New energy automobile high-voltage wire harness thermal management method and system based on big data

Abstract

The invention relates to the technical field of high-voltage wiring harness thermal management, and discloses a new energy automobile high-voltage wiring harness thermal management method and system based on big data, and the method comprises the steps: obtaining vehicle operation environment data and wiring harness data, inputting the vehicle operation environment data into a pre-trained temperature change prediction model, and predicting the temperature change of the vehicle operation environment data; the method comprises the following steps: acquiring wire harness data, outputting a temperature change trend chart, performing heating calculation according to the wire harness data to obtain wire harness heating power, performing temperature wire harness arrangement optimization according to wire harness resistance, the wire harness heating power and the temperature change trend chart to obtain a thermal management wire harness arrangement scheme, and performing thermal management on the vehicle operation environment data according to the thermal management wire harness arrangement scheme. And inputting the thermal management wiring harness arrangement scheme into a pre-trained load demand model, outputting a load demand, and performing bus wiring harness arrangement optimization according to the thermal management wiring harness arrangement scheme and the load demand to obtain an optimal wiring harness arrangement scheme. The method can realize dynamic high-voltage wire harness thermal management.

Description

Technical Field

[0001] The present invention relates to the technical field of high-voltage wire harness thermal management, and in particular to a method and system for thermal management of high-voltage wire harnesses of new energy vehicles based on big data. Background Art

[0002] At present, as the vehicle-side requirements for the current carrying capacity of the wire harness are becoming more and more stringent, the temperature of the wire harness is also getting higher and higher, which brings greater challenges to the thermal management of the wire harness on the vehicle side. On the one hand, the cooling system is used to reduce the temperature of the wire harness, but the cooling system is expensive and requires a lot of energy consumption; on the other hand, the shape, material, and current carrying capacity of the wire harness are adjusted to achieve the purpose of reducing the temperature of the wire harness, but this method puts higher requirements on the space of the vehicle and is limited by the space of the entire vehicle and the current carrying capacity of the wire harness.

[0003] In the prior art, the temperature of the wiring harness is mainly controlled by static design and passive cooling systems. The methods used include optimizing the material selection, cross-sectional design and layout of the wiring harness to improve thermal conductivity and current carrying capacity. In order to reduce the temperature of the wiring harness, some solutions also use external cooling systems, such as fans or liquid cooling systems, to consume the heat generated inside the wiring harness through forced heat exchange. In addition, in some cases, special materials or coatings are used to enhance the temperature resistance of the wiring harness to delay the aging of the insulation and prevent overheating.

[0004] The static optimization method in the prior art has significant deficiencies. The prior art mainly focuses on static design and lacks the ability to adapt to temperature changes in real time under dynamic conditions, thus failing to achieve dynamic thermal management of high-voltage wiring harnesses. Summary of the invention

[0005] The present invention provides a method and system for thermal management of high-voltage wire harnesses of new energy vehicles based on big data, so as to realize dynamic thermal management of high-voltage wire harnesses.

[0006] In the first aspect, in order to solve the above technical problems, the present invention provides a method for thermal management of high-voltage wiring harnesses of new energy vehicles based on big data, comprising: Obtain vehicle operating environment data and wiring harness data; Input the vehicle operating environment data into a pre-trained temperature change prediction model, and output a temperature change trend graph; Performing heat calculation according to the wiring harness data to obtain the wiring harness heat power; According to the harness resistance, the harness heating power and the temperature change trend diagram, the temperature harness arrangement is optimized to obtain a thermal management harness arrangement scheme; Inputting the vehicle operating environment data into a pre-trained load demand model and outputting the load demand; According to the thermal management harness arrangement scheme and the load requirements, the bus harness arrangement is optimized to obtain an optimal harness arrangement scheme.

[0007] Preferably, the vehicle operating environment data includes: driving speed, motor speed, external temperature, humidity, battery charge, battery temperature, motor temperature, traffic density and road speed limit; The harness data includes harness curvature, harness current, harness cross-sectional area and harness length.

[0008] Preferably, the vehicle operating environment data is input into a pre-trained temperature change prediction model, and a temperature change trend graph is output, including: The temperature change prediction model is an LSTM neural network model; The training process of the temperature change prediction model is as follows: Based on the ATSS algorithm, the pre-prepared training set is optimized to obtain the optimized training set; Inputting the optimized training set into the temperature change prediction model to perform time series feature learning to obtain an implicit state; The implicit state is plotted through the output layer of the temperature prediction model to obtain a predicted temperature change trend graph; The predicted temperature change trend graph and the actual temperature change trend graph are calculated point by point to obtain a loss value; Based on the loss value, executing a back propagation algorithm to update the temperature change prediction model parameters and optimize the temperature change prediction model; Repeat the above process and continuously optimize the temperature change prediction model until the output accuracy of the temperature change prediction model on the training set reaches the preset output accuracy requirement, and then stop training.

[0009] Preferably, performing heat calculation according to the wire harness data to obtain the wire harness heat power includes: The harness resistance is calculated using the following formula: in, is the harness resistance, is the resistivity of the wire harness material, is the harness length, is the cross-sectional area of ​​the harness; The heating power of the wiring harness is calculated by the following formula: in, is the heating power of the wiring harness, is the harness current.

[0010] Preferably, the temperature harness arrangement optimization is performed according to the harness heating power and the temperature change trend diagram to obtain a thermal management harness arrangement scheme, including: Performing temperature evaluation according to the heating power of the wiring harness and the temperature change trend diagram to obtain wiring harness temperature distribution information; The wiring harness temperature distribution information is combined with the thermal management requirements for optimization to obtain a thermal management wiring harness arrangement solution.

[0011] Preferably, the step of inputting the vehicle operating environment data into a pre-trained load demand model and outputting the load demand comprises: The load demand model is an MLP neural network model; The training process of the load demand model is: Based on the reinforcement learning algorithm, the pre-prepared training set is optimized to obtain the optimized training set; Inputting the optimized training set into the load demand model, performing layer-by-layer calculations, and obtaining a feature vector; The characteristic vector is calculated through the output layer of the load demand model to obtain the predicted load demand; Performing a difference calculation based on the predicted load demand and the actual load demand to obtain an output error value; Based on the output error value, executing a back propagation algorithm to update load demand model parameters and optimize the load demand model; Repeat the above process and continuously optimize the load demand model until the output accuracy of the load demand on the training set reaches the preset output accuracy requirement, and then stop training.

