An adaptive control method and system for energy storage high voltage connector

Abstract

The application provides a kind of energy storage high-voltage connector adaptive control method and system, it is related to energy storage system safety technical field.The method includes the following steps: according to the multi-source data of connector, extract the heat deviation characteristics and calculate the thermoelectric relationship deviation degree coefficient and identify the aging state grade of connector;According to the aging state grade identified, the current-temperature rise mapping relationship is corrected and the residual safety time is calculated;According to the thermal risk level where the residual safety time is located, the corresponding current control instruction is output and the thermoelectric relationship deviation degree coefficient is continuously updated and closed loop verification.The method of the application aims to solve the technical problems of thermoelectric relationship deviation caused by contact resistance aging, and then passive temperature rise control of connector and charging efficiency reduction, can realize the active perception and prediction of real-time thermal state of connector, eliminate the thermoelectric relationship deviation caused by implicit aging, so as to realize the dynamic early warning and accurate current control of connector temperature rise risk.

Description

Technical Field

[0001] This invention relates to the field of energy storage system safety technology, and more specifically, to an adaptive control method and system for high-voltage connectors in energy storage systems. Background Technology

[0002] In the operation of fast charging and energy storage systems for new energy vehicles, high-voltage connectors, as critical nodes for energy transmission, directly determine the system's operational safety and efficiency through the physical state of their contact interfaces. With prolonged service, wear and tear from insertion and removal, along with environmental oxidation, lead to the formation of a microscopic oxide layer at the contact interface, resulting in a gradual increase in contact resistance. Existing control strategies typically rely on the static current-temperature rise mapping relationship calibrated at the factory, which is based on the ideal resistance characteristics of a new connector. When a connector ages, its actual contact resistance deviates from the factory reference value, causing the actual heat generated by the connector under the same current to be significantly higher than the theoretically expected value.

[0003] Because the control system lacks real-time sensing capabilities for the latent aging state of the contact interface, it still relies on static mapping tables for temperature rise prediction and current adjustment, resulting in the system's inability to promptly identify deviations in heat generation power. This lagging control logic causes the system to maintain a high current output even when the connector temperature rises rapidly, only triggering passive current reduction protection when the temperature approaches the safety threshold. This passive adjustment method not only compresses the connector's safe operating boundary, keeping it under high thermal stress for extended periods, but also leads to decreased charging efficiency due to frequent over-temperature protection triggers, failing to achieve optimal current distribution while ensuring connector thermal safety. Therefore, how to eliminate thermoelectric relationship deviations caused by contact resistance aging through real-time monitoring and dynamic correction, and achieve proactive prediction and precise control of connector temperature rise risks, has become a critical issue that urgently needs to be addressed in the field of thermal management of high-voltage connectors for energy storage.

[0004] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0005] The purpose of this invention is to provide an adaptive control method and system for high-voltage energy storage connectors, which aims to solve the technical problem of passive temperature rise control and reduced charging efficiency caused by thermoelectric relationship deviation due to contact resistance aging. It can realize the active perception and prediction of the real-time thermal state of the connector, eliminate the thermoelectric relationship deviation caused by latent aging, and thus realize dynamic early warning and precise current control of connector temperature rise risk.

[0006] In a first aspect, the present invention provides an adaptive control method for an energy storage high-voltage connector, comprising the following steps: S1. Synchronously acquire multi-source data from the connector and align it with the time reference; S2. Based on the multi-source data and preset reference thermal parameters, perform theoretical thermal response deduction and calculate the theoretical temperature rise sequence that the connector should theoretically generate under the current current. S3. The actual collected temperature sequence is differentiated from the theoretical temperature rise sequence, and the heat deviation characteristics reflecting the change in contact resistance are extracted. S4. Calculate the thermoelectric relationship deviation coefficient based on the heat deviation characteristics, and identify the aging status level of the connector based on the thermoelectric relationship deviation coefficient; S5. Based on the identified aging state level, dynamically correct the preset current-temperature rise mapping relationship to adjust the upper limit of the sustainable current and the expected temperature rise slope at different temperature nodes. S6. Based on the corrected current-temperature rise mapping relationship, and combined with the remaining temperature rise space between the real-time temperature of the connector and the preset safety threshold, risk simulation is performed to calculate the remaining safe time expected to reach the dangerous state. S7. Output the corresponding current control command based on the thermal risk level of the remaining safety time; S8. Based on the actual temperature rise response feedback after each round of adjustment, the thermoelectric relationship deviation coefficient is continuously updated and closed-loop verified.

[0007] The adaptive control method for high-voltage energy storage connectors provided by this invention has the advantages of being able to sense the latent aging state of the connector contact interface in real time, eliminating the thermoelectric relationship deviation caused by the aging of contact resistance, realizing the proactive prediction and precise control of the connector temperature rise risk, thereby improving the system operating efficiency while ensuring the thermal safety of the connector.

[0008] Secondly, the present invention provides an adaptive control system for an energy storage high-voltage connector, comprising: The data acquisition unit is used to synchronously acquire multi-source data from the connector and perform time base alignment. The first calculation unit is used to perform theoretical thermal response deduction based on the multi-source data and preset reference thermal parameters, and calculate the theoretical temperature rise sequence that the connector should theoretically generate under the current current. The feature extraction unit is used to differentiate the actual collected temperature sequence from the theoretical temperature rise sequence and extract the heat deviation features that reflect the change in contact resistance. An aging identification unit is used to calculate a thermoelectric relationship deviation coefficient based on the heat deviation characteristics, and to identify the aging status level of the connector based on the thermoelectric relationship deviation coefficient. The mapping correction unit is used to dynamically correct the preset current-temperature rise mapping relationship based on the identified aging state level, so as to adjust the upper limit of the sustainable current and the expected temperature rise slope at different temperature nodes. The second calculation unit is used to perform risk extrapolation based on the corrected current-temperature rise mapping relationship and the remaining temperature rise space between the real-time temperature of the connector and the preset safety threshold, and to calculate the remaining safe time expected to reach the dangerous state. The control output unit is used to output a corresponding current control command based on the thermal risk level of the remaining safety time. The feedback unit is used to continuously update and perform closed-loop verification of the thermoelectric relationship deviation coefficient based on the actual temperature rise response feedback after each round of adjustment.

[0009] As can be seen from the above, the adaptive control method for high-voltage connectors in energy storage provided by this invention, by collecting multi-source data of the connector in real time and comparing the theoretical thermal response model with the actual temperature rise sequence, not only eliminates the interference of thermal inertia on the evaluation, but also achieves accurate perception of the connector's latent aging state by dynamically identifying contact resistance deviations. Based on this, through dynamic correction logic based on aging levels, the current-temperature rise mapping relationship can be adjusted in real time, overcoming the hysteresis adjustment defects caused by traditional fixed control parameters. This method transforms connector operation from "passive over-temperature protection" to "active thermal risk prediction and control," maximizing the current carrying capacity and charging efficiency of the energy storage system under aging conditions while ensuring the thermal safety of the high-voltage connector.

[0010] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing embodiments of the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings. Attached Figure Description

[0011] Figure 1 This is a flowchart of an adaptive control method for a high-voltage energy storage connector provided in an embodiment of the present invention.

[0012] Figure 2 This is a schematic diagram of a structure of an adaptive control system for a high-voltage energy storage connector provided in an embodiment of the present invention.

[0013] Label Explanation: 100. Data acquisition unit; 200. First calculation unit; 300. Feature extraction unit; 400. Aging identification unit; 500. Mapping correction unit; 600. Second calculation unit; 700. Control output unit; 800. Feedback unit. Detailed Implementation

[0014] 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, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0015] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this invention, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0016] In the fast charging scenario of new energy vehicles, the contact resistance of the high-voltage connector of energy storage increases due to oxidation caused by long-term use. However, the control system still uses the static current-temperature rise mapping relationship at the factory, which leads to the underestimation of heat generation power, lag in temperature rise regulation, and passive compression of safety margin, thus disrupting the balance between charging efficiency and temperature rise safety.

[0017] For example, when a vehicle is plugged into a fast charger and charging begins, after repeated plugging and unplugging or prolonged operation, the microscopic oxide layer at the interface between the connector pins and the socket gradually thickens, causing the baseline value of the contact resistance to increase. At this point, the actual resistance of the contact interface is greater than the factory-calibrated value, but the control logic still uses the original current-temperature rise mapping table. When the system outputs a predetermined current, due to the increased contact resistance, the actual heat generation is higher than the expected value in the mapping table, leading to a faster actual temperature rise. The temperature value read by the control logic corresponds to the temperature that should appear at a higher current according to the old mapping table. Because the system cannot detect the latent oxidation at the contact interface, it mistakenly believes that the current has not exceeded the expected heat generation, and thus continues to maintain or even attempts to increase the current until the temperature approaches the safety threshold before passively reducing the current. At this point, the controllable safety margin is occupied by the additional heat generation, and the system loses the space to actively balance efficiency and temperature rise.

[0018] If the above problems are not addressed, the control system will struggle to recognize the gradual aging of the contact interface, causing current regulation during charging to lag behind actual temperature rise changes. This leaves the connector under prolonged high thermal stress, eroding its safety boundaries. Simultaneously, frequent triggering of over-temperature current reduction protection results in the average charging current falling below a safe level, reducing charging efficiency. Furthermore, the system cannot adjust its strategy based on the connector's actual health condition, leading to decreased control accuracy.

[0019] For reference, see the appendix. Figure 1 This invention provides an adaptive control method for energy storage high-voltage connectors, comprising the following steps: S1. Synchronously acquire multi-source data of the connector and align it with the time reference; the multi-source data includes the connector's real-time current, real-time temperature, and ambient temperature; S2. Based on multi-source data and preset reference thermal parameters, perform theoretical thermal response deduction and calculate the theoretical temperature rise sequence that the connector should theoretically generate under the current; S3. The actual temperature sequence and the theoretical temperature rise sequence are processed to differentiate them, and the thermal deviation characteristics reflecting the change in contact resistance are extracted by stripping away the influence of thermal inertia. S4. Calculate the thermoelectric relationship deviation coefficient based on the heat deviation characteristics, and identify the aging status level of the connector based on the thermoelectric relationship deviation coefficient; S5. Based on the identified aging state level, dynamically correct the preset current-temperature rise mapping relationship to adjust the upper limit of the sustainable current and the expected temperature rise slope at different temperature nodes. S6. Based on the corrected current-temperature rise mapping relationship, and combined with the remaining temperature rise space between the real-time temperature of the connector and the preset safety threshold, risk extrapolation is performed to calculate the remaining safe time expected to reach the dangerous state; specifically, the remaining temperature rise space is divided by the expected temperature rise slope to obtain the remaining safe time. S7. Output the corresponding current control command based on the thermal risk level of the remaining safety time; S8. Based on the actual temperature rise response feedback after each round of adjustment, the thermoelectric relationship deviation coefficient is continuously updated and closed-loop verified.

[0020] For ease of understanding, the following explains some key terms in this embodiment: High-voltage connectors for energy storage: These are electrical connection devices used to connect the high-voltage circuits of energy storage systems. Their main function is to enable the transmission and disconnection of high-voltage electrical energy. In fast-charging scenarios for new energy vehicles, these connectors carry large currents, and the performance of their contact interfaces directly affects charging efficiency and safety.

[0021] Multi-source data: refers to various types of data collected from the energy storage high-voltage connector and its operating environment, including real-time current, real-time temperature, and ambient temperature. This data is the basis for assessing the connector's operating status and aging degree.

[0022] Time reference alignment: This refers to timestamping different types of collected data to ensure that all data points correspond precisely on the time axis. This is crucial for subsequent analysis of the causal relationship between current changes and temperature response.

[0023] Reference thermal parameters: These refer to the thermal characteristic parameters of the energy storage high-voltage connector obtained through calibration when it leaves the factory or is brand new, such as reference thermal resistance, reference thermal capacity, and reference contact resistance. These parameters are used to construct the theoretical thermal model of the connector, serving as a reference for assessing its aging condition.

[0024] Theoretical thermal response deduction: refers to the calculation of the theoretical temperature rise sequence that the connector should produce under ideal, non-aging conditions based on the connector's reference thermal parameters and real-time current data, using a thermodynamic model.

[0025] Theoretical temperature rise sequence: refers to a series of theoretical temperature values ​​that change over time, calculated based on the thermal model of the connector under non-aging conditions, during the theoretical thermal response deduction process.

[0026] Thermal deviation characteristic: This refers to the additional thermal information reflecting changes in contact resistance extracted after differentiating the actual temperature sequence from the theoretical temperature rise sequence by removing the influence of thermal inertia. This characteristic is a key indicator for identifying the degree of connector aging.

[0027] Thermoelectric Relationship Deviation Coefficient: This is a dimensionless coefficient calculated based on thermal deviation characteristics, used to quantify the degree to which the current thermoelectric characteristics of the connector deviate from the factory standard. This coefficient directly reflects the aging condition of the connector.

[0028] Aging condition level: refers to classifying the aging degree of connectors into different levels based on the thermoelectric relationship deviation coefficient, such as reference condition, slight deviation condition and significant deviation condition.

[0029] Current-temperature rise mapping relationship: This refers to a pre-defined lookup table or function that describes the correspondence between connector current and temperature rise. This relationship is used to guide the adjustment of charging current to balance charging efficiency and temperature rise safety.

