A smart charging pile cascade waste heat recovery and circulation system

Abstract

This invention relates to the field of waste heat recovery technology for charging piles, and discloses an intelligent tiered waste heat recovery circulation system for charging piles. The system includes a tiered waste heat monitoring module, a heat level control module, a loop dynamic adjustment module, a recovery anomaly response module, and a recovery efficiency optimization module. Specifically, the tiered waste heat monitoring module collects heat energy distribution and flow parameters in real time and generates distribution characteristics; the heat level control module uses this to classify heat recovery priorities and calculate efficiency thresholds, generating a tiered parameter set; the loop dynamic adjustment module optimizes the loop flow rate and medium ratio based on this parameter set; the recovery anomaly response module monitors node temperature fluctuation rate and pressure deviation to generate anomaly datasets; and the recovery efficiency optimization module adjusts the medium circulation cycle and temperature / pressure range accordingly, generating an optimized configuration table to achieve efficient tiered recovery and dynamic optimization of charging pile waste heat.

Description

Technical Field

[0001] This invention relates to the field of waste heat recovery technology for charging piles, specifically to an intelligent cascade waste heat recovery and circulation system for charging piles. Background Technology

[0002] With the rapid popularization of new energy vehicles, the number of charging piles, as an important energy supply facility, is increasing daily, and their operating power is also constantly improving. During the long-term high-load operation of charging piles, core components such as power units will generate a large amount of heat. If this heat is not handled in a timely and effective manner, it will not only affect the operational stability and service life of the equipment, but also cause unnecessary waste of energy.

[0003] Charging piles typically employ traditional air or liquid cooling methods for heat dissipation. These methods aim only to maintain the normal operating temperature of the equipment and do not effectively recover and utilize the generated waste heat. Some existing waste heat recovery attempts lack precise sensing of the heat source intensity in different areas of the charging pile, often employing a uniform recovery mode, resulting in low heat recovery efficiency. Because the heating characteristics of each power unit within the charging pile differ, and the operating load varies at different times, fixed heat dissipation circuit configurations are difficult to adapt to dynamic heat distribution, easily leading to localized heat accumulation or insufficient waste heat recovery.

[0004] Existing systems lack sufficient monitoring of anomalies during the heat exchange process. When abnormal temperature fluctuations or pressure deviations occur at heat exchange nodes, they cannot be identified and adjusted in a timely manner, potentially leading to decreased heat transfer efficiency and even affecting the normal operation of charging stations. Furthermore, the waste heat recovery efficiency lacks a continuous optimization mechanism; recovery parameters are fixed once set and cannot be dynamically adjusted according to actual operating conditions, making it difficult for the system to maintain high-efficiency waste heat recovery over long-term operation. These problems result in a significant amount of waste heat generated by charging stations not being utilized effectively, causing energy waste and failing to provide additional thermal value to surrounding facilities or users. Summary of the Invention

[0005] The purpose of this invention is to provide an intelligent charging pile cascade waste heat recovery and circulation system to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides an intelligent charging pile cascade waste heat recovery and circulation system, the system comprising:

[0007] The cascade waste heat monitoring module collects the heat energy distribution characteristics and heat dissipation circuit flow parameters of the charging pile power unit in real time, analyzes the matching degree between the heat source intensity level and the heat dissipation path, and generates cascade waste heat distribution characteristics.

[0008] Based on the tiered waste heat distribution characteristics, the heat recovery priority of different temperature zones of the charging pile is divided into the heat recovery priority of different temperature zones, the heat conduction efficiency threshold of the waste heat transmission path is calculated, and a tiered heat recovery parameter set is generated.

[0009] Based on the graded heat recovery parameter set, the loop dynamic adjustment module calibrates the flow distribution deviation in the multi-stage heat exchange loop, reconfigures the ratio of loop flow rate to heat exchange medium, and generates dynamic loop control results.

[0010] Based on the dynamic loop control results, the recovery anomaly response module monitors the temperature fluctuation rate and pressure offset of the heat exchange nodes, identifies the impact of the abnormal fluctuation range on the heat transfer efficiency, and generates an anomaly marker dataset.

[0011] Based on the anomaly-marked dataset, the waste heat recovery efficiency optimization module analyzes the overall energy efficiency ratio of the cascade heat recovery path, adjusts the circulation cycle and temperature and pressure control range of the heat exchange medium, and generates a waste heat recovery optimization configuration table.

[0012] Preferably, the step of obtaining the cascade waste heat distribution characteristics specifically includes:

[0013] Extract thermal imaging data of the charging pile power unit and real-time flow of the heat dissipation circuit, correlate the coordinates of the heat source location with the temperature of the circuit node, and statistically analyze the correspondence between the heat intensity of each temperature zone and the flow of the circuit.

[0014] Based on the correspondence, the matching deviation value between the heat source intensity level and the heat dissipation path is calculated. The heat distribution weight is corrected by combining the specific heat capacity parameter of the heat dissipation medium, and the heat source level matching analysis result is generated.

[0015] Based on the heat source level matching analysis results, heat recovery potential ranges are divided, the location distribution and heat conduction path characteristics of high-potential heat sources are marked, and cascade waste heat distribution characteristics are generated.

[0016] Preferably, the steps for obtaining the staged heat recovery parameter set are as follows:

[0017] Analyze the heat recovery potential range in the cascade waste heat distribution characteristics, arrange the heat recovery priority sequence in descending order of temperature zone, and associate the maximum heat load threshold of the heat exchange loop.

[0018] Based on the heat recovery priority sequence and heat load threshold, the difference in heat conduction rate of the waste heat transfer path is calculated to identify the transfer path that is below the preset efficiency threshold.

[0019] Based on the efficiency defect analysis results of the transmission path, the flow quota of the heat exchange medium is reallocated to generate a graded heat recovery parameter set.

[0020] Preferably, the steps for obtaining the dynamic loop control result are as follows:

[0021] The flow distribution scheme of the multi-stage heat exchange loop is extracted from the set of graded heat recovery parameters. The deviation between the actual flow rate and the target flow rate is compared to calibrate the flow imbalance range between the loop nodes.