[0012] Preferably, the optimizing the wiring harness arrangement according to the thermal management wiring harness arrangement scheme and the load requirement to obtain the optimal wiring harness arrangement scheme includes: Performing preliminary optimization according to the thermal management harness arrangement scheme and the load requirements to obtain a preliminary harness arrangement scheme; The preliminary wiring harness arrangement scheme is iteratively optimized based on a genetic algorithm to obtain an optimal wiring harness arrangement scheme.

[0013] In a second aspect, the present invention provides a new energy vehicle high voltage wire harness thermal management system based on big data, comprising: A data acquisition module, used to acquire vehicle operating environment data and wiring harness data; A temperature change prediction module, used to input the vehicle operating environment data into a pre-trained temperature change prediction model and output a temperature change trend graph; A heating power module, used for performing heating calculation according to the wiring harness data to obtain the wiring harness heating power; A thermal management arrangement module, used to optimize the temperature harness arrangement according to the harness resistance, the harness heating power and the temperature change trend diagram, and obtain a thermal management harness arrangement solution; A load demand module, used to input the vehicle operating environment data into a pre-trained load demand model and output the load demand; The optimal wiring harness arrangement module is used to optimize the wiring harness arrangement of the entire bus according to the thermal management wiring harness arrangement scheme and the load demand to obtain the optimal wiring harness arrangement scheme.

[0014] In a third aspect, the present invention further provides an electronic device comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements any one of the above-mentioned methods for thermal management of high-voltage wire harnesses for new energy vehicles based on big data.

[0015] In a fourth aspect, the present invention also provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute any one of the above-mentioned methods for thermal management of high-voltage wire harnesses for new energy vehicles based on big data.

[0016] Compared with the prior art, the present invention has the following beneficial effects: The method obtains vehicle operating environment data and wiring harness data, and inputs the vehicle operating environment data into a temperature change prediction model to obtain a temperature change trend graph. Then, the wiring harness data is used to perform heat calculations to obtain the heating power of the wiring harness. Based on these data, the temperature wiring harness arrangement is optimized in combination with the temperature change trend graph, and finally a thermal management wiring harness arrangement plan is obtained. Next, the vehicle operating environment data is input into a load demand model to obtain the load demand, and then the bus harness arrangement is optimized in combination with the thermal management wiring harness arrangement plan and the load demand to obtain the optimal wiring harness arrangement plan. The method can achieve dynamic high-voltage wiring harness thermal management. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a schematic flow chart of a method for thermal management of high-voltage wiring harnesses for new energy vehicles based on big data provided by the first embodiment of the present invention; Figure 2 It is a schematic diagram of a high-voltage wire harness thermal management system for new energy vehicles based on big data provided by the second embodiment of the present invention. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] Reference Figure 1 The first embodiment of the present invention provides a method for thermal management of high-voltage wiring harnesses of new energy vehicles based on big data, comprising the following steps: S11, obtaining vehicle operating environment data and wiring harness data; S12, inputting the vehicle operating environment data into a preset temperature change prediction model, and outputting a temperature change trend graph; S13, performing heating calculation according to the wiring harness data to obtain wiring harness heating power; S14, optimizing the temperature harness arrangement according to the harness resistance, the harness heating power and the temperature change trend diagram, and obtaining a thermal management harness arrangement scheme; S15, inputting the vehicle operating environment data into a preset load demand model, and outputting a load demand; S16, optimizing the wiring harness arrangement according to the thermal management wiring harness arrangement scheme and the load demand to obtain an optimal wiring harness arrangement scheme.

[0020] In step S11, the vehicle operating environment data includes: driving speed, motor speed, external temperature, humidity, battery power, battery temperature, motor temperature, traffic density and road speed limit; The harness data includes harness curvature, harness current, harness cross-sectional area and harness length.

[0021] It is worth noting that in step S11, the vehicle operating environment data and wiring harness data of the smart car need to be obtained first. Each parameter is obtained by real-time monitoring and recording based on vehicle sensors, computing systems and external environments.

[0022] For vehicle operating environment data, the first is the driving speed, which is acquired in real time by the vehicle-mounted speed sensor (such as GPS or wheel speed sensor). Through GPS, the system can accurately calculate the real-time driving speed of the vehicle, and the data acquisition frequency is completed every second or even shorter to ensure that the driving status of the vehicle can be reflected in real time.

[0023] Next is the motor speed, which is obtained through the speed sensor on the motor. The motor speed is an important indicator to measure the working status of the motor, which affects the acceleration performance and energy efficiency of the vehicle. By collecting the motor speed through the vehicle control system, the working status of the motor can be understood in real time, providing the necessary input for subsequent temperature prediction and thermal management optimization.

[0024] External temperature refers to the air temperature of the vehicle's environment, which is measured in real time by temperature sensors installed on the outside of the vehicle. External temperature is critical to the vehicle's thermal management system, especially in cold or hot environments, as it directly affects the temperature changes and heat dissipation requirements of the wiring harness. The temperature data outside the vehicle is collected in real time and transmitted to the on-board computing system for processing and analysis.

[0025] Humidity is provided by humidity sensors, which can measure the humidity level of the environment in real time. Changes in humidity affect the heat capacity and heat exchange efficiency of the air, which in turn affects the vehicle's thermal management system. In an environment with high humidity, the heat exchange efficiency of the air decreases, and the vehicle's thermal management requirements increase accordingly. Therefore, the acquisition of humidity information is of great significance for accurately predicting the temperature changes of the wiring harness.

[0026] The battery power is monitored in real time by the vehicle's battery management system (BMS). The battery management system collects battery power, status, and health data to ensure effective use of the battery. The battery power directly affects the vehicle's driving distance and energy management strategy, so this data is particularly important for subsequent load demand calculation and optimization.

[0027] The battery temperature is collected by the temperature sensor installed in the battery pack. Battery temperature is a key factor affecting battery performance and safety. Too high or too low temperature will affect the battery's charging and discharging efficiency and life. Therefore, accurate monitoring of battery temperature is an important means to achieve battery protection and optimized use.