[0030] Sustainable current limit: refers to the maximum current value at which the connector can safely and continuously operate under a specific temperature condition.

[0031] Expected temperature rise slope: refers to the rate at which the connector temperature changes over time under specific current and temperature conditions.

[0032] Remaining temperature rise margin: refers to the temperature difference between the real-time temperature of the connector and the preset safety threshold, indicating the temperature rise that the connector can still withstand before reaching a dangerous state.

[0033] Remaining safe time: This refers to the remaining time expected to reach an absolutely dangerous state under the current heating condition. This time is an important basis for risk assessment and current control.

[0034] Thermal risk level: This refers to classifying the current thermal risk of a connector into different levels based on the remaining safe time, such as no risk, low risk, medium risk, and high risk.

[0035] Current control command: refers to the command output by the system to adjust the charging current based on the identified thermal risk level, including current increase, current decrease or forced current decrease, etc.

[0036] Closed-loop verification: refers to the process by which the system continuously updates and verifies the deviation coefficient of the thermoelectric relationship based on the actual temperature rise response feedback after each round of adjustment, so as to ensure the long-term accuracy and adaptability of the control strategy.

[0037] This embodiment provides an adaptive control method for high-voltage energy storage connectors, aiming to address the problem that when the contact resistance of high-voltage energy storage connectors ages and drifts due to long-term use, the control system still uses the static current-temperature rise mapping relationship from the factory, leading to an underestimation of heat generation power, delayed temperature rise regulation, and passive compression of safety margin, thus affecting charging efficiency and temperature rise safety. This method constructs a closed-loop adaptive control mechanism to achieve real-time perception of the connector's aging state and dynamic evolution of the control strategy. The core idea is to transform the physical aging process of the connector into a quantifiable thermoelectric relationship deviation, thereby breaking the limitations of traditional static mapping tables.

[0038] In step S1, multi-source data from the connector is acquired synchronously and time-referenced. The multi-source data includes the connector's real-time current, real-time temperature, and ambient temperature. This step can be achieved in various ways. For example, the connector's temperature and ambient temperature at different currents can be manually recorded, and timestamps can be manually calibrated. However, this method is inefficient and prone to human error, making it difficult to meet real-time control requirements. Another approach is to synchronously activate a sensor network deployed inside and around the energy storage high-voltage connector. This sensor network includes a high-precision negative temperature coefficient thermistor sensor distributed at the crimping point of the male connector pins, a similar temperature sensor within the outer insulation layer of the female connector socket, an ambient temperature sensor, and a high-frequency Hall current sensor located in the charging pile's DC output circuit. To capture rapidly changing electrical characteristics and slowly changing physical thermal characteristics, the system sets differentiated sampling mechanisms for different types of sensors. The analog-to-digital converter continuously acquires the weak voltage signal from the Hall current sensor at a high-frequency sampling rate of 100 Hz to ensure complete recording of transient electrical characteristics such as current steps, ramps, and high-frequency fluctuations. Meanwhile, considering the large physical heat capacity of the connector housing and internal metal components, resulting in relatively slow temperature changes, the analog-to-digital converter (ADC) collects data from each temperature sensor at a sampling rate of 10 Hz. Due to strong electromagnetic interference under high voltage and high current conditions, the raw temperature sequence output by the ADC is often accompanied by high-frequency noise and random glitches. The system allocates a continuous circular buffer in memory, with a length set to include all sampling points from the past two seconds. Whenever a new temperature sample value enters, it overwrites the oldest data in the buffer. The system accumulates all valid data in the buffer and calculates the average value, using this average as the smoothed temperature value for the current moment. To eliminate the phase delay caused by the moving average, the system combines the historical temperature change rate and uses a first-order Taylor expansion formula to perform forward time compensation for the current smoothed temperature. More importantly, since current changes are controlled by the charging pile power module and have a response time in the millisecond range, while temperature changes are limited by heat conduction and have a response time in the second or minute range, there is a huge time scale difference between the two. If strict timestamp alignment is not performed, subsequent calculations of the temperature response to the current will be misaligned. Therefore, the system introduces a global high-precision hardware timer to assign a unified absolute timestamp to the current value, male terminal temperature, female terminal temperature, and ambient temperature collected in each frame. This multi-source data with timestamps is packaged and combined into a multi-dimensional state vector, stored in the system's high-speed static random access memory, forming a real-time operating state sequence with a perfectly aligned timeline. This high-precision, time-aligned data foundation is the basis for all subsequent state identification and thermoelectric relationship deviation feature extraction, ensuring that the control logic will not make misjudgments due to misalignment or distortion of the underlying input data.

[0039] In step S2, based on multi-source data and preset reference thermal parameters, a theoretical thermal response deduction is performed to calculate the theoretical temperature rise sequence that the connector should theoretically generate under the current current. One implementation is to obtain the average values ​​of thermal resistance, thermal capacitance, and contact resistance from the connector manufacturer's specifications as reference thermal parameters, and then manually input real-time current data, using a simplified thermal balance equation for calculation. However, this method ignores the nonlinear thermal characteristics of the connector material and the influence of ambient temperature, resulting in insufficient accuracy of the theoretical temperature rise sequence. Another implementation is that the system uses reference thermal time constant, reference thermal resistance, and reference thermal capacitance parameters, pre-calibrated at the factory and stored in non-volatile memory, to calculate the theoretically expected pure thermal inertia temperature rise curve under the current actual current step, assuming the connector contact resistance remains at its factory minimum value. In the specific calculation process, the theoretical temperature change rate at the current moment is equal to the difference between the theoretical heat generation power and the theoretical heat dissipation power, divided by the reference thermal capacitance. The theoretical heating power is equal to the square of the current real-time current multiplied by the factory reference contact resistance, and the theoretical heat dissipation power is equal to the difference between the current smoothed temperature and the ambient temperature, divided by the reference thermal resistance. The system uses the Runge-Kutta numerical integration method to derive the theoretical temperature sequence.

[0040] In step S3, the actual temperature sequence and the theoretical temperature rise sequence are differentiated, and the thermal deviation feature reflecting the change in contact resistance is extracted by removing the influence of thermal inertia. One implementation is to directly subtract the actual temperature sequence from the theoretical temperature rise sequence point by point to obtain the temperature difference, and use this as the thermal deviation feature. However, this method fails to effectively remove the influence of thermal inertia and may misjudge the temperature lag caused by the connector's own thermal capacity as a thermal deviation caused by changes in contact resistance. Another implementation is that the system calculates the first-order difference of the current sequence in real time. When the absolute value of the difference is greater than a set step threshold for three consecutive sampling periods, it is marked as the starting point of the current step, and a dynamic observation time window is initiated. Within this time window, the system subtracts the smoothed temperature sequence collected by the actual sensor from the theoretical temperature sequence point by point to obtain a net temperature rise deviation curve. Integrating this net temperature rise deviation curve over the entire dynamic observation time window yields the accumulated thermal deviation. This accumulated thermal deviation completely eliminates the delay effect caused by the normal physical thermal capacity of the connector, and purely and directly reflects the additional heat generation caused by the increased contact resistance due to long-term insertion and removal oxidation and fretting wear at the contact interface. Through this step, the system successfully prevents the temperature feedback lag from masking the expression of the true heat generation risk, and transforms the implicit physical deterioration of the interface into an explicit digital feature that can be directly quantified by subsequent algorithms.

[0041] In step S4, the thermoelectric relationship deviation coefficient is calculated based on the heat deviation characteristics, and the aging status level of the connector is identified based on the thermoelectric relationship deviation coefficient. One implementation is to simply compare the heat deviation characteristics with a preset threshold to directly determine whether the connector is aging. However, this method lacks precision, cannot quantify the degree of aging, and fails to consider the influence of ambient temperature on resistance. Another implementation is that the system divides the cumulative heat deviation obtained in the previous step by the length of the dynamic observation time window to calculate the average additional heat generation power. Adding this average additional heat generation power to the theoretical reference heat generation power restores the actual total heat generation power inside the connector. According to Joule's law, the actual total heat generation power equals the square of the effective current value multiplied by the current actual contact resistance. The system uses this to inversely calculate the actual contact resistance value of the current contact interface. Considering that changes in ambient temperature will have a linear effect on the basic resistivity of conductive materials such as copper alloy pins, the system must perform ambient temperature compensation to avoid misjudging the slight increase in resistance caused by high ambient temperature as contact interface aging. The system subtracts the standard reference temperature of 25 degrees Celsius from the current ambient temperature to obtain the temperature difference value. Multiplying the temperature difference by the inherent temperature coefficient of resistance of the conductive material and adding 1 yields the temperature compensation factor. The system then divides the calculated actual contact resistance by this temperature compensation factor to eliminate the interference of ambient temperature, obtaining the equivalent contact resistance purely due to oxide layer thickening and wear. Subsequently, the system subtracts the factory reference contact resistance from this equivalent contact resistance and divides it by the factory reference contact resistance to calculate a dimensionless thermoelectric relationship deviation coefficient. This coefficient directly quantifies the severity of the current thermoelectric relationship deviation from the factory reference. The system internally presets two key state judgment thresholds: a first deviation threshold and a second deviation threshold. The system compares the real-time calculated deviation coefficient with these two thresholds, thus entering different processing branches. When the deviation coefficient is less than the first deviation threshold, the system determines that the connector is in the reference state branch, indicating that the connector is relatively new, the micro-contact spots are intact, there is no significant oxidation, and the heat generation is entirely within design expectations. When the deviation coefficient is greater than or equal to the first deviation threshold and less than the second deviation threshold, the system determines that the connector has entered a slight deviation state branch. This indicates that after long-term fast charging cycles, the microstructure of the contact interface has begun to degrade, an oxide layer has initially formed, and the heat dissipation capacity has deteriorated but has not yet fully manifested. Traditional control is prone to misjudgment at this stage. When the deviation coefficient is greater than or equal to the second deviation threshold, the system determines that the connector has entered a significant deviation state branch. This indicates that a thick irreversible oxide film has formed on the contact interface, which is in a high-resistivity state and is highly susceptible to local thermal breakdown and over-temperature runaway. This step directly addresses the critical breakpoint in the state sensing stage of the background technology, which cannot identify the aging state, making the implicit physical aging process of the connector transparent and traceable in the digital space.

[0042] In step S5, the preset current-temperature rise mapping relationship is dynamically corrected based on the identified aging state level to adjust the upper limit of the sustainable current and the expected temperature rise slope at different temperature nodes. One implementation is to preset a fixed current-temperature rise mapping relationship for each aging state level, and directly switch to the corresponding mapping relationship when an aging state level is identified. However, this method lacks smoothness and may cause sudden current changes when switching aging states, affecting charging stability. Another implementation is that when the system is in the baseline state branch, the control logic maintains the original factory mapping table unchanged and does not modify the memory data, ensuring that the new connector can operate at full power and efficiency. When the system enters the slightly offset state branch, the system extracts the deviation coefficient as a linear correction factor. The system traverses each cell in the mapping table, multiplies the original expected temperature rise slope by a correction factor equal to 1 plus the deviation coefficient, thereby comprehensively increasing the expected temperature rise slope, allowing the control logic to predict in advance that the temperature will rise at a faster rate. Simultaneously, the system divides the original allowable sustaining time boundary within the cell by the correction factor and proportionally lowers the sustainable current limit corresponding to each temperature node, shrinking the safety boundary in advance to prevent excessive heat accumulation. When the system enters a branch with a significant offset state, the thick oxide layer under high current may trigger a nonlinear heat accumulation effect, and simple linear correction can no longer cover the actual risk. At this time, the system introduces an exponential nonlinear correction function. The system not only exponentially amplifies the expected temperature rise slope but also sets extremely high safety penalty weights in the high current range of the mapping table, significantly and nonlinearly lowering the sustainable current boundary to force the system to operate within a lower safe current range. To ensure that the correction process does not interfere with the ongoing real-time control task, the system adopts a double-buffered memory architecture. After completing the reconstruction, interpolation smoothing, and data verification of the entire two-dimensional mapping table in the background shadow memory area, the system uses an atomic-level pointer switching operation to allow the foreground control logic to seamlessly switch to the new mapping table in the next microsecond-level instruction cycle. This step completely disrupts the transmission chain of the old mapping's erroneous interpretation of temperature feedback, enabling the control system to regain a judgment basis that fully matches the current physical degradation state of the connector, fundamentally eliminating the hidden danger of underestimated heat generation power.

[0043] In step S6, based on the corrected current-temperature rise mapping relationship, and considering the remaining temperature rise space between the connector's real-time temperature and the preset safety threshold, a risk simulation is performed to calculate the remaining safe time before reaching a dangerous state. Specifically, the remaining temperature rise space is divided by the expected temperature rise slope to obtain the remaining safe time. One implementation is to judge the risk solely based on whether the real-time temperature exceeds the safety threshold and directly stop charging. However, this approach is too lagging and fails to provide proactive risk warnings, potentially leading to an over-compression of the safety margin. Another implementation is that the system performs a proactive risk simulation based on the corrected current-temperature rise mapping table that has just taken effect, combined with the smoothed real-time temperature and real-time current at the current moment. First, the system reads the set absolute safety temperature threshold from memory and subtracts the current real-time temperature from this threshold to obtain the remaining temperature rise space. Then, based on the current real-time temperature and real-time current, the system looks up the corresponding expected temperature rise slope in the corrected mapping table. Dividing the remaining temperature rise space by this expected temperature rise slope allows for an accurate calculation of the remaining safe time before reaching an absolutely dangerous state under the current heating condition.

