Electric vehicle cable insulation layer aging test method based on data analysis

Abstract

The invention relates to the field of cable insulation layer aging test, in particular to an electric vehicle cable insulation layer aging test method based on data analysis, which comprises the following steps: firstly, synchronously acquiring multi-dimensional data such as voltage, current, cable surface temperature and cooling system duty ratio based on a high-frequency sampling protocol; and then, the system constructs a virtual internal energy accumulation observation model by using the law of conservation of energy, and effectively eliminates a thermal hysteresis monitoring blind area caused by heat conduction of the insulating layer by calculating Joule thermal power input and an active heat dissipation correction term. Meanwhile, electric field auxiliary dissociation characteristics are constructed through the coupling voltage ratio and the heat accumulation degree, material activation energy is dynamically corrected through a power weight gating factor, and misinformation in the trickle charging stage is avoided. And finally, the virtual internal energy and the dynamic activation energy are mapped to the corrected Arrhenius equation, the insulation life loss is accurately calculated, and physical fidelity evaluation and real-time early warning of the insulation damage under the fast charge transient impact are realized.

Description

Technical Field

[0001] This invention relates to the field of cable insulation aging testing, and more specifically to a data analysis-based method for testing the aging of electric vehicle cable insulation. Background Technology

[0002] In high-power DC charging scenarios, the performance degradation of the insulation layer of the charging cable, which connects the charging pile and the electric vehicle and transmits high-voltage power, directly affects the safety and reliability of the charging process.

[0003] Currently, existing technical solutions for assessing the aging of cable insulation typically employ the Arrhenius equation, based on chemical kinetics, as the core algorithm. The specific implementation method generally involves collecting real-time temperature data using temperature sensors installed on the surface of the cable insulation. This data, combined with pre-defined activation energy parameters for the fixed material, is then substituted into the equation to calculate the reaction rate, thereby deriving the cumulative lifespan loss of the cable.

[0004] However, the above method has certain drawbacks in practical high-power fast charging applications. First, insulating materials are generally poor conductors of heat, exhibiting thermal hysteresis in physical conduction. When a large current is injected instantaneously, the temperature of the cable core rises sharply, which the surface sensors cannot detect in time, leading to severe distortion of the temperature parameters input into the algorithm. Second, the traditional Arrhenius model typically assumes that the activation energy is a static constant, failing to consider the destructive effect on the material's microstructure when a high-voltage electric field and a high-current thermal field coexist. It also cannot distinguish the stress differences between trickle charging and high-power throughput conditions, resulting in misjudgments or omissions in aging assessment results when facing transient shocks or complex operating conditions. Summary of the Invention

[0005] To address the issue of misjudgment or omission in the aforementioned aging assessment results, this invention proposes a data analysis-based method for testing the aging of electric vehicle cable insulation. The method includes: acquiring real-time operating parameters of the charging cable, including at least charging current, charging voltage, cable surface temperature, and cooling system duty cycle; iteratively updating the virtual internal energy accumulation based on the difference between Joule thermal power input and active heat dissipation reduction, where the Joule thermal power input is proportional to the square of the charging current, and the active heat dissipation reduction is proportional to the cooling system duty cycle; weighting the electric field-assisted dissociation characteristic value according to the ratio of charging power to a preset maximum power to obtain the dynamic reduction in activation energy; substituting the virtual internal energy accumulation and the dynamic reduction in activation energy into a modified Arrhenius model to calculate the instantaneous aging rate of the charging cable; and finding that the ratio of the electric field-assisted dissociation characteristic value to the charging voltage of the charging cable and the logarithm of the virtual internal energy accumulation are both positively correlated.

[0006] Unlike existing technologies that typically rely solely on cable surface temperature or static models to assess insulation aging, this invention iteratively updates the virtual internal energy by using the difference between Joule heating and active heat dissipation, which more realistically reflects the thermal hysteresis and heat accumulation effects inside the cable. At the same time, it dynamically corrects the activation energy in the Arrhenius model based on charging power and voltage, effectively solving the problem that traditional models have difficulty quantifying the impact of electrothermal coupling on the accelerated aging of the insulation layer, thereby achieving accurate real-time monitoring of cable aging rate under complex charging conditions.