[0022] The influence of the flow imbalance interval on the temperature distribution of the heat exchange medium was analyzed, and the medium flow rate ratio and heat exchange duration of the high-load loop were adjusted.

[0023] Based on the adjustment results of medium flow rate and heat exchange duration, the balance parameters of heat conduction path are reconstructed to generate dynamic loop control results.

[0024] Preferably, the steps for obtaining the anomaly marker dataset are as follows:

[0025] Monitor the temperature fluctuation rate of the heat exchange nodes in the dynamic loop control results, correlate with real-time pressure sensor data, and eliminate abnormal fluctuation values ​​caused by equipment failures;

[0026] Calculate the attenuation coefficient of effective temperature fluctuation range and pressure offset on heat conduction rate, and identify fluctuation ranges that exceed the safety threshold;

[0027] Based on the safety risk analysis results of the fluctuation range, the coordinates and fluctuation parameters of the heat exchange nodes requiring emergency intervention are marked, and an anomaly marking dataset is generated.

[0028] Preferably, the steps for obtaining the waste heat recovery optimization configuration table are as follows:

[0029] The parameters of the intervention nodes in the anomaly-marked dataset are analyzed to re-evaluate the energy efficiency loss rate of the cascade heat recovery path and correlate the specific heat capacity characteristics of the heat exchange medium.

[0030] Based on the analysis results of energy efficiency loss rate and medium characteristics, the circulation cycle control range and temperature and pressure compensation parameters of the heat exchange medium are adjusted.

[0031] Based on the adjustment scheme of medium circulation and temperature and pressure compensation, a collaborative working configuration table for multi-stage heat recovery paths is generated, and an optimized configuration table for waste heat recovery is generated.

[0032] Preferably, the system further includes a thermal capacity reconstruction module:

[0033] Based on the waste heat recovery optimization configuration table, the circulation cycle control range of the heat exchange medium is extracted, and the matching difference between the medium heat capacity saturation and the loop heat load is analyzed.

[0034] Based on the matching difference, the medium replenishment cycle and the heat capacity buffer are recalculated to generate a heat capacity reconstruction parameter set.

[0035] The heat capacity reconstruction parameter set is fed back to the heat grade control module to update the heat load threshold of the graded heat recovery parameter set.

[0036] Preferably, the system further includes a distributed update module:

[0037] Receive the updated thermal load threshold data fed back from the thermal capacity reconstruction parameter set, and rescan the temperature zone thermal intensity distribution of the charging pile power unit;

[0038] Based on the updated heat load threshold and temperature zone heat intensity distribution, the location coordinates and heat conduction paths of newly added high-potential heat sources are determined.

[0039] The characteristics of the waste heat distribution in the cascade are corrected based on the characteristics of the newly added heat source path, and the updated results are input into the loop dynamic adjustment module.

[0040] Preferably, the system further includes a performance verification module:

[0041] Collect the multi-level heat recovery path collaborative work data of the waste heat recovery optimization configuration table, and associate the medium replenishment cycle of the heat capacity reconstruction parameter set;

[0042] Verify the deviation range between the actual heat recovery energy efficiency improvement rate and the target value, and identify the node locations of inefficient collaborative paths;

[0043] The location and deviation data of inefficient nodes are fed back to the anomaly response module to expand the monitoring scope of the anomaly marker dataset.

[0044] Preferably, the system further includes an output integration module:

[0045] Summarize the data in the buffer zone of the heat exchange medium circulation parameters and heat capacity reconstruction parameter set in the waste heat recovery optimization configuration table, as well as the energy efficiency improvement rate verification results of the efficiency verification module;

[0046] The system integrates multi-source parameters to generate a real-time operation configuration instruction set for the heat recovery system, driving the heat exchange loop actuator to complete the cascade waste heat recovery operation.

[0047] Compared with the prior art, the beneficial effects of the present invention are:

[0048] This intelligent charging pile tiered waste heat recovery and circulation system provides a more accurate and efficient solution for charging pile waste heat recovery through the coordinated operation of multiple modules. The tiered waste heat monitoring module can capture the heat energy distribution and heat dissipation circuit parameters of the charging pile power unit in real time. By analyzing the matching degree between the heat source intensity level and the heat dissipation path, the system gains a clear understanding of the internal heat situation of the charging pile, avoiding the problem of vague heat distribution perception in traditional recovery methods, and enabling subsequent waste heat recovery work to be targeted.

[0049] The heat recovery grading and control module prioritizes heat recovery based on the tiered waste heat distribution characteristics and calculates the heat transfer efficiency threshold, generating a tiered heat recovery parameter set. This tiered processing method fully considers the heating differences in different temperature zones of the charging pile, changing the limitations of the previous uniform recovery mode. It enables waste heat recovery to prioritize high-calorific-value areas, improving the targeting and effectiveness of waste heat recovery.

[0050] The dynamic adjustment module calibrates the flow distribution deviation of the multi-stage heat exchange loops based on the graded heat recovery parameter set, and reconfigures the ratio of loop flow rate to heat exchange medium. This process can dynamically adapt to heat changes under different operating conditions of the charging pile, avoiding the problem that fixed loop configurations cannot cope with dynamic heat distribution, making the distribution of heat exchange medium more reasonable, helping to improve heat conduction efficiency, and reducing local heat accumulation or insufficient recovery.

[0051] The anomaly response module monitors the temperature fluctuation rate and pressure deviation of the heat exchange nodes, identifies the impact of abnormal fluctuations on heat transfer efficiency, and generates anomaly marker datasets. By promptly detecting anomalies in the heat recovery process, the system can perceive potential problems earlier, providing a basis for subsequent adjustments and optimizations, reducing the continued impact of anomalies on waste heat recovery performance, and ensuring the stability of system operation.