[0044] In step S7, the corresponding current control command is output based on the thermal risk level of the remaining safety time. One implementation is to uniformly output a current reduction command regardless of the remaining safety time. However, this approach is too conservative and may unnecessarily sacrifice charging efficiency. Another implementation is to classify the current thermal risk into four levels based on the remaining safety time: a risk-free level when the remaining safety time is greater than 300 seconds; a low-risk level when it is between 120 and 300 seconds; a medium-risk level when it is between 60 and 120 seconds; and a high-risk level when it is less than 60 seconds. Next, the system calculates the target current adjustment boundary according to different risk levels. Under the risk-free or low-risk levels, it indicates that the current is fully sustainable and still has sufficient heat dissipation margin, and the system calculates the maximum allowable current increase. This increase is equal to the maximum current limit allowed by the corrected mapping table at the current temperature minus the current actual current. This positive margin is output to the battery management system, allowing it to further increase the charging power without overheating, thereby maximizing charging efficiency. At the medium-risk level, indicating that the temperature has not yet exceeded the limit but is rapidly approaching the corrected boundary, the system immediately activates a pre-limiting current strategy. The system calculates a target heat dissipation power that is exactly equal to the maximum heat dissipation power under the current environment, thus ensuring that the temperature does not continue to rise. Dividing this target heat dissipation power by the previously calculated actual contact resistance and then taking the square root yields the target safe current. Subtracting this target safe current from the current actual current gives the minimum current reduction that must be implemented. At the high-risk level, the system directly calculates a large forced current reduction command, even requiring the current to be halved. Finally, the control system encapsulates the calculated thermal risk level, the maximum allowable current increase, and the minimum current reduction into a standard Controller Area Network (CLAN) communication message. This message is assigned a high-priority identifier and sent to the system bus inside the charging pile via an isolated transceiver, ensuring that the power distribution module can respond immediately in the next control cycle after receiving the message. This step achieves effective regulation before the temperature truly approaches the threshold, perfectly balancing energy transfer efficiency and temperature rise safety.

[0045] In step S8, the thermoelectric relationship deviation coefficient is continuously updated and closed-loop verified based on the actual temperature rise response feedback after each adjustment. One implementation is to calculate the thermoelectric relationship deviation coefficient only once at system startup and not update it thereafter. However, this method cannot adapt to the continuous aging process of the connector, causing the control accuracy to decrease over time. Another implementation is that when the downstream power module adjusts the current output according to the target current of the system output, the system does not stop the adaptive process but automatically enters a new round of closed-loop observation. Under the new current platform, the system continuously collects the actual smoothed temperature of the connector and simultaneously uses the corrected mapping table and theoretical thermodynamic model in the background to deduce the theoretical expected temperature curve that should appear under the current new current. The system calculates the dynamic residual between the actual temperature sequence and the theoretical expected temperature sequence in real time. To avoid misjudgment caused by short-term disturbances, the system sets a sliding verification window of 30 seconds and calculates the root mean square error of the dynamic residual within this window. If the absolute value of the root mean square error (RMSE) remains consistently below the set convergence dead zone threshold, it indicates that the previous state identification, deviation calculation, and mapping table correction were highly accurate, and the actual heating perfectly matches the corrected expectations. In this case, the system stores the current deviation coefficient in non-volatile memory as an initial reference for the entire subsequent charging phase and even the next charging cycle, confirming that the key invariants have been restored. However, if the actual temperature is significantly higher than the expected temperature, and the RMSE exceeds the positive dead zone threshold, it indicates that the connector's aging and deterioration rate exceeds the previous static assessment, and the previous correction was insufficient. In this case, the system extracts the RMSE, multiplies it by a dynamically adjusted learning rate parameter, and adds it as a compensation increment to the current deviation coefficient. The learning rate parameter is dynamically and adaptively adjusted based on the error change rate; the faster the error changes, the larger the learning rate, to accelerate convergence. An increase in the deviation coefficient will immediately trigger the system to return to the previous steps for a deeper secondary downward correction of the mapping table. Conversely, if the actual temperature is significantly lower than the expected temperature, it indicates that the previous correction was too conservative, unnecessarily limiting the charging current and sacrificing charging efficiency. The system then subtracts an attenuation factor from the deviation coefficient, appropriately relaxing the sustainable current boundary and freeing up more charging space. This continuous closed-loop feedback and verification mechanism ensures that the control system does not become stuck in any single calculation that may have a deviation, but can continuously iterate and converge with the slight evolution of the connector's physical state, always anchoring the control logic at the optimal trade-off between efficiency and safety.

[0046] The following example will provide a more detailed explanation of the above technical solution: Imagine a new energy vehicle charging at a fast charging station. At the start of charging, the system first synchronously acquires multi-source data, including real-time current and temperature from the connector, as well as ambient temperature, and aligns them with a time reference. For example, a Hall effect current sensor collects current data at 100Hz, and a thermistor sensor collects temperature data at 10Hz. A global high-precision hardware timer timestamps all data, ensuring precise synchronization of current and temperature data on the timeline.

[0047] Next, based on this time-aligned multi-source data, the system performs theoretical thermal response simulations using preset reference thermal parameters. For example, using the connector's factory-calibrated reference thermal resistance, reference thermal capacitance, and reference contact resistance, combined with the current real-time current, the system calculates the theoretical temperature rise sequence that the connector should produce under non-aging conditions using the Runge-Kutta numerical integration method. This theoretical temperature rise sequence represents the connector's temperature response curve under ideal conditions.

[0048] Subsequently, the system performs differential processing on the actual collected temperature sequence and the theoretical temperature rise sequence, and extracts the thermal deviation characteristics reflecting changes in contact resistance by removing the influence of thermal inertia. For example, when the charging current undergoes a step change, the system opens a dynamic observation window. Within this window, the system subtracts the actual measured smoothed temperature sequence from the theoretical temperature rise sequence point by point to obtain the net temperature rise deviation curve. Integrating this curve yields the accumulated thermal deviation. This accumulated thermal deviation excludes the temperature hysteresis caused by the connector's own thermal capacity and purely reflects the additional heat generated due to the increase in contact resistance.

[0049] Based on the extracted heat deviation characteristics, the system calculates a thermoelectric relationship deviation coefficient and identifies the connector's aging condition level using this coefficient. For example, the system divides the cumulative heat deviation by the observation window duration to obtain the average additional heat generation power. Combined with the theoretical baseline heat generation power, the actual total heat generation power is calculated, and the actual contact resistance is then deduced. Through ambient temperature compensation, the equivalent contact resistance is obtained and normalized to the factory baseline contact resistance to obtain the thermoelectric relationship deviation coefficient. If this coefficient is less than a first deviation threshold, it is determined to be in the baseline state; if it is between the first and second deviation thresholds, it is determined to be in a slight deviation state; if it is greater than or equal to the second deviation threshold, it is determined to be in a significant deviation state.

[0050] Based on the identified aging condition level, the system dynamically corrects the preset current-temperature rise mapping relationship. For example, if the connector is in a slightly offset state, the system extracts the deviation coefficient as a linear correction factor and applies it to the current-temperature rise mapping table. Specifically, the preset expected temperature rise slope in the mapping table is multiplied by (1 + deviation coefficient), thereby increasing the expected temperature rise slope; simultaneously, the sustainable current limit is divided by (1 + deviation coefficient), thereby decreasing the sustainable current limit. If the connector is in a significantly offset state, the system introduces an exponential nonlinear correction function, significantly and nonlinearly decreasing the sustainable current boundary and exponentially increasing the expected temperature rise slope to address more severe aging risks. This dynamic correction ensures that the control logic matches the actual aging condition of the connector.

[0051] Based on the corrected current-temperature rise mapping, the system performs risk simulation by considering the remaining temperature rise space between the connector's real-time temperature and the preset safety threshold, calculating the remaining safe time to reach a dangerous state. For example, if the current real-time temperature is 45℃ and the preset safety threshold is 60℃, then the remaining temperature rise space is 15℃. According to the corrected mapping, the expected temperature rise slope at the current temperature and current is found to be 0.1℃ / second. Therefore, the expected remaining safe time to reach a dangerous state is 15℃ / 0.1℃ / second = 150 seconds.

[0052] Based on the thermal risk level of the remaining safe time, the system outputs corresponding current control commands. For example, if the remaining safe time is 150 seconds, the system classifies it as a low-risk level (between 120 and 300 seconds). In this case, the system calculates the maximum allowable current increase and outputs corresponding current control commands, allowing the battery management system to moderately increase the charging current within a safe range to maximize charging efficiency. If the remaining safe time is less than 60 seconds, the system classifies it as a high-risk level and generates a forced current reduction command, even requiring the current to be halved, to quickly reduce the connector temperature and avoid overheating risks.

[0053] Finally, based on the actual temperature rise response feedback after each adjustment, the system continuously updates and performs closed-loop verification of the thermoelectric relationship deviation coefficient. For example, under a new current platform, the system continuously collects the actual temperature and compares it with the theoretically expected temperature derived from the corrected mapping table, calculating the dynamic residual. If the root mean square error of the residual is consistently less than the convergence dead zone threshold, it indicates that the deviation coefficient is accurate and is fixed. If the actual temperature is significantly higher than the expected temperature, and the root mean square error exceeds the positive dead zone threshold, it indicates that the aging and deterioration rate exceeds expectations. The system extracts this error, multiplies it by the learning rate parameter, adds it to the deviation coefficient, and triggers a second downward adjustment of the mapping table. Conversely, if the actual temperature is lower than expected, the attenuation amount is subtracted from the deviation coefficient, and the current boundary is appropriately relaxed. This closed-loop feedback mechanism ensures that the control strategy can continuously iterate and converge with the small evolution of the connector's physical state, always anchoring the control logic at the optimal trade-off between efficiency and safety.

[0054] The above examples demonstrate how the adaptive control method for high-voltage energy storage connectors in this application effectively solves the problems of traditional control methods in connector aging scenarios through the close coordination of various technical features. Unlike existing technologies that rely on static current-temperature rise mapping relationships, this application acquires multi-source data in real time and aligns it with a time reference, laying the foundation for subsequent accurate analysis. Traditional methods, when experiencing current steps, often fail to distinguish between normal thermal inertia delay and additional heat generation caused by increased contact resistance. This often leads to the illusion that the temperature has not yet risen, mistaking it for low current heat generation, thus maintaining a high current and secretly consuming the safety margin. This application, by performing theoretical thermal response deduction based on reference thermal parameters and differentiating it from the actual temperature sequence, while simultaneously removing the influence of thermal inertia, can accurately extract the thermal deviation characteristics reflecting changes in contact resistance. This crucial step makes the implicit physical aging process of the connector transparent and traceable in the digital space, overcoming the deficiency of existing technologies in being unable to perceive the gradual aging of the contact interface.

[0055] Furthermore, this application calculates the thermoelectric relationship deviation coefficient based on heat deviation characteristics and identifies the aging state level of the connector accordingly. This allows the system to quantify the degree of aging, rather than simply making a "yes" or "no" judgment. In existing technologies, due to the lack of precise perception of the aging state, the control system can only passively begin to reduce the current when the temperature approaches the safety threshold. At this point, the controllable safety margin is occupied by additional heat generation, and the system loses the space to actively balance efficiency and temperature rise. This application achieves adaptive adjustment of the control strategy by dynamically correcting the current-temperature rise mapping relationship and adjusting the maintainable current upper limit and expected temperature rise slope according to the actual aging state of the connector. This forward-looking correction allows the control system to regain a judgment criterion that perfectly matches the current physical degradation state of the connector, fundamentally eliminating the hidden danger of underestimating the heat generation power.

[0056] Furthermore, this application, based on the modified mapping relationship and combined with the remaining temperature rise space, performs risk extrapolation, calculates the remaining safe time expected to reach a dangerous state, and outputs corresponding current control commands according to the thermal risk level. This makes the output of current control commands more forward-looking and refined, avoiding the efficiency loss caused by overly aggressive or lagging protection strategies in traditional methods. For example, at low risk levels, a moderate current increase is allowed to maximize charging efficiency, while at high risk levels, a forced current decrease is enforced to ensure safety. Finally, this application continuously updates and verifies the thermoelectric relationship deviation coefficient based on the actual temperature rise response feedback after each adjustment, forming a self-correcting closed-loop system. This ensures that the control strategy can be continuously optimized as the connector ages, maintaining control accuracy over long-term operation, solving the adjustment lag and safety boundary compression problems caused by traditional control methods when facing connector aging, and achieving the optimal trade-off between charging efficiency and temperature rise safety.

[0057] In some embodiments, the specific steps in step S2 include: S21. Obtain the reference thermal resistance, reference thermal capacity, and reference contact resistance of the connector; S22. Calculate the theoretical heat generation power of the connector based on the reference contact resistance and the real-time current; S23. Based on the reference thermal resistance, combined with the real-time temperature and ambient temperature, calculate the theoretical heat dissipation power of the connector; S24. Based on the theoretical heating power, theoretical heat dissipation power, and reference heat capacity, the theoretical temperature rise sequence of the connector under non-aging conditions is derived through a preset numerical integration algorithm.

