Electric tricycle controller circuit board welding quality monitoring system

Abstract

The present invention discloses a welding quality monitoring system for the controller circuit board of an electric tricycle, which relates to the technical field of welding quality monitoring. The system includes: a power unit connected to the target electric tricycle; establishing a circuit simulation model based on the target electric tricycle; identifying welding quality characteristic indicators, conducting parameter drift analysis, and determining a first parameter drift set; conducting parameter drift analysis on the connection between the power conversion components in the power unit and the electric tricycle controller to obtain a second parameter drift set; and establishing a welding quality evaluation model. The present invention solves the technical problems in the prior art that there is a lack of effective monitoring during the welding process of the controller circuit board of an electric tricycle and it is impossible to accurately evaluate the welding quality, and achieves the technical effects of comprehensively monitoring the welding process of the controller circuit board of an electric tricycle, accurately evaluating the welding quality, effectively identifying potential fault points, thereby ensuring the welding quality of the power unit of the electric tricycle and improving the performance and reliability of the whole vehicle.

Description

Technical Field

[0001] The present invention relates to the technical field of welding quality monitoring, and particularly to a welding quality monitoring system for the controller circuit board of an electric tricycle. Background Art

[0002] With the wide application of electric tricycles in the fields of logistics transportation, short-distance travel, etc., their performance and reliability have attracted much attention. As one of the core components of an electric tricycle, the welding quality of the circuit board of the controller directly affects the function of the controller and the performance of the whole vehicle. In the current production process of electric tricycles, there are many technical challenges in the welding link of the controller circuit board. The traditional welding quality control method mainly relies on manual sampling inspection, which is not only inefficient but also highly subjective, making it difficult to ensure that the welding quality of each circuit board can be effectively monitored. Due to the lack of real-time and comprehensive monitoring means, during the welding process, the changes in key factors such as the shape of the solder joint, the temperature of the solder, and the amount of solder cannot be accurately grasped in a timely manner. For example, a poor solder joint shape may lead to an increase in contact resistance, affecting the power transmission efficiency; too high or too low solder temperature may cause problems such as solder joint voids and excessive oxidation of the solder, reducing the mechanical strength and conductivity of the solder joint; an unreasonable amount of solder may pose a risk of short circuit or loose connection of the solder joint.

[0003] There are technical problems in the prior art that there is a lack of effective monitoring during the welding process of the controller circuit board of an electric tricycle and the welding quality cannot be accurately evaluated. Summary of the Invention

[0004] This application provides a welding quality monitoring system for the controller circuit board of an electric tricycle, which is used to solve the technical problems in the prior art that there is a lack of effective monitoring during the welding process of the controller circuit board of an electric tricycle and the welding quality cannot be accurately evaluated.

[0005] In view of the above problems, this application provides a welding quality monitoring system for the controller circuit board of an electric tricycle.

[0006] This application provides a welding quality monitoring system for the controller circuit board of an electric tricycle, and the system includes:

[0007] Power unit connection module, which is used to connect the power unit of the target electric tricycle. The power unit includes a motor assembly, a power conversion assembly, and an electric tricycle controller; Circuit simulation model establishment module, which establishes a circuit simulation model based on the target electric tricycle. The circuit simulation model includes multiple control loops, and marks several welding points on each control loop; Welding monitoring unit adoption module, which is used to adopt a welding monitoring unit to synchronously monitor the welding of each control loop and collect a monitoring data set. The monitoring data set includes a solder joint morphology subset, a solder temperature subset, and a solder quantity subset. The welding monitoring unit is deployed on a welding workbench; First parameter drift set determination module, which is used to analyze the solder joint morphology subset, the solder temperature subset, and the solder quantity subset, identify welding quality characteristic indicators, and perform parameter drift analysis on the connection between the motor assembly and the electric tricycle controller in the power unit according to the circuit simulation model to determine the first parameter drift set and calibrate the first solder joint potential fault queue; Second parameter drift set acquisition module, which performs parameter drift analysis on the connection between the power conversion assembly and the electric tricycle controller in the power unit based on the welding quality characteristic indicators according to the circuit simulation model to obtain the second parameter drift set and calibrate the second solder joint potential fault queue; Welding quality evaluation model establishment module, which establishes a welding quality evaluation model based on the first parameter drift set and the first solder joint potential fault queue, the second parameter drift set and the second solder joint potential fault queue. The welding quality evaluation model is used to monitor and remind the welding quality of the electric tricycle controller.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0009] Power unit connection module, connecting the power unit of the target electric tricycle; Circuit simulation model establishment module, establishing a circuit simulation model based on the target electric tricycle; Welding monitoring unit adoption module, adopting a welding monitoring unit to synchronously monitor the welding of each control loop and collect a monitoring data set; First parameter drift set determination module, analyzing the solder joint morphology subset, solder temperature subset, and solder quantity subset, identifying welding quality characteristic indicators, and based on the circuit simulation model, performing parameter drift analysis on the connection between the motor component in the power unit and the electric tricycle controller to determine the first parameter drift set and calibrate the first potential failure queue of solder joints; Second parameter drift set acquisition module, based on the welding quality characteristic indicators and according to the circuit simulation model, performing parameter drift analysis on the connection between the power conversion component in the power unit and the electric tricycle controller to obtain the second parameter drift set and calibrate the second potential failure queue of solder joints; Welding quality evaluation model establishment module, establishing a welding quality evaluation model based on the first parameter drift set and the first potential failure queue of solder joints, the second parameter drift set and the second potential failure queue of solder joints. It achieves the technical effects of comprehensively monitoring the welding process of the electric tricycle controller circuit board, accurately evaluating the welding quality, effectively identifying potential failure points, thereby ensuring the welding quality of the power unit of the electric tricycle and improving the performance and reliability of the whole vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0011] Figure 1 It is a schematic structural diagram of a welding quality monitoring system for an electric tricycle controller circuit board provided by an embodiment of the present application.

[0012] Figure 2 It is a schematic flow diagram of establishing a circuit simulation model for a welding quality monitoring system of an electric tricycle controller circuit board provided by an embodiment of the present application.

