An intelligent monitoring system for new energy battery packs

Abstract

The present invention discloses an intelligent monitoring system for a new energy battery pack, which relates to the field of battery pack safety technology, including a monitoring module, a monitoring module and a data processing module. The monitoring module includes: a trigger unit, at least one group of trigger units are installed at key parts of the battery pack, and corresponding state changes are performed based on the temperature of each key part; an identification unit, which is the same in number as the trigger unit and is provided with a corresponding trigger threshold; after the state change of the trigger unit reaches the trigger threshold, the corresponding identification unit is started and a startup message is sent. The present invention realizes real-time monitoring of key parts of the battery pack through the cooperation of the trigger unit and the identification unit. When the state change of the trigger unit reaches the trigger threshold, the identification unit responds quickly and sends a startup message, so that the system can start the monitoring module and the data processing module in time, thereby quickly detecting the expansion and heating conditions of the battery pack.

Description

Technical Field

[0001] The present invention relates to the field of battery pack safety technology, and in particular to an intelligent monitoring system for a new energy battery pack. Background Art

[0002] New energy battery packs are widely used in electric vehicles, energy storage systems and other fields, and real-time monitoring of their safety has become a key issue in technological development. During long-term use or high-power charging and discharging, battery packs are susceptible to the combined effects of internal chemical reactions and the external environment, leading to expansion and deformation, local overheating and even thermal runaway. These phenomena not only affect the performance of the battery pack, but may also cause serious safety accidents. Therefore, monitoring new energy battery packs is an important part of the safe use of new energy battery packs.

[0003] After searching, a Chinese patent (publication number: CN108878698A) discloses a battery pack, a battery energy storage system and an electric vehicle. The patent includes: a battery module and a pressure sensing device. The battery module includes a plurality of hard battery cells and two end plates. The two end plates are respectively arranged along the thickness direction of the hard battery cells, and are used to compress the plurality of hard battery cells and the pressure sensing device arranged between the two end plates; the pressure sensing device includes a first pressing plate and at least one pressure sensor arranged on the first pressing plate. The first pressing plate is arranged on the first hard battery cell and completely covers the surface of the first hard battery cell. The first pressing plate is used to transmit the expansion force in the thickness direction of the battery cell to the at least one pressure sensor, and the at least one pressure sensor is used to monitor the expansion force.

[0004] In the prior art, how to use low-power and low-cost monitoring technology to monitor the safety status of the battery pack is one of the research and development directions. When monitoring the battery pack, it is necessary to provide early warning and rapid positioning to achieve efficient monitoring requirements. Therefore, the present invention proposes an intelligent monitoring system for new energy battery packs. Summary of the invention

[0005] The purpose of the present invention is to provide an intelligent monitoring system for a new energy battery pack to solve the problems mentioned in the above background technology.

[0006] The present invention can be implemented by the following technical solutions: an intelligent monitoring system for a new energy battery pack, comprising a monitoring module, a monitoring module and a data processing module;

[0007] The monitoring module includes a trigger unit and an identification unit;

[0008] At least one set of trigger units is installed at key parts of the battery pack, and the corresponding state changes are performed based on the temperature of each key part;

[0009] The number of identification units is the same as the number of trigger units, and corresponding trigger thresholds are set;

[0010] When the state change of the trigger unit reaches the trigger threshold, the corresponding recognition unit starts and sends the start information;

[0011] The monitoring module includes an execution unit, a deformation monitoring unit and a heat distribution monitoring unit;

[0012] The execution unit controls the working states of the monitoring unit and the heat distribution monitoring unit based on the startup information or a preset monitoring cycle;

[0013] After the identification unit sends the start information, the execution unit immediately controls the deformation monitoring unit and the heat distribution unit to monitor the battery pack;

[0014] Or after reaching a preset monitoring cycle, the execution unit starts automatically to control the deformation monitoring unit and the heat distribution unit to monitor the battery pack;

[0015] At least one group of the deformation monitoring units identifies the surface of the battery pack and outputs the identification result in the form of a plurality of monitoring points distributed in an array, and the deformation monitoring unit is preset with preset position information corresponding to each monitoring point, and after the deformation monitoring unit is started, the monitoring position information of each monitoring point is obtained;

[0016] The deformation monitoring unit compares the monitoring position information of each monitoring point with the corresponding preset position information to obtain the expansion displacement vector data of the corresponding monitoring point of the battery pack;

[0017] After the heat distribution unit is started, the temperature of the battery pack is detected to obtain heat distribution data of the battery pack;

[0018] The data processing module includes a mapping unit and an analysis unit;

[0019] The mapping unit establishes a mapping model based on the structure of the battery pack;

[0020] The analysis unit is used to receive expansion displacement vector data and thermal distribution data;

[0021] After receiving, the analysis unit obtains expansion position data by analyzing each expansion displacement vector, and obtains heating area data by analyzing the heat distribution data;

[0022] The analysis unit matches the expansion location data and the heating area data with the mapping model, identifies the corresponding area, and determines the fault location of the battery pack.