[0058] The reference thermal resistance (RTR) refers to the temperature difference generated when a unit heat power passes through the heat conduction path of the high-voltage energy storage connector under stable thermal equilibrium. It quantifies the connector's ability to dissipate internal heat to the surrounding environment. This RRT can be obtained through detailed thermal simulation analysis during the connector design phase, or before the connector leaves the factory, by accurately measuring the difference between the steady-state temperature rise of the connector under a specific power input and the ambient temperature on a controlled thermal testing platform, and then deducing it in reverse by combining the input power. The reference thermal capacity refers to the heat absorbed or stored by the high-voltage energy storage connector when the temperature rises by one degree Celsius. It reflects the connector's response speed to temperature changes, i.e., its inherent thermal inertia. This reference thermal capacity can be calculated by weighted averaging the specific heat capacity and mass of the connector's main constituent materials (e.g., copper alloys, insulating plastics, etc.), or by experimentally measuring the dynamic curve of the connector's temperature change over time under a known heat power input, and extracting the thermal capacity parameters through curve fitting techniques. The reference contact resistance refers to the resistance value at the contact interface between the pins and the socket of a high-voltage energy storage connector in its brand-new or factory-shipped condition. This resistance is one of the main sources of Joule heat generated during the connector's power-on process. This reference contact resistance can be accurately measured using a high-precision four-wire resistance meter after the connector is manufactured, or it can be determined according to the typical value specified in the connector design specifications.

[0059] The step of calculating the theoretical heat generation power of a connector based on a reference contact resistance and real-time current aims to quantify the rate at which heat is generated inside the high-voltage energy storage connector due to current flowing through the contact interface under ideal, non-aging conditions. Specifically, the calculation of theoretical heat generation power is based on Joule's law, which states that heat generation power is proportional to the square of the current and also proportional to the resistance value. By squarening the real-time current value and multiplying it by the pre-obtained reference contact resistance, the theoretical heat generation power that the connector should generate under the current current, assuming no aging, can be obtained.

[0060] The step of calculating the theoretical heat dissipation power of a connector based on a reference thermal resistance, combined with real-time temperature and ambient temperature, aims to quantify the rate at which a high-voltage energy storage connector dissipates heat to its surroundings under ideal, non-aging conditions. The calculation of theoretical heat dissipation power is based on Newton's law of cooling or the principle of heat conduction, meaning that heat dissipation power is directly proportional to the temperature difference between the connector's real-time temperature and the ambient temperature, and inversely proportional to the thermal resistance. The theoretical heat dissipation power that the connector should have under the current temperature difference is obtained by subtracting the ambient temperature from the connector's real-time temperature and then dividing by the pre-obtained reference thermal resistance.

[0061] Based on the theoretical heat generation power, theoretical heat dissipation power, and reference heat capacity, a pre-defined numerical integration algorithm is used to deduce the theoretical temperature rise sequence of the connector under non-aging conditions. This aims to simulate the dynamic temperature response process of the high-voltage connector in an ideal non-aging state. Within each small time step, the net heat change of the connector equals the difference between the theoretical heat generation power and the theoretical heat dissipation power. Dividing this net heat change by the reference heat capacity yields the theoretical temperature change within that time step. By accumulating these theoretical temperature changes—that is, using a pre-defined numerical integration algorithm (e.g., Euler method, improved Euler method, or higher-order Runge-Kutta method)—the dynamic temperature change of the connector over time under non-aging conditions can be continuously deduced, thus forming a theoretical temperature rise sequence.

[0062] This solution introduces reference thermal parameters at the physical level of the connector to construct a temperature rise prediction model based on physical mechanisms, providing a benchmark reference for subsequent identification of aging conditions. By acquiring reference thermal resistance, reference thermal capacity, and reference contact resistance, the thermal characteristics of the connector are parameterized, so that theoretical deduction no longer relies on empirical formulas, but is modeled based on the physical properties of the connector itself. Specifically, the system first acquires the reference thermal resistance, reference thermal capacity, and reference contact resistance of the connector in its factory or brand-new state. These parameters represent the inherent thermal characteristics of the connector in an ideal, non-aging state. Second, based on the real-time acquired current data and reference contact resistance, the system uses Joule's law to calculate the theoretical heat generation power that the connector should generate under the current current, ensuring the physical basis for the heat generation power calculation. Then, combining the real-time temperature, ambient temperature, and reference thermal resistance, the system calculates the theoretical heat dissipation power of the connector dissipating heat to the environment based on Newton's law of cooling or the principle of heat conduction, accurately quantifying the thermal equilibrium state of the connector in the current environment. Finally, using the theoretical heat generation power, theoretical heat dissipation power, and reference heat capacity as inputs, a pre-set numerical integration algorithm is employed to simulate the dynamic process of heat accumulation and dissipation within the connector, thereby deriving the theoretical temperature rise sequence that the connector should exhibit under non-aging conditions. This theoretical temperature rise sequence derivation based on the connector's own physical parameters provides an accurate and reliable reference system for subsequent differential processing of the actual collected temperature sequence and the theoretical temperature rise sequence, as well as for removing the influence of thermal inertia and extracting the thermal deviation characteristics reflecting changes in contact resistance.

[0063] As a specific implementation method, before the high-voltage connector for energy storage is put into use, its manufacturer provides a detailed technical specification, which includes the connector's reference thermal resistance, reference thermal capacity, and reference contact resistance. For example, the reference thermal resistance may be calibrated as 0.5 K / W, the reference thermal capacity as 100 J / K, and the reference contact resistance as 50 microohms. These parameters are stored in the non-volatile memory of the control system. During system operation, when the real-time current is 200 amps, the control unit calculates the theoretical heating power P_heating = (200A) based on the reference contact resistance of 50 microohms. 2 *50µΩ = 2000 milliwatts. Simultaneously, if the real-time temperature is 40 degrees Celsius and the ambient temperature is 25 degrees Celsius, the control unit will calculate the theoretical heat dissipation power as P_heat dissipation = (40℃ - 25℃) / 0.5K / W = 30 watts based on the reference thermal resistance of 0.5K / W. Subsequently, the control unit will use these calculated theoretical heat dissipation power, theoretical heat generation power, and reference heat capacity of 100J / K to iteratively calculate the theoretical temperature change of the connector in each sampling period using a preset numerical integration algorithm, such as the fourth-order Runge-Kutta method. For example, within a certain time step Δt, the theoretical temperature change rate dT / dt = (P_heat generation - P_heat dissipation) / reference heat capacity. Through continuous integration calculations, the system can deduce a theoretical temperature rise sequence reflecting the temperature change of the connector over time under non-aging conditions. Specifically, in the calculation process, the theoretical temperature change rate at the current moment is equal to the difference between the theoretical heat dissipation power and the theoretical heat generation power, divided by the reference heat capacity. The theoretical heating power is equal to the square of the current real-time current multiplied by the factory reference contact resistance, and the theoretical heat dissipation power is equal to the difference between the current smoothed temperature and the ambient temperature, divided by the reference thermal resistance. The system uses the Runge-Kutta numerical integration method to derive the theoretical temperature sequence.

[0064] Through the above technical solution, this application effectively solves the problem of deviation between theoretical temperature rise sequence and actual heating process caused by dynamic drift of connector physical parameters in traditional methods. By introducing the connector's own reference thermal resistance, reference thermal capacity, and reference contact resistance, and calculating theoretical heating power and heat dissipation power based on physical mechanisms, and then combining numerical integration algorithms to deduce the theoretical temperature rise sequence, this solution can generate a highly accurate theoretical temperature rise curve that matches the actual physical characteristics of the connector under aging-free conditions. This provides a solid foundation for subsequent differential processing of the actual collected temperature sequence and the theoretical temperature rise sequence, removing the influence of thermal inertia, and extracting the thermal deviation characteristics reflecting changes in contact resistance. Compared with traditional methods that rely on general or static parameters, this solution significantly improves the accuracy of thermal deviation feature extraction, thereby enabling more accurate identification of the connector's aging state, avoiding temperature rise adjustment lag and passive compression of safety boundaries caused by inaccurate theoretical deductions, and ultimately achieving more precise and reliable adaptive control of energy storage high-voltage connectors.

[0065] In some embodiments, step S3, specifically the steps of extracting the thermal deviation characteristics reflecting changes in contact resistance, include: S31. Real-time monitoring of the rate of change of real-time current; S32. When the rate of change exceeds the preset step threshold, the preset observation window is opened, and the cumulative difference between the actual collected temperature sequence and the theoretical temperature rise sequence is continuously calculated within the observation window. S33. The cumulative difference is determined as the characteristic of heat deviation.

[0066] Specifically, real-time monitoring of the rate of change of real-time current aims to capture the dynamic behavior of current, particularly rapid rises or falls in current, which are key signals triggering subsequent thermal deviation analysis. This monitoring can be achieved by performing a first-order difference operation on continuously acquired real-time current data. For example, within each sampling period, the current value at the current moment is subtracted from the current value at the previous moment to obtain the instantaneous rate of change of current. Alternatively, digital filters can be used to process the current signal and extract its high-frequency components; the magnitude of these high-frequency components reflects the drastic nature of current changes.

[0067] When the rate of change exceeds a preset step threshold, a preset observation window is opened, and the cumulative difference between the actual collected temperature sequence and the theoretical temperature rise sequence is continuously calculated within the observation window. This step is crucial for triggering the extraction of thermal deviation features. When the current rate of change exceeds the preset threshold, it indicates a significant step or ramp change in the current. At this point, the connector's thermal response will be most pronounced, making it the optimal time to observe the additional heat generated by changes in contact resistance. The preset step threshold can be a fixed current rate of change value, for example, when the current rate of change is greater than 10A / ms for three consecutive sampling periods, it is considered to have reached the step threshold; or, the threshold can be dynamically adjusted, for example, set proportionally based on the absolute value of the current to adapt to step judgments under different current ranges. The opened observation window can be a fixed time period, for example, continuously calculating for 30 seconds after the current step occurs; or, the window can be dynamically adjusted, for example, the window length can be determined based on the connector's thermal time constant or the amplitude of the current step to ensure that the thermal response is fully captured. Within the observation window, the cumulative difference can be obtained by summing or integrating the difference between the actual collected temperature sequence and the theoretical temperature rise sequence at each sampling point; alternatively, a weighted average method can be used to accumulate the difference, assigning different weights to the differences at different time points within the observation window. For example, the difference weight is greater closer to the moment when the current step occurs.

[0068] Determining the cumulative difference as a thermal deviation characteristic is to quantify the difference between the actual and theoretical temperature rise of the connector. After removing the influence of thermal inertia, this difference directly reflects the additional heat generation caused by changes in contact resistance. This cumulative difference can be directly used as the original thermal deviation characteristic for subsequent calculations; alternatively, it can be normalized, for example, by dividing it by the duration of the observation window, to obtain the average thermal deviation rate, which can then be used as the thermal deviation characteristic.

[0069] This application's solution achieves precise control over the timing of thermal deviation feature extraction by introducing a current change rate monitoring mechanism. In the aforementioned adaptive control method for high-voltage energy storage connectors, multi-source data such as real-time current, real-time temperature, and ambient temperature of the connector are first acquired synchronously and aligned with a time reference. Then, based on preset reference thermal parameters, theoretical thermal response is extrapolated, calculating the theoretical temperature rise sequence that the connector should theoretically generate under the current. Furthermore, this solution effectively filters out noise interference during the current stability phase by monitoring the real-time current change rate. The observation window is triggered only when the current undergoes a significant step, ensuring that the extraction of thermal deviation features is performed under specific dynamic excitation, thus more clearly identifying thermal anomalies caused by changes in contact resistance. The observation window is opened after the current change rate exceeds a preset step threshold. This design utilizes the thermal response change caused by the current step, enabling the system to capture the temperature rise response differences of the connector under specific load excitation. By continuously calculating the cumulative difference between the actual acquired temperature sequence and the theoretical temperature rise sequence within the observation window, the instantaneous temperature rise deviation is converted into a cumulative amount, effectively smoothing out single-point measurement errors and making the extracted thermal deviation features more stable and representative. The cumulative difference was identified as a characteristic of thermal deviation, providing a reliable data basis for the subsequent accurate calculation of the deviation coefficient of thermoelectric relationship.

[0070] The following is a specific example to illustrate this. In the fast charging scenario of new energy vehicles, when the output current of the charging pile undergoes a step change, the system calculates the first-order difference of the current sequence in real time. When the absolute value of the difference exceeds the set step threshold for three consecutive sampling periods—for example, when the current instantly rises from 100A to 200A, and its rate of change exceeds the preset threshold at consecutive sampling points—the system marks this as the start of a current step and initiates a dynamic observation time window, for example, lasting 30 seconds. Within this observation window, the system subtracts the smoothed temperature sequence collected by the actual sensors (e.g., the temperature at the crimped end of the male connector pin and the temperature inside the outer insulation layer of the female connector socket) from the theoretical temperature rise sequence derived in step S2 point by point, obtaining a net temperature rise deviation curve. Subsequently, this net temperature rise deviation curve is integrated over the entire dynamic observation time window to obtain the accumulated heat deviation. This accumulated thermal deviation eliminates the delay effect caused by the normal physical thermal capacity of the connector, and purely and directly reflects the additional heat generation caused by the increased contact resistance due to long-term insertion and removal oxidation and fretting wear at the contact interface, and identifies it as the thermal deviation characteristic.