[0052] The waste heat recovery efficiency optimization module analyzes the overall energy efficiency ratio based on anomaly-marked datasets, adjusts the circulation cycle and temperature / pressure control range of the heat exchange medium, and generates an optimized configuration table. This continuous optimization process enables the system to adjust parameters according to actual operating conditions, avoiding the problem of system efficiency declining over time under fixed parameters. This ensures the waste heat recovery system maintains a good working condition throughout long-term operation, fully utilizing the waste heat of charging piles and reducing energy waste. Attached Figure Description

[0053] Figure 1 This is a timing diagram of the intelligent charging pile cascade waste heat recovery circulation system described in this invention.

[0054] Figure 2 Flowchart for obtaining the parameter set for staged heat recovery;

[0055] Figure 3 A flowchart for obtaining the anomaly-labeled dataset;

[0056] Figure 4 A flowchart illustrating the operation of the thermal capacity reconfiguration module. Detailed Implementation

[0057] 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. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0058] Please see Figure 1 This invention provides an intelligent charging pile tiered waste heat recovery and circulation system. The system comprises five core modules operating collaboratively: tiered waste heat monitoring, heat level control, loop dynamic adjustment, recovery anomaly response, and recovery efficiency optimization. This enables refined management of waste heat from the charging pile power unit throughout the entire process. Specific implementation details are as follows:

[0059] The cascade waste heat monitoring module first collects thermal imaging data of the power unit and flow parameters of the heat dissipation loop, correlates the heat source coordinates with the node temperature, and generates cascade waste heat distribution characteristics through matching deviation calculation and specific heat capacity correction. The heat graded control module analyzes the heat recovery potential range based on this, sorts the priority by temperature zone in descending order, identifies inefficient transmission paths based on heat load thresholds, reallocates medium flow quotas, and generates a graded heat recovery parameter set. The loop dynamic adjustment module extracts the flow scheme from the parameter set, compares the flow rate deviation, adjusts the medium flow rate and exchange time in high-load loops, and reconstructs path balance parameters to output dynamic control results. The recovery anomaly response module monitors the node temperature fluctuation rate and pressure offset, removes equipment fault interference values, and marks fluctuating nodes exceeding the safety threshold, forming an anomaly marker dataset. The recovery efficiency optimization module evaluates the energy loss rate based on this dataset, adjusts the cycle period and temperature and pressure compensation parameters based on the specific heat capacity characteristics of the medium, and finally generates a waste heat recovery optimization configuration table. Simultaneously, it continuously corrects parameters in conjunction with heat capacity reconstruction and distribution update modules to comprehensively improve waste heat recovery efficiency and system stability.

[0060] Example 1: See Figure 2 The core of the collaborative operation of the cascade waste heat monitoring module and the heat level control module lies in the real-time acquisition of the heat energy distribution characteristics of the charging pile power unit and the generation of an optimized heat recovery parameter set. The following details this implementation method through specific operating procedures and data processing methods:

[0061] The cascade waste heat monitoring module first deploys an infrared thermal imager to scan the charging pile's power units, collecting surface temperature distribution data. The thermal imager scans the power unit surface at a fixed frequency, generating a temperature distribution map for each scan cycle. Each pixel in the map corresponds to a temperature value and is mapped to the actual physical location of the power unit. Simultaneously, an electromagnetic flowmeter in the heat dissipation circuit records the flow rate of the cooling medium in real time. The flow rate data and temperature data are synchronized via timestamps to ensure spatiotemporal consistency in the analysis.

[0062] The location coordinates of the heat source are determined based on a three-dimensional structural model of the power unit. The surface is divided into several uniform grids, and the coordinates of the center point of each grid are associated with its temperature value. Loop node temperatures are collected using embedded temperature sensors, which are evenly distributed at key locations in the heat dissipation loop, such as inlets / outlets, bends, and branches. After combining temperature and flow data, the system calculates the heat flux density of each grid unit, reflecting the intensity of heat release in that area. The calculation of heat flux density comprehensively considers the material's thermal conductivity, ambient temperature, and the distance between the grid unit and the heat dissipation loop.

[0063] The heat source intensity level is classified using a clustering algorithm, categorizing heat flux density values ​​into high, medium, and low levels. High-level heat source areas typically correspond to areas with high current density or poor heat dissipation conditions within power units, such as near power semiconductor devices. The system statistically analyzes the area proportion of each level of heat source area and, combined with actual flow data from the heat dissipation loops, assesses the matching degree between heat sources and heat dissipation paths. The matching degree is evaluated using a thermal resistance model, calculating the thermal resistance value of each heat dissipation loop and comparing it with the theoretical optimal value to determine the matching deviation. The specific heat capacity parameter of the heat dissipation medium is used to correct the heat distribution weight, ensuring higher priority for heat recovery in high-temperature areas.

[0064] The division of heat recovery potential zones is based on corrected heat distribution weights and heat flux density data. The surface of a power unit is divided into several evaluation units, and the heat recovery potential value of each unit is obtained by weighted summation of the heat flux densities of all its constituent grid units. High-potential heat source regions are defined as the top 20% of evaluation units in terms of potential value. Their coordinates are determined through centroid calculation, and their spatial distribution characteristics are recorded. The topology of the heat conduction paths is constructed using a triangulation algorithm to reflect the heat transfer relationships between high-potential heat sources. The final generated tiered waste heat distribution characteristics include heat source coordinates, a heat flux density matrix, and a conduction path topology map, providing a data foundation for subsequent graded heat regulation.

[0065] After receiving the tiered waste heat distribution characteristics, the heat intensity control module first normalizes the heat intensity of each temperature zone to eliminate dimensional differences. The normalized heat intensity values ​​are used to generate a heat recovery priority sequence, with higher priority temperature zones corresponding to higher heat recovery value. The maximum heat load threshold of the heat exchange loop is calculated using a heat transfer model, considering the loop's flow rate limit, the medium's specific heat capacity, and the maximum allowable temperature rise. Based on the priority sequence and heat load threshold, the system calculates the difference in heat conduction rate for each loop, identifying less efficient paths.