[0028] The motor temperature is also measured by the motor temperature sensor. The motor generates heat when it is working. If the motor temperature is too high, the motor efficiency will be reduced or even cause failure. Therefore, real-time monitoring of the motor temperature can help the vehicle system to issue early warnings and make timely adjustments to prevent motor damage.

[0029] Traffic density is obtained through the on-board traffic monitoring system or on-board radar. Traffic density reflects the traffic conditions of the vehicle's environment and is of great significance for the evaluation of dynamic traffic flow and the adjustment of vehicle driving strategies. On roads with high traffic density, vehicles run slower and the risk of heat accumulation increases, so it is necessary to adjust the thermal management strategy to ensure the safe operation of the wiring harness.

[0030] The speed limit information is provided by the vehicle's navigation system, which obtains the speed limit information of the vehicle's current driving section through the GPS positioning system. The road speed limit affects the vehicle's driving speed, which in turn affects the vehicle's load and heat generation. On roads with lower speed limits, the vehicle is in a low-speed driving state for a long time, resulting in heat accumulation. Therefore, the speed limit data plays an important role in dynamically adjusting the vehicle's thermal management system.

[0031] Next, regarding the acquisition of wiring harness data, these data include multiple parameters such as wiring harness curvature, wiring harness current, wiring harness cross-sectional area and wiring harness length. The curvature of the wiring harness is measured by sensors installed around the cable or wiring harness, which can detect the degree of curvature of the wiring harness during use. Excessive curvature will increase the resistance to the flow of current, thereby increasing resistance and causing more heat generation, so the curvature of the wiring harness is a key parameter in thermal management. The wiring harness current is obtained in real time through the current sensor. The magnitude of the current directly affects the degree of heating of the wiring harness. Excessive current will cause the heating power of the wiring harness to increase, thereby increasing the temperature. The wiring harness cross-sectional area and wiring harness length are directly obtained through measuring tools. The cross-sectional area determines the width of the channel for current flow, and the length affects the distance that the current passes through the wiring harness, thereby affecting the resistance of the wiring harness. These parameters are monitored and measured in real time by sensors and measuring equipment installed in the vehicle system to ensure that the system can obtain and accurately record the status of each wiring harness in a timely manner.

[0032] These data are collected in real time through the vehicle computing system and serve as the basic data for subsequent temperature change prediction and thermal management optimization. These input parameters interact with each other and affect the overall thermal management strategy of the vehicle. Accurately and in real time, acquiring and processing these data is of vital importance for optimizing the vehicle's thermal management system, improving system efficiency, and ensuring safe operation.

[0033] In step S12, the vehicle operating environment data is input into a pre-trained temperature change prediction model, and a temperature change trend graph is output, including: The temperature change prediction model is an LSTM neural network model; The training process of the temperature change prediction model is as follows: Based on the ATSS algorithm, the pre-prepared training set is optimized to obtain the optimized training set; Inputting the optimized training set into the temperature change prediction model to perform time series feature learning to obtain an implicit state; The implicit state is plotted through the output layer of the temperature prediction model to obtain a predicted temperature change trend graph; The predicted temperature change trend graph and the actual temperature change trend graph are calculated point by point to obtain a loss value; Based on the loss value, executing a back propagation algorithm to update the temperature change prediction model parameters and optimize the temperature change prediction model; Repeat the above process and continuously optimize the temperature change prediction model until the output accuracy of the temperature change prediction model on the training set reaches the preset output accuracy requirement, and then stop training.

[0034] It is worth noting that in step S12, the preparation of the training set is a crucial step. In this technical solution, the training set includes temperature data under different vehicle operating environments, which are measured under various vehicle speeds, ambient temperatures, loads and other working conditions. These data need to be fully representative when collected to cover various operating conditions. In order to further optimize the training set, we use the ATSS algorithm. The core idea of ​​the ATSS algorithm is to select the most helpful samples for model training from the original training set, and remove unimportant or redundant samples, thereby improving the training effect of the model. By using the ATSS algorithm, the optimized training set can train the model more efficiently, reduce the impact of noise on model training, and improve the prediction accuracy and generalization ability of the model. In the task of temperature change prediction, since temperature is a time-dependent variable, the LSTM (Long Short-Term Memory) neural network model is very suitable for processing this type of time series data. The LSTM model is a special recursive neural network (RNN) that can effectively capture long-term dependencies and solve the gradient vanishing problem of traditional RNN when processing long time series. For temperature change prediction, the vehicle's operating status (such as speed, load, etc.) will affect each other at different time points. The LSTM model can combine the current temperature change with previous historical data through the memory and forgetting mechanism to make accurate predictions.

[0035] During the model training process, the training set optimized by ATSS is input into the input layer of the LSTM model. The input layer of the LSTM receives these time series data and passes them to the hidden layer layer by layer. In each layer, the LSTM network processes the data through the built-in gating mechanism (including input gate, forget gate and output gate), retains the most useful historical information for temperature change prediction, and discards unnecessary parts. Through these gating mechanisms, LSTM can learn the important features in the time series and obtain the final implicit state.

[0036] In the LSTM model, the implicit state refers to the model's understanding of the current moment and historical information. Through time series feature learning, the LSTM model generates an implicit state representing the current moment. This implicit state contains information in the past time steps and can help predict future temperature change trends. In the output layer of the LSTM, the predicted result of temperature change is calculated based on the implicit state and the input data at the current moment. This prediction result is a continuous value that represents the temperature change trend at a certain point in the future.

[0037] Specifically, the output layer of the LSTM model passes through a fully connected layer, multiplies the implicit state vector by the weight matrix, and obtains the final prediction output after the activation function. These outputs constitute a temperature change trend graph, reflecting the temperature change in the future time period.

[0038] After obtaining the predicted temperature trend graph, the next step is to compare the trend graph with the actual temperature trend graph point by point. This comparison is achieved by calculating the loss function, using the mean square error (MSE) as the loss function. The mean square error is a common method for calculating the difference between the predicted value and the actual value. Its formula is as follows: in, is the loss value, It is The predicted temperature value, It is The real temperature value, is the total number of samples. By calculating point by point, we can get a loss value, which represents the gap between the predicted result and the actual situation.

