. The gaps between battery cells, the interfaces of electrolyte pipes, and potential leakage points of the cooling plates were identified as internal leakage sources. It was predicted that if a battery cell leaks, the electrolyte will flow along the gaps between the battery cells, then pass through the bottom or sidewalls of the battery pack, and enter the outside of the battery pack. A second leakage path was defined, starting from the gaps between the battery cells, passing through the bottom or sidewalls of the battery pack, and reaching the outside of the battery pack or the first leakage path (such as the gap between the battery pack and the chassis).

[0072] By combining the first leakage path (the gap between the battery pack and the chassis) and the second leakage path (from the gap between battery cells to the outside of the battery pack), a complete leakage control path was formed. The coverage of the path was optimized to ensure that it covers all potential leakage points inside and outside the battery pack. Monitoring points were also added at key locations along the path to more accurately monitor leakage. Through these steps, the second leakage path was successfully identified and the leakage control path was synthesized, providing important reference information for subsequent sensor installation and leakage monitoring. This helps ensure that leakage in the battery pack can be detected and addressed in a timely manner, thus ensuring the safe operation of the vehicle.

[0073] Therefore, in the leakage control path, the leakage control path is divided into multiple leakage control segments. Based on each leakage control segment, the corresponding leakage detection points are marked to determine multiple leakage detection points, thus introducing multiple leakage detection points.

[0074] At this point, a detailed analysis of the leakage control path is conducted to understand its length, width, shape, and potential obstacles or points of change. Based on the characteristics of the path, the principles for segmentation are determined, including natural breaks in the path (such as turns and interfaces), length limitations (such as each segment not exceeding a certain length), and monitoring requirements (such as the need for denser monitoring points). Based on the segmentation principles, the leakage control path is divided into multiple leakage control segments. Each segment should have a clear start and end point and maintain consistency in shape and width as much as possible.

[0075] Within each leak control section, select one or more suitable detection points. These points should be easily accessible for sensor installation, observation, and observation, and should accurately reflect the leak situation in that section. When selecting detection points, consider the liquid flow characteristics (such as gravity, velocity, and flow rate), the geometric features of the path (such as width, inclination, and curvature), and potential obstacles or interference factors. Use drawings, photographs, or digital models to mark and record the detection points within each leak control section. Ensure that each detection point has a unique identifier and indicates its location, coordinates, and monitoring requirements.

[0076] The detection points of each leakage control section are compiled into a complete list of leakage detection points. The layout of the detection points is optimized according to actual needs and monitoring requirements, including adjusting the location of the detection points, increasing or decreasing the number of detection points, and changing the monitoring method. After optimization, the locations and monitoring requirements of multiple leakage detection points are finally determined. These points will be used to install sensors for real-time monitoring to ensure that leakage of the battery pack can be detected and dealt with in a timely manner.

[0077] Specifically, suppose there is a battery pack for an electric vehicle, whose leakage control path has been determined according to the previous steps; in order to determine multiple leakage detection points, the leakage control path was analyzed and found to be a path that starts from the bottom of the battery pack, extends along the edge of the chassis to the outside of the vehicle; the total length of the path is about 2 meters and the width is about 10 centimeters; it was decided to segment the path according to the natural breaks and length limitations; the path was divided into four leakage control segments, each segment being about 0.5 meters long.

[0078] Within each leak control section, locations that are easy to install sensors on and can accurately reflect leak conditions were selected as detection points. For example, in the first section, the lowest point of the path was chosen as the detection point because liquid tends to accumulate there under gravity. The flow characteristics of the liquid and the geometry of the path were considered to ensure that the detection points could cover the entire width of the section and be as close as possible to potential leak sources. Each detection point was marked and recorded, and its location, coordinates, and monitoring requirements were noted.

[0079] The detection points of the four leakage control sections were compiled into a leakage detection point list containing four detection points. The layout of the detection points was optimized to ensure that they are evenly distributed throughout the leakage control path and cover all potential leakage points. Finally, the locations and monitoring requirements of the four leakage detection points were determined. These points will be used to install sensors for real-time monitoring to ensure that leakage in the battery pack can be detected and dealt with in a timely manner. Through these steps, multiple leakage detection points were successfully identified, providing important reference information for subsequent sensor installation and leakage monitoring. This helps to ensure that leakage in the battery pack can be detected in a timely manner and measures can be taken to ensure the safe operation of the vehicle.

[0080] In some embodiments of this application, a leakage control segment matching table is collected, as shown in Table 2:

[0081] Table 2. Leakage Control Section Matching Table

[0082] Leakage control section Description of detection point location Inspection point number Section A The lowest point of the path, near the chassis support. DP-A1 Section B At the bend, near the wiring harness DP-B1 Section B Middle section of the path, obstacle-free area DP-B2 Section C The lowest point on the path, away from obstacles. DP-C1 Section D The end of the route, near the exit outside the vehicle. DP-D1

[0083] A score matching table was collected, as shown in Table 3:

[0084] Table 3 Score Matching Table

[0085] Inspection point number Weighting (factors to consider) Score DP-A1 High flow rate, easy access 9 DP-B1 Near the bend, near the potential source of leakage 8 DP-B2 Medium flow speed, no obstacles 7 DP-C1 Low flow velocity, away from obstacles 6 DP-D1 End of the route, important exit 10

[0086] Based on the scores, it was ultimately decided to install sensors at three detection points: DP-A1, DP-B1, and DP-D1, as they had higher scores and could more effectively monitor leaks. DP-B2 and DP-C1 were also considered, but due to their lower scores, they were relegated to alternative points in the subsequent monitoring plan. This method not only identified multiple leak detection points but also prioritized them according to their importance and monitoring needs, which helps ensure that resources are used effectively and that timely responses are taken when leaks occur.

[0087] refer to Figure 4 In step S13, multiple operating parameters of the battery pack are collected, and the current state of the battery pack is determined based on the multiple operating parameters and the data detected by multiple leakage detection points. The current state is a leakage state and a non-leakage state.

[0088] In the specific implementation of this invention, the specific steps are as follows:

[0089] S131: Monitor the operation of the battery pack in real time and collect multiple operating parameters of the battery pack. When the battery pack is in operation, perform real-time detection on multiple leakage detection points and determine the data detected by multiple leakage detection points.