[0013] Description of reference numerals: Power unit connection module 10, Circuit simulation model establishment module 20, Welding monitoring unit adoption module 30, First parameter drift set determination module 40, Second parameter drift set acquisition module 50, Welding quality evaluation model establishment module 60. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0014] This application provides a welding quality monitoring system for the controller circuit board of an electric tricycle, aiming to solve the technical problems in the prior art that there is a lack of effective monitoring during the welding process of the controller circuit board of an electric tricycle and it is impossible to accurately evaluate the welding quality.

[0015] Next, the technical solutions in the embodiments of this application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in this application without creative efforts shall fall within the protection scope of this application.

[0016] Embodiment, as Figure 1 shown, this application provides a welding quality monitoring system for the controller circuit board of an electric tricycle, and the system includes:

[0017] A power unit connection module 10, which is used to connect the power unit of the target electric tricycle, and the power unit includes a motor assembly, a power conversion assembly, and an electric tricycle controller.

[0018] Specifically, the power unit connection module 10 establishes a stable and reliable connection link with the power unit of the target electric tricycle. This power unit covers the motor assembly that is crucial for vehicle operation, the power conversion assembly responsible for power conversion, and the electric tricycle controller that plays a core control role. The power unit connection module 10 precisely realizes electrical connection with the motor assembly through a carefully designed adapter interface and high-quality connection cables, ensuring that electrical signals related to the motor operation state can be accurately obtained, such as the motor speed feedback signal, torque signal, etc., so as to accurately monitor the collaborative working state between the motor and the controller in the subsequent process. When connecting to the power conversion assembly, it ensures the unobstructed power transmission line. Whether it is the process of supplying power from the battery to the controller or supplying power to the motor under the control of the controller, it can accurately monitor the changes in parameters such as current and voltage, thereby providing a reliable data source for evaluating the power conversion efficiency and stability. For the connection of the electric tricycle controller, the power unit connection module 10 further ensures the accurate transmission of control signals, enabling the monitoring system to obtain the output instructions of the controller in real time, as well as the regulation information of the controller on the motor assembly and the power conversion assembly, laying a solid foundation for comprehensively analyzing the working state of the entire power unit. Through this comprehensive and accurate connection method, the power unit connection module 10 provides a stable data transmission channel for subsequent circuit simulation model establishment, welding quality monitoring and evaluation, etc., ensuring that the entire monitoring system can operate accurately and efficiently, and effectively guaranteeing the reliability and stability of the welding quality of the electric tricycle power unit.

[0019] The circuit simulation model building module 20 builds a circuit simulation model based on the target electric tricycle. The circuit simulation model includes multiple control loops, and several welding points on each control loop are marked.

[0020] Specifically, first, comprehensively collect the basic model parameters of the target electric tricycle, including the rated output power and speed range of the motor assembly, which are key factors for determining the working characteristics and performance requirements of the motor; the rated conversion current and current capacity of the power conversion component, which are related to the efficiency and stability of electric energy conversion; and the rated control voltage and voltage range of the electric tricycle controller, which are crucial for the normal operation of the controller and the precise control of other components. Based on these detailed basic model parameters, build a basic model architecture that fully considers the load working states of the electric tricycle under different operating scenarios, such as the high-current impact state during startup, the dynamic load change state during acceleration, the stable load state during uniform driving, and the load state related to energy recovery during deceleration. To make the model more in line with the actual operating conditions, the module further deeply optimizes the basic model architecture using rich historical operating parameters. Through a detailed analysis of the historical operating parameters, such as scientifically sorting them in the order of the duration of the load working state from long to short, multiple historical operating parameter subsets closely related to different load working states are obtained. At the same time, combined with carefully designed regular usage scenarios, such as the initial current change of the motor in the startup usage scenario, the torque demand change in the acceleration usage scenario, the stable power output in the uniform driving usage scenario, and the energy feedback characteristics in the deceleration usage scenario, use this information to perform iterative training on the basic model architecture. The finally determined circuit simulation model can accurately present the complex characteristics of the target electric tricycle circuit system. The multiple control loops clearly included in it accurately depict the transmission paths of current and signals in the entire circuit, and several welding points are accurately marked on each control loop. These welding points, as the key connection parts in the circuit, are the core focus of subsequent welding quality monitoring and analysis, providing an important structural basis and theoretical basis for comprehensively evaluating the welding quality, ensuring that the entire monitoring system can accurately and effectively monitor the welding quality of the electric tricycle controller circuit board.

[0021] The welding monitoring unit adoption module 30 is used to adopt the welding monitoring unit to synchronously monitor the welding of each control loop and collect a monitoring data set. The monitoring data set includes a solder joint morphology subset, a solder temperature subset, and a solder quantity subset. The welding monitoring unit is deployed on the welding workbench.

[0022] Specifically, the welding monitoring unit uses Module 30 to deploy an advanced welding monitoring unit on the welding workbench. With its high-precision sensors and intelligent monitoring system, this monitoring unit can conduct real-time and synchronous welding monitoring for each control loop. During the welding operation, high-definition imaging technology and image analysis algorithms are used to accurately capture the solder joint morphology subset, and key appearance features such as the shape, size, flatness of the solder joint, and the connection state with surrounding components are detailedly recorded. These features are directly related to the physical structure integrity and electrical connection reliability of the solder joint. At the same time, through the built-in high-precision temperature sensor, the solder temperature subset is closely monitored, and the temperature change curve during the melting to solidification process of the solder is tracked throughout, strictly controlling the temperature range during the welding process, because appropriate solder temperature is crucial for ensuring welding quality and avoiding problems such as false soldering or overheating damage to components. In addition, with the help of advanced weight sensing or flow monitoring technology, the solder quantity subset is accurately obtained to ensure that the solder quantity applied to each solder joint is just right. An appropriate amount of solder can not only ensure good electrical conductivity but also provide sufficient mechanical strength to stabilize the solder joint. Through the comprehensive collection of these monitoring data sets covering multi-dimensional information such as solder joint morphology, solder temperature, and solder quantity, a rich data basis is provided for subsequent in-depth analysis of welding quality. The effective operation of Module 30 of the welding monitoring unit enables the entire monitoring system to promptly detect potential problems during the welding process, providing a strong preliminary guarantee for improving the welding quality of the electric tricycle controller circuit board, ensuring that each solder joint meets high-quality standards, and thus enhancing the performance and reliability of the controller and even the entire electric tricycle.

