High-safety anti-overload fast-charging electronic wire harness system and electronic wire harness

Abstract

The invention discloses a high-safety anti-overload fast-charging electronic wire harness system and an electronic wire harness, and belongs to the technical field of electronic wire harness safety protection, and the system comprises a sensing module, a load prediction modeling module, an anomaly detection module, a bypass control module, a central fast-charging regulation and control module and an optimization module. A local load prediction function is constructed by collecting a current change rate and a conductor thermal response curve at the initial stage of fast charging in real time, a resistance abrupt change area is judged, and dynamic current limiting and bypass shunting are carried out; the central module can adjust charging parameters of the next cycle according to historical load characteristics, and the optimization module corrects a prediction model through multi-cycle learning to realize adaptive control; the electronic wire harness integrates a thermoelectric sensor, a resistance sampling unit and a controllable bypass path, and has quick response and local shunting functions; the overload protection capability and the operation safety of the electronic wire harness in the fast charging system can be effectively improved, and the method is suitable for high-power application scenes such as new energy automobiles and high-speed charging piles.

Description

Technical Field

[0001] This invention relates to the field of electronic wire harness safety protection technology, specifically to a high-safety overload-resistant fast-charging electronic wire harness system and electronic wire harness. Background Technology

[0002] With the development of new energy vehicles and high-speed transportation systems, the reliance on high-power fast charging technology is deepening. Especially in scenarios such as urban emergency power replenishment and rapid turnaround power replenishment in highway service areas, electronic wire harnesses frequently endure instantaneous peak voltage and current surges, often involving short-duration high-power transmission. In these application environments, common thin-diameter electronic wire harnesses, in order to ensure lightweight and flexibility, generally use small-section copper wires or alloy wires, which have low heat capacity and poor heat dissipation capabilities.

[0003] When a fast charging system is activated, poor contact at the charging interface, abnormal chip control, or sluggish response of the power control module can easily cause microsecond-level high-current surges. Within seconds, this generates a dramatic resistance heating effect, leading to a sudden increase in the local temperature of the wiring harness, exceeding the material's temperature resistance limit. This can result in overload, melting, or even fire of the wiring harness. Current systems generally rely on external temperature sensors or fuse protection devices, but due to response delays and limitations in their placement, it is difficult to detect localized heating points in the wiring harness in a timely manner, creating a technical blind spot where "overload occurs first, response lags behind." Summary of the Invention

[0004] The purpose of this invention is to provide a high-safety, overload-resistant, fast-charging electronic wiring harness system and electronic wiring harness to address the shortcomings of the prior art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a high-safety, overload-resistant, fast-charging electronic harness system, comprising:

[0006] Sensing module: Acquires the real-time current change rate within t<10ms during the fast charging initialization phase. And detect the initial thermal response curve of the micro-region conductor. ;

[0007] Load prediction modeling module: based on and The nonlinear coupling relationship between them is used to construct a local load prediction function model P(x,t) for the fast charging electronic harness, where x is the axial position variable of the harness;

[0008] Anomaly detection module: Maps P(x,t) to the piecewise resistance model R(x) of the wire harness structure to determine the resistance abrupt change threshold of local segments. ;

[0009] Bypass control module: for In areas exceeding the preset safety limit Rmax, current limiting is performed, and current is diverted through the bypass conductor path B(x);

[0010] Central fast charging control module: Simultaneously records the location and time of the current-limiting area and continuous load characteristic values, and adjusts the charging voltage for the next cycle. With current Make dynamic adjustments;

[0011] Optimization module: Based on the overload region change trend recorded during continuous charging cycles, the prediction weight coefficient of P(x,t) is corrected.

[0012] Preferably, the sensing module includes: sampling the instantaneous current signal of the main conductor and branch conductors during the fast charging initialization phase and converting it into a current change rate I'; and performing time synchronization processing on the I' signal and the temperature change signal output by the micro thermoelectric sensing element arranged on the conductor surface to obtain the initial thermal response curve of the conductor micro-region. .