[0023] A further technical improvement of the present invention is that: the trigger unit adopts a memory alloy unit, at least one group of memory alloy units is installed at the key parts of the battery pack, and corresponding thermal deformation is performed based on the temperature of each key part;

[0024] The number of the identification units is the same as the number of the memory alloy units, and each of them corresponds to a group of memory alloy units;

[0025] When the memory alloy unit undergoes thermal deformation and reaches the trigger threshold, the corresponding identification unit starts, sends the startup information, and detects the deformation rate of the memory alloy unit after it reaches the trigger threshold. The deformation rate of the memory alloy unit reflects the severity of the temperature change and can assist in judging the severity of the battery pack failure.

[0026] A further technical improvement of the present invention is that the key parts of the battery pack include:

[0027] A. Weak heat dissipation area;

[0028] Based on the thermal design of the battery pack, identify areas with insufficient heat dissipation or prone to heat accumulation;

[0029] B. High heat flux areas;

[0030] Detect the temperature between the cells to determine whether there are local overheating areas. If so, mark them as critical areas.

[0031] C. Heat distribution area;

[0032] Perform multiple charge and discharge cycle tests on the battery pack, record and sort the distribution hot spots of each test, and select the corresponding hot spots as key locations in order;

[0033] D. High-risk areas;

[0034] The historical data of the battery pack of the same model are counted, the fault records in the historical data are sorted, and the corresponding parts are selected as key parts in sequence.

[0035] A further technical improvement of the present invention is that: the recognition unit detects the deformation rate of the memory alloy unit after reaching the trigger threshold, and the deformation rate of the memory alloy unit reflects the severity of the temperature change, which can assist in judging the severity of the battery pack failure;

[0036] The identification unit is provided with a warning threshold. After the deformation rate of the memory alloy unit exceeds the warning threshold, the identification unit sends out a warning message for skipping the monitoring module and the data processing module to issue a quick alarm.

[0037] A further technical improvement of the present invention is that the step of analyzing the expansion displacement vector by the analysis unit comprises:

[0038] S1. Determine the expansion shape of the battery pack through each expansion displacement vector;

[0039] First calculate the expansion amplitude of each monitoring point, the formula is:

[0040] ;

[0041] in, is the current coordinate of the corresponding monitoring point, that is, the coordinate information of the monitoring location information;

[0042] is the initial coordinate of the corresponding detection, that is, the coordinate information of the preset position information;

[0043] is the expansion amplitude of the i-th detection point;

[0044] Then calculate the uniformity of the expansion amplitude, the formula is:

[0045] , ;

[0046] is the variance of the expansion amplitude, reflecting the uniformity of the expansion;

[0047] is the average value of the expansion amplitude of all monitoring points;

[0048] It is then compared with the preset variance threshold ε to determine the expansion shape;

[0049] like , then it expands to a uniform shape;

[0050] like , then the expansion shape may be a local or asymmetric expansion;

[0051] S2, determine the expansion position;

[0052] First determine the maximum expansion area, the formula is:

[0053] , filter expansion amplitude above the threshold The set of monitoring points P;

[0054] ;

[0055] is the maximum expansion amplitude, P is the set of monitoring points with obvious expansion, is the screening threshold, used to adjust the range of the obvious expansion area;

[0056] The weighted center of the dilated area is then calculated using the formula:

[0057] ;

[0058] is the weighted center of the area with obvious expansion, which uses the expansion amplitude As a weight, the coordinates of the current monitoring point A weighted average is performed, which is calculated only for points whose expansion amplitude belongs to the obvious expansion area P;

[0059] S3, determining the expansion edge;

[0060] First determine the expansion edge point set, the formula used is: ;

[0061] in, , is the center of the obvious area of ​​expansion position;

[0062] is the local boundary radius of the area where the expansion position is obvious;

[0063] S4. Determine the degree of expansion;

[0064] Matching the expansion height of the battery pack located inside the expansion edge with a preset risk height table to obtain a risk level;

[0065] S5. Mark the part of the battery pack within the expansion edge and match it with the risk level to generate expansion part data.

[0066] A further technical improvement of the present invention is that the step of analyzing the thermal distribution data by the analysis unit comprises:

[0067] Q1. Preliminary screening of fever areas based on temperature anomalies in temperature data;

[0068] Divide the battery pack surface into multiple identification points, obtain temperature data of each identification point on the battery pack surface, and screen out temperature abnormal points based on the basic temperature and the temperature threshold of each identification point. When the difference between the identification point and the basic temperature exceeds the temperature threshold, it is a temperature abnormal point.