[0023] The first parameter drift set determination module 40 is used to analyze the solder joint morphology subset, the solder temperature subset, and the solder quantity subset, identify the welding quality characteristic indicators, conduct parameter drift analysis on the connection between the motor assembly in the power unit and the electric tricycle controller according to the circuit simulation model, determine the first parameter drift set, and calibrate the first solder joint potential failure queue.

[0024] Specifically, the first parameter drift set determination module 40 deeply analyzes the solder joint morphology subset, solder temperature subset, and solder volume subset collected by the welding monitoring unit. By applying image processing algorithms, temperature data analysis models, and solder volume statistical models, key characteristic indicators affecting welding quality, such as solder joint fullness and roundness, are accurately identified from the solder joint morphology subset; indicators reflecting the stability of the welding process, such as the temperature fluctuation range and heating and cooling rates, are extracted from the solder temperature subset; and characteristic indicators such as whether the solder volume is within a reasonable range are determined from the solder volume subset. Based on the constructed circuit simulation model, this module focuses on the connection part between the motor assembly in the power unit and the electric tricycle controller. With the help of the accurate description of the electrical characteristics and signal transmission logic of this connection link in the circuit simulation model, combined with the identified welding quality characteristic indicators, a comprehensive parameter drift analysis of this connection is carried out. During the analysis process, factors such as the change in contact resistance caused by poor solder joint morphology, the change in the solder joint metal structure caused by abnormal solder temperature, which in turn affects electrical parameters, and the change in conductivity caused by inappropriate solder volume are considered. The influence of these factors on the key parameters (such as resistance, inductance, capacitance, etc.) of the connection part is comprehensively evaluated to determine the first parameter drift set, which accurately quantifies the degree of parameter deviation from the normal range due to welding quality problems. At the same time, based on the parameter drift situation, by comparing with the preset thresholds and standards, the first solder joint potential failure queue is calibrated. Those solder joints with a high potential failure risk due to large parameter drift are identified, providing a clear direction for subsequent targeted detection, repair, or adjustment, effectively improving the efficiency and accuracy of troubleshooting welding quality problems between the motor assembly and the controller, and ensuring the reliability and stability of this key connection part of the electric tricycle power unit.

[0025] The second parameter drift set acquisition module 50, based on the welding quality characteristic indicators and according to the circuit simulation model, conducts a parameter drift analysis on the connection between the power conversion component in the power unit and the electric tricycle controller, acquires the second parameter drift set, and calibrates the second solder joint potential failure queue.

[0026] Specifically, the second parameter drift set acquisition module 50 is based on the welding quality characteristic indexes obtained and analyzed from the welding process. These indexes include various aspects of information such as the solder joint morphology, solder temperature, and solder volume, comprehensively reflecting the quality level of the welding operation. Relying on these rich index data, it works deeply based on the circuit simulation model. The circuit simulation model accurately depicts key information such as the circuit structure, electrical characteristics, and signal transmission path of the connection between the power conversion components in the power unit and the electric tricycle controller. The module combines the welding quality characteristic indexes with the circuit simulation model to conduct a detailed parameter drift analysis of this specific connection. Considering the importance of the power conversion components in the power conversion process and the accuracy requirements for signal interaction between them and the controller, analyze the possible impact of the solder joint morphology on the current conduction path, such as whether an irregular solder joint shape will increase the contact resistance and thus affect the power conversion efficiency; the interference of the solder temperature fluctuation on the performance of the internal electronic components of the power conversion components and the resulting electrical parameter changes; factors such as the change in connection stability and conductivity due to insufficient or excessive solder volume. Through calculation and simulation, the second parameter drift set is obtained, which accurately quantifies the deviation degree of each parameter of this connection part from the normal standard due to welding quality problems. Furthermore, according to the degree and trend of the parameter drift, by strictly comparing with the pre-set safety threshold and fault determination criteria, the second solder joint potential fault queue is calibrated. Identify the positions of the solder joints on the connection line between the power conversion components and the controller that have a relatively high potential fault risk due to welding problems, providing key technical support and decision-making basis for taking effective maintenance measures in a timely manner, ensuring the reliable connection between the power conversion components and the controller, and the stable operation of the entire electric tricycle power system.

[0027] The welding quality assessment model establishment module 60, the welding quality assessment model establishment module 60 establishes a welding quality assessment model based on the first parameter drift set and the first solder joint potential fault queue, the second parameter drift set and the second solder joint potential fault queue. The welding quality assessment model is used to monitor and remind the welding quality of the electric tricycle controller.

[0028] Specifically, the welding quality assessment model establishment module 60 uses the first parameter drift set and the corresponding first potential fault queue of welding points, the second parameter drift set and the associated second potential fault queue of welding points as key input information. By comprehensively considering the deviation of various parameters reflecting the connection part between the motor component and the electric tricycle controller in the first parameter drift set, such as the resistance drift amount, inductance change value, capacitance fluctuation degree, etc., and combining the solder joint positions where problems may exist specified in the first potential fault queue of welding points, the welding quality status and its potential risks of this connection link are comprehensively evaluated. At the same time, in-depth analysis is carried out on the parameter change data regarding the connection between the power conversion component and the controller in the second parameter drift set, including the drift conditions of the stability of power conversion related parameters, the accuracy of signal transmission parameters, etc., as well as the solder joints with potential fault hazards indicated by the second potential fault queue of welding points, to accurately grasp the welding quality status of this key connection part. On this basis, the support vector machine (SVM) algorithm is used to construct a welding quality assessment model. This model can receive various monitoring data from the actual welding process in real time, and based on its internal complex logic and algorithms, accurately judge the welding quality level of the electric tricycle controller. When abnormal fluctuations in welding quality are detected, such as parameter drift exceeding the safe range and the potential fault risk increasing, the model will promptly issue a welding quality monitoring reminder, notifying relevant staff in an intuitive alarm form. They can quickly take corresponding measures according to the reminder, such as adjusting welding process parameters, repairing potential fault solder joints, or strengthening quality inspection, so as to effectively guarantee the welding quality of the electric tricycle controller, ensure the stable operation of the entire electric tricycle power system, and improve the reliability and safety of the product.