[0007] Furthermore, the product of the square of the charging current and the preset reference resistor constitutes the Joule thermal power input.

[0008] By incorporating the product of the square of the charging current and the reference resistance into the calculation, and combining it with insulation heat capacity for normalization, the intensity of heat source input inside the cable can be accurately quantified from a physical perspective. Compared to simply monitoring the current amplitude, this calculation method can more accurately reflect the actual heat generation rate of cables of different materials and specifications under different current loads, providing a precise incremental basis for the iteration of virtual internal energy.

[0009] Furthermore, the active heat dissipation reduction is constituted by the product of the cooling system duty cycle, the preset heat dissipation coefficient, and the difference between the cable surface temperature and the ambient temperature.

[0010] By incorporating the duty cycle of the cooling system, ambient temperature difference, and heat dissipation coefficient into the calculation model, the active heat dissipation effect of liquid cooling or air cooling systems can be dynamically quantified. This overcomes the shortcomings of traditional methods that often ignore the effect of external ambient temperature changes or the operating status of the cooling system on delaying aging, ensuring that the model can accurately reflect the dissipation of internal energy when the cooling system is activated, and avoiding overestimation of the aging degree of cables.

[0011] Furthermore, the square of the ratio of the charging voltage to the rated voltage is multiplied by a logarithmic function term that includes the virtual internal energy accumulation to obtain the electric field-assisted dissociation characteristic value.

[0012] Furthermore, obtaining the dynamic reduction in activation energy also includes multiplying the electric field-assisted dissociation characteristic value by a preset material sensitivity coefficient.

[0013] By introducing a preset material sensitivity coefficient, the calculation model can be adapted to insulation materials with different chemical compositions. This enhances the versatility and flexibility of the testing method, ensuring that when testing electric vehicle cables of different models or materials, the activation energy correction amount that conforms to the material characteristics can be obtained by adjusting this coefficient, thereby improving the accuracy of aging estimation.

[0014] Furthermore, substituting the virtual internal energy accumulation and the dynamic reduction of activation energy into the modified Arrhenius model includes: replacing the temperature term in the standard Arrhenius model with an equivalent temperature term proportional to the virtual internal energy accumulation; and replacing the activation energy term in the standard Arrhenius model with the difference between the initial activation energy and the dynamic reduction of activation energy.

[0015] By replacing the temperature term in the standard model with virtual internal energy and replacing the fixed activation energy with dynamic activation energy, this improvement enables the Arrhenius model to move beyond lifetime prediction under constant temperature and pressure conditions and to respond in real time to power fluctuations and heat dissipation changes during charging, significantly improving the dynamic tracking of instantaneous aging rate calculation.

[0016] Furthermore, the instantaneous aging rate is integrated over time to obtain the cumulative lifespan loss of the charging cable.

[0017] Furthermore, it also includes: in response to the cumulative lifespan loss exceeding a preset threshold, generating and sending a derating charging request or maintenance alarm.

[0018] Furthermore, if the cable surface temperature is not detected within a preset time, the cable surface temperature is set to the sum of the current ambient temperature and a preset safety margin value.

[0019] Furthermore, obtaining the real-time operating parameters of the charging cable also includes: data acquisition at a sampling frequency of 10Hz to 100Hz.

[0020] The technical effects of this invention are as follows: This invention breaks through the limitations of traditional aging assessments that rely solely on surface temperature. It introduces a virtual internal energy accumulation to characterize the dynamic thermal balance between Joule heating and active heat dissipation, and adjusts the dynamic reduction in activation energy based on charging power weighting to reflect the impact of electrical stress. This mechanism enables accurate calculation and lifetime prediction of the instantaneous aging rate of cable insulation under complex charging conditions. Attached Figure Description