[0071] The above technical solution can effectively solve the technical problem of accurately quantifying the degree of contact resistance aging under complex current conditions, making the extracted heat deviation characteristics more stable and representative, thus providing a reliable data basis for the subsequent accurate calculation of the thermoelectric relationship deviation coefficient, thereby improving the accuracy and reliability of the adaptive control method for energy storage high voltage connectors.

[0072] In some embodiments, step S4, specifically calculating the thermoelectric relationship deviation coefficient based on the heat deviation characteristics, includes: S41. Calculate the additional heating power corresponding to the connector based on the heat deviation characteristics; S42. Calculate the actual total heat generation power of the connector based on the additional heat generation power and the connector's reference heat generation power; S43. Calculate the actual contact resistance of the connector based on the actual total heat generation power and real-time current; S44. Based on the ambient temperature, perform temperature compensation on the actual contact resistance to obtain the equivalent contact resistance of the connector; S45. Normalize the equivalent contact resistance with the reference contact resistance of the connector to obtain the thermoelectric relationship deviation coefficient.

[0073] This scheme achieves a quantitative assessment of connector aging by transforming the macroscopic thermal deviation characteristics extracted in the preceding step S3 into microscopic physical parameter indicators. Specifically, calculating the additional heat generation power of the connector aims to quantify the extra heat generated beyond normal expectations due to connector aging (such as increased contact resistance). The thermal deviation characteristic is the accumulated heat obtained in the preceding step S3 after removing the influence of thermal inertia; it directly reflects the difference between the actual heat generation and the theoretically expected heat generation of the connector. Dividing the accumulated thermal deviation by the duration of the observation time window yields the average additional heat generation power; alternatively, by filtering and differentiating the thermal deviation characteristic and combining it with the average current within the time window, the instantaneous additional heat generation power can be estimated. Obtaining the actual total heat generation power of the connector aims to obtain the true total heat generation power of the connector under the current operating conditions. The reference heat generation power refers to the theoretical heat generation power generated by the current real-time current when the connector is in a brand-new state (i.e., the factory reference contact resistance). Adding the additional heat generation power to the reference heat generation power yields the actual total heat generation power of the connector under the current aging state and real-time current, providing accurate input for subsequent inversion of the actual contact resistance. The reference heating power can be calculated by multiplying the square of the real-time current by the reference contact resistance calibrated at the factory, and then directly adding this reference heating power to the additional heating power; alternatively, an energy balance model incorporating both the reference heating power and the additional heating power can be established, and the actual total heating power can be solved using iterative or optimization algorithms. Calculating the actual contact resistance of the connector utilizes Joule's law, transforming macroscopic electrical power and current data into microscopic physical parameters, namely the actual contact resistance. This is a crucial step in inferring the electrical essence from thermal phenomena. By dividing the actual total heating power by the square of the real-time current, the current true contact resistance value of the connector can be directly derived, overcoming the limitations of traditional methods that cannot directly measure or perceive changes in internal contact resistance; alternatively, after considering the internal structure and material properties of the connector, a more complex electrothermal coupling model can be established, and the actual contact resistance can be solved using numerical methods. Temperature compensation for the actual contact resistance aims to eliminate the influence of ambient temperature changes on the contact resistance measurement results, ensuring the accuracy and consistency of the evaluation results. The resistivity of conductive materials changes with temperature; therefore, even if the contact interface has not aged, an increase in ambient temperature may lead to a slight increase in the actual contact resistance. Temperature compensation can calibrate the actual contact resistance to the equivalent value at a certain standard reference temperature, thereby more accurately reflecting the resistance changes caused by aging (such as oxide layer thickening and wear).Based on the temperature coefficient of resistance of the conductive material, the actual contact resistance is compensated to its equivalent value at a standard reference temperature (e.g., 25 degrees Celsius). Specifically, a temperature compensation factor can be calculated, based on the difference between the ambient temperature and the standard reference temperature, as well as the material's temperature coefficient of resistance. The actual contact resistance is then divided by this compensation factor. Alternatively, a lookup table or fitting curve between ambient temperature and contact resistance can be pre-established, allowing for direct lookup or curve-based compensation at different ambient temperatures. The equivalent contact resistance is then normalized to the connector's reference contact resistance to obtain a thermoelectric deviation coefficient. This step compares the temperature-compensated equivalent contact resistance with the connector's original factory reference contact resistance and normalizes it, resulting in a dimensionless, intuitive indicator reflecting the connector's aging degree—the thermoelectric deviation coefficient. This coefficient quantifies the connector's degradation relative to its new state, providing precise quantitative input for subsequent adaptive control, enabling the system to adjust based on the connector's actual physical aging state. The relative deviation can be obtained by subtracting the reference contact resistance from the equivalent contact resistance and then dividing by the reference contact resistance; alternatively, the ratio of the equivalent contact resistance to the reference contact resistance can be transformed using a logarithmic or exponential function to provide a more sensitive or smoother deviation coefficient at different aging stages.

[0074] This solution further refines the multi-source data, theoretical thermal response deduction, and thermal deviation characteristics provided in the preceding steps S1, S2, and S3 into a precise quantification of the physical aging state of the connector. It not only solves the problem of directly quantifying the degree of degradation of internal physical parameters based solely on thermal deviation characteristics, but also ensures the accuracy and precision of the aging assessment by eliminating the influence of ambient temperature. This transformation from macroscopic thermal phenomena to microscopic physical parameters enables the system to perceive the true aging state of the connector in real time and accurately, thus providing a solid data foundation for the dynamic correction of the current-temperature rise mapping relationship in the subsequent step S5. It effectively solves the control lag problem caused by physical parameter drift and significantly improves the intelligence level and safety of the adaptive control method for energy storage high-voltage connectors.

[0075] As a specific implementation method, the thermoelectric relationship deviation coefficient can be calculated based on the heat deviation characteristics as follows: The system receives the cumulative heat deviation output from the preceding step S3. Assume that the cumulative heat deviation is ΔQ joules within a dynamic observation time window, and the duration of this time window is Δt seconds. Then, the additional heat generation power P_extra corresponding to the connector can be easily calculated using the formula P_extra = ΔQ / Δt, in watts. Next, the system needs to calculate the actual total heat generation power P_total of the connector. Assume that the reference contact resistance R_base calibrated at the time of manufacture is 0.1 milliohms, and the current real-time current I_real is 200 amperes. Then, the reference heat generation power P_base of the connector can be calculated using Joule's law as P_base = I_real. 2 *R_base=(200A) 2 *(0.1*10 -3 Ω) = 4 watts. Adding P_extra to P_base gives the actual total heat dissipation power P_total = P_extra + P_base. Next, based on the actual total heat dissipation power P_total and the real-time current I_real, calculate the actual contact resistance R_actual of the connector. According to Joule's law, R_actual = P_total / I_real 2 For example, if P_total is 5 watts and I_real is 200 amperes, then R_actual = 5W / (200A). 2=0.000125 ohms, or 0.125 milliohms. Then, temperature compensation is applied to the actual contact resistance R_actual. Assume the current ambient temperature T_ambient is 45 degrees Celsius, the standard reference temperature T_ref is 25 degrees Celsius, and the temperature coefficient of resistance α of the connector's conductive material (such as copper alloy) is 0.00393 / ℃. First, calculate the temperature difference ΔT = T_ambient - T_ref = 45 - 25 = 20 degrees Celsius. Then calculate the temperature compensation factor F_temp = 1 + α * ΔT = 1 + 0.00393 * 20 = 1.0786. Finally, divide the actual contact resistance R_actual by this compensation factor to obtain the equivalent contact resistance R_equivalent = R_actual / F_temp = 0.125 mΩ / 1.0786 ≈ 0.1159 mΩ. Finally, the equivalent contact resistance R_equivalent and the reference contact resistance R_base are normalized to obtain the thermoelectric deviation coefficient K_deviation. K_deviation = (R_equivalent - R_base) / R_base. For example, K_deviation = (0.1159mΩ - 0.1mΩ) / 0.1mΩ = 0.159. This dimensionless coefficient intuitively represents the degree of deviation of the connector contact resistance from its factory condition.

[0076] Through the above technical solution, this application can transform the macroscopic thermal deviation characteristics extracted in the preceding steps into microscopic, quantifiable physical parameter indicators, namely, the thermoelectric relationship deviation coefficient. Specifically, by calculating the additional heating power, the unexpected heating portion caused by contact resistance degradation can be effectively isolated, thereby establishing a direct link between thermal effects and resistance changes. Based on this, the actual total heating power is derived by combining the reference heating power, providing an accurate power reference for subsequent calculation of the actual contact resistance. By calculating the correlation between the actual total heating power and the real-time current, the current actual contact resistance value of the connector can be inverted in real time, overcoming the shortcomings of traditional methods that cannot detect internal resistance changes. To eliminate the interference of ambient temperature on resistance measurement, a temperature compensation mechanism is introduced to convert the actual contact resistance into an equivalent contact resistance, ensuring the consistency of resistance evaluation under different operating conditions. Finally, by normalizing the equivalent contact resistance and the reference contact resistance, a thermoelectric relationship deviation coefficient is generated. This coefficient can intuitively reflect the degree of degradation of the connector relative to its factory condition, providing accurate quantitative input for subsequent adaptive control. Therefore, this solution effectively addresses the problem of directly quantifying the degradation degree of connector internal physical parameters based solely on thermal deviation characteristics. It avoids misinterpreting changes in heat dissipation caused by environmental factors as increased contact resistance, thus significantly improving the accuracy and precision of connector aging status assessment. This allows subsequent current-temperature rise mapping corrections to more accurately match the connector's true physical state, fundamentally eliminating the risk of underestimated heat generation power and ensuring an optimal trade-off between charging efficiency and temperature rise safety.

[0077] In some embodiments, the specific steps in step S5 include: S51. Determine the correction factor based on the aging status level; S52. Multiply the preset expected temperature rise slope in the current-temperature rise mapping relationship by the correction factor to obtain the corrected expected temperature rise slope. S53. Divide the preset sustainable current upper limit in the current-temperature rise mapping relationship by the correction factor to obtain the corrected sustainable current upper limit.

[0078] The aging status level is a qualitative or semi-quantitative indicator characterizing the current degree of aging of the connector, identified by the system based on the thermoelectric deviation coefficient of the connector. It can be divided into multiple discrete levels, such as no aging, mild aging, moderate aging, and severe aging, or represented by a continuous numerical range. This level serves as the basis for all subsequent parameter adjustments, ensuring that the control system can respond differently based on the actual physical degradation state of the connector. The correction factor is a dimensionless value that directly reflects the quantification of the impact of connector aging on thermoelectric performance. This factor can be determined based on the aging status level using a pre-defined functional relationship or lookup table. For example, for mild aging, the correction factor can be a linear value slightly greater than 1, such as 1.1 or 1.2; for severe aging, the correction factor can be a larger non-linear value, such as 2.0 or 3.0, to reflect the exponential impact of aging on performance. The determination of the correction factor aims to transform the abstract aging level into a concrete, quantifiable weight that can be used to adjust control parameters. The current-temperature rise mapping relationship is a pre-established model or dataset used by the system to describe the expected temperature rise behavior of the connector under different currents. This can be represented as a two-dimensional lookup table stored in memory, containing parameters such as the expected temperature rise slope and the upper limit of the sustainable current under different current and temperature conditions; or as a mathematical model that calculates the expected temperature rise based on the input current and ambient temperature. This mapping relationship is the basis for the control system to predict temperature rise and regulate current. The expected temperature rise slope is a key parameter in the current-temperature rise mapping relationship, representing the rate at which the connector temperature changes over time under specific current conditions. This slope is typically expressed in degrees Celsius per ampere squared per second (°C / (A)). 2 The current is expressed in units such as s (s) or similar. By adjusting this slope, the system can dynamically change its expectation of the connector's temperature rise rate, thereby triggering current regulation earlier or later to adapt to the connector's actual thermal response characteristics. The sustainable current limit is another key parameter in the current-temperature rise mapping relationship; it represents the maximum current the connector can safely and continuously carry under specific temperature conditions. This limit is a hard constraint that the system must adhere to when performing current control, designed to prevent the connector from overheating due to overcurrent. By dynamically adjusting this limit, the system can proactively narrow or widen the safe current range based on the connector's aging level to balance charging efficiency and operational safety.