[0069] Q2. Analyze each identification point through temperature gradient and conduct secondary screening to narrow the scope;

[0070] Calculate the temperature gradient of each identification point, select the points with temperature gradient higher than the temperature threshold, and mark them as abnormal temperature gradient points;

[0071] Q3. Integrate each temperature anomaly point to obtain a temperature anomaly point set;

[0072] Integrate each temperature gradient anomaly point to obtain a temperature gradient anomaly point set;

[0073] Calculate the weighted center of the intersection of the temperature anomaly point set and the gradient anomaly point set;

[0074] Q4. Fit the surrounding points of the core area to obtain the radius of the heating area;

[0075] The range of the final heating area is centered on the weighted center and is within the radius of the heating area.

[0076] A further technical improvement of the present invention is that: a plurality of deformation monitoring units identify the same side of the battery pack, and each deformation monitoring unit can independently cover a corresponding side of the battery pack;

[0077] Each deformation monitoring unit independently scans the corresponding side of the battery pack and generates its own array monitoring point data;

[0078] The data processing module receives the monitoring point data of all deformation monitoring units and generates an overall detection result based on the monitoring point data;

[0079] When it is generated, if the data of the corresponding monitoring points are the same, the same data is used; if the data of the corresponding monitoring points are different, the average value is taken or calculated by weighting;

[0080] In addition, multiple deformation monitoring units repeatedly cover the same surface to generate redundant data, which can improve the accuracy of fault diagnosis.

[0081] A further technical improvement of the present invention is that: a plurality of deformation monitoring units continuously identify the same surface of the battery pack to form a regional scan, and the scanning areas of each adjacent deformation monitoring unit are provided with overlapping coverage to ensure seamless connection;

[0082] Adjacent deformation monitoring units fuse the monitoring point data of overlapping coverage areas to ensure consistency;

[0083] Through fusion processing, data redundancy in overlapping areas is eliminated, and multiple data are merged into a unified result to avoid data duplication and improve computing efficiency;

[0084] At the same time, the fusion processing solves the possible "fault" or inconsistency problem between adjacent monitoring units, ensuring the continuity of the array distribution of monitoring points;

[0085] Through overlapping area calibration, battery packs of different sizes can be adapted at a lower cost.

[0086] The further technical improvement of the present invention is:

[0087] Compared with the prior art, the present invention has the following beneficial effects:

[0088] The present invention realizes real-time monitoring of key parts of the battery pack through the cooperation of the trigger unit and the identification unit. When the state change of the trigger unit reaches the trigger threshold, the identification unit responds quickly and sends the start information, so that the system can start the monitoring module and the data processing module in time, thereby quickly detecting the expansion and heating conditions of the battery pack. Through the comprehensive analysis of the expansion displacement vector and the heat distribution data, the system can accurately determine the fault location and risk level of the battery pack, significantly improving the accuracy and efficiency of fault diagnosis;

[0089] Through the design of the trigger unit, when the battery pack is in normal working condition, the monitoring module is only started when the trigger threshold or the preset monitoring cycle is reached, thus effectively reducing the system power consumption. At the same time, the modular design reduces hardware costs and energy consumption, ensures the efficient operation of the system, and is suitable for long-term online monitoring needs;

[0090] The data processing module establishes a mapping model of the battery pack structure through the mapping unit, and fuses and analyzes the expansion location data and the heating area data. The analysis unit can extract information such as the expansion shape, location and severity from the expansion displacement vector, and accurately locate the fault area in combination with the heat distribution data. This multi-dimensional data fusion analysis method significantly improves the depth of fault diagnosis and the intelligence level of the system;

[0091] In addition, the deformation monitoring unit and heat distribution monitoring unit can adapt to battery pack structures of different sizes and complexities at the same time. Through precise monitoring and data fusion, they can be widely used in various scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0092] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0093] Figure 1 It is a system block diagram of the present invention. DETAILED DESCRIPTION

[0094] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the specific implementation methods, structures, features and effects of the present invention are described in detail below in conjunction with the accompanying drawings and preferred embodiments.

[0095] Example 1

[0096] See also Figure 1 As shown, the present invention provides an intelligent monitoring system for a new energy battery pack, including a monitoring module, a monitoring module and a data processing module;

[0097] The monitoring module includes a trigger unit and an identification unit;

[0098] N groups of trigger units are installed in key parts of the battery pack, and the corresponding state changes are made based on the temperature of each key part;

[0099] The key parts of the battery pack include:

[0100] A. Weak heat dissipation area;

[0101] Based on the thermal design of the battery pack, identify areas with insufficient heat dissipation or prone to heat accumulation;

[0102] In this embodiment, the battery pack is inspected by a thermal imager, the heat distribution is simulated and analyzed, and key locations are marked, including the gaps between battery cells and locations close to heat sources;

[0103] B. High heat flux areas;

[0104] Detect the temperature between the cells to determine whether there are local overheating areas. If so, mark them as critical areas.