[0013] Preferably, the load prediction modeling module includes:

[0014] Received current change rate I' and initial thermal response curve The sample was then normalized to eliminate sampling biases caused by different harness structures and ambient temperatures.

[0015] Based on normalization and A multivariate nonlinear regression algorithm was used to establish the thermo-electric coupling response relationship and extract the coefficient vector β(x) that characterizes the thermosensitive properties of different conductor positions.

[0016] By combining the coefficient vector β(x) with the wire harness structural parameters, a local load prediction function model P(x,t) is constructed using the finite element discrete modeling method to simulate the local heat accumulation and current density change trend of the wire harness during fast charging.

[0017] Preferably, the anomaly detection module includes:

[0018] The local load prediction function model P(x,t) is received, and based on the spatial discrete characteristics of the wire harness, the wire harness axis is divided into several detection unit intervals Δx of equal length;

[0019] For each Δx interval, the instantaneous equivalent resistance is calculated using real-time current and voltage sampling data, and compared with the steady-state resistance in the reference model. By performing a difference comparison, the resistance change ΔR(x,t) is obtained;

[0020] The correlation between ΔR(x,t) and the local load prediction function model P(x,t) is fitted to construct the resistance mutation identification function φ(x,t) to screen out abnormal segments caused by thermal-electric overload.

[0021] When φ(x,t) corresponds to When the preset safety limit Rmax is exceeded, a local abnormality signal is sent to trigger corresponding diversion or load limiting actions.

[0022] Preferably, the bypass control module includes:

[0023] It receives local anomaly signals from the anomaly detection module and locks the corresponding axial position x and its resistance mutation threshold. ;

[0024] Based on position x, the control electronic switch array drives the bypass conductor path B(x) to conduct, where B(x) is a low-resistance conductor shunt channel pre-laid at the parallel position of the main wire harness;

[0025] Adjust the current-carrying capacity of the main body at position x to dynamically guide part of the current to B(x) in order to reduce the thermal load at abnormal points.

[0026] Preferably, the central fast charging control module includes:

[0027] Receive current limiting execution event information from the bypass control module in real time, and record the axial position (x) of the current limiting area and the occurrence time. and the duration of the current limiting Δt;

[0028] Extract the local load prediction function model P(x,t) change curve of the region in time Δt, and combine it with the change of the main branch current before and after the current diversion to generate the load intensity characteristic value ψ(x);

[0029] Based on the historical trend and spatial distribution characteristics of ψ(x), the corresponding power limiting strategy in the control strategy library is invoked to dynamically adjust the target charging voltage for the next cycle. With current This ensures that it meets the local load-bearing capacity.

[0030] The revised and The system synchronizes with the fast charging host or vehicle power control unit via a communication bus to complete closed-loop control.

[0031] Preferably, the optimization module includes:

[0032] The current limiting trigger frequency, load intensity characteristic value ψ(x), and resistance change threshold at each location x during multiple charging cycles were continuously recorded. This forms a time-series dataset of the overload region;

[0033] Cluster analysis and trend fitting were performed on the dataset to extract high-frequency overload segments and their change trajectories, and a historical risk weight function w(x) was constructed.

[0034] The weighting function w(x) is introduced as a correction factor into the local load prediction function model P(x,t);

[0035] After each round of prediction correction, the coefficient vector β(x) is updated and optimized through the error backpropagation mechanism.

[0036] The present invention also provides an electronic wiring harness, which forms a signal and control linkage with a sensing module, a load prediction modeling module, an anomaly detection module, a bypass control module, a central fast charging regulation module and an optimization module, for realizing dynamic overload protection and thermal risk diversion management in fast charging mode.