[0029] In a possible implementation manner, as Figure 2 shown, based on the target electric tricycle, establishing a circuit simulation model further includes:

[0030] Obtaining the basic model parameters of the target electric tricycle, where the basic model parameters include the rated output power and speed range of the motor component, the rated conversion current and current capacity of the power conversion component, and the rated control voltage and voltage range of the electric tricycle controller; establishing a basic model architecture identified by the basic model parameters, where the basic model architecture includes different load working states; optimizing the basic model architecture through historical operation parameters to determine the circuit simulation model.

[0031] Specifically, in order to build an accurate circuit simulation model, the basic model parameters of the target electric tricycle are comprehensively obtained. These parameters are the basis for model construction. Among them, the rated output power and speed range of the motor assembly are particularly crucial. The rated output power determines the power that the motor can provide under normal operating conditions. For example, if the rated output power of the motor is 1000W, it means that under ideal conditions, the motor can continuously and stably output 1000W of power to drive the vehicle. The speed range limits the operating speed interval of the motor. For example, if the speed range is 0 - 3000 rpm, it indicates that the motor can achieve different operating states within this speed range, from low speed at startup to high speed during driving. The rated conversion current and current capacity of the power conversion component are also indispensable. The rated conversion current determines the magnitude of the current that the component can safely and effectively handle during the power conversion process. For example, if the rated conversion current is 20A, it means that during normal operation, the current passing through the component should fluctuate around this value. The current capacity represents the maximum current value that the component can withstand. If the current capacity is 30A, when the current exceeds this value, it will damage the component. The rated control voltage and voltage range of the electric tricycle controller are important parameters to ensure the normal operation of the controller. The rated control voltage, such as 48V, is the standard voltage value for the controller's designed operation, and the voltage range (such as 42V - 54V) specifies the upper and lower limits of the voltage within which the controller can operate normally. When the actual voltage exceeds this range, the controller may not be able to accurately control the motor, affecting the vehicle's performance.

[0032] Taking the obtained basic model parameters as identifiers, a basic model architecture is established. This architecture aims to simulate various working states of the electric tricycle during actual operation, and different load working states are key considerations. For example, when starting, the motor needs to overcome a large static friction force, and at this time, it is in a high-load state with a large current demand; during the acceleration process, the vehicle requires additional power, and the load is also relatively high; when driving at a constant speed, the load is relatively stable, and the motor output power remains at a relatively constant level; when decelerating, the motor may be in a power generation state, converting the vehicle's kinetic energy into electrical energy and feeding it back to the battery, and the load state changes. The basic model architecture constructs a basic framework that can reflect the real operating conditions of the electric tricycle by simulating these different load working states, laying a foundation for subsequent precise simulation and analysis.

[0033] To make the basic model architecture more accurately reflect the actual circuit characteristics of the target electric tricycle, it is optimized using rich historical operation parameters. The historical operation parameters include various data during the vehicle's actual use in the past, such as the actual output power of the motor under different road conditions, current conversion situations, and the actual working voltage of the controller. By deeply analyzing these historical data, for example, calculating statistical indicators such as the average value and standard deviation of each parameter under different load working conditions, and analyzing the change trend of parameters over time, this information is incorporated into the basic model architecture. An iterative optimization algorithm is adopted to continuously adjust the parameter values and logical relationships in the model according to the historical operation parameters, making the output results of the model when simulating different load working conditions more consistent with the actual historical data. After multiple iterations of optimization, a highly accurate circuit simulation model that can truly reflect the circuit characteristics of the target electric tricycle is finally determined, providing a reliable theoretical basis for subsequent welding quality monitoring and analysis.

[0034] In a possible implementation manner, optimizing the basic model architecture through historical operation parameters to determine the circuit simulation model further includes:

[0035] Based on the historical operation parameters, sort them in descending order of the duration of the load working state to obtain multiple subsets of historical operation parameters associated with different load working states; formulate a conventional use scenario, where the conventional use scenario includes a start-up use scenario, an acceleration use scenario, a constant-speed driving use scenario, and a deceleration use scenario; and perform iterative training on the basic model architecture according to the conventional use scenario and the multiple subsets of historical operation parameters associated with different load working states.

[0036] Specifically, the collected rich historical operating parameters are analyzed in depth. These historical operating parameters cover various data records of electric tricycles in actual use, including the actual output power change of the motor, the current conversion of the power conversion component, the working voltage fluctuation of the controller and other information. These historical operating parameters are sorted in order from long to short according to the duration of the load working state. For example, in long-term actual use, the vehicle may be in a uniform driving state for most of the time, which lasts the longest; the acceleration and deceleration states are relatively rare, and the start state lasts for a shorter time. Through this sorting method, the proportion and time distribution characteristics of different load working states in actual operation can be clearly identified. Based on this, the historical operating parameters are divided into multiple subsets, each of which is closely related to a specific load working state. In this way, for the uniform driving state, a historical operating parameter subset containing a large number of changes in various parameters during uniform driving can be obtained, which contains information such as the stable output power range of the motor in this state, the relatively constant current value and the stable working voltage of the controller. Similarly, for load working states such as starting, acceleration and deceleration, corresponding historical operating parameter subsets are obtained respectively, providing a targeted data basis for subsequent model optimization.