[0105] C. Heat distribution area;

[0106] Perform multiple charge and discharge cycle tests on the battery pack, record and sort the distribution hot spots of each test, and select the corresponding hot spots as key locations in order;

[0107] D. High-risk areas;

[0108] Collect historical data of the same model of battery pack, sort the fault records in the historical data, and select the corresponding parts as key parts in order;

[0109] The number of identification units is the same as the number of trigger units, and corresponding trigger thresholds are set;

[0110] When the state change of the trigger unit reaches the trigger threshold, the corresponding recognition unit starts and sends the start information;

[0111] In this embodiment, the trigger unit adopts a memory alloy unit, and N groups of memory alloy units are installed at key positions of the battery pack, and corresponding thermal deformation is performed based on the temperature of each key position;

[0112] The number of the identification units is the same as the number of the memory alloy units, and each of them corresponds to a group of memory alloy units;

[0113] When the memory alloy unit undergoes thermal deformation and reaches the trigger threshold, the corresponding identification unit starts, sends the startup information, and detects the deformation rate of the memory alloy unit after reaching the trigger threshold. The deformation rate of the memory alloy unit reflects the severity of the temperature change and can assist in judging the severity of the battery pack failure.

[0114] The recognition unit detects the deformation rate of the memory alloy unit after it reaches the trigger threshold. The deformation rate of the memory alloy unit reflects the severity of the temperature change and can assist in judging the severity of the battery pack failure.

[0115] The recognition unit is provided with a warning threshold. When the deformation rate of the memory alloy unit exceeds the warning threshold, the recognition unit sends out a warning message to skip the monitoring module and the data processing module and issue a quick alarm.

[0116] The monitoring module includes an execution unit, a deformation monitoring unit and a heat distribution monitoring unit;

[0117] The execution unit controls the working state and position movement of the monitoring unit and the heat distribution monitoring unit based on the startup information or the preset monitoring cycle;

[0118] After the identification unit sends the start information, the execution unit immediately controls the deformation monitoring unit and the heat distribution unit to monitor the battery pack;

[0119] Or after reaching a preset monitoring cycle, the execution unit starts automatically to control the deformation monitoring unit and the heat distribution unit to monitor the battery pack;

[0120] The multiple groups of deformation monitoring units identify the surface of the battery pack and output the identification results in the form of multiple monitoring points distributed in an array, and the deformation monitoring units are preset with preset position information corresponding to each monitoring point. After the deformation monitoring units are started, the monitoring position information of each monitoring point is obtained;

[0121] Multiple deformation monitoring units identify the same side of the battery pack, and each deformation monitoring unit can independently cover the corresponding side of the battery pack;

[0122] Each deformation monitoring unit independently scans the corresponding side of the battery pack and generates its own array monitoring point data;

[0123] The data processing module receives the monitoring point data of all deformation monitoring units and generates an overall detection result based on the monitoring point data;

[0124] When it is generated, if the data of the corresponding monitoring points are the same, the same data is used; if the data of the corresponding monitoring points are different, the average value is taken or calculated by weighting;

[0125] In this embodiment, each deformation monitoring unit can independently complete full coverage, ensuring that when a single deformation monitoring unit fails, other deformation monitoring units can still work;

[0126] Furthermore, multiple deformation monitoring units repeatedly cover the same surface, generating redundant data, which can improve the accuracy of fault diagnosis;

[0127] The deformation monitoring unit compares the monitoring position information of each monitoring point with the corresponding preset position information to obtain the expansion displacement vector data of the corresponding monitoring point of the battery pack;

[0128] After the heat distribution unit is started, the temperature of the battery pack is detected to obtain the heat distribution data of the battery pack;

[0129] The data processing module includes a mapping unit and an analysis unit;

[0130] The mapping unit establishes a mapping model based on the structure of the battery pack;

[0131] The process of building a mapping model includes:

[0132] Z1. Obtain the structural information of the battery pack;

[0133] Generate a 3D geometric model of the battery pack based on its physical size, shape, structural characteristics, and internal cell arrangement;

[0134] Use CAD software or scanning technology to digitize the external contour and internal structure information of the battery pack;

[0135] Z2, define the mapping area;

[0136] The battery pack is divided into several functional areas, including:

[0137] Cell area, the area where battery cells are arranged;

[0138] Connection area, distribution area of ​​conductive connection plates or busbars;

[0139] Heat dissipation area, the area where heat dissipation components and heat dissipation paths are located;

[0140] And assign a unique area label to each area and establish a logical mapping table;

[0141] Z3. Establish the corresponding relationship between monitoring points and regions;

[0142] Match the monitoring point coordinates of the expansion monitoring unit and the heat distribution monitoring unit with the three-dimensional structural model of the battery pack;

[0143] Through geometric projection, the monitoring point data distributed in the array is mapped to the specific location in the battery pack model;

[0144] Z4. Establish dynamic mapping;

[0145] Considering the deformation or thermal expansion effect of the battery pack during operation, the coordinates of the monitoring points are updated in real time to adjust the correspondence between the monitoring data and the mapping model;