[0037] The technical effects and advantages provided by the present invention in the above technical solution are as follows:

[0038] 1. This invention achieves high-precision monitoring of transient current surges and local thermal responses in the early stages of fast charging through the coordinated operation of a sensing module, a predictive modeling module, and an anomaly detection module. The system can identify overload trends in the wiring harness within milliseconds and dynamically reflect local risk points based on the local load prediction function model P(x,t), effectively compensating for the shortcomings of traditional temperature control protection in terms of response lag and insufficient resolution, and improving the active sensing capability and response speed of the wiring harness system.

[0039] 2. This invention establishes a closed-loop adaptive mechanism for fast charging power control by integrating bypass control, central regulation, and optimization iteration modules. The system can adjust the charging voltage and current in real time based on overload behavior during multi-cycle operation and continuously correct the prediction model, effectively avoiding local thermal runaway and wiring harness fatigue aging problems. This significantly improves the safety, stability, and intelligence level of the fast charging system, making it suitable for high-power applications such as new energy vehicles, energy storage systems, and high-speed charging infrastructure. Attached Figure Description

[0040] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0041] Figure 1 This is a flowchart of a high-safety, overload-resistant, fast-charging electronic harness system according to the present invention.

[0042] Figure 2 This is a schematic diagram of the electronic wire harness module of the present invention. Detailed Implementation

[0043] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0044] Example 1, please refer to Figure 1 As shown in the figure, the high-safety overload-resistant fast-charging electronic harness system described in this embodiment includes:

[0045] Sensing module: Acquires the real-time current change rate within t<10ms during the fast charging initialization phase. And detect the initial thermal response curve of the micro-region conductor. ;

[0046] Load prediction modeling module: based on and The nonlinear coupling relationship between them is used to construct a local load prediction function model P(x,t) for the fast charging electronic harness, where x is the axial position variable of the harness;

[0047] Anomaly detection module: Maps P(x,t) to the piecewise resistance model R(x) of the wire harness structure to determine the resistance abrupt change threshold of local segments. ;

[0048] Bypass control module: for In areas exceeding the preset safety limit Rmax, current limiting is performed, and current is diverted through the bypass conductor path B(x);

[0049] Central fast charging control module: Simultaneously records the location and time of the current-limiting area and continuous load characteristic values, and adjusts the charging voltage for the next cycle. With current Make dynamic adjustments;

[0050] Optimization module: Based on the overload region change trend recorded during continuous charging cycles, the prediction weight coefficient of P(x,t) is corrected.

[0051] In this invention, the sensing module is used to acquire the current-carrying and thermal response behavior of the electronic wire bundle during the fast charging initialization phase, so as to achieve rapid prediction and response control of local overload trends. The sensing module includes a current change rate acquisition unit and a micro-area temperature response acquisition unit, which work together to establish the thermo-electric dynamic response relationship of the conductor micro-region under the initial state of fast charging.

[0052] In practical operation, after the fast charging system starts, the sensing module initiates a high-speed data acquisition process within a time t of less than 10 milliseconds, which specifically includes the following steps:

[0053] The sensing module uses high-speed Hall current collectors distributed on the main conductor and branch conductors to sample the current signal flowing through the conductor at the microsecond level. The collected data is transmitted to the central data processing unit via an analog-to-digital converter circuit. After processing by a first-order differential calculation model, the rate of change of current per unit time, I', is extracted. That is, the rate of change of current I' is defined as the difference between the sampled current values ​​at two adjacent time points divided by the time interval. Taking the i-th sampling as an example, if the current values ​​at the sampling points are Ii and... If the corresponding time interval is Δt, then Δt is selected to be no greater than 100 microseconds to ensure that the transient response behavior at the beginning of fast charging is reflected.

[0054] Synchronized with the above steps, the sensing module uses miniature thermoelectric temperature sensing elements (such as thin-film thermocouples or MEMS thermistors) deployed on the outer surface of the conductor to sample the surface temperature rise of the micro-region of the conductor. The temperature sensor's acquisition frequency is consistent with the current acquisition frequency to ensure the correspondence between thermal response data and current data on the time axis.