[0090] S132: Determine multiple data combinations based on data detected by multiple operating parameters and multiple leakage detection points, and determine the corresponding state characteristics based on the identification of multiple data combinations, so as to collect multiple state characteristics;

[0091] S133: Collect the working signal of the battery pack and determine the current power supply mode of the battery pack based on the analysis of the working signal of the battery pack. Determine the current state of the battery pack based on multiple state features, the current power supply mode of the battery pack and the state mapping relationship. The current state covers the leakage state and the non-leakage state.

[0092] In the embodiments of this application, the operation of the battery pack is monitored in real time, and multiple operating parameters of the battery pack are collected. When the battery pack is in operation, multiple leakage detection points are detected in real time, and the data detected by the multiple leakage detection points is determined, thus introducing the data detected by multiple leakage detection points.

[0093] At this point, when the battery pack starts working, the monitoring system automatically starts and begins to monitor the battery pack comprehensively. Multiple sensors, including but not limited to temperature sensors, voltage sensors, and current sensors, are deployed at key locations on the battery pack to monitor key operating parameters such as temperature, voltage, and current in real time. The data acquisition frequency of the sensors is set according to the characteristics and safety requirements of the battery pack to ensure the real-time performance and accuracy of the data.

[0094] The sensor transmits the real-time collected operating parameters to the monitoring system; the monitoring system performs preliminary processing on the received data, including data cleaning and format conversion, to ensure the availability and accuracy of the data; and stores the processed data in a designated database or data warehouse for subsequent analysis and use.

[0095] Leakage detection points are set up in potential leakage areas of the battery pack. These points are usually located in critical locations such as between battery cells, at the bottom of the battery pack, and connectors. Specialized leakage sensors are used to monitor the detection points in real time. The sensors work based on resistance, capacitance, optical or chemical principles and can detect the presence of trace amounts of liquid. When leakage is detected, the sensor will immediately trigger an alarm mechanism and send an alarm signal to the monitoring system.

[0096] The monitoring system reads data sent by the leak sensor, including the status of the leak detection point and the amount of leak; it verifies the read data to ensure its accuracy and reliability, including comparison with preset thresholds and cross-verification between data; and it records the verified data in the monitoring system's log for subsequent analysis and tracking.

[0097] Furthermore, multiple data combinations are determined based on data detected by multiple operating parameters and multiple leakage detection points. Based on the identification of multiple data combinations, corresponding state features are determined to collect multiple state features. This approach takes into account the overall consideration of multiple operating parameters and data detected by multiple leakage detection points, ensuring the accuracy of multiple data combinations.

[0098] At this point, operating parameters (such as temperature, voltage, and current) from different sensors and data from leakage detection points are integrated. These data, originating from multiple locations within the battery pack, reflect the overall operating status of the battery pack and potential leakage conditions. Based on the type and source of the data, different combinations are formed. For example, temperature data is combined with voltage data to analyze the relationship between the battery pack's temperature and voltage; or data from a specific leakage detection point is combined with data from adjacent detection points to analyze the spread of leakage. All data is ensured to be synchronized in time to accurately reflect the battery pack's operating status and leakage conditions at any given moment.

[0099] Each data set is analyzed to extract key features reflecting the battery pack's condition. These features include temperature range, voltage stability, current fluctuations, and changes in the state of leak detection points. Machine learning algorithms or expert systems are used to identify these extracted features to determine if they correspond to known battery pack conditions. For example, if the temperature continues to rise and the voltage drops, it indicates an overheating risk in the battery pack; if a leak detection point continuously alarms, it indicates a leak at that location. The identified features are then categorized into different state characteristics, such as healthy state characteristics, overheating state characteristics, and leaking state characteristics.

[0100] Identified and categorized status features are recorded in the monitoring system's logs or database for subsequent analysis and tracking; as the battery pack's operating status changes, new status features are continuously updated and recorded, which helps to understand the battery pack's health status and potential risks in real time; multiple status features are aggregated to form a comprehensive set of status features for assessing the overall status of the battery pack.

[0101] Specifically, the battery pack is equipped with a comprehensive monitoring system and leakage detection mechanism; the monitoring system integrates data from temperature sensors, voltage sensors, current sensors and leakage detection points, and this data is divided into different combinations, such as temperature-voltage combinations, current-time combinations, leakage detection point status combinations, etc.; all data is synchronized in time to ensure accuracy.

[0102] Temperature-Voltage Combination: Analyzing the relationship between temperature and voltage; assuming a temperature of 30℃ and a voltage of 400V at a certain time point; through feature extraction and identification, it is found that the temperature is within the normal range and the voltage remains stable, indicating that the battery pack is in a healthy state. Current-Time Combination: Analyzing the change of current over time; assuming that the current data shows stable fluctuations within a certain time period, without abnormal peaks or troughs; through feature extraction and identification, it is confirmed that the current fluctuations are within the normal range, indicating that the battery pack's charging and discharging process is stable. Analyzing the status data of leakage detection points; assuming that a leakage detection point alarms at a certain time point, indicating the detection of a trace leakage; through feature extraction and identification, it is confirmed that there is a risk of leakage at this detection point, requiring further investigation.

[0103] The identified status characteristics are recorded in the monitoring system's log, including the battery pack's health status characteristics, current stability characteristics, and leakage risk characteristics at a specific leakage detection point. As the battery pack's operating status changes, new status characteristics are continuously updated and recorded. For example, if the temperature continues to rise or the voltage fluctuates abnormally, the health status characteristics will be updated promptly. Multiple status characteristics are aggregated to form a comprehensive status characteristic set, used to assess the overall status of the battery pack. In this example, the status characteristic set indicates that the battery pack is generally in a healthy state, but there is a leakage risk at a certain location, requiring appropriate measures for inspection and repair.