[0021] Figure 1 This is a schematic flowchart illustrating the data analysis-based aging test method for electric vehicle cable insulation in an embodiment of the present invention. Figure 2 This is a schematic diagram illustrating the cross-sectional structure of the liquid-cooled charging cable in an embodiment of the present invention; Figure 3 This is a schematic diagram illustrating the changes in charging voltage and charging current over time in an embodiment of the present invention. Figure 4 This is a schematic diagram illustrating the changes in cable surface temperature, ambient temperature, and cooling system duty cycle over time in an embodiment of the present invention. Figure 5 This is a schematic logic block diagram illustrating the heat transfer path and thermal hysteresis region of the charging cable in an embodiment of the present invention. Figure 6 This is a schematic thermodynamic simulation cloud diagram illustrating the temperature field distribution inside the cable cross-section in an embodiment of the present invention; Figure 7 These are schematic diagrams illustrating various three-dimensional surface plots showing the changes in cable temperature over time and radial position in embodiments of the present invention; Figure 8 This is a schematic diagram illustrating the curves showing the change of dynamic activation energy reduction and power gating factor over time in embodiments of the present invention. Detailed Implementation

[0022] 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, not all, of the embodiments of the present invention. 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.

[0023] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0024] Example of a data analysis-based test method for aging of electric vehicle cable insulation: In a specific embodiment of the present invention, the data analysis-based electric vehicle cable insulation aging test method is implemented in the main control unit of a high-power DC charging pile or the battery management system (BMS) of an electric vehicle. The main control unit exemplarily employs a 32-bit high-performance microprocessor based on an ARM Cortex-M7 core, with its clock frequency preferably set in the range of 200MHz to 400MHz to ensure millisecond-level data processing capabilities. The microprocessor is connected to multiple high-precision sensors and actuators via optocoupler isolation modules.

[0025] Specifically, the hardware architecture includes: a Hall current sensor installed on the busbar side of the charging cable to collect real-time charging current, with a response bandwidth of no less than 10kHz; an NTC thermistor array closely attached to the cable insulation layer to collect cable surface temperature; and a cooling system controller connected via CAN bus or PLC power line carrier communication to acquire real-time duty cycle data of the liquid cooling pump or fan. In addition, the system is equipped with 1GB of non-volatile memory to store historical aging data and calibration parameters.

[0026] like Figure 1 As shown, the data analysis-based aging test method for electric vehicle cable insulation of the present invention includes: S100: Establish a low-level communication link based on a high-frequency sampling protocol and synchronously collect multi-dimensional electrical physical quantities and environmental state variables to construct thermal field differential boundary conditions.

[0027] like Figure 2 As shown, the cross-sectional structure of the charging cable, from the inside out, includes a central copper conductor, an XLPE insulation layer covering the outside of the conductor, a coolant channel located outside the insulation layer, and an outermost outer sheath.

[0028] To capture transient current surges during fast charging, a low-level communication link is first established with the vehicle's BMS and vehicle controller. In this embodiment, the system is configured with a high-frequency synchronous acquisition strategy, with the sampling frequency preferably set to 10Hz. However, if computing power allows, this frequency can be adjusted within the range of 10Hz to 100Hz to adapt to different levels of transient capture requirements.

[0029] The processor reads multi-dimensional data in real time through the analog front-end (AFE), specifically including: high-voltage bus voltage. Real-time fast charging current Cable surface temperature and ambient temperature .

[0030] like Figure 3 As shown, this illustrates the changes in electrical parameters over a complete charging cycle. The continuously rising, sloping curve represents the charging voltage. This indicates that the battery terminal voltage increases with increasing charge; the curve that maintains a high plateau and then decays exponentially represents the charging current. This reflects the switching process from the constant current charging stage to the constant voltage charging stage. The system captures the transient increment of Joule heating based on this millisecond-level changing current data.

[0031] At the same time, the system introduces active cooling state variables, which read the real-time duty cycle of cooling system actuators, such as liquid pumps or fans, via the communication bus. This value ranges from 0.0 to 1.0. Furthermore, the system obtains the maximum allowed charging power for the current charging session during the handshake phase. This is stored as a baseline constant for subsequent normalization calculations.

[0032] like Figure 4As shown, the smooth curve with a large amplitude that increases significantly during charging represents the cable surface temperature; the curve that remains flat at the bottom represents the ambient temperature; and the curve region exhibiting a high-frequency pulse oscillation pattern represents the duty cycle of the cooling system. It can be seen that as the surface temperature increases, the duty cycle of the cooling system is intensively intervened through pulse width modulation (PWM) signals for active heat dissipation. This embodiment utilizes this duty cycle data to correct for the reduction in heat dissipation.