[0079] This application's solution introduces a correction factor to construct a quantitative bridge from connector aging status level to control parameter adjustment, achieving precise compensation for changes in connector thermal characteristics. After acquiring multi-source data such as real-time current, real-time temperature, and ambient temperature of the connector, and deriving a theoretical temperature rise sequence based on this data, the system differentiates it from the actual acquired temperature sequence, removing the influence of thermal inertia and extracting the thermal deviation characteristics reflecting changes in contact resistance. The system can then calculate the thermoelectric relationship deviation coefficient and identify the connector's aging status level accordingly. Based on this, the solution first determines a correction factor according to the identified aging status level. This correction factor transforms the qualitative degree of aging into a quantitative adjustment weight, providing a unified calculation benchmark for subsequent parameter adjustments. Subsequently, the system multiplies the preset expected temperature rise slope in the current-temperature rise mapping relationship by this correction factor. Since connector aging typically leads to increased heating and a faster actual temperature rise rate, the correction factor is usually greater than 1, thus comprehensively increasing the expected temperature rise slope. This allows the control logic to anticipate a faster temperature rise, enabling it to extrapolate the remaining safety time based on a temperature rise slope more aligned with the current connector's physical characteristics, avoiding assessment biases caused by using the factory-standard slope. Simultaneously, the system divides the preset sustainable current limit in the current-temperature rise mapping by this correction factor. This reverse adjustment mechanism proactively lowers the current limit when connector aging leads to increased heating, thus suppressing the risk of further connector overheating at its source. This bidirectional dynamic adjustment of the mapping relationship through multiplication and division of the correction factor ensures that the control system automatically shrinks the safety boundary and slows the expected temperature rise as connector aging progresses, achieving adaptive management of the charging current while ensuring connector operational safety. In this way, this solution effectively solves the problems of underestimated heat generation power, delayed temperature rise adjustment, and passive compression of the safety boundary caused by reliance on static mapping tables in traditional control strategies. It allows the control system to perfectly match the actual physical degradation state of the connector, completing effective adjustment before the temperature truly approaches the threshold, perfectly balancing energy transfer efficiency and temperature rise safety.

[0080] The following is a concrete example. Suppose that after long-term use, the system analyzes the thermal deviation characteristics of the energy storage high-voltage connector, calculates a thermoelectric relationship deviation coefficient of 0.2, and identifies the connector as being in a "slight deviation state level." At this point, the system can calculate a correction factor of 1.2 based on a preset function, for example, correction factor = 1 + deviation coefficient. As a specific implementation, the current-temperature rise mapping relationship can be stored in a two-dimensional lookup table in the non-volatile memory of a microcontroller (e.g., an STM32 series microcontroller). The row index of this lookup table can represent different real-time temperature nodes, the column index can represent different current values, and each cell stores the expected temperature rise slope and the upper limit of the sustainable current at that temperature and current. When the correction factor is determined to be 1.2, the system will traverse the lookup table. For example, for a certain temperature node and current value, if its original expected temperature rise slope is 0.5°C / A... 2 If the slope is ·s, then the corrected expected temperature rise slope will become 0.5 * 1.2 = 0.6°C / A. 2 Similarly, if the original sustainable current limit for this cell is 200A, the corrected sustainable current limit will become 200 / 1.2≈166.7A. To ensure that the correction process does not interfere with the ongoing real-time control task, the system can adopt a double-buffered memory architecture. After the reconstruction, interpolation smoothing, and data verification of the entire two-dimensional mapping table are completed in the background shadow memory area, the system uses an atomic pointer switching operation to allow the foreground control logic to seamlessly switch to the new mapping table in the next microsecond instruction cycle. In this way, the system can dynamically adjust its expected thermal behavior and current limit according to the actual aging degree of the connector, thereby achieving fine-grained adaptive control.

[0081] Through the above technical solution, this application effectively solves the problem of lacking clear execution logic when converting abstract aging levels into specific control parameter adjustments in traditional methods. This solution introduces a correction factor to directly quantify the identified aging state level into adjustments to the expected temperature rise slope and the upper limit of the sustainable current in the current-temperature rise mapping relationship. This quantitative adjustment mechanism enables the system to dynamically and accurately adjust the expected thermal behavior and current carrying capacity of the connector based on its actual aging degree, thereby achieving a refined dynamic response to different degrees of aging. Furthermore, combined with the identification of the connector aging state level in the basic solution, the dynamic correction mechanism of this solution ensures that subsequent risk extrapolation and current control command generation are based on a mapping relationship that highly matches the current physical state of the connector. This fundamentally eliminates the risk of lag in temperature rise regulation and passive compression of safety boundaries caused by underestimated heat generation power, enabling the control system to maximize charging efficiency while ensuring connector operational safety, avoiding frequent triggering of over-temperature current reduction protection in traditional methods, and thus significantly improving charging performance and safety in fast charging scenarios for new energy vehicles.

[0082] In some embodiments, the specific steps in step S51 include: S511. Calculate the correction factor using a preset function based on the thermoelectric relationship deviation coefficient; the preset function maintains continuity at the threshold of the aging state level to ensure that the correction factor can transition smoothly, thereby determining the correction factor based on subtle changes in the aging state level.

[0083] This solution addresses the technical problem of insufficient matching between correction factors and actual aging conditions when there are continuous or non-linear transitions between connector aging status levels. Existing methods often use discrete correction factor mappings, which fail to accurately reflect subtle changes in aging degree, leading to inadequate alignment between correction factors and actual aging conditions. Existing methods may simply divide aging conditions into several discrete levels and assign a fixed correction factor to each level. When the connector aging degree is between two levels or at a critical point, this discrete mapping results in a mismatch between the correction factor and the actual aging condition, affecting the accuracy of the current-temperature rise mapping and the robustness of the control system. By obtaining a continuous quantitative index reflecting the connector aging degree—the thermoelectric relationship deviation coefficient—and calculating the correction factor based on this coefficient using a preset continuous function, while ensuring the function remains continuous at the threshold of the aging status level, the problem of insufficient matching between the correction factor and the actual aging condition is solved. The core technical concept lies in transforming the discrete aging level mapping into a function calculation based on continuous physical quantities, and ensuring a smooth transition of the function at the level boundaries, enabling the correction factor to accurately and continuously reflect subtle changes in the connector aging degree.

[0084] Specifically, a correction factor is calculated based on the thermoelectric relationship deviation coefficient. The thermoelectric relationship deviation coefficient is a dimensionless value that quantifies the degree to which the actual thermoelectric characteristics of the connector deviate from the factory standard, directly reflecting the extent to which the connector contact resistance increases due to aging. The correction factor is a proportional coefficient used to adjust the current-temperature rise mapping relationship; its value directly affects the correction magnitude of the expected temperature rise slope and the maintainable upper limit of the current. This correction factor can be calculated using various preset functions, such as polynomial functions (e.g., linear functions, quadratic functions), exponential functions, logarithmic functions, piecewise functions, or lookup table interpolation functions.

[0085] Furthermore, the preset function maintains continuity at the aging state level threshold. Continuity means that the function does not abruptly change or jump at a specific point (i.e., the aging state level threshold), and its left and right limits are equal to the function value. The aging state level threshold is the critical point that distinguishes different degrees of aging, such as the boundary from "slight shift" to "significant shift". To achieve this continuity, a piecewise function can be designed, ensuring that the function value and the first derivative (or even higher-order derivatives) are continuous at the piecewise points. For example, spline interpolation (such as cubic splines) can be used to construct the function, ensuring a smooth transition at the threshold points. Alternatively, continuity constraints can be introduced during function fitting or design to ensure a smooth transition of the function output value at the preset threshold points, avoiding abrupt changes.

[0086] By employing the above method, the correction factor is ensured to transition smoothly, thus determining the correction factor based on subtle changes in the aging condition level. This means that the correction factor should not abruptly change at the aging condition level threshold, but rather change continuously and smoothly with minute variations in the thermoelectric deviation coefficient. Even if the thermoelectric deviation coefficient changes only slightly, the correction factor can be fine-tuned accordingly, thereby more accurately reflecting the actual aging condition of the connector.

[0087] This application's solution uses the thermoelectric relationship deviation coefficient, a continuous quantitative indicator, as input and directly calculates the correction factor using a preset continuous function, thus avoiding the inaccuracies of traditional discrete mapping. This preset function is carefully designed to ensure continuity at the threshold of aging state levels. This means that as the connector's aging degree gradually crosses different preset level boundaries, the calculated correction factor will not experience abrupt jumps but will adjust smoothly and gradually. This smooth transition mechanism allows the correction factor to accurately respond to subtle changes in the connector's aging state, thereby providing a continuous and highly matched adjustment parameter for the subsequent dynamic correction of the current-temperature rise mapping relationship (step S5). Throughout the process, the thermoelectric relationship deviation coefficient, as a quantitative representation of the connector's aging degree, fully utilizes its continuity, transforming it into a smooth correction factor through a continuous function, thereby ensuring that the correction of the current-temperature rise mapping relationship is also continuous and precise. This mechanism effectively solves the problem of control parameter step changes caused by aging level classification, enabling the resistance change at the micro level of the connector to be quantified into specific adjustment parameters. As a result, the correction of the current-temperature rise mapping relationship no longer depends on the rough level determination, but can respond to the slight fluctuations in the deviation coefficient.

[0088] The following example illustrates this. Assume the thermoelectric deviation coefficient ranges from 0 to 1, and two aging state levels are set with thresholds: a first deviation threshold of 0.2 (indicating the start of a slight deviation) and a second deviation threshold of 0.5 (indicating the start of a significant deviation). To ensure a smooth transition of the correction factor, a piecewise polynomial function can be used as the preset function. For example, when the thermoelectric deviation coefficient is less than 0.2, the correction factor can be set to 1 (i.e., no correction is performed). When the deviation coefficient is between 0.2 and 0.5, the correction factor can be calculated using a quadratic polynomial function, for example: Correction factor = 1 + k_1 * (deviation coefficient - 0.2) + k_2 * (deviation coefficient - 0.2) 2 Where k_1 and k_2 are preset coefficients. When the deviation coefficient is greater than or equal to 0.5, the correction factor can be calculated by another polynomial function or exponential function, for example: correction factor = 1 + k_3 * (deviation coefficient - 0.5) + k_4 * (deviation coefficient - 0.5) 2 +k_5*e (偏离度系数-0.5)Here, k_3, k_4, and k_5 are preset coefficients. The key is that at the thresholds of 0.2 and 0.5, the function value and its first derivative (and even higher derivatives) are continuous. This can be achieved by using spline interpolation algorithms (such as cubic spline functions) to fit correction factors for different intervals during function design, thus ensuring a smooth transition at connection points. In practical applications, the coefficients or spline interpolation points of these piecewise functions can be pre-stored in a microcontroller (such as an STM32 series microcontroller). During runtime, the system obtains the corresponding correction factor based on the real-time calculated thermoelectric deviation coefficient through table lookup and calculation. For example, when the thermoelectric deviation coefficient is 0.3, a correction factor calculated using the above piecewise function is slightly larger than the correction factor at 0.2, but without any abrupt changes. When the deviation coefficient is 0.51, the correction factor is slightly larger than at 0.5, also resulting in a smooth transition.

[0089] Through the above technical solution, this application ensures that the correction factor accurately and continuously reflects subtle changes in the aging degree of the connector, thereby avoiding the step problem of control parameters caused by discrete aging level division. This smooth transition mechanism ensures that the current control command can be adjusted gradually and continuously as the connector aging degree gradually deepens, avoiding sudden current changes caused by control logic switching. This improves the stability and reliability of the charging process while ensuring the safety of connector temperature rise. Compared with the above basic solution, this application introduces a continuous function to calculate the correction factor and ensures its continuity at the aging state level threshold, making the correction of the current-temperature rise mapping relationship more refined and accurate. Thus, throughout the entire connector life cycle, it can more effectively balance charging efficiency and temperature rise safety, improving the system's adaptability and robustness.

[0090] In some embodiments, the thermal risk level includes no risk, low risk, medium risk, and high risk; The specific steps in step S7 include: S71. If the thermal risk level is no risk or low risk, calculate the maximum allowable current rise based on the upper limit of the sustainable current, and output a current control command based on the maximum current rise. S72. If the thermal risk level is medium risk level, calculate the target safe current that makes the target heating power equal to the maximum heat dissipation power, determine the minimum current reduction based on the target safe current, and output the current control command based on the minimum current reduction. If the thermal risk level of S73 is high, then a forced current reduction command is generated and output as a current control command.

[0091] These thermal risk levels are quantitative assessments of the connector's current thermal state and future trends, designed to provide a refined basis for subsequent current control decisions. A no-risk level indicates the connector's thermal state is stable with sufficient safety margin; a low-risk level indicates the connector's thermal state is good, but the safety margin is relatively reduced compared to the no-risk level; a medium-risk level indicates the connector's thermal state is approaching a critical point, requiring preventative measures; and a high-risk level indicates the connector's thermal state is extremely dangerous, necessitating immediate and forceful intervention. These levels can be defined based on different remaining safety time thresholds. For example, they can be divided into different intervals based on the length of the remaining safety time, with each interval corresponding to a thermal risk level. Alternatively, multiple dimensions such as the connector's real-time temperature, temperature rise rate, and aging status level can be combined, using fuzzy logic or machine learning models for comprehensive judgment to determine the current thermal risk level.

[0092] When the thermal risk level is no risk or low risk, the maximum allowable current boost is calculated based on the sustainable current limit, and a current control command is output based on the maximum current boost. This step aims to fully utilize the connector's current-carrying potential and improve charging efficiency while ensuring safety. The maximum allowable current boost can be calculated based on the current connector's real-time temperature, the corrected current-temperature rise mapping, and a preset safety margin. For example, the system can query the corrected mapping to find the highest current value that the connector can safely maintain at the current temperature, and then compare it with the current actual current; the difference is the maximum allowable current boost. Alternatively, the system can predict whether the connector temperature will exceed a safety threshold in the future after increasing the current by a certain amount, based on the connector's thermal model, thereby dynamically determining the maximum current boost.