[0066] For efficiency-deficient paths, the system employs an optimization algorithm to reallocate the flow quota of the heat exchange medium. The optimization objective is to maximize the total heat recovery without exceeding pump power limits. The flow allocation scheme comprehensively considers the actual flow rate, heat load capacity, and priority weight of each loop to ensure that the heat energy in high-priority temperature zones is fully recovered. The final generated set of tiered heat recovery parameters includes the target flow allocation ratio, heat exchange time constraints, and priority weights for each temperature zone, providing a control basis for the loop dynamic adjustment module.

[0067] In actual operation, the cascade waste heat monitoring module continuously updates temperature and flow data, dynamically adjusting the heat source level matching analysis results. The heat level control module iteratively optimizes the cascade heat recovery parameter set based on the latest data, ensuring that the system always adapts to the thermal energy changes of the charging pile power unit. This closed-loop control mechanism enables the waste heat recovery system to efficiently respond to heat load fluctuations under different operating conditions of the charging pile, improving the overall thermal energy utilization rate.

[0068] Example 2: See Figure 3 The collaborative operation of the loop dynamic adjustment module and the recovery anomaly response module is based on dynamically optimizing the heat exchange loop according to the staged heat recovery parameter set and monitoring the system operating status in real time to identify abnormal situations. The following details this implementation method through specific operating procedures and system interaction methods:

[0069] The loop dynamic adjustment module first receives a set of graded heat recovery parameters from the heat graded control module. This dataset contains key parameters such as the target flow distribution ratio for each temperature zone and heat exchange duration constraints. The system monitors the actual medium flow velocity in each heat exchange loop in real time using a high-precision ultrasonic flow meter, with a sampling frequency set to 20 times per second to ensure data real-time performance. The monitored data is compared with the target values ​​set in the parameter set to generate a matrix containing the flow velocity deviation values ​​for each loop. This deviation matrix reflects the degree of difference between the current system operating state and the ideal control target.

[0070] For identified flow imbalance zones, the system employs a multi-parameter coupled analysis method to assess their impact on heat exchange efficiency. Temperature distribution data for each loop node is acquired via distributed fiber optic temperature sensors, achieving a spatial resolution of 1 cm. The system constructs a three-dimensional temperature field model to analyze localized temperature anomalies caused by flow velocity deviations. In a specific operational case, when the actual flow velocity in a branch was detected to be 15% lower than the target value, the outlet temperature of that loop increased by approximately 8°C compared to normal conditions. The system then marked this area as a high-load loop requiring priority adjustment.

[0071] The adjustment of the medium flow rate ratio is accomplished through the coordinated operation of the variable frequency pump unit and the electric regulating valve. The system calculates the required flow rate compensation based on the degree of flow deviation and the range of temperature anomalies. The adjustment strategy employs a gradual approximation method, first changing the valve opening or pump speed by a small margin, observing the temperature field response, and then making a secondary fine-tuning. In one actual operation, the system detected insufficient flow rate in the No. 3 main loop. First, the variable frequency pump speed was increased. After the temperature sensor feedback showed a stable downward trend in the outlet temperature, the speed was further adjusted, ultimately restoring the loop to the target flow rate range.

[0072] The dynamic adjustment of heat exchange duration is based on real-time heat load data for each loop. By analyzing the inlet and outlet water temperature difference and flow rate change, the system predicts the time required to complete the target heat exchange volume. When a sudden increase in the heat load of a loop is detected, the system automatically extends the heat exchange cycle of that loop while correspondingly shortening the operating time of low-load loops. This dynamic duration allocation mechanism ensures that system resources are always tilted towards high heat load areas.

[0073] The abnormal response module continuously monitors the status parameters of key nodes during loop adjustment. Temperature fluctuation rate is calculated using a sliding time window algorithm to analyze the temperature change trend of each sensor over the past 30 seconds. Pressure offset is collected by a high-response pressure sensor with a sampling frequency of up to 100Hz to capture instantaneous fluctuations. The system establishes a temperature-pressure correlation model to distinguish between normal operating condition fluctuations and actual abnormal situations.

[0074] The abnormal data screening process employs a multi-level filtering mechanism. Raw sensor data first undergoes hardware filtering to eliminate high-frequency noise, and then a digital filtering algorithm removes occasional interference signals. In one operational instance, the pressure sensor detected a momentary peak, but the temperature data did not show a corresponding change. The system determined this to be sensor interference rather than a genuine anomaly and did not trigger a response procedure. For data confirmed as a genuine anomaly, the system calculates its impact weight on heat transfer efficiency and generates an anomaly level assessment based on a preset safety threshold.

[0075] The anomaly-marked dataset is generated using a structured storage method. Each anomaly record includes fields such as anomaly type, location of occurrence, start time, duration, and maximum deviation. The dataset is organized by time series, and a spatial index is also established to quickly locate high-incidence areas of anomalies. During system operation, periodic temperature fluctuations were recorded at the inlet of a heat exchanger. Analysis of historical data revealed that this anomaly was related to a specific operating mode, and the control parameters under this condition were optimized accordingly.

[0076] The system employs a tiered response strategy for identified abnormal fluctuations. For minor anomalies, data is simply recorded and observation continues; moderate anomalies trigger alerts and prompt maintenance personnel to pay attention; severe anomalies immediately activate protection mechanisms, such as reducing pump speed or switching to a backup circuit. All response operations are recorded in the anomaly-tagged dataset, forming a complete anomaly handling closed loop. This dataset also provides an analytical basis for the recycling efficiency optimization module, enabling continuous improvement of the system's operational status.

[0077] Example 3 focuses on the operational mechanism of the heat recovery efficiency optimization module, which performs comprehensive energy efficiency optimization on the cascade heat recovery system based on anomaly-labeled datasets. The system re-evaluates the heat transfer performance of each heat recovery path by analyzing abnormal fluctuation data at heat exchange nodes and dynamically adjusts the medium circulation parameters to improve overall energy efficiency. The specific implementation process and technical features of this module are described in detail below.