[0146] The analysis unit is used to receive expansion displacement vector data and thermal distribution data;

[0147] After receiving, the analysis unit obtains expansion position data by analyzing each expansion displacement vector, and obtains heating area data by analyzing the heat distribution data;

[0148] The step of analyzing the expansion displacement vector by the analysis unit includes:

[0149] S1. Determine the expansion shape of the battery pack through each expansion displacement vector;

[0150] First calculate the expansion amplitude of each monitoring point, the formula is:

[0151] ;

[0152] in, is the current coordinate of the corresponding monitoring point, that is, the coordinate information of the monitoring location information;

[0153] is the initial coordinate of the corresponding detection, that is, the coordinate information of the preset position information;

[0154] is the expansion amplitude of the i-th detection point;

[0155] Then calculate the uniformity of the expansion amplitude, the formula is:

[0156] , ;

[0157] is the variance of the expansion amplitude, reflecting the uniformity of the expansion;

[0158] is the average value of the expansion amplitude of all monitoring points;

[0159] It is then compared with the preset variance threshold ε to determine the expansion shape;

[0160] like , then it expands to a uniform shape;

[0161] like , then the expansion shape may be a local or asymmetric expansion;

[0162] S2, determine the expansion position;

[0163] First determine the maximum expansion area, the formula is: , filter expansion amplitude above the threshold The set of monitoring points P;

[0164] ;

[0165] is the maximum expansion amplitude, P is the set of monitoring points with obvious expansion, is the screening threshold, used to adjust the range of the obvious expansion area;

[0166] The weighted center of the dilated area is then calculated using the formula:

[0167] ;

[0168] is the weighted center of the area with obvious expansion, which uses the expansion amplitude As a weight, the coordinates of the current monitoring point A weighted average is performed, which is calculated only for points whose expansion amplitude belongs to the obvious expansion area P;

[0169] S3, determining the expansion edge;

[0170] First determine the expansion edge point set, the formula used is: ;

[0171] in, , is the center of the obvious area of ​​expansion position;

[0172] is the local boundary radius of the area where the expansion position is obvious;

[0173] In this embodiment, Clustering algorithm is used to group the expansion areas and calculate , the process includes:

[0174] Construct features: Position the points and expansion amplitude As clustering features;

[0175] Perform clustering: Use K-Means clustering to divide the monitoring points into K clusters;

[0176] Select the expansion area: the cluster with the largest expansion amplitude is selected as the obvious expansion area;

[0177] calculate :Calculated by the average distance from the points in the cluster to the cluster center in the dilated obvious area;

[0178] S4. Determine the degree of expansion;

[0179] Matching the expansion height of the battery pack located inside the expansion edge with a preset risk height table to obtain a risk level;

[0180] S5. Mark the part of the battery pack within the expansion edge and match it with the risk level to generate expansion part data;

[0181] In this embodiment, the risk levels include slight risk, medium risk and high risk, which are obtained by matching the preset expansion height;

[0182] The steps of analyzing the thermal distribution data by the analysis unit include:

[0183] Q1. Preliminary screening of fever areas based on temperature anomalies in temperature data;

[0184] Divide the battery pack surface into multiple identification points, obtain temperature data of each identification point on the battery pack surface, and screen out temperature abnormal points based on the basic temperature and the temperature threshold of each identification point. When the difference between the identification point and the basic temperature exceeds the temperature threshold, it is a temperature abnormal point.

[0185] Q2. Analyze each identification point through temperature gradient and conduct secondary screening to narrow the scope;

[0186] Calculate the temperature gradient of each identification point, select the points with temperature gradient higher than the temperature threshold, and mark them as abnormal temperature gradient points;

[0187] Q3. Integrate each temperature anomaly point to obtain a temperature anomaly point set;

[0188] Integrate each temperature gradient anomaly point to obtain a temperature gradient anomaly point set;

[0189] Calculate the weighted center of the intersection of the temperature anomaly point set and the gradient anomaly point set;

[0190] Q4. Fit the surrounding points of the core area to obtain the radius of the heating area;

[0191] The range of the final heating area is centered on the weighted center and is within the radius of the heating area.

[0192] The analysis unit matches the expansion location data and the heating area data with the mapping model, identifies the corresponding area, and determines the fault location of the battery pack;

[0193] The analysis unit matches the monitoring point coordinates of the expansion data with the regional labels of the mapping model to identify the specific area where the expansion occurred;

[0194] And the analysis unit compares the center point and boundary range of the fever area with the mapping model through weighted center and fever radius calculation to locate the abnormal fever area;

[0195] At the same time, the analysis unit superimposes the expansion location data and the heating area data on the mapping model to determine the intersection or correlation area of ​​the expansion and heating data, and judges whether the fault location is concentrated in a certain area based on the historical trend of the data monitored multiple times;

[0196] If the expansion and heating data are concentrated in a specific area of ​​the battery pack, then that area is considered the fault location;