[0104] Therefore, the operating signals of the battery pack are collected, and the current power supply mode of the battery pack is determined based on the analysis of the operating signals. The current state of the battery pack is determined based on multiple state features, the current power supply mode of the battery pack, and the state mapping relationship. This current state covers both leakage and non-leakage states, and takes into account the overall consideration of multiple state features, the current power supply mode of the battery pack, and the state mapping relationship to ensure the accuracy of the current state of the battery pack. At the same time, it takes into account the overall consideration of multiple operating parameters and the data detected by multiple leakage detection points to ensure the accuracy of the current state of the battery pack. This current state is the leakage state and the non-leakage state.

[0105] At this time, the operating signals usually come from the battery management system (BMS) or other control systems, which are responsible for monitoring and controlling the operation of the battery pack. The operating signals include the battery pack's voltage, current, temperature, charging / discharging status, fault alarms, etc. These operating signals are collected in real time through sensor networks or data interfaces to ensure the accuracy and real-time nature of the data.

[0106] The operating signals are analyzed to identify the current operating state of the battery pack, such as charging, discharging, and standby. Based on the analysis results, the current power supply mode of the battery pack is determined. For example, if the battery pack is receiving power from an external power source, it is in charging mode; if the battery pack is supplying power to a load, it is in discharging mode.

[0107] The system integrates multiple previously collected state characteristics (such as temperature stability, voltage fluctuations, and leakage detection point status); it combines these state characteristics with the power supply mode using a predefined state mapping relationship to determine the current state of the battery pack; the state mapping relationship is derived based on historical data, expert knowledge, or machine learning models; based on the state mapping relationship, it determines whether the battery pack is in a healthy state, an overheated state, an overcharged / over-discharged state, a leakage state, or other specific state. These states cover various situations encountered by the battery pack, including leakage and non-leakage states.

[0108] The current status is displayed through the monitoring system interface, alarm system, or other means so that operators or maintenance personnel can understand the status of the battery pack in a timely manner; the current status is recorded in the log or database for subsequent analysis and tracking.

[0109] Specifically, suppose there is a battery pack for an electric vehicle equipped with a comprehensive monitoring system and status determination mechanism. The monitoring system collects the battery pack's operating signals in real time through a data interface, including voltage, current, temperature, and charging / discharging status. For example, at a certain point in time, the voltage is 380V, the current is 100A, the temperature is 25°C, and the battery pack is in a discharging state. The monitoring system analyzes these operating signals and determines that the battery pack is currently in a discharging mode because it is supplying power to the electric vehicle's drive system.

[0110] The monitoring system integrates previously collected status characteristics, including the battery pack's temperature stability (fluctuations within the normal range), voltage fluctuations (stable), and the status of leakage detection points (no alarms at any detection point). Using a predefined state mapping relationship, combined with the current power supply mode (discharge mode) and status characteristics, the monitoring system determines the battery pack's current healthy state. This state mapping relationship, derived from extensive historical data and expert knowledge, accurately reflects the battery pack's state under different conditions. The monitoring system displays the determined current state (health status) on the interface and records it in the log. Operators or maintenance personnel can promptly understand the battery pack's status through the monitoring system and perform corresponding operations or maintenance as needed. If any abnormality occurs in the battery pack, such as excessively high temperature, abnormal voltage fluctuations, or leakage alarms, the monitoring system will immediately trigger an alarm mechanism.

[0111] In some embodiments of this application, a battery pack status matching table is collected, as shown in Table 4:

[0112] Table 4 Battery Pack Status Matching Table

[0113] State characteristics Energy supply mode Battery pack status Temperature is normal, no alarm. Discharge health status The temperature is too high, an alarm will sound. Discharge Overheating Large voltage fluctuations, no alarm. Discharge Unstable voltage state Leak detection point alarm any Leakage status

[0114] Assuming the current collected status characteristics are "normal temperature, stable voltage, current fluctuation within normal range, and no leakage alarm", and the battery pack is in discharge mode; according to the matching table, these conditions match "healthy state"; therefore, the current state of the battery pack is determined to be "healthy state".

[0115] refer to Figure 5 In step S14, if the current state of the battery pack is a leakage state, an abnormal data set is determined based on multiple battery parameters and data detected by multiple leakage detection points, and the current location of the leakage is determined based on the abnormal data set and the leakage control path.

[0116] In the specific implementation of this invention, the specific steps are as follows:

[0117] S141: Real-time monitoring of the current status of the battery pack. If the current status of the battery pack is leakage, trigger leakage detection of the battery pack and bottom shell to perform anomaly detection on the data detected by multiple battery parameters and multiple leakage detection points.

[0118] S142: Based on the abnormal detection of data from multiple battery parameters and multiple leakage detection points, determine multiple abnormal data, mark the location of multiple abnormal data, and determine the abnormal data set based on the multiple abnormal data and the location of multiple abnormal data.

[0119] S143: Collect multiple abnormal data sets, and determine the corresponding abnormal features based on the identification of multiple abnormal data sets. Determine multiple abnormal regions based on the shape and location of each abnormal feature. Determine the leakage region based on the synthesis of multiple abnormal regions and leakage control path. Determine the current location of the leakage based on the traversal of the leakage region. At this time, the current location of the leakage is within the area of ​​the bottom shell.

[0120] In the embodiments of this application, the current state of the battery pack is monitored in real time. If the current state of the battery pack is a leakage state, leakage detection of the battery pack and the bottom shell is triggered to perform anomaly detection on the data detected by multiple battery parameters and multiple leakage detection points. Anomaly detection is performed on the data detected by multiple battery parameters and multiple leakage detection points.

[0121] At this point, the system collects various parameters of the battery pack in real time through a sensor network, including but not limited to key indicators such as voltage, current, temperature, and internal resistance. These sensors are usually distributed at various key locations in the battery pack to ensure the comprehensiveness and accuracy of the data. The collected data is processed and analyzed to assess the health status of the battery pack in real time. This involves steps such as data smoothing, filtering, and trend analysis to eliminate noise and extract useful information. Based on preset thresholds or algorithm logic, the system determines whether the battery pack is in a normal state, an abnormal state, or a specific problem state (such as leakage).