[0078] The recycling efficiency optimization module first receives a structured anomaly tag dataset from the anomaly response module. This dataset contains temperature fluctuation rate, pressure offset, and corresponding spatial location information. The module processes this data using spatiotemporal correlation analysis to establish a mapping relationship between anomaly events and the location of heat exchange loops. Each anomaly record includes fields such as timestamp, physical coordinates, anomaly type code, and fluctuation amplitude. The system indexes and stores this data through a distributed database, with query response time controlled within 50 milliseconds.

[0079] The energy efficiency loss assessment model is constructed based on the second law of thermodynamics, calculating the energy loss coefficient for each heat recovery path. For the p-th heat recovery path, its energy efficiency loss rate is... Calculated using the following formula:

[0080]

[0081] in: and These represent the inlet and outlet temperatures of the medium along path p, respectively. This represents the highest permissible operating temperature for this path. This formula quantifies the difference between the actual heat transfer efficiency of the path and its theoretical maximum. The closer the value is to 1, the more severe the energy efficiency loss. The system periodically calculates the energy efficiency of each path. The values ​​are used to generate an energy efficiency loss trend chart to analyze the performance degradation pattern.

[0082] The specific heat capacity of the heat exchange medium was obtained through laboratory calibration, and differential scanning calorimetry was used to measure the specific heat capacity of the medium at different temperatures. Value. Among them, Let be the specific heat capacity of the heat exchange medium in the p-th heat recovery path. The measurement result is fitted as a polynomial function of temperature and input into the system database. When the medium temperature changes, the system automatically calls this function to update the current specific heat capacity value, ensuring the accuracy of thermodynamic calculations. In a certain operating instance, when the medium temperature rose from 40℃ to 60℃, the system detected a decrease in its specific heat capacity and subsequently adjusted the flow parameters of the relevant loop.

[0083] The adjustment of the cycle control parameters is based on the thermal relaxation time constant τ, which reflects the time required for the system to recover from a non-equilibrium state to an equilibrium state. The value of τ is obtained by fitting a curve of the medium temperature changing over time, and the system dynamically sets the cycle range according to the value of τ. For loops with a larger τ value, the cycle is appropriately extended to ensure sufficient heat exchange; for loops with a smaller τ value, the cycle is shortened to improve the response speed. The control algorithm uses fuzzy logic to seek the optimal balance between the cycle adjustment and system stability.

[0084] The temperature and pressure compensation parameters are determined based on the medium's state equation and phase change critical point data. The system maintains a lookup table containing medium property parameters at different temperatures and pressures, allowing real-time lookup of the property values ​​corresponding to the current operating condition. When the temperature or pressure of a loop is detected to be close to the phase change critical point, the compensation mechanism is automatically triggered. This adjusts the flow rate of adjacent loops to share the heat load, preventing a phase change in the medium that could lead to a decrease in heat transfer efficiency. The compensation coefficient matrix is ​​dynamically generated based on the degree of thermal coupling between loops, ensuring the accuracy of the compensation operation.

[0085] The collaborative optimization of multi-stage heat recovery paths is implemented using a non-dominated sorting genetic algorithm (NSGA-II). This algorithm has two objectives: maximizing total heat recovery and minimizing system energy consumption, taking into account flow constraints, temperature limitations, and equipment operating parameters for each loop. The optimization process generates a Pareto optimal solution set, from which the system selects the configuration scheme that best meets the current operational requirements. In each optimization iteration, the algorithm evaluates hundreds of possible parameter combinations, with computation time controlled within 5 seconds to meet real-time requirements.

[0086] The waste heat recovery optimization configuration table is generated using a hierarchical structure. The top layer records system-level parameters, including the total heat recovery target and the maximum allowable pump power. The middle layer contains independent parameters for each loop, such as target temperature, flow range, and cycle time. The bottom layer stores node-level control commands, such as valve opening and pump speed setpoints. The configuration table is stored in JSON format for easy parsing and execution by various actuators. The system updates the configuration table every 30 minutes or triggers a recalculation immediately upon detecting a major anomaly.

[0087] The configuration table execution process employs a gradual adjustment strategy. When there are significant differences between the parameters in the old and new configuration tables, the system gradually approaches the target value in multiple steps to avoid impacting the equipment. After each adjustment, the system monitors the changing trends of key parameters, and only proceeds to the next adjustment after confirming stability. All adjustment operations are recorded in the system log, including adjustment time, parameter change amount, execution results, and other information, providing data support for analysis.

[0088] The system maintains a configuration repository that stores configuration tables generated from each optimization. The repository supports retrieving historical configurations by time range, operating conditions, and other criteria. When the current system state is detected to be similar to a historical configuration, the corresponding optimization parameters can be quickly used as initial values, accelerating convergence. The version comparison function helps identify optimal configuration patterns under different operating conditions, continuously improving the optimization algorithm.

[0089] The efficiency optimization module maintains real-time data interaction with other modules in the system. It receives updated thermal property data of the medium from the heat capacity reconstruction module and obtains the latest heat source distribution information from the distribution update module. Simultaneously, it feeds back the optimization results to the actuators. This closed-loop operation mode ensures that the system always makes decisions based on the latest status data, maximizing waste heat recovery efficiency.

[0090] Example 4: See Figure 4 The collaborative operation of the heat capacity reconstruction module and the distribution update module is described, focusing on how the system adjusts the heat exchange medium parameters and dynamically updates the heat source distribution information based on the optimized configuration. The following details this implementation method through specific operational procedures and data processing methods, and includes a table of key parameter examples.

[0091] The heat capacity reconstruction module first analyzes the waste heat recovery optimization configuration table from the recovery efficiency optimization module, extracting control parameters related to the heat exchange medium circulation cycle. The system monitors the medium's state in real time through multi-parameter sensors installed in the medium storage tank, including key indicators such as temperature, density, and heat capacity saturation. The monitoring data is updated twice per second, forming a trend graph of medium state changes. The module's built-in heat capacity saturation calculation model compares the real-time monitored values ​​with the theoretical maximum values ​​to determine the current energy carrying capacity of the medium.