[0055] The temperature change signal and the current change rate signal are time-aligned and interpolated to form the initial thermal response curve of the conductor micro-region. This curve represents the trend of conductor surface temperature change with fast charging start-up time, and can reflect the thermal inertial response characteristics of the local conductor caused by the sudden increase in current.

[0056] To further quantify the current-carrying heat sensitivity of the conductor's local area, the system will and A nonlinear fitting is performed to construct a thermo-electric coupling model, and the thermo-electric response coefficient λ is calculated. λ is defined as the rate of temperature rise per unit time on the conductor surface under a unit current change rate, and can be expressed as: Where ΔT is the change in temperature, The current change rate increment is used to fit the maximum change interval within the same time period. The thermal-electric coupling coefficient λ will be used as one of the input parameters of the subsequent local load prediction function model P(x,t) to determine the difference in thermal load capacity at different wiring harness locations during the fast charging start-up phase.

[0057] The load prediction modeling module in this invention is used to construct a local load prediction function model P(x,t) based on the data collected by the sensing module. This model can dynamically predict the risk of local heat accumulation and the trend of current carrying capacity changes in the electronic harness during fast charging. Here, x is the spatial position variable along the harness axis, and t is the time variable during fast charging.

[0058] First, the load prediction modeling module receives two pieces of raw data from the sensing module: the rate of change of current I', which is the rate at which the conductor current changes per unit time; and the initial thermal response curve. This represents the trajectory of the conductor surface temperature over time during the initial stage of fast charging. Since the wiring harness may be deployed under different ambient temperatures or structural parameters (such as cross-sectional area and conductor material), directly comparing the original data may lead to a decrease in model accuracy. Therefore, the system compares I' with... Normalization is performed: The normalization process uses the extremum standardization method. For a given sampled sequence F(t), its normalized value F_norm(t) is calculated as follows: Where F_max and F_min are the maximum and minimum values ​​within the sampling interval, respectively. Normalized and It can eliminate systematic biases and enhance the model's ability to predict the consistency of different harness environments.

[0059] Based on normalized data, a multivariate nonlinear regression algorithm is used to fit the coupling relationship between current change and thermal response. The algorithm selects the following regression function form: ;in, , , Let be the vector of thermo-electric response coefficients to be fitted, x represent the discrete axial position of the wire bundle, and ε(x) be the fitting residual. This regression modeling is performed independently at each discrete position x, yielding a set of coefficients β(x), whose physical meaning is as follows: : Represents the initial temperature rise at position x; : Represents the linear current response sensitivity; This indicates the degree of aggravation of the nonlinear thermal response. By controlling the fitting error within an acceptable range (e.g., residual ε less than 0.05), the accuracy of the model's predictions can be ensured.

[0060] After extracting the β(x) coefficients, and combining them with the electronic wire bundle structural parameters, including: cross-sectional area A(x), in square millimeters; material resistivity ρ(x), in ohms·mm² / m; and heat dissipation constant k(x), in watts / m·Kelvin, a local load prediction function model P(x,t) is constructed to dynamically estimate the thermal load and current density response trends of the wire bundle at time t and position x. ;in: P(x,t) represents the real-time current change rate; I(t) is the current value before fast charging; Δt is the time integration step, with a value less than 1 millisecond; C is the thermal capacity constant per unit length of the conductor bundle, in joules per meter Kelvin. The value of P(x,t) reflects the intensity of heat accumulation risk per unit volume of the conductor and can be used as a core parameter to determine whether there is a potential overload risk in a local area. The larger the value of P(x,t), the more severe the temperature rise in that area, and the easier it is to trigger thermal runaway.

[0061] The anomaly detection module in this invention is used to dynamically monitor and analyze the local resistance changes of the electronic harness during fast charging. By fitting and comparing the predicted thermal-electric load function with the actual resistance behavior, it identifies and judges whether there are abrupt risk points in the harness caused by thermal overload, thereby providing a basis for subsequent current limiting control strategies.