[0197] Fault locations include:

[0198] Battery cell area: battery cells swell or overheat locally;

[0199] Connection area: Overheating or mechanical deformation at the connection plate or busbar due to high current;

[0200] Heat dissipation area: heat accumulation caused by poor heat dissipation;

[0201] Causes of failure include:

[0202] Battery cell failure: If the expansion displacement vector is abnormal and the expansion area matches the battery cell position, it is judged to be mechanical expansion caused by gas generation inside the battery cell or material aging;

[0203] If the temperature is abnormally concentrated, it is judged to be local overheating caused by a single cell short circuit or overcharging;

[0204] Connection failure:

[0205] If the heating data is concentrated on the connection board or busbar, it is judged that the resistance heating is caused by high current or poor contact;

[0206] Heat dissipation problem:

[0207] If the heat distribution data shows high temperatures in multiple areas but the expansion data is normal, it is judged that it is due to insufficient heat sink performance or obstructed heat dissipation path;

[0208] Comprehensive failure:

[0209] If the expansion and heating data are concentrated in the same area and occur near the battery cell, it is judged to be a serious thermal runaway problem;

[0210] After the system makes a judgment, it is transmitted to the control host or user as an auxiliary result;

[0211] Auxiliary results include fault location, fault type, fault cause and risk assessment. The risk assessment combines historical data and current conditions to provide a risk assessment of the fault.

[0212] Example 2

[0213] An intelligent monitoring system for a new energy battery pack includes a monitoring module, a monitoring module and a data processing module;

[0214] The monitoring module includes a trigger unit and an identification unit;

[0215] Multiple groups of trigger units are installed at key locations of the battery pack, and the corresponding state changes are made based on the temperature of each key location;

[0216] The number of identification units is the same as the number of trigger units, and corresponding trigger thresholds are set;

[0217] When the state change of the trigger unit reaches the trigger threshold, the corresponding recognition unit starts and sends the start information;

[0218] In this embodiment, the trigger unit adopts a combination of a memory alloy unit and a thermocouple unit. N groups of memory alloy units and thermocouple units are respectively installed at key positions of the battery pack, and corresponding thermal deformation is performed based on the temperature of each key position.

[0219] The number of the identification units is the same as the number of the memory alloy units, and each of them corresponds to a group of memory alloy units;

[0220] The probe of the thermocouple unit is close to the surface of the battery pack to ensure accurate temperature sensing. The signal is amplified and A / D converted to output the temperature value in real time.

[0221] The recognition unit monitors the real-time temperature value output by the thermocouple unit and the deformation data and amplitude of the memory alloy unit, and the recognition unit is provided with a real-time temperature threshold corresponding to the thermocouple unit and a trigger threshold corresponding to the memory alloy unit;

[0222] The identification unit is provided with a trigger logic based on the real-time temperature threshold and the trigger threshold. When the identification unit is working, it selects to execute the trigger logic and send the start information when either or both of the real-time temperature threshold and the trigger threshold are met based on the demand.

[0223] The monitoring module includes an execution unit, a deformation monitoring unit and a heat distribution monitoring unit;

[0224] The execution unit controls the working state and position movement of the monitoring unit and the heat distribution monitoring unit based on the startup information or the preset monitoring cycle;

[0225] After the identification unit sends the start information, the execution unit immediately controls the deformation monitoring unit and the heat distribution unit to monitor the battery pack;

[0226] Or after reaching a preset monitoring cycle, the execution unit starts automatically to control the deformation monitoring unit and the heat distribution unit to monitor the battery pack;

[0227] The plurality of groups of deformation monitoring units identify the surface of the battery pack and output the identification results in the form of a plurality of monitoring points distributed in an array, and the deformation monitoring units are preset with preset position information corresponding to each monitoring point, and after the deformation monitoring units are started, the monitoring position information of each monitoring point is obtained;

[0228] The deformation monitoring unit compares the monitoring position information of each monitoring point with the corresponding preset position information to obtain the expansion displacement vector data of the corresponding monitoring point of the battery pack;

[0229] After the heat distribution unit is started, the temperature of the battery pack is detected to obtain heat distribution data of the battery pack;

[0230] The data processing module includes a mapping unit and an analysis unit;

[0231] The mapping unit establishes a mapping model based on the structure of the battery pack;

[0232] The analysis unit is used to receive expansion displacement vector data and thermal distribution data;

[0233] After receiving, the analysis unit obtains expansion position data by analyzing each expansion displacement vector, and obtains heating area data by analyzing the heat distribution data;

[0234] The analysis unit matches the expansion location data and the heating area data with the mapping model, identifies the corresponding area, and determines the fault location of the battery pack;

[0235] The monitoring module includes an execution unit, a deformation monitoring unit and a heat distribution monitoring unit;

[0236] The execution unit controls the working state and position movement of the monitoring unit and the heat distribution monitoring unit based on the startup information or the preset monitoring cycle;