[0039] Based on the calculated loss value, the parameters of the LSTM model are updated through the back-propagation algorithm. The back-propagation algorithm is a commonly used optimization algorithm in deep learning. It guides the update of model parameters by calculating the gradient of the loss function with respect to the model parameters. Specifically, the back-propagation algorithm adjusts each weight and bias according to the loss function so that the output of the model tends to the true value. During the optimization process, the gradient descent method is used to continuously update the model parameters to reduce the prediction error and thus improve the accuracy of the model.

[0040] The training process of the LSTM model relies on multiple iterations and back-propagation algorithms. In each training iteration, the model calculates the loss based on the current training data, and updates the weights and biases of the network through back-propagation, gradually optimizing the parameters of the model. The training process will continue until the output accuracy of the model on the training set reaches the preset requirements. In this technical solution, the output accuracy requirement is measured by the mean square error (MSE), and the MSE is set to be less than 0.01, that is, the temperature change error predicted by the model needs to be controlled within 0.01 degrees. The setting of this accuracy requirement is based on several considerations: First, the temperature change of the high-voltage wiring harness of new energy vehicles is crucial to the regulation of the thermal management system and the safety of the wiring harness. Excessive prediction errors lead to inaccurate temperature control, affecting the operating safety and efficiency of the system. Therefore, a lower MSE can ensure that the prediction results of the model have higher reliability. Secondly, in actual operation, temperature changes are affected by many factors, such as vehicle speed, ambient temperature, battery load, etc. These factors often have certain noises. MSE less than 0.01 can effectively balance these noises and ensure the stability and accuracy of the model. In addition, choosing this accuracy standard also helps to optimize the use of computing resources, avoid overfitting, and meet the real-time requirements in engineering applications. During the training process, the model will continuously update its parameters through back propagation according to the loss value until the set MSE standard is reached on the training set, marking the completion of the training. At this point, the temperature change prediction accuracy of the LSTM model has reached an ideal level and can be applied to the thermal management of high-voltage wire harnesses, ensuring real-time optimization of the temperature and current carrying capacity of the wire harnesses and providing efficient thermal management solutions.

[0041] In step S13, heat generation calculation is performed according to the wiring harness data to obtain wiring harness heating power, including: The harness resistance is calculated using the following formula: in, is the wiring harness resistance, is the resistivity of the wire harness material, is the harness length, is the cross-sectional area of ​​the harness; The heating power of the wiring harness is calculated by the following formula: in, is the heating power of the wiring harness, is the harness current.

[0042] It is worth noting that in step S13, the purpose of performing heat calculation based on the harness data is to calculate the heat generated by the current flowing through the harness. This process first relies on the resistance calculation of the harness, and the resistance calculation directly depends on multiple factors such as the material, length and cross-sectional area of ​​the harness. Next, we will explain this step in detail, especially the selection of material resistivity, the measurement of harness length and cross-sectional area, etc.

[0043] First, the calculation of resistance is done according to Ohm's law in physics. The resistance of the wiring harness is the key factor in the heat generated when the current flows through the wiring harness, because the current encounters resistance when flowing in the resistor, which is converted into heat energy. Different materials have different resistivities, which directly affect the resistance size of the wiring harness and its heating characteristics, so it is crucial to select the right material.

[0044] The resistivity of a wire harness refers to the resistance of the material to current per unit length and per unit cross-sectional area. Common wire materials include copper, aluminum, iron, etc., and their resistivities vary greatly. Copper is a very common electrical conductive material, and its resistivity is about Because copper has very superior electrical conductivity, it is used in electrical applications that require high conductivity, such as cables, wires, and motor windings. Copper's low resistivity enables it to reduce heat generation and improve transmission efficiency at smaller currents, making it the material of choice for efficient electrical circuit design.

[0045] Aluminum is another common conductive material with a resistivity of approximately . Compared with copper, aluminum has a higher resistivity, but its density is lower, which makes aluminum sometimes used as a substitute for copper in circuit design, especially when weight is required. For example, fields such as aerospace or electric vehicles often choose aluminum as a wire material. Although aluminum is slightly less conductive than copper, its lighter weight can offset the increase in resistance to a certain extent. Therefore, designers will consider multiple factors such as current requirements, weight, cost, and durability when selecting materials.

[0046] The resistivity of iron is relatively high, about , so iron is not a common wire material. Iron is not as conductive as copper and aluminum, but it is still used in some special applications, such as the coils and cores of motors. Iron is used in applications that require magnetism or low cost, rather than in high-performance wire and cable. Iron has a higher resistivity, which means it generates more heat when conducting current, so it has poor heat dissipation ability under high load conditions, which can easily lead to overheating problems.

[0047] Once the materials to be used and their resistivity are determined, the next step is to calculate the resistance of the wire harness. The length L of the wire harness refers to the length of the wiring path between components such as the battery and the motor, and the shortest path of the wiring is determined according to the layout of the vehicle design. The wiring design of the electrical system inside the vehicle needs to minimize the length of the wire harness to reduce the energy loss of the current during transmission.

[0048] The cross-sectional area A of the wire harness determines the area through which the current can flow, and it is selected according to the current load of the wire harness. When the current flows through a conductor, the resistance is inversely proportional to the cross-sectional area. Therefore, in order to ensure that the wire harness can safely transmit a specific current, an appropriate cross-sectional area must be selected. If the cross-sectional area is too small, the current will encounter too much resistance when passing through, resulting in a large amount of heat generation, which may cause a fire or damage electrical components.

[0049] The selection of the cross-sectional area of the wire harness is determined according to the current intensity to be transmitted. The greater the current intensity, the larger the required cross-sectional area. A standard wire size table will provide the cross-sectional area of the wire harness required under different currents to ensure that the wire harness will not be damaged due to overheating when the current flows through. For applications with currents such as 15A to 30A, 2.5 mm² cables are used to ensure the safe transmission of the current.

[0050] Next, by calculating the resistance and current, we can obtain the heating power of the wire harness. When the current flows through the resistance, due to the resistance's hindrance to the current, electrical energy is converted into heat energy, resulting in heat generation. Through the formula , we can obtain the heating power P, where P represents power, I represents current, and R represents resistance. The calculation of this heating power can help designers understand how much heat the wire harness will generate during operation and evaluate whether heat dissipation measures need to be taken to avoid overheating. In addition, designers also need to consider the current fluctuations because during actual use, the current will be affected by factors such as load changes, acceleration, and braking, resulting in instantaneous fluctuations in the current. These fluctuations will affect the heating situation of the wire harness, so the maximum and minimum current situations need to be considered during design to ensure the safety of the wire harness under various working conditions.