[0062] The anomaly detection module first receives the local load prediction function model P(x,t) output from the load prediction modeling module, where x is the axial spatial position of the harness and t is a time variable. To improve positioning accuracy, the system divides the harness into several detection units Δx of equal length along the spatial axis. Each Δx represents a resistance monitoring segment, and its length depends on the harness wiring density and sensor sampling accuracy, preferably between 1 cm and 5 cm.

[0063] For each Δx segment, the system collects the real-time current I(x,t) and voltage U(x,t) at that location, and calculates the instantaneous equivalent resistance R(x,t) at that location at time t using Ohm's law. The calculation method is as follows: Where U(x,t) is the measured voltage across the segment, and I(x,t) is the current flowing through the segment, both being real-time sampled values. Simultaneously, a reference resistor model stored in the harness parameter database is invoked. This model represents the standard resistance value for each section under normal temperature and steady-state current conditions, typically obtained through experimental pre-setting or factory calibration. The difference between these two values ​​is calculated to obtain the resistance change ΔR(x,t), expressed as: ΔR(x,t) can reflect the dynamic change trend of the resistance during the charging process and is the basic data for judging whether a sudden change in thermal resistance has occurred.

[0064] To further determine whether the resistance change is caused by the predicted thermal-electrical overload trend, the system performs a correlation fitting between ΔR(x,t) and the local load prediction function model P(x,t) in the time and location dimensions. The resistance change identification function φ(x,t) is defined as follows: ; where φ(x,t) is the resistance change response coefficient under unit predicted load. The larger the value, the more drastic the actual resistance change corresponding to the current predicted load, indicating that there may be abnormal heating, abnormal contact or local aging in this section.

[0065] To achieve effective identification, a resistance change response threshold Rmax is set. This threshold is preset based on factors such as wire harness material type, service life, and ambient temperature, with an optimal resistance deviation rate ranging from 5% to 20%. When the calculated result of φ(x,t) corresponds to... When the value (i.e., the maximum ΔR(x,t)) exceeds Rmax, it will be determined that there is a potential overload anomaly in the section, and a local anomaly signal will be sent immediately.

[0066] The bypass control module in this invention is used to provide current-limiting protection for areas in the fast-charging electronic harness where local overload or abnormal resistance changes occur. It also achieves dynamic current shunting through a controllable bypass channel to prevent harness damage caused by heat accumulation. This module, in conjunction with the anomaly detection module, current-limiting execution unit, and electronic switch array, forms a closed-loop fast protection mechanism. The specific implementation steps are as follows:

[0067] The bypass control module receives local anomaly signals from the anomaly detection module in real time. These signals include two key pieces of information:

[0068] Position parameter x: represents the axial spatial coordinate of the wire harness where the resistance change occurs;

[0069] Change in resistance : That is, the change in resistance increment detected at this location compared to the steady-state resistance R0(x).

[0070] The module first compares ΔR t Whether the preset safety limit Rmax is exceeded. Rmax is the resistance change response threshold set by the system, which is generally set according to the conductor material characteristics and the maximum allowable temperature rise. The typical value range is the rated value. 10% to 25%. When When the module determines that there is a potential risk of thermal runaway, it initiates the bypass control process.

[0071] After locking onto the abnormal location x, the system controls the electronic switch array (such as MOSFETs or solid-state relays) deployed at both ends of the main conductor segment to conduct, connecting its corresponding bypass conductor path B(x). B(x) is a low-resistance metal conductor channel pre-connected in parallel with the main conductor, typically made of silver-plated copper or composite high-conductivity materials, with a resistance value much smaller than R(x) of the abnormal section of the main conductor. After the electronic switches are turned on, B(x) forms an electrically parallel branch, providing a path for subsequent current shunting.