[0092] The heat capacity buffer zone calculation is based on the thermodynamic characteristic curve of the medium and historical operating data. It analyzes the temperature fluctuation range of the medium over the past 24 hours and combines it with the current heat load forecast to dynamically define the safe operating boundary. When the medium temperature of a certain loop is detected to be close to the boundary value, the buffer mechanism is automatically triggered, and the heat load is shared by adjusting the flow rate of adjacent loops. The buffer zone parameters are set in stages according to the importance of the loops, with wider buffer zones for critical loops to ensure system stability.

[0093] The media replenishment cycle is determined using an adaptive algorithm, considering the following factors: the current rate of decrease in the media's thermal saturation, the predicted heat load for the next 15 minutes, and the amount of spare media in reserve. The system maintains a media replenishment decision matrix, as shown in Table 1, which lists the replenishment strategy selection criteria under different operating conditions. The data in this table is derived from long-term operational statistics and is periodically updated to reflect the latest system status.

[0094] Table 1: Decision Matrix for Media Replenishment Strategy

[0095]

[0096] The generation of the thermal capacity reconfiguration parameter set adopts a hierarchical structure, comprising three levels: the basic parameter layer records the current physical property state of the medium, the control parameter layer sets the replenishment period and buffer thresholds, and the execution parameter layer specifies the adjustment amounts for each loop. The parameter set is shared with all modules of the system through a distributed database, and its update frequency is synchronized with the medium status monitoring.

[0097] The workflow of the distribution update module begins with receiving updated thermal load threshold data from the thermal capacity reconstruction parameter set. The module then initiates a power unit temperature zone scanning procedure, using a mobile infrared thermometer to scan the entire surface of the charging pile's power unit. The scanning path employs a serpentine trajectory to cover the entire surface, with a dwell time of no more than 0.1 seconds at each scanning point to ensure timely data acquisition. The scan data is aligned with the power unit's 3D model to generate an updated thermal intensity distribution map of the temperature zone.

[0098] The identification of newly added high-potential heat sources employs a change detection algorithm, comparing the differences between the old and new versions of the heat intensity distribution map. When the temperature rise in a certain area exceeds a set threshold (e.g., 15℃) and lasts for more than 5 minutes, the system marks it as a newly added high-potential heat source. The coordinates of the heat source are accurately located using a spatial interpolation algorithm, with an accuracy of ±2mm. The system automatically assigns a unique identifier to each new heat source and records its first appearance time, initial temperature, and other attributes.

[0099] The heat conduction path reconstruction process employs finite element analysis (FEM), calculating the heat flow path between the new heat source and the existing heat dissipation loop based on the power unit structural model and material thermal conductivity parameters. The analysis results generate a new path topology map, labeling the thermal resistance values ​​and recommended flow ranges for each path. The path reconstruction algorithm prioritizes the shortest heat transfer distance while avoiding conflicts with existing high-load paths. The updated cascade waste heat distribution characteristics include the following core elements: a heat source spatial distribution matrix recording the location and intensity level of all heat sources; a heat flow path topology map describing the connection relationships of the conduction paths; and a thermal resistance parameter table listing the heat transfer characteristic data for each path. This data is transmitted to the loop dynamic adjustment module via a standardized interface, triggering a new round of system parameter optimization.

[0100] During system operation, a new high-temperature concentration area was detected in the southeast corner of the power unit under actual operating conditions. Infrared scanning showed that the temperature in this area rose by 18°C ​​within 10 minutes, and the system immediately marked it as a new high-potential heat source. The thermal capacity reconstruction module simultaneously detected an accelerated decrease in the medium's thermal capacity saturation, triggering a supplementary warning. The distribution update module analyzed that the distance between this heat source and the nearest heat dissipation loop was 12cm and generated a new heat conduction path scheme. Based on the updated distribution characteristics, the loop dynamic adjustment module reallocated the flow rate ratio of loop number three, keeping the temperature in the newly added heat source area within a safe range.

[0101] The system maintains a version control system that records detailed parameters for each hot-capacity refactoring and distributed update. Version records include timestamps, operation types, lists of modified parameters, and associated modules. When system anomalies are detected, historical versions can be quickly traced back to analyze the correlation between parameter changes and the anomalies. Version comparison tools help identify optimal parameter combinations, providing a reference for optimization.

[0102] The hot-capacity reconstruction module and the distributed update module interact using a publish-subscribe pattern. The hot-capacity reconstruction module publishes media status update events, and the distributed update module subscribes to these events and triggers corresponding processing flows. This loosely coupled architecture ensures that each module can operate independently yet collaboratively, improving the system's scalability and maintainability. All data exchange is completed through a message middleware, avoiding direct module dependencies.

[0103] The system employs a multi-level caching mechanism to ensure timely data processing. Real-time monitoring data is stored in an in-memory database for fast access, while historical data is periodically archived to disk. Key indicators such as thermal saturation calculations are updated in real-time using a sliding window algorithm, with the window size dynamically adjusted based on system load. When processing large amounts of concurrent data, the system automatically enables data downsampling to balance computational accuracy with real-time performance requirements.

[0104] Example 5: The collaborative operation of the performance verification module and the output integration module is described, focusing on how the system verifies heat recovery performance and generates the final execution instruction set. The performance verification module continuously monitors the execution status of the waste heat recovery optimization configuration table and collects actual operating data for each heat recovery loop. The module acquires real-time parameters such as temperature, pressure, and flow rate through distributed data acquisition nodes, with a sampling frequency set to 10 times per second to ensure data integrity. The collected data is compared with the target values ​​in the optimization configuration table to calculate the actual heat recovery efficiency value of each loop. The calculation of the efficiency value comprehensively considers factors such as heat transfer, medium flow loss, and equipment energy consumption, reflecting the overall performance of the loop.