[0051] In summary, the key to calculating the heat generation of the wire harness lies in accurately measuring the resistivity of the material, the length, and the cross-sectional area of the wire harness, and then calculating the resistance and the heating power. These parameters are essential elements in electrical design, which can help designers predict the working state of the electrical system and ensure the stable and safe operation of the vehicle's electrical system.

[0052] In step S14, based on the heating power of the wire harness and the temperature change trend graph, the wire harness layout is optimized to obtain a thermal management wire harness layout scheme, including: Performing temperature evaluation according to the heating power of the wiring harness and the temperature change trend diagram to obtain wiring harness temperature distribution information; The wiring harness temperature distribution information is combined with the thermal management requirements for optimization to obtain a thermal management wiring harness arrangement solution.

[0053] It is worth noting that in step S14, the temperature harness layout is optimized mainly based on the harness heating power and temperature change trend diagram, so as to obtain a thermal management harness layout plan. The key to this step is how to use the temperature change trend diagram to guide the optimization of the harness layout, so as to make the thermal management of the vehicle electrical system more efficient. Here, first we need to clarify the concept of the temperature change trend diagram and how it plays a role in the thermal management system.

[0054] The temperature trend chart is calculated by analyzing the vehicle operating environment data and the heating power of the wiring harness. This chart reflects the temperature change of the wiring harness under different working conditions, especially when the current flows through the wiring harness, over time. The temperature trend chart is not just a simple temperature record, it is actually a dynamic thermodynamic model that integrates the vehicle's operating status, environmental factors, and the load of the electrical system, and can accurately predict the temperature change trend of the wiring harness during actual operation. This temperature trend chart helps designers understand how the temperature changes with time, location, working conditions and other factors during the process of transmitting current in the wiring harness, and then make more accurate wiring harness layout optimization decisions.

[0055] In practical applications, the temperature change trend graph is generated by a temperature change prediction model, which relies on deep learning and calculates the prediction results by inputting the real-time operating data of the vehicle. This model first needs to process a large amount of vehicle operating environment data, including external temperature, battery temperature, vehicle speed and other information, and further use the temperature change prediction model to infer the temperature of the wire harness at each moment during operation. Through these calculations, a dynamic temperature change curve is finally formed, which can accurately show the temperature changes of the wire harness in the vehicle electrical system under different loads and ambient temperature changes.

[0056] After obtaining the temperature change trend chart, this chart can be used as a basis for optimizing the wiring harness layout. Based on the temperature change information in the chart, the working status of the wiring harness can be evaluated, the areas that cause overheating can be found, and the temperature in these areas can be avoided by optimizing the layout. Specifically, the wiring harness needs to avoid running at high load for too long in high-temperature areas, while the load and current can be reasonably distributed in low-temperature areas. In this way, the wiring harness can be effectively prevented from being damaged due to excessive temperature, and the stability and safety of the system can be improved.

[0057] In the process of using the temperature change trend chart to optimize the wiring harness layout, it is first necessary to analyze each data point in the chart to confirm the law of temperature change. For example, when the vehicle is traveling at different speeds, there will be obvious differences in temperature changes. The temperature change trend chart can clearly show the temperature change trend of the wiring harness under various vehicle speeds, battery power, load and other conditions. Based on these data, local optimization can be performed to adjust the position and path of the wiring harness to ensure that it is not in an area with excessively high temperatures. In specific operations, designers will choose to keep the wiring harness with higher heat generation away from high temperature areas, or add heat dissipation devices, change the line layout and other methods to optimize the heat dissipation effect, thereby ensuring that the temperature of the wiring harness is within a safe range.

[0058] In addition, the temperature trend graph can be combined with thermal management requirements for further optimization and adjustment. Thermal management requirements are specific standards for the temperature control and heat dissipation performance of each component in the system (such as wiring harness, battery, motor, etc.) under different working conditions. It includes the upper and lower temperature limits, temperature control range, heat dissipation capacity requirements, etc., aiming to ensure that each component in the system operates within a safe temperature range to avoid damage, reduced efficiency or performance degradation due to overheating.

[0059] A specific example of thermal management requirements can be the temperature control of the battery pack. Assume that the battery pack of a new energy vehicle has a temperature control requirement, which clearly states: Upper temperature limit requirement: The temperature of the battery pack cannot exceed 65 degrees Celsius. When this temperature is exceeded, the chemical reaction rate of the battery is too fast, resulting in battery damage, or even thermal runaway, causing a fire risk. Lower temperature limit requirement: The temperature of the battery pack cannot be lower than -20 degrees Celsius. Low temperature will cause the internal chemical reaction of the battery to slow down, reduce battery efficiency, and in severe cases, cause battery capacity loss or inability to charge. Temperature control range: The battery temperature should be maintained between 20 degrees Celsius and 45 degrees Celsius. Within this temperature range, the battery has the best charging and discharging efficiency and the longest service life. Heat dissipation capacity requirements: Under high load or long-term driving conditions, the battery pack will generate higher heat, so the heat dissipation system needs to ensure that the battery operates within the above temperature range. For example, the heat dissipation system needs to automatically start when the battery temperature reaches 45 degrees Celsius to ensure that the battery temperature does not continue to rise.

[0060] This thermal management requirement ensures that the battery operates within the normal operating temperature range to avoid performance loss or safety issues caused by overheating or overcooling. In the battery management system (BMS) of electric vehicles, based on this thermal management requirement, designers will add temperature sensors, heat exchange systems, fans and other devices to monitor the battery temperature in real time and automatically adjust the working state of the cooling system based on the temperature data. This method effectively avoids the battery's performance degradation or damage caused by excessively high or low temperatures during operation, thereby ensuring the safety and service life of electric vehicles.

[0061] When using the temperature trend chart to optimize the layout, some dynamic factors also need to be considered, such as load fluctuations during vehicle driving, instantaneous changes in acceleration and braking, etc. These factors cause the temperature of the wiring harness to rise instantly, so designers need to adjust the load distribution of the vehicle's electrical system according to the high-temperature areas in the temperature trend chart to avoid concentrating too much load in these areas. By reasonably distributing current and optimizing line layout, the temperature fluctuation of the wiring harness can be minimized without affecting vehicle performance, ensuring the safe and stable operation of the entire electrical system.