[0072] The module dynamically controls the current distribution at this location based on the current carrying capacity of the main conductor, the overload level, and the conduction state of B(x). The current limiting strategy is as follows:

[0073] Let the total expected current at the abnormal location x of the dominant body be I_total(x), then the adjusted current distribution of the system satisfies: ,and Where 0 < α < 1, according to Adaptive setting with temperature rise rate.

[0074] By reducing I_main(x) through the current limiting unit, and simultaneously guiding the current of the proportional α into the bypass conductor path B(x), local load transfer is achieved, thereby mitigating the overheating of the main body and reducing the risk of failure.

[0075] In practical applications, the value of α can be based on the historical overload level, the predicted value of P(x,t), and ΔR. t The rate of change is dynamically adjusted; for example, α is set to 0.3~0.5 in the early stages of mutation. If it continues to rise, it will gradually increase to above 0.8 until it is completely transferred.

[0076] The central fast charging control module in this invention is used to adjust the fast charging power parameters (including the target voltage) for the next cycle based on the thermal load characteristics of the abnormal region after current limiting or shunting occurs in the electronic wiring harness. With current This module performs adaptive dynamic adjustments to avoid repeated overload risks and achieve comprehensive optimization control of safety and efficiency. It relies on system-level data acquisition, strategy library calls, and closed-loop communication mechanisms, and specifically includes the following steps:

[0077] The central fast-charging control module receives current-limiting execution event information from the bypass control module in real time, specifically including: current-limiting trigger position x: the axial coordinate of the corresponding harness when an abnormal response occurs; and the current-limiting occurrence time. : Refers to the time point when the flow control is first triggered; Flow control duration Δt: Represents the time period from the first flow control to bypass recovery at this location.

[0078] Within the time interval Δt, the system calls the historical local load prediction function model P(x,t) for the current-limiting area, and combines it with the actual current change data of the main conductor and bypass conductor during this time interval to calculate the load intensity characteristic value ψ(x) at that location. ψ(x) is defined as follows: ; where ΔI_bypass(x) represents the current change amplitude of the bypass conductor before and after the shunt. This characteristic value comprehensively reflects the degree of thermal-electrical load per unit time. The larger the value, the more intense the thermal shock that the wire harness experiences during this cycle. A safe load threshold ψ_max can be set as a control reference. The preferred value range is set empirically based on the material's heat capacity and current density, such as ψ_max being 10 ampere-seconds-watts / cm.

[0079] The module has a built-in control strategy library, including various voltage / current power limiting strategies to match different ψ(x) levels. By looking up a table or interpolating, a strategy suitable for the current ψ(x) is selected, and the target charging voltage for the next cycle is calculated. With current Correction value. An example of the adjustment mechanism is as follows: if ψ(x) ≥ 0.8·ψ_max, then set... , Where: I_prev and U_prev are the voltage and current values ​​of the previous cycle; β and γ are dynamic adjustment factors, generally ranging from 0.6 to 0.95, and are adaptively set according to the ratio between ψ(x) and ψ_max.

[0080] After the calculation is completed, the central fast charging control module transmits the corrected data via the communication bus (preferably using the vehicle-mounted CAN-FD bus or the PLC communication link defined by the fast charging interface). and The instructions are synchronized to: fast charging host controller (such as the charging pile power management unit); vehicle power control unit (such as VCU, BMS and other modules).

[0081] The optimization module in this invention is used to perform periodic adaptive optimization of the local load prediction function model P(x,t) to improve its prediction accuracy and system environmental adaptability under multi-cycle operation conditions. This module achieves continuous learning and model accuracy improvement of the thermal-electrical overload behavior of the same wiring harness structure in different service cycles through historical data analysis, trend modeling, and iterative adjustment of model weights. Specifically, it includes the following steps:

[0082] During each charging cycle, the following key data, fed back by the anomaly detection module and the central control module, are continuously recorded: Current limiting trigger frequency F(x): the number of times current limiting or bypass actions occur at a certain location x per unit time; Load intensity characteristic value ψ(x): calculated by the central control module within the current limiting cycle, in amperes per second per centimeter; Resistance change threshold. : Represents the resistance increment compared to the steady-state resistance R0(x), reflecting the degree of local thermal runaway.