[0237] After the identification unit sends the start information, the execution unit immediately controls the deformation monitoring unit and the heat distribution unit to monitor the battery pack;

[0238] Or after reaching a preset monitoring cycle, the execution unit starts automatically to control the deformation monitoring unit and the heat distribution unit to monitor the battery pack;

[0239] The plurality of groups of deformation monitoring units identify the surface of the battery pack and output the identification results in the form of a plurality of monitoring points distributed in an array, and the deformation monitoring units are preset with preset position information corresponding to each monitoring point, and after the deformation monitoring units are started, the monitoring position information of each monitoring point is obtained;

[0240] The deformation monitoring unit compares the monitoring position information of each monitoring point with the corresponding preset position information to obtain the expansion displacement vector data of the corresponding monitoring point of the battery pack;

[0241] Multiple deformation monitoring units continuously identify the same side of the battery pack to form a regional scan, and the scanning areas of each adjacent deformation monitoring unit are set with overlapping coverage to ensure seamless connection;

[0242] Adjacent deformation monitoring units fuse the monitoring point data of the overlapping coverage area to ensure consistency. The data fusion formula is:

[0243] , ;

[0244] in, is the final fusion value of point (i, j) in the overlapping area;

[0245] is the monitoring value of the kth deformation monitoring unit at point (i, j);

[0246] is the set of all points in the overlapping coverage area;

[0247] Through overlapping area calibration, it can adapt to battery packs of different sizes at a lower cost;

[0248] After the heat distribution unit is started, the temperature of the battery pack is detected to obtain heat distribution data of the battery pack;

[0249] The data processing module includes a mapping unit and an analysis unit;

[0250] The mapping unit establishes a mapping model based on the structure of the battery pack;

[0251] The analysis unit is used to receive expansion displacement vector data and thermal distribution data;

[0252] After receiving, the analysis unit obtains expansion position data by analyzing each expansion displacement vector, and obtains heating area data by analyzing the heat distribution data;

[0253] The step of analyzing the expansion displacement vector by the analyzing unit comprises:

[0254] S1. Determine the expansion shape of the battery pack through each expansion displacement vector;

[0255] First, the expansion amplitude of each monitoring point is calculated, and then the uniformity of the expansion amplitude is calculated, and then compared with the preset variance threshold ε to determine the expansion shape;

[0256] S2, determine the expansion position;

[0257] First determine the maximum expansion area, then calculate the weighted center of the expansion area;

[0258] S3, determining the expansion edge;

[0259] First determine the set of dilation edge points, then calculate the local boundary radius of the area where the dilation position is obvious;

[0260] S4. Determine the degree of expansion;

[0261] Matching the expansion height of the battery pack located inside the expansion edge with a preset risk height table to obtain a risk level;

[0262] S5. Mark the part of the battery pack within the expansion edge and match it with the risk level to generate expansion part data;

[0263] The step of analyzing the heat distribution data by the analysis unit comprises:

[0264] Q1. Preliminary screening of fever areas based on temperature anomalies in temperature data;

[0265] Q2. Analyze each identification point through temperature gradient and conduct secondary screening to narrow the scope;

[0266] Q3. Integrate each temperature anomaly point to obtain a temperature anomaly point set;

[0267] Q4. Fit the surrounding points of the core area to obtain the radius of the heating area;

[0268] The range of the final heating area is centered on the weighted center and is within the radius of the heating area;

[0269] The analysis unit matches the expansion location data and the heating area data with the mapping model, identifies the corresponding area, and determines the fault location of the battery pack.

[0270] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment as above, it is not used to limit the present invention. Any technical personnel in this field can make some changes or modify the technical contents disclosed above into equivalent embodiments without departing from the scope of the technical solution of the present invention. However, any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present invention without departing from the content of the technical solution of the present invention still fall within the scope of the technical solution of the present invention.

Claims

1. An intelligent monitoring system for a new energy battery pack, comprising a monitoring module, a monitoring module and a data processing module, characterized in that: The monitoring module comprises: Trigger units, at least one group of trigger units is installed at a key part of the battery pack, and performs corresponding state changes based on the temperature of the corresponding key part; Identification units, the number of which is the same as that of triggering units, and are provided with corresponding triggering thresholds; When the state change of the trigger unit reaches the trigger threshold, the corresponding recognition unit starts and sends the start information; The monitoring modules include: A deformation monitoring unit, at least one group of which identifies the surface of the battery pack when in operation, and outputs the identification result in the form of a plurality of monitoring points distributed in an array, obtains monitoring position information of each monitoring point, and the deformation monitoring unit is preset with preset position information corresponding to each monitoring point; The deformation monitoring unit compares the monitoring position information of each monitoring point with the corresponding preset position information to obtain the expansion displacement vector data of the corresponding monitoring point of the battery pack; A heat distribution unit detects the temperature of the battery pack and obtains heat distribution data of the battery pack; An execution unit controls the working states of the monitoring unit and the heat distribution monitoring unit based on the startup information or a preset monitoring cycle; The data processing module includes: A mapping unit, which establishes a mapping model based on the structure of the battery pack; An analysis unit receives the expansion displacement vector data and the heat distribution data, and obtains the expansion position data by analyzing each expansion displacement vector, and obtains the heating area data by analyzing the heat distribution data; The analysis unit matches the expansion location data and the heating area data with the mapping model, identifies the corresponding area, and determines the fault location of the battery pack.