[0062] In summary, the temperature trend chart is not only a tool for evaluating the temperature of the wiring harness, but also plays a vital role in optimizing the thermal management system. Through accurate temperature trend prediction, designers can understand the temperature dynamics of the wiring harness in the electrical system, and then optimize the line layout, load distribution, etc., to improve the thermal management efficiency and safety of the vehicle electrical system. Through this series of operations, not only can the occurrence of overheating problems be effectively prevented, but also the service life of the wiring harness and electrical system can be extended, ensuring the reliability and stability of new energy vehicles.

[0063] In step S15, the vehicle operating environment data is input into a pre-trained load demand model to output the load demand, including: The load demand model is an MLP neural network model; The training process of the load demand model is: Based on the reinforcement learning algorithm, the pre-prepared training set is optimized to obtain the optimized training set; Inputting the optimized training set into the load demand model, performing layer-by-layer calculations, and obtaining a feature vector; The characteristic vector is calculated through the output layer of the load demand model to obtain the predicted load demand; Performing a difference calculation based on the predicted load demand and the actual load demand to obtain an output error value; Based on the output error value, executing a back propagation algorithm to update load demand model parameters and optimize the load demand model; Repeat the above process and continuously optimize the load demand model until the output accuracy of the load demand on the training set reaches the preset output accuracy requirement, and then stop training.

[0064] It is worth noting that the training set is the basis of model learning, and its quality directly affects the training effect of the model. In order to build an efficient training set, it is first necessary to collect real load demand data from the vehicle operating environment. These data contain vehicle status information under different working conditions. For example, data sources may include but are not limited to: vehicle speed, battery power, ambient temperature, traffic density, road speed limit and other information, as well as corresponding load demand data. These data can be obtained through sensors installed on the vehicle and the on-board computing system. The sensors record the operating status and load demand of the vehicle under different working conditions in real time.

[0065] In order to ensure the diversity and representativeness of the training set, data needs to be collected under a variety of driving conditions, including different road environments (such as urban roads and highways), different driving modes (such as acceleration, deceleration, and constant speed driving), and different load states (such as battery power and changes in motor power). By integrating data from a variety of driving conditions, the training set can be made more comprehensive and cover a variety of working conditions, thereby improving the generalization ability of the model.

[0066] After obtaining the original training set, the reinforcement learning algorithm is used to optimize the training set because there is noise or redundant data in the original data, or some data points are not very helpful for model training. The goal of this process is to identify which data samples are most important for model training through the exploration and feedback mechanism of reinforcement learning, and to eliminate redundant or irrelevant samples, thereby improving the training efficiency and prediction accuracy of the model. The reinforcement learning algorithm defines a reward mechanism to allow the algorithm to dynamically adjust the composition of the training set according to the contribution to model training during the training process, thereby obtaining an optimized training set and ensuring that the final training set can help the model learn the most effective features.

[0067] After data optimization, the optimized training set will be input into the load demand model. The model is a multi-layer perceptron (MLP) neural network, which consists of multiple neurons and gradually extracts features from the input data through layer-by-layer calculations. The input layer of the MLP model receives the training data and calculates through the hidden layer to learn the deep features in the data layer by layer.

[0068] Specifically, the input layer passes the received vehicle operating environment data (such as vehicle speed, ambient temperature, etc.) to the next layer. The neurons in each layer pass the information to the next layer through weighted summation and activation processing, until an intermediate representation called a "feature vector" is finally obtained. The feature vector is the characteristic of the vehicle load demand learned by the model. It contains the key patterns in the input data and provides an important basis for predicting load demand.

[0069] After layer-by-layer calculation, the feature vector is passed to the output layer of the MLP model. In the output layer, the model converts the feature vector into an actual load demand forecast value through linear transformation and activation functions (such as ReLU or Sigmoid). This forecast value represents the load demand under a given vehicle operating environment. The model optimizes these parameters through learning so that the output result is as close to the actual load demand data as possible.

[0070] For example, when the input data is the vehicle speed, temperature, and battery power at a certain moment, the result calculated by the output layer is the load demand at that moment (for example, a current of 10A). This prediction result will be used for subsequent error calculations and the model parameters will be gradually adjusted through optimization steps.

[0071] After obtaining the predicted results of the load demand, the next step is to calculate the error between the predicted results and the actual load demand. Error calculation is a crucial step in model training, which determines the update direction of the model parameters. Specifically, the error is obtained by calculating the difference between the predicted load demand and the actual load demand.

[0072] The error value is calculated using the following formula: in, is the error value, For the The predicted load demand value, It is The actual load demand value, is the total number of samples.

[0073] After calculating the error value, the model needs to be optimized through the back propagation algorithm. The back propagation algorithm is a key algorithm in deep learning. It guides the model to adjust parameters by calculating the gradient of the loss function to each parameter of the model (including weights and biases). In this step, the back propagation algorithm calculates the gradient of each layer based on the output error value and uses the optimization algorithm to update the parameters of the model.

[0074] The goal of backpropagation is to minimize the loss function, that is, to gradually reduce the prediction error of the model by gradually updating the weights and biases. The optimization process adjusts the weight of each neuron according to the current error information, so that the model can better predict the load demand in the next iteration.

[0075] The optimization process of the load demand model is an iterative process. Each time the parameters are updated through back propagation, the model will recalculate the load demand and calculate a new error value. The training will be repeated until the error value is small enough and the accuracy of the model output meets the preset requirements.

[0076] In this technical solution, the output accuracy requirement is set to a mean square error (MSE) less than 0.05. The reason for choosing this accuracy requirement is that in practical applications, the prediction results of load demand need to have a high degree of accuracy, because this is related to the safe and efficient operation of the high-voltage wiring harness. MSE less than 0.05 can ensure that the gap between the predicted load demand and the actual load demand is within an acceptable range, avoiding excessive errors from having adverse effects on thermal management and current carrying capacity distribution. MSE less than 0.05 means that the prediction results of the model are credible in practical applications and can provide effective data support to ensure the stable operation of the high-voltage wiring harness.