[0033] Specifically, to improve system robustness, if the processor does not receive a valid signal from the surface temperature sensor for three consecutive sampling periods, the system will automatically trigger a safety redundancy mechanism, which will default to... Set to the current ambient temperature Add a preset safety margin value, such as 20 degrees Celsius, and generate a fault log to prevent monitoring blind spots caused by sensor failure.

[0034] S200, based on the iterative calculation of the Joule thermal power input term and the active heat dissipation correction term, to construct a virtual internal energy accumulation observation model that can eliminate the thermal hysteresis blind zone.

[0035] In response to the thermal hysteresis phenomenon caused by the cable insulation layer being a poor conductor of heat, resulting in a hotter core and a lower surface temperature, this embodiment no longer relies solely on the hysteresis surface temperature reading, but instead constructs a virtual internal energy accumulation observer based on the law of conservation of energy.

[0036] like Figure 5 As shown, this heat transfer logic model clarifies the mechanism of thermal hysteresis. The heat flow path starts from the conductor, which acts as the heat source, passes sequentially through the insulating layer (acting as thermal resistance), and then through the cooling layer (acting as a heat dissipation stage), finally dissipating into the environment. The arrows in the figure indicate the direction of heat flow, with the insulating layer region marked as the thermal hysteresis region. This indicates that the heat generated by the conductor cannot be instantaneously conducted to the cooling layer or surface sensor, confirming the physical blind spots that exist when relying solely on surface temperature monitoring.

[0037] The system constructs an iterative calculation model based on the first law of thermodynamics in physics. This model is based on the premise that the heat accumulation inside the insulation layer is a dynamic balance between the Joule heat generated by the electric current and the heat removed by the cooling system. The processor executes the following discrete iterative formula to calculate the virtual internal energy accumulation at the current moment. : ; in This represents the virtual residual internal energy from the previous moment. The current at the current moment; The conductor reference resistance can be set to a value that is specified in this embodiment. However, it is possible to use different wire diameters. to Values ​​can be taken from between; The comprehensive heat dissipation power coefficient is expressed in units of ( ). This reflects the overall efficiency of the active cooling system in removing heat from the insulation layer; This represents the sampling time interval.

[0038] Simultaneously, the step response method was used to... Perform offline calibration. This includes: in a constant-temperature laboratory, first applying a constant high current to the cable until the temperature stabilizes; then cutting off the current and simultaneously turning on the cooling system to maximum power. Next, record the surface temperature. The decreasing curve is then observed; finally, the least squares method is used to fit the cooling term formula, and the extracted attenuation coefficient is... .

[0039] From the logic of the above formula, we can see that the first term... This represents the Joule thermal power input, which is proportional to the square of the current. When a sudden surge occurs, this value increases exponentially, enabling a millisecond-level response to high-current surges without waiting for heat to be conducted to the surface. The second term represents the cooling reduction, which is related to the cooling duty cycle. The correlation is positive, accurately reflecting the system's active heat dissipation capability. Through thermal field difference calculations, even the surface temperature... No significant changes yet, calculated It will also rise rapidly due to high current input, thus logically eliminating the thermal hysteresis caused by physical conduction.

[0040] like Figure 6 and Figure 7 As shown, where Figure 6 The calculated temperature field distribution cloud map of the cable cross-section further validates the above model. The bright area at the center of the cross-section represents the high-temperature region, mainly concentrated in the conductor; while the darker areas extending outwards represent gradually decreasing temperatures. The dashed ring marks the boundary between the conductor and the insulation layer. This figure visually demonstrates that under high-current conditions, a significant radial temperature gradient exists within the cable, meaning the core temperature is significantly higher than the surface temperature.

[0041] Figure 7 The three-dimensional surface plot illustrates the dynamic evolution of temperature over time and radial position. It can be seen that the surface height is highest at a radial position of 0 mm over time, indicating the highest temperature; while the surface height gradually decreases as the radial distance increases. This plot vividly illustrates the accumulation and conduction of heat in both time and space.