[0122] The system monitors changes in specific parameters that directly or indirectly indicate leakage. For example, the leakage of certain chemicals can cause increased internal pressure, abnormal temperature, or changes in the readings of specific sensors in the battery pack. Thresholds are set for these key parameters, and the system considers a risk of leakage when the parameter value exceeds or falls below these thresholds. By combining the changes in multiple parameters, the system makes a comprehensive judgment on whether the battery pack is indeed in a leakage state, which requires certain algorithmic logic or machine learning models to assist in the decision-making.

[0123] Once a battery pack is confirmed to be leaking, the system immediately activates the leak detection mechanism. This involves activating specific sensors or actuators to monitor the leak in more detail. The system begins collecting more data about the leak, including changes in the state of the leak detection points and real-time monitoring of the internal environment of the battery pack. This data will be used for further analysis to determine the specific location and extent of the leak. Anomaly detection is performed on the collected data to identify data points or patterns that are significantly different from the normal state. These abnormal data points indicate the specific location or extent of the leak.

[0124] Specifically, suppose there is a battery pack for an electric vehicle equipped with a comprehensive monitoring system and a leak detection mechanism. The system collects parameters such as voltage, current, temperature, and pressure of the battery pack in real time. For example, at a certain point in time, the system detects a sudden increase in the temperature of the battery pack, and at the same time, the pressure sensor also shows abnormal readings. The system analyzes the changes in these parameters according to preset thresholds and algorithm logic. In this example, the simultaneous abnormal changes in temperature and pressure indicate that there is a problem inside the battery pack, such as an increase in temperature and pressure caused by electrolyte leakage. After comprehensive judgment, the system concludes that the battery pack is in a leaking state.

[0125] Once a battery pack is confirmed to be leaking, the system immediately activates the leak detection mechanism. This includes activating specific sensors on the battery pack and bottom casing to monitor the leak in more detail. For example, the system activates the humidity sensor and optical sensor on the bottom casing to detect if electrolyte has leaked into the bottom casing. During leak detection, the system collects and analyzes data from multiple sensors. In this example, the humidity sensor detects an abnormal increase in humidity in a certain area of ​​the bottom casing, while the optical sensor detects signs of electrolyte leakage in that area. The system marks these abnormal data points as potential leak locations and prepares for further analysis and processing. Through this series of steps, the system can achieve real-time monitoring and anomaly detection of battery pack leaks, providing important information for subsequent leak location and repair.

[0126] Furthermore, multiple abnormal data are identified based on the anomaly detection of data from multiple battery parameters and multiple leakage detection points, and the locations of these multiple abnormal data are marked. An abnormal data set is determined based on these multiple abnormal data and their locations, taking into account the overall consideration of multiple abnormal data and their locations, thus ensuring the accuracy of the abnormal data set.

[0127] At this point, the system first collects real-time data from multiple battery parameters (such as voltage, current, temperature, etc.) and multiple leakage detection points. This data contains noise or outliers, so preprocessing, such as noise reduction and smoothing, is required to improve the accuracy and reliability of the data. The preprocessed data is then analyzed using a preset anomaly detection algorithm (such as statistical methods, machine learning models, etc.) to identify data points that are significantly different from the normal state, i.e., abnormal data. These abnormal data indicate that there is a problem inside the battery pack, such as electrolyte leakage or battery cell failure.

[0128] For each identified abnormal data, the system needs to record its location information, including the sensor or detection point number, physical location (such as which part of the battery pack or which battery cell), etc. This information is crucial for subsequent fault location and analysis. The identified abnormal data and its location information are marked as "abnormal" to distinguish them from other normal data. This is achieved by adding a label field to the dataset and using different colors or icons to represent them in the visualization interface.

[0129] Data marked as "abnormal" and their location information are integrated into a set to form an abnormal data set. This set contains all identified abnormal data and related information, providing a foundation for subsequent analysis and processing. As new data is continuously collected and analyzed, the abnormal data set needs to be continuously updated, including adding newly identified abnormal data and deleting resolved abnormal data.

[0130] Specifically, suppose there is a battery pack monitoring system for an electric vehicle. This system monitors the voltage, current, temperature, and status of multiple leakage detection points of the battery pack in real time. The system collects a series of battery parameters and leakage detection point data. For example, at a certain point in time, the system detects that the voltage of a certain battery cell in the battery pack suddenly drops, and at the same time, the temperature sensor near the cell shows an abnormal temperature rise. The system uses an anomaly detection algorithm to analyze these data and identify these two data points as abnormal data.

[0131] The system records the location information of these two anomalous data points. In this example, the location information includes the battery pack number, the battery cell number, and the physical location of the sensor or detection point. The system marks this information in the dataset and highlights the anomalous data in red to distinguish it from other normal data. The system integrates these two data points marked as "abnormal" and their location information into an anomalous data set, which contains all currently identified anomalous data and their related information. As new data is continuously collected and analyzed, the system will continue to update this set, adding newly identified anomalous data and deleting resolved anomalous data.

[0132] Therefore, multiple abnormal data sets are collected, and corresponding abnormal features are determined based on the identification of multiple abnormal data sets. Multiple abnormal regions are determined based on the shape and location of each abnormal feature. The leakage region is determined by combining multiple abnormal regions and leakage control paths. The current location of the leakage is determined by traversing the leakage region. At this time, the current location of the leakage is within the area of ​​the bottom shell. This method takes into account the overall consideration of combining multiple abnormal regions and leakage control paths, ensuring the accuracy of the leakage region.

[0133] At this point, the system has collected and integrated multiple sets of abnormal data from the previous steps. These sets contain abnormal data and their location information identified at different times or under different conditions. During the integration process, the system further cleans the data to remove duplicate, invalid, or noisy data to ensure the accuracy of subsequent analysis.

[0134] The system analyzes each abnormal data set and extracts common or significant features, including the numerical range, trend, and frequency of occurrence of the data. The extracted features are then matched with a pre-defined abnormal feature library to determine the specific type or cause of the abnormal data. For example, a specific voltage drop pattern is associated with battery cell failure, while an abnormal temperature rise is related to the thermal effect caused by electrolyte leakage.

[0135] Based on the location information and the form of abnormal features (such as numerical distribution and trend) of the abnormal data, the system identifies multiple abnormal regions inside the battery pack; these abnormal regions are then divided in the physical layout of the battery pack to provide a more intuitive understanding of the distribution of the abnormal data.