[0093] When the thermal risk level is medium, the system calculates the target safe current that makes the target heat dissipation power equal to the maximum heat dissipation power, determines the minimum current reduction based on the target safe current, and outputs a current control command based on the minimum current reduction. This step aims to maintain the connector's thermal balance, prevent further temperature increases, and retain charging current as much as possible. The target heat dissipation power can be calculated based on the connector's thermal resistance, ambient temperature, and target temperature (e.g., a temperature slightly below the safety threshold). For example, the system can calculate the maximum heat dissipation power the connector can generate under the current environment based on the connector's thermal resistance and ambient temperature, and set this as the target heat dissipation power. Then, based on Joule's law and the connector's actual contact resistance, the current that can generate this target heat dissipation power is deduced, which is the target safe current. The minimum current reduction is the difference between the current actual current and the target safe current.

[0094] When the thermal risk level is high, a forced current reduction command is generated and output as a current control command. This step aims to rapidly reduce the connector's heat output, avoid the risk of thermal runaway, and ensure system safety. The forced current reduction command can be a preset fixed current reduction ratio, such as directly reducing the current by 50% or more. Alternatively, it can directly cut off the current, entering a stop-charging state. In some implementations, the forced current reduction command can also be dynamically calculated based on the difference between the connector's current temperature and a safe threshold, as well as the connector's thermal inertia, to generate a current reduction curve that allows the temperature to drop back to a safe range in the shortest possible time.

[0095] This application's solution achieves differentiated management of the connector's thermal state by subdividing thermal risk levels into four levels: no risk, low risk, medium risk, and high risk, thereby maximizing charging efficiency while ensuring safety. In the aforementioned adaptive control method for high-voltage energy storage connectors, the system first performs risk extrapolation in step S6 based on the corrected current-temperature rise mapping relationship and the remaining temperature rise space between the connector's real-time temperature and the preset safety threshold, calculating the remaining safe time expected to reach a dangerous state. Based on this, the solution further refines the output of corresponding current control commands according to the thermal risk level at which this remaining safe time falls.

[0096] Specifically, when the system determines that the connector is at a risk-free or low-risk level, it indicates that the connector is currently operating well and has sufficient temperature rise margin. At this time, the system calculates the maximum allowable current boost based on the upper limit of the sustainable current at the current temperature node in the corrected current-temperature mapping relationship. This current boost represents the current value that the system can actively increase without exceeding the safety boundary. By outputting this maximum current boost as a current control command, the system can fully utilize the connector's current carrying capacity and actively increase the charging current, thereby effectively shortening the charging time and improving charging efficiency.

[0097] When a connector enters the medium-risk level, it means that although the temperature has not yet reached the danger threshold, its rate of increase has accelerated, rapidly approaching the corrected safety boundary. To prevent the temperature from rising further and maintain thermal equilibrium, the system activates a pre-current limiting strategy. At this point, the system no longer blindly reduces the current but precisely calculates a target safe current. This target safe current is the current value that makes the connector's heat dissipation power exactly equal to its maximum heat dissipation power, thus ensuring that the connector temperature no longer rises and reaches a thermal equilibrium state. By comparing the current actual current with this target safe current, the system determines the minimum current reduction that must be implemented and uses this as the current control command. This strategy avoids efficiency losses caused by excessive current reduction, achieving a dynamic balance between thermal safety and charging efficiency.

[0098] When a connector is at a high-risk level, it means its thermal state is extremely dangerous and thermal runaway may be imminent. In this emergency, the system immediately generates a large forced current reduction command and outputs it as a current control command. This strategy sacrifices charging speed to rapidly reduce the connector's heat output, thereby ensuring the connector's thermal stability and preventing physical damage or safety accidents caused by overheating.

[0099] Through this hierarchical control strategy, this solution transforms a single current regulation logic into an adaptive response mechanism for different risk scenarios. It not only solves the problem of mismatched responses from a single control logic at different risk levels, but also achieves closed-loop control of the connector's thermal state throughout its entire lifecycle through refined management of current boosting, stabilizing, and reducing. This refined hierarchical management enables the system to maximize charging efficiency while ensuring the connector's safe operation, significantly improving the system's safety, reliability, and operational efficiency.

[0100] As a specific implementation method, the above-mentioned technical means can be implemented with reference to the following example. In the fast charging scenario of new energy vehicles, the system first calculates the remaining safe time expected to reach the dangerous state through step S6. Assume that the system sets the following thermal risk level classification: a remaining safe time greater than 300 seconds is determined to be a no-risk level; between 120 seconds and 300 seconds is determined to be a low-risk level; between 60 seconds and 120 seconds is determined to be a medium-risk level; and less than 60 seconds is determined to be a high-risk level.

[0101] When the system calculates a remaining safe time of 350 seconds, it determines the risk level to be no risk. At this point, the system will query the current-temperature rise mapping relationship. For example, when the current real-time temperature of the connector is 40℃, the mapping relationship shows that the maximum current limit can be maintained at 300A. If the current actual charging current is 250A, the system calculates the maximum allowable current increase to be 300A - 250A = 50A. The system sends this 50A current increase command to the charging pile's power module, allowing it to increase the charging current to 300A to accelerate the charging speed.

[0102] When the system calculates a remaining safety time of 90 seconds, it classifies the risk as medium. At this point, the system immediately activates a pre-limiting current-limiting strategy. The system first calculates the maximum heat dissipation power based on the connector's thermal resistance (e.g., 0.5 K / W), ambient temperature (e.g., 25°C), and target temperature (e.g., 60°C, slightly below the safety threshold). Assuming the maximum heat dissipation power is (60°C - 25°C) / 0.5 K / W = 70W, the system then deduces the target safe current based on the current connector's actual contact resistance (e.g., 0.001Ω). According to P=I... 2R, I≈264.5A. If the current actual charging current is 280A, the system calculates the minimum current reduction to be 280A-264.5A=15.5A. The system sends this 15.5A current reduction command to the charging station, reducing the charging current to 264.5A to maintain thermal balance and prevent the temperature from rising further.

[0103] When the system calculates that the remaining safe time is 45 seconds, it is classified as a high-risk level. At this point, the system will immediately generate a forced current reduction command. For example, the system can directly send a command requesting the charging station to reduce the current by 50%. If the current actual charging current is 200A, the command will request that the current be reduced to 100A. In more extreme cases, the system can also directly send a charging stop command to ensure the safety of the connector to the greatest extent possible.

[0104] Through the above technical solutions, this application can achieve refined and differentiated current control for high-voltage connectors in energy storage under different thermal risk levels. In no-risk or low-risk states, the system can actively identify and utilize the remaining temperature rise margin of the connector, calculate and output the maximum allowable current increase, enabling the charging system to maximize the charging current within a safe range, thereby significantly improving charging efficiency and shortening charging time. This solves the problem of traditional methods failing to fully utilize current-carrying potential in low-risk conditions. In medium-risk states, this solution accurately calculates the target safe current that makes the target heat generation power equal to the maximum heat dissipation power, and determines the minimum current reduction based on this, avoiding blindly large current reductions. While ensuring the connector does not heat up further, it retains as much charging current as possible, achieving a dynamic balance between thermal safety and charging efficiency. This effectively solves the problem of efficiency loss caused by the single response strategy of traditional methods. In high-risk states, the system can quickly generate and output forced current reduction commands, ensuring timely and effective reduction of heat generation power under extreme thermal risk conditions. This prevents physical damage or safety accidents to the connector due to overheating, solving the problem of traditional methods failing to timely avoid the risk of thermal runaway. This hierarchical control strategy, combined with risk extrapolation based on the modified current-temperature rise mapping relationship in the basic scheme, enables the entire control system to perceive the thermal state of the connector more comprehensively and accurately, and to make more intelligent and timely responses. Thus, throughout the entire life cycle of the connector, it always maintains the optimal trade-off between charging efficiency and temperature rise safety, significantly improving the overall reliability of the system and the user experience.

[0105] In some embodiments, the specific steps in step S8 include: S81. After each round of adjustment, acquire multiple sets of actual temperature rise response data; S82. Based on multiple sets of actual temperature rise response data and theoretical expected temperature rise response data, calculate the difference between the actual temperature rise response and the theoretical expected temperature rise response; S83. Smooth the difference to obtain the smoothed difference; S84. Update the thermoelectric relationship deviation coefficient based on the difference after smoothing, and perform closed-loop verification on the thermoelectric relationship deviation coefficient.

[0106] "After each adjustment" refers to the system dynamically correcting the current-temperature rise mapping relationship based on the identified aging state level, outputting a current control command, and then entering a new operating cycle. In this new operating cycle, the system needs to evaluate the effect of the previous adjustment. "Acquiring multiple sets of actual temperature rise response data" means that after a new current control command takes effect, the system continuously monitors the actual temperature changes of the connector and records a series of temperature values ​​and their corresponding timestamps. This can be achieved through continuous or periodic sampling using temperature sensors (e.g., negative temperature coefficient thermistors) deployed inside and around the connector, and smoothing the raw data (e.g., moving average, Kalman filtering) to eliminate noise and form an actual temperature rise response data sequence. Alternatively, the system can use a high-precision analog-to-digital converter to acquire temperature sensor signals at a preset sampling frequency (e.g., 10Hz) and store the acquired digital signals in a circular buffer, forming a set of actual temperature data points within a certain time window (e.g., 30 seconds). "Theoretical expected temperature rise response data" refers to the system's prediction of the temperature change trend that the connector should exhibit under no aging or specific aging conditions, based on the corrected current-temperature rise mapping relationship and theoretical thermodynamic model under the current current control command. This can be achieved by using the corrected current-temperature rise mapping relationship, combined with the current current and ambient temperature, and performing numerical integration (e.g., Runge-Kutta method) using a preset thermal model (e.g., a first-order thermal network model) to deduce the theoretical temperature sequence. Alternatively, the system can calculate the theoretical temperature rise rate at each time step based on the corrected current-temperature rise mapping relationship, using the current current and the connector's thermal parameters (e.g., thermal resistance, thermal capacity), and accumulate this to obtain the theoretical expected temperature sequence. "Calculating the difference between the actual temperature rise response and the theoretical expected temperature rise response" refers to comparing the actual temperature sequence with the theoretically predicted temperature sequence point by point to quantify the deviation between the two. This can be achieved by subtracting the theoretical expected temperature value from the actual temperature value at the same time point to obtain the instantaneous temperature difference. Alternatively, the area difference between the actual temperature rise curve and the theoretical temperature rise curve can be calculated, or statistical methods such as mean square error, absolute error, etc., can be used to quantify the difference. "Smoothing the difference" refers to filtering the calculated instantaneous difference sequence to eliminate random noise and short-term fluctuations, extracting more stable and representative trend information. This can be done by using a moving average filter to average the difference over a certain time window. Alternatively, digital signal processing methods such as exponentially weighted moving average (EWMA) or Kalman filter can be used to smooth the difference, better balancing real-time performance and smoothing effect. "Updating the thermoelectric relationship deviation coefficient" refers to adjusting the previously calculated thermoelectric relationship deviation coefficient based on the smoothed difference, making it more accurately reflect the actual aging state of the connector.If the smoothed difference indicates that the actual temperature rise is higher than expected, the deviation coefficient is increased; conversely, it is decreased. The update magnitude can be adjusted based on the size of the difference and a preset learning rate. Alternatively, a proportional-integral (PI) controller or adaptive algorithm can be used, with the smoothed difference as the error input, to dynamically adjust the deviation coefficient to make the actual temperature rise consistent with the theoretical expected temperature rise. "Closed-loop verification of the thermoelectric relationship deviation coefficient" refers to continuously monitoring the accuracy of the updated deviation coefficient in subsequent control cycles for temperature rise prediction, and further adjusting the coefficient based on the verification results to form a self-correcting feedback loop. This can be achieved by setting a verification window and calculating the root mean square error between the theoretical temperature rise predicted using the updated deviation coefficient and the actual temperature rise within that window. If the error is within the allowable range, the verification is considered successful; otherwise, adjustment continues. Alternatively, the system can set a convergence dead zone threshold and positive / negative dead zone thresholds, and determine whether the deviation coefficient needs to be fixed, increased, or decreased based on the comparison between the root mean square error and these thresholds, thereby achieving continuous optimization of the closed loop.