[0037] In order to enable the circuit simulation model to better simulate the various working conditions of electric tricycles in actual use, conventional usage scenarios are proposed. The startup usage scenario is the initial stage of vehicle operation. At this time, the motor needs to provide a large torque to overcome the static inertia of the vehicle, and the current demand increases instantly. The controller needs to accurately control the motor startup process to ensure smooth startup. In the acceleration usage scenario, the vehicle needs additional power to increase the speed, the motor output power increases rapidly, and the current rises accordingly. The controller adjusts the motor speed and torque according to the acceleration demand. This process involves complex power matching and power management. The uniform speed driving scenario is a common state of vehicle operation. The motor output power remains relatively stable, and the current and voltage are also at a relatively stable level. At this time, the energy consumption and performance of the vehicle are relatively stable. The deceleration usage scenario involves an energy recovery mechanism. The motor changes to a generator mode when the vehicle decelerates, converting the vehicle's kinetic energy into electrical energy and feeding it back to the battery. This process requires the controller to accurately control the energy recovery efficiency, while ensuring the smoothness and safety of the vehicle's deceleration. These conventional usage scenarios cover the main working conditions of electric tricycles in actual operation, providing a comprehensive simulation scenario framework for model optimization.

[0038] Iterative training is carried out on the basic model architecture based on the proposed conventional usage scenarios and multiple subsets of historical operating parameters associated with different load operating states. During the training process, each conventional usage scenario is taken as a training unit, and the subset of historical operating parameters corresponding to the load operating state is input. For example, when training for the acceleration usage scenario, the subset of historical operating parameters in the acceleration state is input into the basic model architecture, and the model simulates the circuit operation during acceleration according to these input data, calculates simulation results such as the motor output power, current change, and voltage fluctuation. Then, these simulation results are compared with the real data in the actual subset of historical operating parameters, the error is calculated, and by continuously adjusting the parameters in the model, such as the equivalent resistance, inductance value, capacitance value of circuit components, and control coefficients in the control algorithm, the error between the simulation results and the real data is reduced. After each parameter adjustment, the subset of historical operating parameters is input again for simulation calculation and error evaluation, and iterative training is repeated in this way until the simulation results of the model in each conventional usage scenario are highly consistent with the real historical data. After multiple iterative trainings, the basic model architecture is gradually optimized into a high-precision circuit simulation model that can accurately simulate the circuit characteristics of an electric tricycle under different usage scenarios and load operating states, providing a reliable theoretical basis and accurate simulation tool for subsequent welding quality monitoring and analysis.

[0039] In a possible implementation manner, welding quality characteristic indicators are identified, parameter drift analysis is performed on the connection between the motor assembly in the power unit and the electric tricycle controller according to the circuit simulation model, a first parameter drift set is determined, and a first potential failure queue of welding points is calibrated. It further includes:

[0040] Based on the circuit simulation model, through the connection between the motor assembly in the power unit and the electric tricycle controller, a first unidirectional pointer pointing to the motor assembly is set; based on the circuit simulation model, through the connection between the motor assembly in the power unit and the electric tricycle controller, a second unidirectional pointer pointing to the electric tricycle controller is set; based on the first unidirectional pointer and the second unidirectional pointer, multiple control loops corresponding to the circuit simulation model are traversed, and according to the first parameter drift set, a first potential failure queue of welding points is calibrated.

[0041] Specifically, when performing fault analysis using a circuit simulation model, first, based on this model, set one-way pointers for the connection between the motor component in the power unit and the electric tricycle controller. Set the first one-way pointer pointing to the motor component, aiming to accurately trace the control signal and the power transmission path along the direction from the controller to the motor component. This pointer clarifies the information flow direction from the controller through the connection line to the motor component, which helps to accurately grasp the circuit characteristics and signal transmission conditions related to the motor component in subsequent analysis. Similarly, set the second one-way pointer pointing to the electric tricycle controller, which points from the motor component side to the controller, enabling the reverse tracing of the transmission path of the motor operating state feedback signal and power consumption and other information to the controller. These two one-way pointers cooperate with each other, providing a clear path guidance for comprehensively analyzing the connection relationship between the motor component and the controller, ensuring that no key information is missed in the complex circuit structure.

[0042] With the set first one-way pointer and second one-way pointer, start traversing the multiple control loops corresponding to the circuit simulation model. Starting from the motor component end pointed by the first one-way pointer, along the direction determined by the pointer, conduct a detailed exploration of each control loop in turn. During the traversal, carefully check the key parts such as each component, line connection point, and solder joint in the loop, and record information such as its electrical parameters and signal transmission characteristics. At the same time, starting from the controller end pointed by the second one-way pointer, traverse the control loop in the reverse direction, and confirm the status of each component in the loop again. Through the traversal in both forward and reverse directions, achieve a comprehensive and detailed inspection of the control loop, ensure obtaining the most complete and accurate information about the loop, and provide sufficient data support for subsequent fault judgment.

[0043] Based on the traversal of the control loop, calibrate the potential fault queue of the first solder joint according to the data in the first parameter drift set. The first parameter drift set contains the parameter change information related to the connection between the motor component and the controller monitored during the actual soldering process, such as the drift value of the solder joint resistance and the change amount of current conduction at the solder joint. When traversing to a certain solder joint, compare the parameters corresponding to this solder joint with the standard range in the first parameter drift set. If the parameters of a certain solder joint exceed the normal drift range, for example, the solder joint resistance drifts too much, which may lead to increased power consumption or unstable signal transmission. At this time, mark this solder joint as a potential fault point and add it to the potential fault queue of the first solder joint. In this way, it is possible to accurately identify the solder joints that may cause faults due to soldering quality problems in the connection part between the motor component and the controller, provide a clear target for subsequent targeted detection, repair, or quality improvement, effectively improve the efficiency and accuracy of fault troubleshooting, and ensure the reliability and stability of this key connection part of the electric tricycle power unit.

[0044] In a possible implementation manner, determining the first parameter drift set further includes:

[0045] Adding a preset welding standard, where the welding quality characteristic index conforms to the preset welding standard; based on the first unidirectional pointer and the second unidirectional pointer, performing a correlation analysis on the parameter drift deviation degree and the welding quality deviation degree, and formulating a first deviation function; based on the first deviation function, performing a parameter drift analysis on the connection between the motor assembly in the power unit and the electric tricycle controller, and determining the first parameter drift set.