[0077] The training will continue until the MSE is less than 0.05, at which point the training process stops and the parameters of the load demand model are considered to have been optimized, capable of providing high-precision load demand prediction for thermal management of high-voltage wiring harnesses.

[0078] In step S16, the bus harness arrangement is optimized according to the thermal management harness arrangement scheme and the load demand to obtain the optimal harness arrangement scheme, including: Performing preliminary optimization according to the thermal management harness arrangement scheme and the load requirements to obtain a preliminary harness arrangement scheme; The preliminary wiring harness arrangement scheme is iteratively optimized based on a genetic algorithm to obtain an optimal wiring harness arrangement scheme.

[0079] It is worth noting that in step S16, the bus harness arrangement is optimized according to the thermal management harness arrangement scheme and the load demand. The purpose of this process is to combine all thermal management harness solutions and load demand solutions, and obtain the optimal harness arrangement scheme through a series of optimization steps to ensure the safety and efficiency of the electrical system. In this optimization process, genetic algorithms are used to optimize the harness arrangement scheme, especially in the preliminary optimization stage. Genetic algorithms can help us explore the optimal solution through evolution, so as to find the most suitable harness arrangement scheme.

[0080] First, the genetic algorithm is an optimization algorithm that simulates natural selection and genetic mechanisms. Its basic principle draws on the evolutionary process of the biological world, including selection, crossover, mutation and other processes, and finally finds the optimal solution to the problem through continuous iteration and optimization. In this step, the genetic algorithm is used to optimize the preliminary wiring harness arrangement plan to ensure that the most suitable wiring harness configuration can be obtained under the given constraints of thermal management and load requirements, thereby improving the efficiency of the thermal management system and reducing the loss of electrical equipment.

[0081] In the preliminary stage of the genetic algorithm, we first encode the solution of the problem into chromosomes, which represent different wiring harness arrangements. Each chromosome contains information about the wiring harness layout, load demand distribution, etc., and all arrangements are expressed in this encoding method. Then, we need to use a fitness function to evaluate the quality of each solution. The design of the fitness function is based on the relationship between the thermal management solution and the load demand. Specifically, the fitness function will comprehensively consider the impact of the wiring harness arrangement on the current load, as well as the temperature changes of each component, and calculate a comprehensive score to evaluate the pros and cons of the arrangement. The higher the score, the better the solution is in terms of thermal management and load demand satisfaction.

[0082] In the crossover operation of the genetic algorithm, a pair of chromosomes is selected as the parent generation, and they are combined into new chromosomes, i.e., the offspring. The purpose of this step is to "inherit" the excellent features from the parent generation and generate a new wiring harness arrangement through the crossover operation. Through crossover, the design of the offspring not only retains some of the excellent characteristics of the parent generation, but also introduces some new permutations and combinations, so that the optimization space is expanded. After the crossover operation, multiple offspring will be generated, and each offspring represents a new wiring harness arrangement.

[0083] At the same time, genetic algorithms also introduce mutation operations. Mutation refers to a small-scale random adjustment of genes in certain chromosomes after the crossover operation to prevent the algorithm from falling into a local optimal solution. Through the mutation operation, new solutions can explore areas that were not previously covered, allowing the genetic algorithm to optimize in a wider search space. The introduction of mutation operations has enhanced the search capabilities of genetic algorithms and helped to find more ideal solutions in complex optimization problems.

[0084] After multiple iterations, the genetic algorithm continuously optimizes the wiring harness arrangement until an optimal solution is found. In each generation of evolution, the fitness of the chromosome will gradually increase, which means that after multiple iterations, the wiring harness arrangement scheme obtained is increasingly in line with the requirements of thermal management and load requirements. In this way, the genetic algorithm can effectively select the optimal solution from a large number of solutions to ensure the optimal performance of the system.

[0085] In this process, the optimal solution generated by the genetic algorithm is the final wiring harness arrangement plan. This plan can meet specific thermal management requirements, while optimizing the distribution of load demand and ensuring the stability and efficiency of the system under different environments. The optimized wiring harness arrangement plan will be implemented in actual applications, thereby improving the overall performance of the high-voltage wiring harness of new energy vehicles, achieving the purpose of reducing losses, improving efficiency, and ensuring the safety of the electrical system.

[0086] In general, the application of genetic algorithms in this step, by simulating the selection, crossover and mutation mechanisms in nature, can effectively explore the optimal wiring harness arrangement scheme in a complex search space, providing an efficient and reliable optimization method.

[0087] Reference Figure 2 The second embodiment of the present invention provides a new energy vehicle high voltage wire harness thermal management system based on big data, including: A data acquisition module, used to acquire vehicle operating environment data and wiring harness data; A temperature change prediction module, used for inputting the vehicle operating environment data and the wiring harness data into a preset temperature change prediction model and outputting a temperature change trend graph; A heating power module, used for performing heating calculation according to the wiring harness data to obtain the wiring harness heating power; A thermal management arrangement module, used to optimize the temperature harness arrangement according to the harness resistance, the harness heating power and the temperature change trend diagram, and obtain a thermal management harness arrangement solution; A load demand module, used to input the vehicle operating environment data into a preset load demand model and output a load demand; The optimal wiring harness arrangement module is used to optimize the wiring harness arrangement of the entire bus according to the thermal management wiring harness arrangement scheme and the load demand to obtain the optimal wiring harness arrangement scheme.

[0088] It should be noted that the high-voltage wire harness thermal management system for new energy vehicles based on big data provided in an embodiment of the present invention is used to execute all the process steps of the high-voltage wire harness thermal management method for new energy vehicles based on big data in the above-mentioned embodiment. The working principles and beneficial effects of the two correspond one to one, so they will not be repeated here.

[0089] The embodiment of the present invention also provides an electronic device. The electronic device includes: a processor, a memory, and a computer program stored in the memory and executable on the processor, such as a thermal management program for high-voltage wire harnesses of new energy vehicles based on big data. When the processor executes the computer program, the steps in the above-mentioned embodiments of the thermal management method for high-voltage wire harnesses of new energy vehicles based on big data are implemented, such as Figure 1 Alternatively, when the processor executes the computer program, the functions of the modules / units in the above-mentioned device embodiments are realized, such as the optimal wiring harness arrangement module.