[0042] S300 constructs electric field-assisted dissociation characteristics by using coupling voltage ratio and thermal accumulation degree, and dynamically corrects the material activation energy barrier using power weight gating factor.

[0043] To quantify insulation damage under the dual extreme conditions of high voltage and high current, this embodiment reconstructs and corrects the stress characteristics and activation energy of the material at the physical level.

[0044] First, the processor calculates the electric field-assisted dissociation eigenvalues. The formula is based on the fact that the chain segmentation of polymer materials is not solely determined by voltage, but rather by a nonlinear process assisted by a thermal field. The calculation formula is as follows: ; in Rated voltage; The reference energy constant is preferred to be within the range of to In this embodiment, it is set to When there is only high voltage and no internal heating, the logarithmic term approaches zero, leading to... Maintain a low position; only when high voltage and high internal heat coexist will the combined effect of the square and logarithmic terms cause... It exhibits a rapid, non-linear growth.

[0045] Furthermore, to address the false aging issue in traditional models during the trickle charging stage, a dynamic activation energy correction mechanism is introduced. The processor calculates the dynamic reduction in activation energy based on the following formula. : ; in The material sensitivity coefficient is set to . That is, under full power and with a large electric field-assisted dissociation characteristic value, the activation energy barrier will decrease. In this embodiment, to ensure model stability, the range of values ​​for this coefficient is limited to [range missing]. to between; This is the power weighting gating factor. It introduces a power gating mechanism: when the system is in a high-power throughput state, i.e. near When the gating factor approaches 1, it significantly lowers the activation energy barrier, indicating accelerated aging; conversely, during the trickle-down phase, the gating factor approaches 0, making... Extremely small, thus avoiding false alarms.

[0046] like Figure 8As shown, this diagram illustrates the logical results of dynamic activation energy correction. The curve exhibiting drastic fluctuations due to noise superposition represents the dynamic reduction in activation energy, while the smoother curve decreasing towards the end represents the power gating factor. It can be observed that when the power gating factor is high, the reduction in activation energy shows dense peaks, indicating a significant decrease in the material's aging barrier. Conversely, as the power gating factor decreases with decreasing current, the reduction in activation energy approaches zero, demonstrating that this method can accurately identify high-stress states under high-power throughput conditions.

[0047] S400: Map the virtual internal energy and dynamic activation energy parameters to the modified Arrhenius equation to calculate the insulation lifetime loss with full-cycle physical fidelity.

[0048] Finally, the system substitutes the implicit physical variables calculated above into the modified chemical kinetic equation to complete the final mapping from electrical quantities to material lifetime loss.

[0049] The equivalent insulation temperature, incorporating virtual internal energy, is used instead of the traditional surface temperature, and the dynamic activation energy is used instead of the static constant to recreate the microscopic aging environment. The processor executes the following formula to calculate the instantaneous aging rate. : ; in Pre-exponential factors; The initial activation energy of the material; is the Boltzmann constant. This determines the energy threshold at which insulation aging occurs. The inherent frequency of the reaction is determined. In this embodiment, accelerated thermal aging tests are conducted according to GB / T 11026.1 (IEC60216) standard. Insulating material samples are selected, and the failure time of the insulating material is measured at three different high-temperature points (e.g., 135°C, 150°C, 165°C). Subsequently, the logarithm of the failure time is used to perform linear regression analysis with the reciprocal of the absolute temperature. In this linear model, the slope of the resulting straight line characterizes the sensitivity of the material to temperature changes, i.e., the initial activation energy of the material. The intercept of the straight line on the vertical axis represents the fundamental frequency of material aging, i.e., the pre-exponential factor. .

[0050] In this embodiment, the initial activation energy of the material is used for automotive-grade XLPE material. Set as In practical applications, this value is usually located at to Between; then the pre-exponential factor Set as Because this parameter is extremely sensitive to material formulation, its order of magnitude range can cover... to .