[0046] Specifically, to ensure the accuracy and consistency of welding quality assessment, a preset welding standard is first introduced. These standards cover the ideal index ranges in multiple aspects such as solder joint morphology, solder temperature, and solder volume, and are formulated based on a large amount of experimental data and industry experience. For example, in terms of solder joint morphology, the ideal shape, size, and surface flatness of the solder joint are specified to ensure good mechanical connection strength and electrical conductivity; for the solder temperature, a suitable welding temperature range is set to ensure that the solder can fully melt and infiltrate the welding part, while avoiding component damage or excessive oxidation of the solder due to too high temperature, and problems such as false soldering may occur due to too low temperature; the solder volume standard defines the optimal solder volume or weight required for each solder joint to ensure that the solder joint has sufficient conductivity without the risk of short circuit or unnecessary weight increase due to too much solder. During the entire welding quality assessment process, the actually monitored welding quality characteristic index is strictly compared with these preset welding standards, which is used as an important basis for judging whether the welding quality is qualified and the deviation degree.

[0047] Based on the already set first unidirectional pointer pointing to the motor assembly and the second unidirectional pointer pointing to the electric tricycle controller, conduct a correlation analysis on the deviation degree of parameter drift and the deviation degree of welding quality, and then formulate the first deviation function. Through the first unidirectional pointer, the changes in the parameters related to the signal and power transmission path from the controller to the motor assembly can be accurately traced. For example, the drift amount of parameters such as the solder joint resistance and inductance along this path due to the change in welding quality; by using the second unidirectional pointer, the parameter fluctuations caused by welding problems during the transmission of the motor assembly operating state feedback signal and power consumption and other information to the controller can be obtained in the reverse direction. Considering the parameter changes on these forward and reverse paths comprehensively, compare them with the preset welding standards, and analyze the degree of deviation of the parameter drift from the standard value and the degree of deviation of the welding quality from the ideal state. For example, excessive drift of the solder joint resistance may lead to an increase in power transmission loss, thereby affecting the power output of the motor, which reflects the close relationship between parameter drift and welding quality. Through the analysis of a large amount of actual data and mathematical modeling, formulate the first deviation function that can accurately describe this correlation relationship. This function usually includes mathematical expressions of variables such as the parameter drift amount, the difference between the welding quality characteristic index and the preset standard, and their mutual relationships, providing a quantitative tool for subsequent accurate analysis of parameter drift.

[0048] With the formulated first deviation function, conduct an in-depth parameter drift analysis on the connection between the motor assembly in the power unit and the electric tricycle controller to determine the first parameter drift set. Substitute the actual measured parameter values of the connection part into the first deviation function to calculate the drift degree of each parameter and the overall parameter drift situation. For example, for key parameters such as solder joint resistance, inductance, and capacitance, calculate their drift ratio or specific numerical change amount relative to the preset standard value under the current welding state according to the function. Organize and summarize these calculated parameter drift data to form the first parameter drift set. This set comprehensively and accurately reflects the parameter changes in the connection part between the motor assembly and the controller due to welding quality problems, providing key data support for further judging whether the welding quality is qualified, calibrating potential fault points, and taking corresponding improvement measures, and helping to ensure the stable operation and performance of the electric tricycle power system.

[0049] In a possible implementation manner, formulating the first deviation function further includes:

[0050] Define the deviation degree of parameter drift and the deviation degree of welding quality ; conduct a correlation analysis on the deviation degree of parameter drift and the deviation degree of welding quality, and the first deviation function ; where represents the parameter drift and its mean value ( ) and standard deviation ( ) reflects the relative size of parameter drift. is an exponential decay term, where α is a positive coefficient, is the hyperbolic tangent function term corresponding to the welding quality deviation, γ, Used to adjust the hyperbolic tangent function term.

[0051] Specifically, in the welding quality monitoring system of the electric tricycle controller circuit board, the parameter drift deviation degree is defined Degree of deviation from welding quality These two important concepts are used to accurately quantify the changes in relevant parameters during the welding process and the degree of deviation of welding quality from the ideal state. By calculating the parameter drift and its mean and standard deviation The absolute value of the ratio This absolute value can clearly reflect the relative size of the parameter drift relative to its normal distribution range, thereby intuitively showing the degree of parameter change. For example, if the calculated value of the parameter drift deviation of a solder joint resistance is large, it means that the actual value of the resistance is significantly different from the mean and standard deviation under normal circumstances, which may indicate that there are factors that affect the resistance performance of the solder joint during the welding process, such as unstable welding temperature causing changes in the metal structure that affect the resistance value. At the same time, the exponential decay term (in is a positive coefficient) is introduced, which can reflect the attenuation trend of the overall impact as the parameter drift deviation increases. When the parameter drift is small, this term has a greater impact on the function value, and as the drift increases, its impact gradually weakens, which helps to reasonably consider the influence weight of different degrees of parameter drift on the system in the model. In addition, the hyperbolic tangent function term corresponding to the welding quality deviation (in The function term used to adjust the function is also taken into consideration. The characteristics of the hyperbolic tangent function enable it to take values ​​between -1 and 1, which can well reflect the variation of the welding quality deviation within a certain range. Moreover, the adjustment can more accurately fit the deviation relationship between the actual welding quality and the ideal state. The first deviation function finally constructed , combining the above factors, through the degree of parameter drift deviation Degree of deviation from welding quality And the correlation coefficient , , The synergistic effect can accurately evaluate the welding quality status of the connection part between the motor component and the electric tricycle controller. This function provides an important calculation basis for determining the first parameter drift set subsequently, enabling the system to obtain accurate parameter drift analysis results through substituting the actual monitoring data into the function calculation, thereby more effectively identifying potential welding quality problems, ensuring the welding quality of the electric tricycle controller circuit board, and improving the performance and reliability of the whole vehicle.

[0052] In a possible implementation manner, formulating the first deviation function further includes:

[0053] Based on the first deviation function corresponding to the first parameter drift set and the second deviation function corresponding to the second parameter drift set, set a welding standard update strategy; through the welding standard update strategy, make an update decision on the preset welding standard.