[0090] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program in the electronic device.

[0091] The electronic device may be a computing device such as a desktop computer, a notebook, a PDA, and a smart tablet. The electronic device may include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that the above components are merely examples of electronic devices and do not constitute a limitation on the electronic device. The electronic device may include more or fewer components than the above components, or may combine certain components, or different components. For example, the electronic device may also include input and output devices, network access devices, buses, etc.

[0092] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the electronic device, and uses various interfaces and lines to connect various parts of the entire electronic device.

[0093] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the electronic device by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0094] Wherein, if the module / unit integrated in the electronic device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0095] It should be noted that the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art may understand and implement it without paying any creative effort.

[0096] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. A thermal management method for high-voltage wiring harnesses of new energy vehicles based on big data, characterized in that: Executed by a computer, including: Obtain vehicle operating environment data and wiring harness data; Input the vehicle operating environment data into a pre-trained temperature change prediction model, and output a temperature change trend graph; Performing heat calculation according to the wiring harness data to obtain the wiring harness heat power; According to the harness resistance, the harness heating power and the temperature change trend diagram, the temperature harness arrangement is optimized to obtain a thermal management harness arrangement scheme; Inputting the vehicle operating environment data into a pre-trained load demand model and outputting the load demand; According to the thermal management harness arrangement scheme and the load requirements, the bus harness arrangement is optimized to obtain an optimal harness arrangement scheme.

2. The method for thermal management of high-voltage wire harnesses for new energy vehicles based on big data according to claim 1 is characterized in that: The vehicle operating environment data includes: driving speed, motor speed, external temperature, humidity, battery power, battery temperature, motor temperature, traffic density and road speed limit; The harness data includes harness curvature, harness current, harness cross-sectional area and harness length.

3. The method for thermal management of high-voltage wire harnesses for new energy vehicles based on big data according to claim 2 is characterized in that: The vehicle operating environment data is input into a pre-trained temperature change prediction model, and a temperature change trend graph is output, including: The temperature change prediction model is an LSTM neural network model; The training process of the temperature change prediction model is as follows: Based on the ATSS algorithm, the pre-prepared training set is optimized to obtain the optimized training set; Inputting the optimized training set into the temperature change prediction model to perform time series feature learning to obtain an implicit state; The implicit state is plotted through the output layer of the temperature prediction model to obtain a predicted temperature change trend graph; The predicted temperature change trend graph and the actual temperature change trend graph are calculated point by point to obtain a loss value; Based on the loss value, executing a back propagation algorithm to update the temperature change prediction model parameters and optimize the temperature change prediction model; Repeat the above process and continuously optimize the temperature change prediction model until the output accuracy of the temperature change prediction model on the training set reaches the preset output accuracy requirement, and then stop training.

4. The method for thermal management of high-voltage wire harnesses for new energy vehicles based on big data according to claim 2 is characterized in that: The step of performing heat calculation according to the wiring harness data to obtain the wiring harness heat power includes: The harness resistance is calculated using the following formula: in, is the harness resistance, is the resistivity of the wire harness material, is the harness length, is the cross-sectional area of ​​the harness; The heating power of the wiring harness is calculated by the following formula: in, is the heating power of the wiring harness, is the harness current.

5. The method for thermal management of high-voltage wire harnesses for new energy vehicles based on big data according to claim 1 is characterized in that: The step of optimizing the temperature harness arrangement according to the harness heating power and the temperature change trend diagram to obtain a thermal management harness arrangement solution includes: Performing temperature evaluation according to the heating power of the wiring harness and the temperature change trend diagram to obtain wiring harness temperature distribution information; The wiring harness temperature distribution information is combined with the thermal management requirements for optimization to obtain a thermal management wiring harness arrangement solution.

6. The method for thermal management of high-voltage wire harnesses for new energy vehicles based on big data according to claim 2 is characterized in that: The step of inputting the vehicle operating environment data into a pre-trained load demand model and outputting the load demand includes: The load demand model is an MLP neural network model; The training process of the load demand model is: Based on the reinforcement learning algorithm, the pre-prepared training set is optimized to obtain the optimized training set; Inputting the optimized training set into the load demand model, performing layer-by-layer calculations, and obtaining a feature vector; The characteristic vector is calculated through the output layer of the load demand model to obtain the predicted load demand; Performing a difference calculation based on the predicted load demand and the actual load demand to obtain an output error value; Based on the output error value, executing a back propagation algorithm to update load demand model parameters and optimize the load demand model; Repeat the above process and continuously optimize the load demand model until the output accuracy of the load demand on the training set reaches the preset output accuracy requirement, and then stop training.

7. The method for thermal management of high-voltage wire harnesses for new energy vehicles based on big data according to claim 1 is characterized in that: The step of optimizing the wiring harness arrangement according to the thermal management wiring harness arrangement scheme and the load requirement to obtain the optimal wiring harness arrangement scheme includes: Performing preliminary optimization according to the thermal management harness arrangement scheme and the load requirements to obtain a preliminary harness arrangement scheme; The preliminary wiring harness arrangement scheme is iteratively optimized based on a genetic algorithm to obtain an optimal wiring harness arrangement scheme.

8. A high-voltage wiring harness thermal management system for new energy vehicles based on big data, characterized in that: include: A data acquisition module, used to acquire vehicle operating environment data and wiring harness data; A temperature change prediction module, used to input the vehicle operating environment data into a pre-trained temperature change prediction model and output a temperature change trend graph; A heating power module, used for performing heating calculation according to the wiring harness data to obtain the wiring harness heating power; A thermal management arrangement module, used to optimize the temperature harness arrangement according to the harness resistance, the harness heating power and the temperature change trend diagram, and obtain a thermal management harness arrangement solution; A load demand module, used to input the vehicle operating environment data into a pre-trained load demand model and output the load demand; The optimal wiring harness arrangement module is used to optimize the wiring harness arrangement of the entire bus according to the thermal management wiring harness arrangement scheme and the load demand to obtain the optimal wiring harness arrangement scheme.

9. An electronic device, characterized in that: It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the thermal management method of high-voltage wire harness for new energy vehicles based on big data as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the big data-based thermal management method for high-voltage wire harnesses of new energy vehicles as described in any one of claims 1 to 7.

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