[0136] The system combines the structural information of the battery pack with known leakage control paths (such as the flow direction of electrolyte and areas of accumulation) to analyze the relationship between abnormal areas and these paths. Based on path analysis and the distribution of abnormal areas, the system identifies the most leaking area, which is the location where electrolyte is most likely to accumulate or leak. At the same time, the system performs a detailed traversal of the identified leakage area to collect more data about the area, such as humidity and chemical composition analysis. Combining the data collected during the traversal and the abnormal characteristics, the system finally determines the current location of the leak, which is located in a specific component, battery cell, or area of ​​the bottom shell of the battery pack.

[0137] Specifically, suppose there is a battery pack monitoring system for an electric vehicle. This system has collected and integrated multiple sets of abnormal data. The system integrates multiple sets of abnormal data from different time points. These sets contain abnormal data from different locations inside the battery pack, such as voltage drop and abnormal temperature rise. The system analyzes these sets of abnormal data and finds that some of the data show specific trends, such as a gradual decrease in voltage accompanied by an abnormal increase in temperature. These characteristics match the "battery cell failure caused by electrolyte leakage" pattern in the preset abnormal feature library.

[0138] Based on the location information and morphological characteristics of the abnormal data, the system identified multiple abnormal areas inside the battery pack, mainly concentrated near a specific battery cell. Combining the battery pack's structural information and known leakage control paths, the system analyzed the relationship between these abnormal areas and the electrolyte flow direction. Ultimately, the system determined a corner of the battery pack's bottom casing as the most leaking area, as this region is located at the end of the electrolyte flow path and where the abnormal data is most concentrated. The system meticulously traversed this corner of the bottom casing, collecting humidity and chemical composition analysis data. Combining this data with the abnormal characteristics, the system finally determined the current location of the leak to be on a specific component inside the bottom casing. This location matched the previous abnormal data analysis and path analysis results, providing accurate location information for subsequent maintenance and handling.

[0139] In some embodiments of this application, a fault cause matching table is collected, as shown in Table 5:

[0140] Table 5 Fault Cause Matching Table

[0141] Abnormal characteristics Cause of the fault Voltage drop of 5V or more Battery cell failure or leakage Temperature rise of 10°C or above Overheating or electrolyte reaction Humidity increased by 20% or more Electrolyte leakage Electrolyte Odor Detection Electrolyte leakage or battery damage

[0142] Based on the characteristics of set A, the system determines that a certain battery cell area in the battery pack has a fault or leakage; based on the characteristics of set B, the system determines that there is electrolyte leakage near the bottom shell; the system analysis found that the fault or leakage of the battery cell caused the electrolyte to flow to the bottom shell through the internal channel, and the humidity and odor detection in set B also support this; therefore, the system determines a certain area of ​​the bottom shell as the most leaking area.

[0143] The system assigns a weight to each anomalous feature, reflecting its importance in indicating the location of the leak. The system calculates a score for each leak location based on the matching degree between the weight and the anomalous feature. Example: Assume the weights for voltage drop and temperature rise are 2 and 1 respectively, and the weights for humidity increase and electrolyte odor are 3. Then, for sets A and B, the system calculates the score for each leak location and selects the location with the highest score as the final leak location. Score calculation example: Location 1 (battery cell area): Voltage drop (2 points) + Temperature rise (1 point) = 3 points; Location 2 (bottom shell area): Humidity increase (3 points) + Electrolyte odor (3 points) = 6 points; Therefore, the system determines Location 2 (bottom shell area) as the current location of the leak.

[0144] refer to Figure 6 In step S15, the leakage point of the battery pack is determined based on the detection of the battery pack. The corresponding leakage warning logic is determined according to the leakage point of the battery pack, the current location of the leakage and the model of the battery pack, triggering the battery pack's own warning measures and the bottom shell warning measures of the location through which the leakage has passed.

[0145] In the specific implementation of this invention, the specific steps are as follows:

[0146] S151: If the current state of the battery pack is a leakage state, the battery pack's autonomous detection is triggered. The internal leakage path of the battery pack is determined based on the battery pack's autonomous detection. The leakage point of the battery pack is determined based on the internal leakage path of the battery pack, the shape of the battery pack, and multiple sides of the battery pack.

[0147] S152: Determine the first warning coefficient based on the leakage point and current location of the battery pack, determine the second warning coefficient based on the leakage point and model of the battery pack, and determine the corresponding leakage warning logic based on the mapping relationship between the first warning coefficient, the second warning coefficient and the warning logic.

[0148] S153: In this leakage warning logic, the sub-warning logic of the battery pack and the sub-warning logic of the bottom shell are determined based on the division of the leakage warning logic. The battery pack's own warning measures are determined according to the traversal of the sub-warning logic of the battery pack. The bottom shell warning measures at the location through which the leakage occurs are determined according to the traversal of the sub-warning logic of the bottom shell.

[0149] In the embodiments of this application, if the current state of the battery pack is a leakage state, the battery pack's autonomous detection is triggered. The internal leakage path of the battery pack is determined based on the autonomous detection. The leakage point of the battery pack is determined based on the internal leakage path, the shape of the battery pack, and multiple sides of the battery pack. This takes into account the overall consideration of the internal leakage path, the shape of the battery pack, and multiple sides of the battery pack, ensuring the accuracy of the leakage point of the battery pack.

[0150] At this point, the system first continuously monitors the status of the battery pack through the built-in sensor network (such as humidity sensor, electrolyte detection sensor, etc.); when the sensor detects signs of electrolyte leakage inside or outside the battery pack (such as abnormal increase in humidity, electrolyte odor detection, etc.), the system determines that the current status of the battery pack is a leakage state.

[0151] Once the battery pack is confirmed to be leaking, the system immediately triggers the battery pack's autonomous detection function.

[0152] The autonomous detection function includes initiating high-resolution sensor scanning and image recognition algorithm analysis to obtain detailed information about the inside of the battery pack. At the same time, the system uses built-in high-precision sensors (such as pressure sensors, temperature sensors, and humidity sensors) and image recognition algorithms to analyze the electrolyte flow inside the battery pack. By analyzing the changing trends of sensor data and image recognition results, the system can determine the flow path of the electrolyte inside the battery pack, including the starting point of leakage, the direction of diffusion, and the accumulation area.