[0105] The system establishes a multi-dimensional performance evaluation framework, analyzing energy efficiency trends over time and comparing performance differences between different loops spatially. The evaluation process employs a sliding time window technique, with each window covering the most recent 15 minutes of operational data, and a sliding step of 1 minute. This design captures both short-term fluctuations and identifies long-term trends. When the energy efficiency value of a loop falls below the target threshold for three consecutive windows, the system marks it as an inefficient collaborative path. The location of inefficient nodes utilizes a hierarchical diagnostic method, analyzing comprehensive energy efficiency indicators at the loop level to determine the scope of the problematic loop; examining the thermodynamic parameters of each segment of the loop to pinpoint the specific interval where the anomaly occurs; and verifying the raw data of all sensor nodes within the interval to identify the root causes of the energy efficiency decline. The diagnostic results include information in three dimensions: anomaly type, location, and possible causes, providing a clear direction for optimization.

[0106] Anomaly diagnostic data is transmitted to the anomaly response module via a standardized interface, expanding its monitoring scope. The transmitted data structure includes fields such as timestamp, spatial coordinates, anomaly code, and detailed description. The system maintains an anomaly knowledge base, recording historical diagnostic cases and solutions, enabling rapid matching of response strategies when similar anomaly patterns are detected. The knowledge base employs a continuous learning mechanism; new diagnostic results are automatically added to the knowledge base after verification, continuously improving the system's self-learning capabilities.

[0107] The output integration module is responsible for summarizing the optimization results of each module in the system and generating an executable instruction set. The module extracts the circulation parameters of the heat exchange medium from the waste heat recovery optimization configuration table, including key control variables such as target temperature, flow range, and cycle time. It integrates the buffer data from the heat capacity reconstruction parameter set to determine the operational safety boundaries of each loop. Combined with the energy efficiency improvement rate verification results from the efficiency verification module, the control parameters are fine-tuned.

[0108] The instruction set is generated using a hierarchical, progressive strategy. Top-level instructions define system-level operating modes, such as normal mode, energy-saving mode, or high-load mode. Mid-level instructions specify the collaborative operating parameters for each loop to ensure optimal overall heat recovery efficiency. Bottom-level instructions detail the operational commands for each actuator, such as the speed setpoint of the variable frequency pump and the opening percentage of the regulating valve. The instruction set uses a hybrid binary and ASCII encoding to balance transmission efficiency and readability.

[0109] The command execution process employs a dual verification mechanism. Before issuing control commands, their feasibility is verified in a virtual simulation environment, and the changes in system state after execution are predicted. During actual execution, the effect of the commands is monitored through real-time data feedback. If the deviation from the expected value exceeds the allowable range, a correction process is immediately initiated. The execution results are recorded in the system log, including detailed information such as command content, issuance time, execution status, and actual effect.

[0110] During system operation, a verification process revealed that the energy efficiency value of loop 2 was 12% lower than the target value. Diagnostic analysis showed a decrease in the heat exchange efficiency of the third section of this loop, and further inspection revealed a drift phenomenon in the temperature sensor of the corresponding area. The system added this anomaly to the anomaly marker dataset and adjusted the temperature compensation parameters in the instruction set. The corrected instructions restored the loop's energy efficiency to normal levels, and the entire process was completed within 3 minutes. Data transfer between modules uses a unified time synchronization mechanism. All data records are marked with timestamps accurate to milliseconds to ensure time consistency of analysis results from different modules. The system clock is periodically calibrated via the NTP protocol, and the clock deviation of each acquisition node is controlled within 10 milliseconds. This strict time synchronization ensures accurate correlation and analysis of distributed data.

[0111] The system employs multi-level caching to improve data processing efficiency. Real-time instruction generation utilizes in-memory computing, with response times controlled within 100 milliseconds. Historical data is stored in a time-series database, supporting rapid retrieval and analysis. When the system load is high, data priority management is automatically enabled to ensure the real-time performance of critical instructions is not affected. The caching strategy is dynamically adjusted based on system operating status, balancing processing speed and resource consumption. A version rollback function enhances system reliability; a snapshot of the current system configuration is automatically saved before each major instruction update. If an anomaly occurs after executing a new instruction, a rollback to the previous stable version can be implemented within 30 seconds. The rollback process records detailed logs, including the rollback reason, operation time, and system status, providing a complete data chain for problem analysis.

[0112] The performance verification module and the output integration module form a closed-loop control system. Verification results are continuously fed back to the optimization algorithm, guiding the generation of instructions. Through this continuous self-correction mechanism, the system gradually improves its overall operating efficiency. All optimization and adjustment processes are recorded, forming a complete operating history, supporting post-analysis and algorithm improvement.

[0113] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0114] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A smart charging pile cascade waste heat recovery and circulation system, characterized in that, The system includes: The cascade waste heat monitoring module collects the heat energy distribution characteristics and heat dissipation circuit flow parameters of the charging pile power unit in real time, analyzes the matching degree between the heat source intensity level and the heat dissipation path, and generates cascade waste heat distribution characteristics. Based on the tiered waste heat distribution characteristics, the heat recovery priority of different temperature zones of the charging pile is divided into the heat recovery priority of different temperature zones, the heat conduction efficiency threshold of the waste heat transmission path is calculated, and a tiered heat recovery parameter set is generated. Based on the graded heat recovery parameter set, the loop dynamic adjustment module calibrates the flow distribution deviation in the multi-stage heat exchange loop, reconfigures the ratio of loop flow rate to heat exchange medium, and generates dynamic loop control results. Based on the dynamic loop control results, the recovery anomaly response module monitors the temperature fluctuation rate and pressure offset of the heat exchange nodes, identifies the impact of the abnormal fluctuation range on the heat transfer efficiency, and generates an anomaly marker dataset. Based on the anomaly-marked dataset, the waste heat recovery efficiency optimization module analyzes the overall energy efficiency ratio of the cascade heat recovery path, adjusts the circulation cycle and temperature and pressure control range of the heat exchange medium, and generates a waste heat recovery optimization configuration table.