[0054] Specifically, deeply analyze the first deviation function corresponding to the first parameter drift set and the second deviation function corresponding to the second parameter drift set. The first deviation function reflects the parameter drift and welding quality deviation of the connection part between the motor component and the electric tricycle controller in the power unit, where is the degree of parameter drift deviation of this connection part, is the degree of welding quality deviation, , , , , are correlation coefficients. The second deviation function (for , whose form is similar to and corresponds to the parameters of the connection part between the power conversion component and the controller) reflects the relevant situation of the connection between the power conversion component and the controller. By comprehensively studying these two deviation functions, set a welding standard update strategy. For example, if in a certain number of welding samples, the calculation result of the first deviation function frequently exceeds the preset threshold range, it indicates that the welding quality stability of the connection part between the motor component and the controller is poor, and the relevant standards for this part of the welding need to be adjusted. At this time, the strategy may stipulate further analyzing whether the standards related to parameter drift and welding quality deviation related to , such as the solder joint morphology, solder temperature, solder volume, etc., need to be optimized. A similar analysis method is also adopted for the second deviation function . If shows that there are many potential problems in the connection part between the power conversion component and the controller, the evaluation process for the welding standards of this part is also started.

[0055] When it is determined that the preset welding standard needs to be updated according to the welding standard update strategy, the update decision begins to be implemented. If it is found that the parameter drift of a certain type of solder joint is generally large during the actual welding process, resulting in a significant impact on the relevant item of the parameter drift deviation degree in the first deviation function or the second deviation function , it is decided to adjust the form standard of this type of solder joint, such as increasing the solder joint area or improving the solder joint roundness standard, to enhance the connection reliability of the solder joint. At the same time, if the deviation degree of the welding quality is obvious in terms of temperature, for example, the fluctuation of the solder temperature causes unstable welding quality, which in turn affects the calculation result of the deviation function, the solder temperature standard may be updated to optimize the welding temperature range. During the update decision-making process, the mutual relationship between different deviation functions and their comprehensive impact on the overall welding quality will also be considered. For example, if and both show abnormalities in certain parameters at the same time, it is necessary to make coordinated adjustments to multiple relevant welding standards to ensure that the connections between the motor components, power conversion components and the controller can all meet higher quality requirements. Through this welding standard update strategy and decision implementation process based on deviation function analysis, the preset welding standard can be continuously optimized to make it more in line with the actual welding situation, improve the welding quality of the electric tricycle controller circuit board, and ensure the performance and reliability of the electric tricycle.

[0056] In a possible implementation manner, establishing a welding quality assessment model further includes:

[0057] According to the warranty period of the target electric tricycle, comparing with the welding quality assessment model, identifying the welding failure modes and time distributions; through the welding failure modes and time distributions, adjusting the welding standard update strategy.

[0058] Specifically, the warranty period of the target electric tricycle is an important time window reflecting product quality and reliability. During this period, the usage conditions of the vehicle and the possible problems can provide valuable feedback for welding quality assessment. By combining the warranty period with the welding quality assessment model, the relationship between welding quality and the long-term performance of the vehicle can be deeply explored. For example, at the beginning of the warranty period, focus on the impact of the initial stability of the welding points on vehicle starting, acceleration and other performances; in the middle of the warranty period, analyze how welding quality affects the power output stability and electric energy conversion efficiency during the continuous operation of the vehicle; at the end of the warranty period, focus on studying the aging and potential failure of the welding points and their effects on the overall performance decline of the vehicle. The welding quality assessment model is constructed based on the first parameter drift set and the first potential welding point failure queue, the second parameter drift set and the second potential welding point failure queue, and can accurately predict and evaluate the impact of welding quality on the operation of the electric tricycle controller. During the warranty period, according to various problems occurring in the actual operation of the vehicle, such as abnormal motor control and reduced power conversion efficiency, compare with the welding quality assessment model to determine whether these problems are related to welding failure modes. For example, if the motor speed is unstable frequently in the middle of the warranty period, analyze through the assessment model whether it is caused by parameter drift or potential failure of the welding points connecting the motor components and the controller, so as to identify possible welding failure modes. With the vehicle failure data and welding quality monitoring data collected during the warranty period, identify the welding failure modes and time distributions in detail. Welding failure modes include various forms such as solder joint cracking, virtual soldering, and solder joint corrosion. Through statistical analysis of welding failure cases occurring in different periods, determine the occurrence probability and time node distribution of each failure mode during the warranty period. For example, it is found that the incidence of solder joint cracking is relatively high at the end of the warranty period, which is due to long-term vibration and temperature cycling causing solder joint fatigue; while virtual soldering appears in a certain proportion at the beginning of the warranty period, and the reason may be improper initial adjustment of the welding process. At the same time, analyze the relationship between welding failure modes and vehicle operating conditions and environmental factors. Observe the change rules of welding failure modes under different driving road conditions (such as bumpy roads, flat roads) and usage environments (such as humid environments, high-temperature environments). For example, in a humid environment, the corrosion speed of the solder joints may accelerate, resulting in earlier occurrence of welding failures; in driving conditions with frequent acceleration and deceleration, the stress on the solder joints changes greatly, easily causing problems such as solder joint cracking. According to these analysis results, draw a detailed chart of welding failure modes and time distributions to provide intuitive data support for subsequent strategy adjustment.

[0059] According to the identified welding failure modes and time distribution, the welding standard update strategy is adjusted in a targeted manner. If it is found that solder joint cracking becomes the main failure mode at the end of the warranty period and is related to long-term vibration and temperature cycling, then in the update strategy, the requirements for the mechanical strength and fatigue resistance of the solder joints are strengthened. For example, the solder joint morphology standard is adjusted, the thickness or area of ​​the solder joint is increased to improve the ability of the solder joint to withstand vibration and temperature stress; the solder composition standard is optimized, and solder materials that are more suitable for long-term stability are selected. For the problem of cold solder joints that occurs more frequently in the early warranty period, the focus is on improving the welding process standards, such as strictly controlling the welding temperature and time, strengthening the training of welding workers' operating skills, and ensuring that each solder joint can achieve good welding quality. At the same time, according to the impact of different environmental factors on welding failure, environmental adaptability welding standards are formulated. In a humid environment, the moisture resistance performance standards of the solder joints are improved, such as adding moisture-proof coatings or improving the packaging process; in a high temperature environment, the solder temperature standard is optimized to ensure the stability of the solder at high temperatures during the welding process, and prevent the solder performance from deteriorating due to excessively high temperatures, which may cause welding failure. Through this adjustment of the welding standard update strategy based on warranty period feedback, the welding quality can be continuously improved, the service life of the electric tricycle controller can be extended, and the reliability and durability of the entire vehicle can be improved.