[0107] This application's solution achieves dynamic calibration and continuous optimization of connector aging status assessment by introducing a closed-loop feedback and smoothing mechanism. After each round of current control command is issued and executed, the system continuously collects the actual smoothed temperature of the connector and simultaneously uses a corrected mapping table and theoretical thermodynamic model in the background to deduce the theoretical expected temperature curve that should appear under the current. The system calculates the dynamic residual between the actual temperature sequence and the theoretical expected temperature sequence in real time. To avoid misjudgments caused by short-term disturbances, the system sets a sliding verification window of 30 seconds and calculates the root mean square error of the dynamic residual within this window. If the absolute value of the root mean square error is consistently less than the set convergence dead zone threshold, it indicates that the previous state identification, deviation calculation, and mapping table correction were very accurate, and the actual heating completely matches the corrected expectation. At this time, the system stores the current deviation coefficient in non-volatile memory as an initial reference for the entire subsequent charging stage and even the next charging, confirming that the key invariants have been restored. However, if the actual temperature is significantly higher than the expected temperature, and the root mean square error exceeds the positive dead zone threshold, it indicates that the connector's aging and deterioration rate exceeds the previous static assessment, and the previous correction was insufficient. In this case, the system extracts the root mean square error, multiplies it by a dynamically adjusted learning rate parameter, and adds this as a compensation increment to the current deviation coefficient. The learning rate parameter is dynamically and adaptively adjusted according to the error change rate; the faster the error changes, the larger the learning rate, to accelerate convergence. An increase in the deviation coefficient immediately triggers the system to return to the previous steps for a deeper secondary downward correction of the mapping table. Conversely, if the actual temperature is significantly lower than the expected temperature, it indicates that the previous correction was too conservative, unnecessarily limiting the charging current and sacrificing charging efficiency. The system then subtracts a decay amount from the deviation coefficient, appropriately relaxing the sustainable current boundary and releasing more charging space. By acquiring multiple sets of actual temperature rise response data after each round of adjustment, the thermal characteristics of the connector under different current loads can be effectively covered, thus providing a more representative data basis for evaluation. By comparing the actual temperature rise response with the theoretically expected temperature rise response, the deviation between the current control model and the actual physical state of the connector can be quantified. This difference directly reflects the accuracy of the model's extrapolation. Smoothing the difference effectively filters out random interference caused by instantaneous current fluctuations or environmental noise, ensuring that the updated deviation coefficient has better stability and anti-interference capability. Updating the thermoelectric relationship deviation coefficient based on the smoothed difference allows the system to continuously correct the aging assessment model based on actual operational feedback, thereby achieving real-time tracking of changes in connector contact resistance. Finally, closed-loop verification ensures that the updated coefficients remain consistent with the actual temperature rise response, thus forming a self-correcting and self-improving closed-loop control system.This continuous closed-loop feedback and verification mechanism ensures that the control system does not become stuck in any single calculation that may have a deviation, but can continuously iterate and converge as the physical state of the connector changes slightly, always anchoring the control logic at the optimal trade-off between efficiency and safety.

[0108] In one specific implementation, after each round of current adjustment command is issued and executed, the system continuously collects temperature data at a sampling frequency of 10Hz using negative temperature coefficient thermistors deployed at the crimping point of the male connector pin and within the outer insulation layer of the female connector socket. This raw data is smoothed using a moving average filter with 20 sampling points to form an actual smoothed temperature sequence. Simultaneously, the system uses a modified current-temperature rise mapping table and a preset first-order thermal network model, combined with the current real-time current and ambient temperature, to deduce the theoretically expected temperature sequence using the fourth-order Runge-Kutta method. Then, the actual smoothed temperature sequence is subtracted point-by-point from the theoretically expected temperature sequence at the same timestamp to obtain the instantaneous temperature difference sequence. This instantaneous temperature difference sequence is smoothed using an exponentially weighted moving average filter with a time constant of 5 seconds to filter out high-frequency noise and transient disturbances, obtaining the smoothed difference. The system sets a 30-second sliding verification window. Within this window, the root mean square error (RMSE) of the smoothed difference is calculated. If the absolute value of RMSE remains consistently below 0.5°C (convergence dead zone threshold), the current deviation coefficient is considered accurate and is fixed and stored in non-volatile memory. If RMSE exceeds the positive dead zone threshold (e.g., 1.5°C), it indicates that the actual temperature rise is significantly higher than expected. The system multiplies RMSE by a dynamic learning rate (e.g., 0.1), adds this increment to the current thermoelectric relationship deviation coefficient, and triggers a secondary correction to the current-temperature rise mapping table. If RMSE is below the negative dead zone threshold (e.g., -1.0°C), it indicates that the actual temperature rise is lower than expected. The system subtracts a decay amount (e.g., 0.05) from the deviation coefficient, moderately relaxing the current limit.

[0109] Through the above technical solution, the closed-loop feedback and smoothing mechanism significantly improves the accuracy of temperature rise prediction and the reliability of current control for connectors during long-term operation. By continuously acquiring actual temperature rise response data and comparing it with theoretical expectations, the system can quantify the deviation between the current control model and the actual physical state of the connector. Smoothing the difference effectively filters out random interference caused by instantaneous current fluctuations or environmental noise, ensuring that the updated deviation coefficient has better stability and anti-interference capability. Updating the thermoelectric relationship deviation coefficient based on the smoothed difference allows the system to continuously correct the aging assessment model according to actual operating feedback, thereby achieving real-time tracking of connector contact resistance changes. Finally, closed-loop verification ensures that the updated coefficients are consistent with the actual temperature rise response, thus forming a self-correcting and self-improving closed-loop control system. This makes the dynamically corrected current-temperature rise mapping relationship more accurate and robust, avoiding control oscillations or insufficient convergence caused by single calculation deviations. Therefore, throughout the entire life cycle of the connector, it can always accurately and stably track its aging state, ensuring the optimal trade-off between charging efficiency and temperature rise safety.

[0110] Reference Appendix Figure 2 This invention provides an adaptive control system for energy storage high-voltage connectors (this adaptive control system for energy storage high-voltage connectors adopts the adaptive control method for energy storage high-voltage connectors described in the above embodiments, and the specific process is described in the corresponding steps above), comprising: The data acquisition unit 100 is used to synchronously acquire multi-source data from the connector and perform time base alignment. The first calculation unit 200 is used to perform theoretical thermal response deduction based on multi-source data and preset reference thermal parameters, and calculate the theoretical temperature rise sequence that the connector should theoretically generate under the current current. The feature extraction unit 300 is used to differentiate the actual temperature sequence from the theoretical temperature rise sequence and extract the heat deviation features that reflect the change in contact resistance. The aging identification unit 400 is used to calculate the thermoelectric relationship deviation coefficient based on the heat deviation characteristics, and to identify the aging status level of the connector based on the thermoelectric relationship deviation coefficient. The mapping correction unit 500 is used to dynamically correct the preset current-temperature rise mapping relationship according to the identified aging state level, so as to adjust the upper limit of the sustainable current and the expected temperature rise slope at different temperature nodes. The second calculation unit 600 is used to perform risk extrapolation based on the corrected current-temperature rise mapping relationship and the remaining temperature rise space between the real-time temperature of the connector and the preset safety threshold, and to calculate the remaining safe time expected to reach the dangerous state. The control output unit 700 is used to output the corresponding current control command according to the thermal risk level of the remaining safety time; Feedback unit 800 is used to continuously update and perform closed-loop verification of the thermoelectric relationship deviation coefficient based on the actual temperature rise response feedback after each round of adjustment.

[0111] In this context, the units described as separate components may or may not be physically separate. Similarly, the components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0112] Furthermore, the functional modules in the various embodiments of the present invention can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0113] In this document, relational terms such as first and second are used only to distinguish one entity or operation from another entity or operation, without necessarily requiring or implying any such actual relationship or order between these entities or operations.

[0114] The above description is merely an embodiment of the present invention and is not intended to limit the scope of protection of the present invention. For those skilled in the art, the present invention can have various modifications and variations. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. An adaptive control method for a high-voltage energy storage connector, characterized in that, Includes the following steps: S1. Synchronously acquire multi-source data from the connector and align it with the time reference; S2. Based on the multi-source data and preset reference thermal parameters, perform theoretical thermal response deduction and calculate the theoretical temperature rise sequence that the connector should theoretically generate under the current current; S3. The actual collected temperature sequence is differentiated from the theoretical temperature rise sequence, and the heat deviation characteristics reflecting the change in contact resistance are extracted. S4. Calculate the thermoelectric relationship deviation coefficient based on the heat deviation characteristics, and identify the aging status level of the connector based on the thermoelectric relationship deviation coefficient; S5. Based on the identified aging state level, dynamically correct the preset current-temperature rise mapping relationship to adjust the upper limit of the sustainable current and the expected temperature rise slope at different temperature nodes. S6. Based on the corrected current-temperature rise mapping relationship, and combined with the remaining temperature rise space between the real-time temperature of the connector and the preset safety threshold, risk simulation is performed to calculate the remaining safe time expected to reach the dangerous state. S7. Output the corresponding current control command based on the thermal risk level of the remaining safety time; S8. Based on the actual temperature rise response feedback after each round of adjustment, the thermoelectric relationship deviation coefficient is continuously updated and closed-loop verified.

2. The adaptive control method for energy storage high-voltage connectors according to claim 1, characterized in that, The multi-source data includes the connector's real-time current, real-time temperature, and ambient temperature.

3. The adaptive control method for energy storage high-voltage connectors according to claim 2, characterized in that, The specific steps in step S2 include: S21. Obtain the reference thermal resistance, reference thermal capacity, and reference contact resistance of the connector; S22. Calculate the theoretical heat generation power of the connector based on the reference contact resistance and the real-time current; S23. Based on the reference thermal resistance, combined with the real-time temperature and the ambient temperature, calculate the theoretical heat dissipation power of the connector; S24. Based on the theoretical heating power, the theoretical heat dissipation power, and the reference heat capacity, the theoretical temperature rise sequence of the connector under non-aging conditions is derived through a preset numerical integration algorithm.

4. The adaptive control method for energy storage high-voltage connectors according to claim 2, characterized in that, In step S3, the specific steps for extracting the thermal deviation characteristics reflecting changes in contact resistance include: S31. Monitor the rate of change of the real-time current in real time; S32. When the rate of change exceeds a preset step threshold, a preset observation window is opened, and the cumulative difference between the actual collected temperature sequence and the theoretical temperature rise sequence is continuously calculated within the observation window. S33. The cumulative difference is determined as the heat deviation characteristic.

5. The adaptive control method for energy storage high-voltage connectors according to claim 2, characterized in that, In step S4, the specific steps for calculating the thermoelectric relationship deviation coefficient based on the heat deviation characteristics include: S41. Calculate the additional heating power corresponding to the connector based on the heat deviation characteristics; S42. Calculate the actual total heat generation power of the connector based on the additional heat generation power and the reference heat generation power of the connector; S43. Calculate the actual contact resistance of the connector based on the actual total heat generation power and the real-time current; S44. Based on the ambient temperature, perform temperature compensation on the actual contact resistance to obtain the equivalent contact resistance of the connector; S45. Normalize the equivalent contact resistance and the reference contact resistance of the connector to obtain the thermoelectric relationship deviation coefficient.

6. The adaptive control method for energy storage high-voltage connectors according to claim 1, characterized in that, The specific steps in step S5 include: S51. Determine the correction factor based on the aging status level; S52. Multiply the preset expected temperature rise slope in the current-temperature rise mapping relationship by the correction factor to obtain the corrected expected temperature rise slope; S53. Divide the preset sustainable current upper limit in the current-temperature rise mapping relationship by the correction factor to obtain the corrected sustainable current upper limit.

7. The adaptive control method for energy storage high-voltage connectors according to claim 6, characterized in that, The specific steps in step S51 include: S511. Calculate the correction factor using a preset function based on the thermoelectric relationship deviation coefficient; the preset function remains continuous at the threshold of the aging state level to ensure that the correction factor can transition smoothly.

8. The adaptive control method for energy storage high-voltage connectors according to claim 1, characterized in that, The thermal risk levels include no risk, low risk, medium risk, and high risk; The specific steps in step S7 include: S71. If the thermal risk level is no risk or low risk, calculate the maximum allowable current rise based on the sustainable current limit, and output a current control command based on the maximum current rise. S72. If the thermal risk level is medium risk level, calculate the target safe current that makes the target heating power equal to the maximum heat dissipation power, determine the minimum current reduction amplitude based on the target safe current, and output a current control command based on the minimum current reduction amplitude. S73 If the thermal risk level is high risk level, then a forced current reduction command is generated and output as a current control command.

9. The adaptive control method for energy storage high-voltage connectors according to claim 1, characterized in that, The specific steps in step S8 include: S81. After each round of adjustment, acquire multiple sets of actual temperature rise response data; S82. Based on the multiple sets of actual temperature rise response data and theoretical expected temperature rise response data, calculate the difference between the actual temperature rise response and the theoretical expected temperature rise response; S83. Smooth the difference to obtain the smoothed difference. S84. Update the thermoelectric relationship deviation coefficient based on the difference after smoothing, and perform closed-loop verification on the thermoelectric relationship deviation coefficient.

10. An adaptive control system for a high-voltage energy storage connector, characterized in that, include: The data acquisition unit is used to synchronously acquire multi-source data from the connector and perform time base alignment. The first calculation unit is used to perform theoretical thermal response deduction based on the multi-source data and preset reference thermal parameters, and calculate the theoretical temperature rise sequence that the connector should theoretically generate under the current current. The feature extraction unit is used to differentiate the actual collected temperature sequence from the theoretical temperature rise sequence and extract the heat deviation features that reflect the change in contact resistance. An aging identification unit is used to calculate a thermoelectric relationship deviation coefficient based on the heat deviation characteristics, and to identify the aging status level of the connector based on the thermoelectric relationship deviation coefficient. The mapping correction unit is used to dynamically correct the preset current-temperature rise mapping relationship based on the identified aging state level, so as to adjust the upper limit of the sustainable current and the expected temperature rise slope at different temperature nodes. The second calculation unit is used to perform risk extrapolation based on the corrected current-temperature rise mapping relationship and the remaining temperature rise space between the real-time temperature of the connector and the preset safety threshold, and to calculate the remaining safe time expected to reach the dangerous state. The control output unit is used to output a corresponding current control command based on the thermal risk level of the remaining safety time. The feedback unit is used to continuously update and perform closed-loop verification of the thermoelectric relationship deviation coefficient based on the actual temperature rise response feedback after each round of adjustment.

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