[0083] The optimization module applies the K-means clustering algorithm and the sliding time window trend fitting algorithm to the dataset D(x,t) to extract high-risk regions that meet the following conditions: F(x) exceeds the set abnormal frequency threshold F_thresh; ψ(x) is in the neighborhood of the set load limit ψ_max for a long time. The fluctuation tolerance remains consistently higher than the baseline tolerance ΔR_tol. High-frequency anomaly areas selected based on the above conditions are defined as "historical overload sensitive areas." The system generates a corresponding historical risk weight function w(x) for each location x, with a value ranging from 0 to 1, representing the influence factor that should be amplified in the prediction model for that location. w(x) is calculated using the exponentially decaying average method: , where s1 is the weight preservation coefficient (e.g., 0.8) and f is the comprehensive normalization function.

[0084] The generated w(x) is used as a correction factor and introduced into the existing local load prediction function model P(x,t) to form a new weight-corrected prediction model. Its expression is as follows: This operation makes the model more sensitive to load growth trends in high-risk areas, thereby issuing early risk warnings and enhancing the model's dynamic response capability. In implementation, P(x,t) is still constructed from the original coefficient vector β(x), but P'(x,t) formed by multiplying by w(x) reflects the importance of local anomaly history in the output layer weights.

[0085] To further optimize the performance of the prediction model, after each prediction cycle, a loss function L is constructed based on the deviation between the actual load response and the prediction result, expressed as: Where R_actual(x,t) is the true local response intensity measured by voltage and current. Based on this loss function, the model parameter β(x) is updated using the backpropagation algorithm: Where η is the learning rate constant (e.g., 0.01~0.1). This represents the gradient of the objective function with respect to β(x). Through repeated iterations, the model gradually approximates the actual operating conditions, thus acquiring adaptive capabilities.

[0086] Example 2, please refer to Figure 2 As shown in this embodiment, an electronic wire harness is provided. The electronic wire harness, together with a sensing module, a load prediction modeling module, an anomaly detection module, a bypass control module, a central fast charging control module, and an optimization module, forms a signal and control linkage to realize dynamic overload protection and thermal risk diversion management in fast charging mode.

[0087] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A high-safety overload-resistant fast-charging electronic harness system, characterized in that: include: Sensing module: Acquires the real-time current change rate within t<10ms during the fast charging initialization phase. And detect the initial thermal response curve of the micro-region conductor. ; Load prediction modeling module: based on and The nonlinear coupling relationship between them is used to construct a local load prediction function model P(x,t) for the fast charging electronic harness, where x is the axial position variable of the harness; Anomaly detection module: Maps P(x,t) to the piecewise resistance model R(x) of the wire harness structure to determine the resistance abrupt change threshold of local segments. ; Bypass control module: for In areas exceeding the preset safety limit Rmax, current limiting is performed, and current is diverted through the bypass conductor path B(x); Central fast charging control module: Simultaneously records the location and time of the current-limiting area and continuous load characteristic values, and adjusts the charging voltage for the next cycle. With current Make dynamic adjustments; Optimization module: Based on the overload region change trend recorded during continuous charging cycles, the prediction weight coefficient of P(x,t) is corrected.

2. The high-safety overload-resistant fast-charging electronic harness system according to claim 1, characterized in that: The sensing module includes: sampling the instantaneous current signals of the main conductor and branch conductors during the fast charging initialization phase and converting them into a current change rate I'; and performing time synchronization processing between the I' signal and the temperature change signal output by a micro thermoelectric sensing element arranged on the conductor surface to obtain the initial thermal response curve of the conductor micro-region. .