2. The intelligent charging pile cascade waste heat recovery and circulation system according to claim 1, characterized in that, The specific steps for obtaining the cascade waste heat distribution characteristics are as follows: Extract thermal imaging data of the charging pile power unit and real-time flow of the heat dissipation circuit, correlate the coordinates of the heat source location with the temperature of the circuit node, and statistically analyze the correspondence between the heat intensity of each temperature zone and the flow of the circuit. Based on the correspondence, the matching deviation value between the heat source intensity level and the heat dissipation path is calculated. The heat distribution weight is corrected by combining the specific heat capacity parameter of the heat dissipation medium, and the heat source level matching analysis result is generated. Based on the heat source level matching analysis results, heat recovery potential ranges are divided, the location distribution and heat conduction path characteristics of high-potential heat sources are marked, and cascade waste heat distribution characteristics are generated.

3. The intelligent charging pile cascade waste heat recovery and circulation system according to claim 2, characterized in that, The specific steps for obtaining the staged heat recovery parameter set are as follows: Analyze the heat recovery potential range in the cascade waste heat distribution characteristics, arrange the heat recovery priority sequence in descending order of temperature zone, and associate the maximum heat load threshold of the heat exchange loop. Based on the heat recovery priority sequence and heat load threshold, the difference in heat conduction rate of the waste heat transfer path is calculated to identify the transfer path that is below the preset efficiency threshold. Based on the efficiency defect analysis results of the transmission path, the flow quota of the heat exchange medium is reallocated to generate a graded heat recovery parameter set.

4. The intelligent charging pile cascade waste heat recovery and circulation system according to claim 3, characterized in that, The specific steps for obtaining the dynamic loop control results are as follows: The flow distribution scheme of the multi-stage heat exchange loop is extracted from the set of graded heat recovery parameters. The deviation between the actual flow rate and the target flow rate is compared to calibrate the flow imbalance range between the loop nodes. The influence of the flow imbalance interval on the temperature distribution of the heat exchange medium was analyzed, and the medium flow rate ratio and heat exchange duration of the high-load loop were adjusted. Based on the adjustment results of medium flow rate and heat exchange duration, the balance parameters of heat conduction path are reconstructed to generate dynamic loop control results.

5. The intelligent charging pile cascade waste heat recovery and circulation system according to claim 4, characterized in that, The specific steps for obtaining the anomaly marker dataset are as follows: Monitor the temperature fluctuation rate of the heat exchange nodes in the dynamic loop control results, correlate with real-time pressure sensor data, and eliminate abnormal fluctuation values ​​caused by equipment failures; Calculate the attenuation coefficient of effective temperature fluctuation range and pressure offset on heat conduction rate, and identify fluctuation ranges that exceed the safety threshold; Based on the safety risk analysis results of the fluctuation range, the coordinates and fluctuation parameters of the heat exchange nodes requiring emergency intervention are marked, and an anomaly marking dataset is generated.

6. The intelligent charging pile cascade waste heat recovery and circulation system according to claim 5, characterized in that, The specific steps for obtaining the waste heat recovery optimization configuration table are as follows: The parameters of the intervention nodes in the anomaly-marked dataset are analyzed to re-evaluate the energy efficiency loss rate of the cascade heat recovery path and correlate the specific heat capacity characteristics of the heat exchange medium. Based on the analysis results of energy efficiency loss rate and medium characteristics, the circulation cycle control range and temperature and pressure compensation parameters of the heat exchange medium are adjusted. Based on the adjustment scheme of medium circulation and temperature and pressure compensation, a collaborative working configuration table for multi-stage heat recovery paths is generated, and an optimized configuration table for waste heat recovery is generated.

7. The intelligent charging pile cascade waste heat recovery and circulation system according to claim 6, characterized in that, The system also includes a thermal capacity reconfiguration module: Based on the waste heat recovery optimization configuration table, the circulation cycle control range of the heat exchange medium is extracted, and the matching difference between the medium heat capacity saturation and the loop heat load is analyzed. Based on the matching difference, the medium replenishment cycle and the heat capacity buffer are recalculated to generate a heat capacity reconstruction parameter set. The heat capacity reconstruction parameter set is fed back to the heat grade control module to update the heat load threshold of the graded heat recovery parameter set.

8. The intelligent charging pile cascade waste heat recovery and circulation system according to claim 7, characterized in that, The system also includes a distributed update module: Receive the updated thermal load threshold data fed back from the thermal capacity reconstruction parameter set, and rescan the temperature zone thermal intensity distribution of the charging pile power unit; Based on the updated heat load threshold and temperature zone heat intensity distribution, the location coordinates and heat conduction paths of newly added high-potential heat sources are determined. The characteristics of the waste heat distribution in the cascade are corrected based on the characteristics of the newly added heat source path, and the updated results are input into the loop dynamic adjustment module.

9. The intelligent charging pile cascade waste heat recovery and circulation system according to claim 8, characterized in that, The system also includes a performance verification module: Collect the multi-level heat recovery path collaborative work data of the waste heat recovery optimization configuration table, and associate the medium replenishment cycle of the heat capacity reconstruction parameter set; Verify the deviation range between the actual heat recovery energy efficiency improvement rate and the target value, and identify the node locations of inefficient collaborative paths; The location and deviation data of inefficient nodes are fed back to the anomaly response module to expand the monitoring scope of the anomaly marker dataset.

10. The intelligent charging pile cascade waste heat recovery and circulation system according to claim 9, characterized in that, The system also includes an output integration module: Summarize the data in the buffer zone of the heat exchange medium circulation parameters and heat capacity reconstruction parameter set in the waste heat recovery optimization configuration table, as well as the energy efficiency improvement rate verification results of the efficiency verification module; The system integrates multi-source parameters to generate a real-time operation configuration instruction set for the heat recovery system, driving the heat exchange loop actuator to complete the cascade waste heat recovery operation.

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