[0153] The system combines the battery pack's morphology (such as size, shape, internal structure, etc.) and observations from multiple sides to further analyze the relationship between the leakage path and the battery pack structure. By comparing sensor data and image recognition results from different sides, the system can accurately locate the leakage point, that is, the specific location where the electrolyte begins to leak.

[0154] Specifically, suppose an electric vehicle's battery pack is detected to have signs of electrolyte leakage during charging. The system detects an abnormal increase in humidity near the bottom of the battery pack using built-in humidity and electrolyte detection sensors, and also detects the odor of electrolyte. The system determines that the battery pack is currently leaking. The system immediately triggers the battery pack's autonomous detection function, initiating high-resolution sensor scanning and image recognition algorithm analysis. Through high-precision sensor and image recognition algorithm analysis, the system discovers that the electrolyte is leaking from one of the battery cells and is flowing along the internal channels of the battery pack towards the bottom. The system determines the flow path of the electrolyte.

[0155] By combining the shape of the battery pack with observations from multiple sides, the system discovered that the leaking battery cell was located on the right side of the battery pack, and there were obvious signs of electrolyte seepage at the bottom seal of the cell. The system precisely located the leak point at the bottom seal of the battery cell. Through the above steps, the system can accurately detect the leakage status of the battery pack and determine the leakage path and leak point inside the battery pack, which provides important information support for subsequent leakage early warning and emergency handling.

[0156] Furthermore, a first warning coefficient is determined based on the leakage point and current location of the battery pack, and a second warning coefficient is determined based on the leakage point and model of the battery pack. The corresponding leakage warning logic is determined based on the mapping relationship between the first warning coefficient, the second warning coefficient, and the warning logic, which takes into account the overall consideration of the mapping relationship between the first warning coefficient, the second warning coefficient, and the warning logic, and ensures the accuracy of the corresponding leakage warning logic.

[0157] At this point, the first warning coefficient is mainly determined based on the leak point of the battery pack and the current location of the leak. The leak point reflects the starting location of the electrolyte leakage, while the current location of the leak shows the diffusion of the electrolyte. The system will evaluate the first warning coefficient based on the importance of the leak point and the severity of the leakage diffusion (such as diffusion speed, diffusion range, whether it affects critical components, etc.). Generally, the more critical the leak point and the more severe the diffusion, the higher the first warning coefficient.

[0158] The second warning coefficient is determined based on the leakage point of the battery pack and the battery pack model. Different models of battery packs differ in structure, materials, and capacity, and therefore have different sensitivities and tolerances to electrolyte leakage. The system will combine the battery pack model information to analyze the specific response of that model of battery pack to electrolyte leakage (such as whether it is easy to accumulate electrolyte or whether it is easy to cause a short circuit), and evaluate the second warning coefficient accordingly. Generally, models that are more sensitive to electrolyte leakage will have a higher second warning coefficient.

[0159] The system internally has a pre-set warning logic mapping table, which corresponds different combinations of first and second warning coefficients to specific warning logic. After determining the first and second warning coefficients, the system will find the corresponding warning logic in the warning logic mapping table based on these two coefficients. The warning logic includes different warning levels (such as low warning, medium warning, high warning, etc.), warning measures (such as stopping charging, discharging, isolating faulty units, starting the emergency drainage system, etc.), and the way warning information is transmitted (such as sound alarm, light alarm, remote notification, etc.).

[0160] Specifically, suppose an electric vehicle's battery pack is detected to have electrolyte leakage during charging, and the system has identified the leak point and its current location; a first warning coefficient is determined: the leak point is located in a critical battery cell of the battery pack, which is responsible for providing the main driving force; at the same time, the leak has spread to the bottom of the battery pack and is affecting other battery cells and the overall structure of the battery pack; based on this information, the system evaluates a high first warning coefficient, indicating that the leakage is relatively serious.

[0161] Determining the second warning coefficient: This battery pack is a high-performance model, employing advanced materials and structural design, making it highly sensitive to electrolyte leakage; at the same time, due to the large capacity of this battery pack model, leakage would lead to more serious consequences; based on this information, the system assesses a high second warning coefficient, indicating that this battery pack model has low tolerance to electrolyte leakage.

[0162] The system searches the warning logic mapping table for the warning logic corresponding to the first and second warning coefficients. Since both warning coefficients are high, the system finds the "advanced warning" logic. This logic includes measures such as immediately stopping charging and discharging, isolating the faulty battery cell, activating the emergency drainage system, and notifying the driver through sound and light alarms. At the same time, the system also sends the warning information to the electric vehicle manufacturer or service center through the remote communication module so that they can take timely measures for repair and replacement. Through the above steps, the system can accurately determine the warning coefficient and construct the corresponding warning logic based on the leakage point and current location of the battery pack and the battery pack model information. This provides important guidance and support for subsequent emergency handling and maintenance.

[0163] Therefore, in this leakage warning logic, the battery pack sub-warning logic and the bottom shell sub-warning logic are determined based on the division of the leakage warning logic. The battery pack's own warning measures are determined according to the traversal of the battery pack's sub-warning logic, and the bottom shell warning measures at the locations through which the leakage occurs are determined according to the traversal of the bottom shell's sub-warning logic. This takes into account the overall consideration of the traversal of the bottom shell's sub-warning logic, ensuring the accuracy of the bottom shell warning measures at the locations through which the leakage occurs. At the same time, the leakage status of the battery pack is monitored and controlled, and the leakage point and the current location of the leakage are introduced to ensure the accuracy of the leakage warning logic. The battery pack's own warning measures and the bottom shell warning measures at the locations through which the leakage occurs are controlled synchronously.