2. The intelligent monitoring system for a new energy battery pack according to claim 1, characterized in that: The trigger unit adopts a memory alloy unit, at least one group of memory alloy units is installed in the key parts of the battery pack, and performs corresponding thermal deformation based on the temperature of each key part; The number of the identification units is the same as the number of the memory alloy units, and each of them corresponds to a group of memory alloy units; When the memory alloy unit undergoes thermal deformation and reaches the trigger threshold, the corresponding recognition unit starts and sends the start information.

3. The intelligent monitoring system for a new energy battery pack according to claim 2 is characterized in that: The identification unit detects the deformation rate of the memory alloy unit after reaching the trigger threshold; The identification unit is provided with a warning threshold. When the deformation rate of the memory alloy unit exceeds the warning threshold, the identification unit sends out a warning message, skips the monitoring module and the data processing module, and issues a rapid alarm.

4. The intelligent monitoring system for a new energy battery pack according to claim 2, characterized in that: The key parts of the battery pack include: A. Weak heat dissipation area; Based on the thermal design of the battery pack, identify areas with insufficient heat dissipation or prone to heat accumulation; B. High heat flux areas; Detect the temperature between the cells to determine whether there are local overheating areas. If so, mark them as critical areas. C. Heat distribution area; Perform multiple charge and discharge cycle tests on the battery pack, record and sort the distribution hot spots of each test, and select the corresponding hot spots as key locations in order; D. High-risk areas; The historical data of the battery pack of the same model are counted, the fault records in the historical data are sorted, and the corresponding parts are selected as key parts in sequence.

5. The intelligent monitoring system for a new energy battery pack according to claim 1, characterized in that: The step of analyzing the expansion displacement vector by the analyzing unit comprises: S1. Determine the expansion shape of the battery pack through each expansion displacement vector; First, the expansion amplitude of each monitoring point is calculated, and then the uniformity of the expansion amplitude is calculated, and then compared with the preset variance threshold to determine the expansion shape; S2, determine the expansion position; First determine the maximum expansion area, then calculate the weighted center of the expansion area; S3, determining the expansion edge; First determine the set of dilation edge points, then calculate the local boundary radius of the area where the dilation position is obvious; S4. Determine the degree of expansion; Matching the expansion height of the battery pack located inside the expansion edge with a preset risk height table to obtain a risk level; S5. Mark the part of the battery pack within the expansion edge and match it with the risk level to generate expansion part data.

6. The intelligent monitoring system for a new energy battery pack according to claim 5, characterized in that: The step of analyzing the heat distribution data by the analysis unit comprises: Q1. Preliminary screening of fever areas based on temperature anomalies in temperature data; Divide the battery pack surface into multiple identification points, obtain temperature data of each identification point on the battery pack surface, and filter out temperature abnormal points based on the basic temperature and temperature threshold of each identification point; Q2. Analyze each identification point through temperature gradient and conduct secondary screening to narrow the scope; Calculate the temperature gradient of each identification point, select the points with temperature gradient higher than the temperature threshold, and mark them as abnormal temperature gradient points; Q3. Integrate each temperature anomaly point to obtain a temperature anomaly point set; Integrate each temperature gradient anomaly point to obtain a temperature gradient anomaly point set; Calculate the weighted center of the intersection of the temperature anomaly point set and the gradient anomaly point set; Q4. Fit the surrounding points of the core area to obtain the radius of the heating area; The range of the final heating area is centered on the weighted center and is within the radius of the heating area.

7. The intelligent monitoring system for a new energy battery pack according to claim 1, characterized in that: Multiple deformation monitoring units identify the same side of the battery pack, and each deformation monitoring unit independently covers the corresponding side of the battery pack; Each deformation monitoring unit independently scans the corresponding side of the battery pack and generates its own array monitoring point data; The data processing module receives the monitoring point data of all deformation monitoring units and generates an overall detection result based on the monitoring point data; When it is generated, if the data of the corresponding monitoring points are the same, the same data is used; if the data of the corresponding monitoring points are different, the average value is taken or calculated by weighting.

8. The intelligent monitoring system for a new energy battery pack according to claim 1, characterized in that: Multiple deformation monitoring units continuously identify the same surface of the battery pack to form a regional scan, and the scanning areas of each adjacent deformation monitoring unit are set with overlapping coverage; Adjacent deformation monitoring units fuse the monitoring point data in the overlapping coverage area.

Images