[0060] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0061] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

[0062] This specification and the drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. Electric tricycle controller circuit board welding quality monitoring system, characterized in that: include: A power unit connection module, the power unit connection module is used to connect the power unit of the target electric tricycle, the power unit includes a motor assembly, a power conversion assembly, and an electric tricycle controller; A circuit simulation model building module, wherein the circuit simulation model building module builds a circuit simulation model based on a target electric tricycle, wherein the circuit simulation model includes a plurality of control loops and marks a plurality of welding points on each control loop; A welding monitoring unit adopts a module, wherein the welding monitoring unit adopts a welding monitoring unit, performs welding synchronization monitoring on each control loop, and collects a monitoring data set, wherein the monitoring data set includes a solder point morphology subset, a solder temperature subset, and a solder quantum subset, and the welding monitoring unit is deployed on a welding workbench; A first parameter drift set determination module, the first parameter drift set determination module is used to parse the solder point morphology subset, the solder temperature subset, and the solder quantum set, identify welding quality characteristic indicators, perform parameter drift analysis on the connection between the motor assembly in the power unit and the electric tricycle controller according to the circuit simulation model, determine the first parameter drift set, and calibrate the first welding point potential fault queue; A second parameter drift set acquisition module, which performs parameter drift analysis on the connection between the power conversion component in the power unit and the electric tricycle controller based on the welding quality characteristic index and the circuit simulation model, acquires a second parameter drift set, and calibrates a potential fault queue of a second welding point; A welding quality assessment model establishment module, which establishes a welding quality assessment model based on the first parameter drift set and the first welding point potential fault queue, the second parameter drift set and the second welding point potential fault queue. The welding quality assessment model is used to monitor and remind the electric tricycle controller of welding quality.

2. The electric tricycle controller circuit board welding quality monitoring system according to claim 1, characterized in that: Based on the target electric tricycle, a circuit simulation model is established, including: Obtaining basic model parameters of the target electric tricycle, the basic model parameters including the rated output power and speed range of the motor assembly, the rated conversion current and current capacity of the power conversion assembly, and the rated control voltage and voltage range of the electric tricycle controller; Establishing a basic model framework identified by the basic model parameters, wherein the basic model framework includes different load working states; The basic model architecture is optimized through historical operating parameters to determine the circuit simulation model.

3. The electric tricycle controller circuit board welding quality monitoring system as claimed in claim 2, characterized in that: The basic model architecture is optimized by using historical operating parameters to determine the circuit simulation model, including: Based on the historical operating parameters, the load working state duration is sorted in descending order to obtain a plurality of historical operating parameter subsets associated with different load working states; Formulate regular usage scenarios, including startup usage scenarios, acceleration usage scenarios, constant speed driving usage scenarios, and deceleration usage scenarios; The basic model architecture is iteratively trained according to the conventional usage scenarios and multiple historical operating parameter subsets associated with the different load working states.

4. The electric tricycle controller circuit board welding quality monitoring system as claimed in claim 3, characterized in that: Identifying welding quality characteristic indicators, performing parameter drift analysis on the connection between the motor assembly in the power unit and the electric tricycle controller according to the circuit simulation model, determining a first parameter drift set, and calibrating a first welding point potential fault queue, including: Based on the circuit simulation model, a first one-way pointer pointing to the motor assembly is set through the connection between the motor assembly in the power unit and the electric tricycle controller; Based on the circuit simulation model, a second one-way pointer pointing to the electric tricycle controller is set through the connection between the motor assembly in the power unit and the electric tricycle controller; Based on the first one-way pointer and the second one-way pointer, multiple control loops corresponding to the circuit simulation model are traversed, and according to the first parameter drift set, a first welding point potential fault queue is calibrated.

5. The electric tricycle controller circuit board welding quality monitoring system as claimed in claim 4, characterized in that: include: Adding a preset welding standard, wherein the welding quality characteristic index complies with the preset welding standard; Based on the first one-way pointer and the second one-way pointer, a correlation analysis is performed on the deviation degree of parameter drift and the deviation degree of welding quality, and a first deviation function is formulated; Based on the first deviation function, a parameter drift analysis is performed on the connection between the motor assembly in the power unit and the electric tricycle controller to determine a first parameter drift set.

6. The electric tricycle controller circuit board welding quality monitoring system as claimed in claim 5, characterized in that: Define the degree of parameter drift deviation , welding quality deviation degree ; The correlation analysis between the parameter drift deviation degree and the welding quality deviation degree is carried out. The first deviation function ; in, represents the parameter drift and its mean ( ) and standard deviation ( ) reflects the relative size of parameter drift. is an exponential decay term, where α is a positive coefficient, is the hyperbolic tangent function term corresponding to the welding quality deviation, γ, Used to adjust the hyperbolic tangent function term.

7. The electric tricycle controller circuit board welding quality monitoring system as claimed in claim 6, characterized in that: Setting a welding standard update strategy based on a first deviation function corresponding to the first parameter drift set and a second deviation function corresponding to the second parameter drift set; Through the welding standard update strategy, an update decision is made on the preset welding standard.

8. The electric tricycle controller circuit board welding quality monitoring system as claimed in claim 7, characterized in that: Establish a welding quality assessment model, including: According to the warranty period of the target electric tricycle, comparing the welding quality assessment model, identifying the welding failure mode and time distribution; The welding standard update strategy is adjusted according to the welding failure mode and time distribution.

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