[0164] At this point, after determining the overall leakage warning logic (such as advanced warning, intermediate warning, etc.), the system will further subdivide it into battery pack sub-warning logic and bottom shell sub-warning logic according to the structure and functional characteristics of the battery pack. The battery pack sub-warning logic mainly focuses on the potential risks of various components inside the battery pack (such as battery cells, connecting lines, heat dissipation system, etc.), while the bottom shell sub-warning logic focuses on the potential impact of the bottom shell and its surrounding structures (such as seals, drainage channels, etc.).

[0165] The system iterates through the sub-warning logics of the battery pack and determines the warning measures for the battery pack itself based on the specific content of each sub-warning logic and the risk assessment results. These measures include stopping the charging and discharging functions of the battery pack, isolating damaged battery cells, and adjusting the operating mode of the battery pack to reduce the impact of leakage on battery performance.

[0166] The system also iterates through the sub-early warning logic of the bottom shell, and determines the bottom shell early warning measures for the location through which the leakage occurs based on the specific content of each sub-early warning logic and the risk assessment results. These measures include strengthening the sealing of the bottom shell, activating the emergency drainage system to remove the accumulated electrolyte, and monitoring the temperature changes of the bottom shell and its surrounding structures.

[0167] Specifically, assuming that in step S152, the system has determined the "advanced warning" logic for a certain electric vehicle battery pack; the system further subdivides the "advanced warning" logic into sub-warning logic for the battery pack and sub-warning logic for the bottom shell; the sub-warning logic for the battery pack focuses on whether the connection lines between battery cells are damaged, whether the heat dissipation system is operating normally, etc.; the sub-warning logic for the bottom shell focuses on whether the bottom shell seals are intact, whether the drainage channels are unobstructed, etc.

[0168] The system iterates through the sub-warning logic of the battery pack and finds that the connection lines between battery cells are at risk of damage, and the heat dissipation system is affected by electrolyte leakage. Therefore, the system determines the following warning measures: immediately stop the charging and discharging functions of the battery pack to avoid short circuits caused by current flowing through the damaged connection lines; at the same time, isolate the damaged battery cells to reduce the impact of leakage on the overall battery performance; in addition, adjust the operating mode of the battery pack to reduce its output power to alleviate the burden on the heat dissipation system.

[0169] The system iterates through the sub-warning logic of the bottom casing and finds that the bottom casing seals are damaged due to electrolyte corrosion and the drainage channels are blocked due to electrolyte accumulation. Therefore, the system determines the following warning measures: strengthen the inspection and maintenance of the bottom casing seals, and replace damaged seals when necessary; activate the emergency drainage system to quickly remove the accumulated electrolyte through the preset drainage channels; and monitor the temperature changes of the bottom casing and its surrounding structures to prevent overheating or fire risks caused by electrolyte leakage. Through the above steps, the system can accurately determine the sub-warning logic of the battery pack and bottom casing according to the division of the leakage warning logic, and implement effective warning measures accordingly. This not only helps to reduce the negative impact of leakage on battery pack performance, but also ensures the overall safety and reliability of electric vehicles.

[0170] In some embodiments of this application, a battery pack warning measure matching table is collected, as shown in Table 6:

[0171] Table 6: Matching Table of Battery Pack Early Warning Measures

[0172]

[0173]

[0174] A matching table for bottom shell early warning measures was collected, as shown in Table 7.

[0175] Table 7 Matching Table for Early Warning Measures for Bottom Shell

[0176]

[0177] Please see Figure 7 , Figure 7 This is a schematic diagram of the structure of a battery pack leakage detection system according to an embodiment of the present invention; the battery pack leakage detection system includes:

[0178] Leakage detection area module 21 is used to determine the leakage detection area of ​​the battery pack based on the position of the battery pack and the coordinate system of the bottom shell when the battery pack is installed on the bottom shell.

[0179] The leakage detection point module 22 is used to determine the leakage control path based on the leakage detection area and the shape of the battery pack, and to determine multiple leakage detection points based on the division of the leakage control path.

[0180] The status module 23 is used to collect multiple operating parameters of the battery pack and determine the current status of the battery pack based on the multiple operating parameters and the data detected by multiple leakage detection points. The current status is a leakage state and a non-leakage state.

[0181] The leakage location module 24 is used to determine the abnormal data set based on multiple battery parameters and data detected by multiple leakage detection points if the current state of the battery pack is a leakage state, and to determine the current location of the leakage based on the abnormal data set and the leakage control path.

[0182] The leakage warning module 25 is used to determine the leakage point of the battery pack based on the detection of the battery pack. According to the leakage point of the battery pack, the current location of the leakage and the model of the battery pack, the corresponding leakage warning logic is determined, which triggers the battery pack's own warning measures and the bottom shell warning measures of the location through which the leakage has passed.

[0183] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

Claims

1. A method for detecting leakage in a battery pack, characterized in that, include: When the battery pack is installed on the bottom shell, the leakage detection area of ​​the battery pack is determined based on the position of the battery pack and the coordinate system of the bottom shell. The leakage control path is determined based on the leakage detection area and the shape of the battery pack, and multiple leakage detection points are determined based on the division of the leakage control path. Collect multiple operating parameters of the battery pack, and determine the current state of the battery pack based on the data detected by multiple operating parameters and multiple leakage detection points. The current state is either a leakage state or a non-leakage state. If the current state of the battery pack is leakage, an abnormal data set is determined based on multiple battery parameters and data detected by multiple leakage detection points. The current location of the leakage is determined based on the abnormal data set and the leakage control path. Based on the detection of the battery pack, the leakage point of the battery pack is determined. According to the leakage point, the current location of the leakage and the model of the battery pack, the corresponding leakage warning logic is determined, which triggers the battery pack's own warning measures and the bottom shell warning measures of the location through which the leakage has passed.

2. The battery pack leakage detection method according to claim 1, characterized in that, The step of determining the leakage detection area of ​​the battery pack based on the position of the battery pack and the coordinate system of the bottom shell when the battery pack is installed on the bottom shell includes: The battery pack is installed on the bottom shell, and the lower side wall of the battery pack is in direct contact with the upper side wall of the bottom shell. At this time, a coordinate system of the bottom shell is constructed based on the shape of the bottom shell, and the contact position between the lower side wall of the battery pack and the upper side wall of the bottom shell is defined as the zero point of the coordinate system. The battery pack control area is determined based on the coordinate system of the bottom shell, the zero point of the coordinate system, and the position of the battery pack. Mark the liquid accumulation point on the bottom shell, and determine the leakage detection area of ​​the battery pack based on the liquid accumulation point on the bottom shell and the battery pack control area.