[0051] The preset insulation heat capacity (unit: This characterizes the energy required for the insulation layer of the charging cable to withstand a unit temperature rise. In this embodiment, it is obtained through pre-calculation based on the cable's geometry and material properties. The calculation formula is as follows: ;in The effective volume of the cable insulation layer per unit length ( The value is obtained by multiplying the area of ​​the insulation layer in the cable cross-section by the length of the monitoring section. For the density of insulating materials, such as XLPE materials, the value range is typically [value range missing]. ; Specific heat capacity of insulating materials (unit: ).

[0052] From the above formula, it can be seen that when When the power is increased due to a high-power impact, the denominator of the exponential term in the numerator decreases, meaning the potential barrier is lowered. Simultaneously, the temperature term in the denominator... The presence of these factors increases the level, and their combined effect leads to... The current surge is exponential. This proves that the high current surge in the first few minutes of a single overcharge can cause losses equivalent to hundreds of hours under normal operating conditions.

[0053] The processor then integrates the instantaneous aging rate over time to obtain the cumulative lifetime loss. : It should be noted that although this embodiment uses discrete summation for integration, in other embodiments where computing power allows, Simpson's integral method or Runge-Kutta method can also be used to achieve higher precision numerical integration. When the preset lifespan threshold is exceeded (e.g., 10% of the initial lifespan), the system will send a derating request or maintenance alarm to the BMS via the communication interface.

Claims

1. A method for testing the insulation layer of an electric vehicle cable based on data analysis, characterized in that, The method comprises: obtaining real-time working parameters of the charging cable, the real-time working parameters at least including a charging current, a charging voltage, a cable surface temperature and a cooling system duty cycle; iteratively updating a virtual internal energy accumulation based on a difference between a Joule heat power input and an active heat dissipation reduction amount, the Joule heat power input being proportional to a square value of the charging current, the active heat dissipation reduction amount being proportional to the cooling system duty cycle; weighting correcting an electric field assisted dissociation eigenvalue based on a ratio of a charging power to a preset maximum power to obtain a dynamic reduction amount of activation energy; and substituting the virtual internal energy accumulation and the dynamic reduction amount of activation energy into a corrected Arrhenius model to calculate an instantaneous aging rate of the charging cable; the ratio of the electric field assisted dissociation eigenvalue to the charging voltage of the charging cable and the logarithmic value of the virtual internal energy accumulation are positively correlated.

2. The method of claim 1, wherein, The product of the square value of the charging current and a preset reference resistance constitutes the Joule heat power input.

3. The method of claim 1, wherein, The product of the cooling system duty cycle, a preset heat dissipation coefficient and the difference between the cable surface temperature and the ambient temperature constitutes the active heat dissipation reduction amount.

4. The method of claim 1, wherein, The square value of the ratio of the charging voltage to the rated voltage is multiplied by a logarithmic function term containing the virtual internal energy accumulation to obtain the electric field assisted dissociation eigenvalue.

5. The method of claim 1, wherein, Obtaining the dynamic reduction amount of activation energy further comprises multiplying the electric field assisted dissociation eigenvalue by a preset material sensitivity coefficient.

6. The method of claim 1, wherein, Substituting the virtual internal energy accumulation and the dynamic reduction amount of activation energy into the corrected Arrhenius model comprises: replacing a temperature term in the standard Arrhenius model with an equivalent temperature term proportional to the virtual internal energy accumulation; and replacing an activation energy term in the standard Arrhenius model with a difference between an initial activation energy and the dynamic reduction amount of activation energy.

7. The method of claim 1, wherein, Time integrating the instantaneous aging rate to obtain a cumulative life consumption of the charging cable.

8. The method of claim 7, wherein the method further comprises: Further comprising: in response to the cumulative life consumption exceeding a preset threshold, generating and sending a derated charging request or a maintenance alarm.

9. The method for testing the insulation layer of the electric vehicle cable based on data analysis according to claim 1, wherein, When the cable surface temperature is not detected within a preset time, setting the cable surface temperature to a sum of the current ambient temperature and a preset safety margin value.

10. The method of claim 1, wherein, Obtaining real-time working parameters of the charging cable comprises: carrying out data acquisition at a sampling frequency of 10 Hz to 100 Hz.

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