3. The high-safety overload-resistant fast-charging electronic harness system according to claim 1, characterized in that: The load prediction modeling module includes: Received current change rate I' and initial thermal response curve The sample was then normalized to eliminate sampling biases caused by different harness structures and ambient temperatures. Based on normalization and A multivariate nonlinear regression algorithm was used to establish the thermo-electric coupling response relationship and extract the coefficient vector β(x) that characterizes the thermosensitive properties of different conductor positions. By combining the coefficient vector β(x) with the wire harness structural parameters, a local load prediction function model P(x,t) is constructed using the finite element discrete modeling method to simulate the local heat accumulation and current density change trend of the wire harness during fast charging.

4. The high-safety overload-resistant fast-charging electronic harness system according to claim 1, characterized in that: The anomaly detection module includes: The local load prediction function model P(x,t) is received, and based on the spatial discrete characteristics of the wire harness, the wire harness axis is divided into several detection unit intervals Δx of equal length; For each Δx interval, its instantaneous equivalent resistance is calculated using real-time current and voltage sampling data, and compared with the steady-state resistance in the reference model. By performing a difference comparison, the resistance change ΔR(x,t) is obtained; The correlation between ΔR(x,t) and the local load prediction function model P(x,t) is fitted to construct the resistance mutation identification function φ(x,t) to screen out abnormal segments caused by thermal-electric overload. When φ(x,t) corresponds to When the preset safety limit Rmax is exceeded, a local abnormal signal is sent to trigger the corresponding diversion or load limiting action.

5. A high-safety overload-resistant fast-charging electronic harness system according to claim 4, characterized in that: The bypass control module includes: It receives local anomaly signals from the anomaly detection module and locks the corresponding axial position x and its resistance mutation threshold. ; Based on position x, the control electronic switch array drives the bypass conductor path B(x) to conduct, where B(x) is a low-resistance conductor shunt channel pre-laid at the parallel position of the main wire harness; Adjust the current carrying capacity of the main body at position x, and dynamically guide part of the current to B(x) to reduce the thermal load at abnormal points.

6. The high-safety overload-resistant fast-charging electronic harness system according to claim 5, characterized in that: The central fast charging control module includes: Receive current limiting execution event information from the bypass control module in real time, and record the axial position (x) of the current limiting area and the occurrence time. and the duration of the current limiting Δt; Extract the local load prediction function model P(x,t) change curve of the region in time Δt, and combine it with the change of main branch current before and after the current diversion to generate the load intensity characteristic value ψ(x); Based on the historical trend and spatial distribution characteristics of ψ(x), the corresponding power limiting strategy in the control strategy library is invoked to dynamically adjust the target charging voltage for the next cycle. With current This ensures that it meets the local load-bearing capacity. The revised and The system synchronizes with the fast charging host or vehicle power control unit via a communication bus to complete closed-loop control.

7. A high-safety overload-resistant fast-charging electronic harness system according to claim 6, characterized in that: The optimization module includes: The current limiting trigger frequency, load intensity characteristic value ψ(x), and resistance change threshold at each location x during multiple charging cycles were continuously recorded. This forms a time-series dataset of the overload region; Cluster analysis and trend fitting were performed on the dataset to extract high-frequency overload segments and their change trajectories, and a historical risk weight function w(x) was constructed. The weighting function w(x) is introduced as a correction factor into the local load prediction function model P(x,t); After each round of prediction correction, the coefficient vector β(x) is updated and optimized through the error backpropagation mechanism.

8. An electronic wire harness for implementing the high-safety overload-resistant fast-charging electronic wire harness system according to any one of claims 1-7, characterized in that: The electronic wiring harness, sensing module, load prediction modeling module, anomaly detection module, bypass control module, central fast charging regulation module, and optimization module form a signal and control linkage to achieve dynamic overload protection and thermal risk diversion management in fast charging mode.

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