3. The battery pack leakage detection method according to claim 1, characterized in that, The process involves determining a leakage control path based on the leakage detection area and the shape of the battery pack, and then identifying multiple leakage detection points based on the division of the leakage control path, including: Collect the leakage detection area, and determine the first leakage path based on the area's shape and location. The second leakage path is determined based on the shape and internal distribution diagram of the battery pack, and the leakage control path is determined by combining the first and second leakage paths. In the leakage control path, the leakage control path is divided into multiple leakage control segments. Based on each leakage control segment, the corresponding leakage detection points are marked to determine multiple leakage detection points.

4. The battery pack leakage detection method according to claim 1, characterized in that, The system collects multiple operating parameters of the battery pack and determines the current state of the battery pack based on these parameters and data detected by multiple leakage detection points. This current state includes a leakage state and a non-leakage state, including: The system monitors the battery pack's operation in real time and collects multiple operating parameters. When the battery pack is in operation, it performs real-time detection on multiple leakage detection points and determines the data detected at these points.

5. The battery pack leakage detection method according to claim 4, characterized in that, The process of collecting multiple operating parameters of the battery pack and determining the current state of the battery pack based on these parameters and data detected by multiple leakage detection points, wherein the current state is a leakage state and a non-leakage state, also includes: Multiple data combinations are determined based on data detected from multiple operating parameters and multiple leakage detection points. Based on the identification of multiple data combinations, the corresponding state characteristics are determined to collect multiple state characteristics. The system collects the operating signals of the battery pack and determines the current power supply mode of the battery pack based on the analysis of the operating signals. The current state of the battery pack is determined based on multiple state features, the current power supply mode of the battery pack, and the state mapping relationship. This current state covers both leakage and non-leakage states.

6. The battery pack leakage detection method according to claim 1, characterized in that, If the current state of the battery pack is a leakage state, then an abnormal data set is determined based on multiple battery parameters and data detected by multiple leakage detection points. Based on the abnormal data set and the leakage control path, the current location of the leakage is determined, including: The current status of the battery pack is monitored in real time. If the current status of the battery pack is leakage, leakage detection of the battery pack and bottom shell is triggered to detect anomalies in multiple battery parameters and data detected at multiple leakage detection points.

7. The battery pack leakage detection method according to claim 6, characterized in that, If the current state of the battery pack is a leakage state, then an abnormal data set is determined based on multiple battery parameters and data detected by multiple leakage detection points. The current location of the leakage is determined based on the abnormal data set and the leakage control path. This also includes: Multiple abnormal data are identified based on the abnormal detection of data from multiple battery parameters and multiple leakage detection points, and the location of the multiple abnormal data is marked. An abnormal data set is determined based on the multiple abnormal data and their locations. Multiple abnormal data sets are collected, and corresponding abnormal features are determined based on the identification of multiple abnormal data sets. Multiple abnormal regions are determined based on the shape and location of each abnormal feature. The leakage region is determined by combining multiple abnormal regions and leakage control paths. The current location of the leakage is determined by traversing the leakage region. At this time, the current location of the leakage is within the area of ​​the bottom shell.

8. The battery pack leakage detection method according to claim 1, characterized in that, The method for determining the leakage point of the battery pack based on battery pack detection, and determining the corresponding leakage warning logic according to the leakage point, the current location of the leakage, and the battery pack model, triggers the battery pack's own warning measures and the bottom shell warning measures at the location traversed by the leakage, including: If the current state of the battery pack is leakage, the battery pack's autonomous detection is triggered. Based on the autonomous detection, the internal leakage path of the battery pack is determined. Based on the internal leakage path of the battery pack, the shape of the battery pack, and multiple sides of the battery pack, the leakage point of the battery pack is determined. A first warning coefficient is determined based on the leak point and current location of the battery pack. A second warning coefficient is determined based on the leak point and model of the battery pack. The corresponding leak warning logic is determined based on the mapping relationship between the first warning coefficient, the second warning coefficient, and the warning logic.

9. The battery pack leakage detection method according to claim 8, characterized in that, The method of determining the leakage point of the battery pack based on battery pack detection, determining the corresponding leakage warning logic according to the leakage point, the current location of the leakage, and the battery pack model, and triggering the battery pack's own warning measures and the bottom shell warning measures at the location traversed by the leakage, also includes: In this leakage warning logic, the sub-warning logic of the battery pack and the sub-warning logic of the bottom shell are determined based on the division of the leakage warning logic. The battery pack's own warning measures are determined according to the traversal of the sub-warning logic of the battery pack. The bottom shell warning measures at the locations through which the leakage occurs are determined according to the traversal of the sub-warning logic of the bottom shell.

10. A battery pack leakage detection system, characterized in that, The battery pack leakage detection system is applied to the battery pack leakage detection method as described in any one of claims 1-9, and the battery pack leakage detection system comprises: The leakage detection area module is used to determine the leakage detection area of ​​the battery pack based on the position of the battery pack and the coordinate system of the bottom shell when the battery pack is installed on the bottom shell. The leakage detection point module is used to determine the leakage control path based on the leakage detection area and the shape of the battery pack, and to determine multiple leakage detection points based on the division of the leakage control path. The status module is used to collect multiple operating parameters of the battery pack and determine the current status of the battery pack based on the data detected by multiple operating parameters and multiple leakage detection points. The current status is either a leakage state or a non-leakage state. The leakage location module is used to determine the abnormal data set based on multiple battery parameters and data detected by multiple leakage detection points if the current state of the battery pack is a leakage state, and to determine the current location of the leakage based on the abnormal data set and the leakage control path. The leakage warning module is used to determine the leakage point of the battery pack based on the detection of the battery pack. According to the leakage point, the current location of the leakage and the model of the battery pack, the corresponding leakage warning logic is determined, which triggers the battery pack's own warning measures and the bottom shell warning measures of the location through which the leakage has passed.

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