High-power liquid-cooled charging pile charging power optimization scheduling method and system

Abstract

The present application belongs to the technical field of charging pile heat and power management, and specifically relates to a high-power liquid-cooled charging pile charging power optimization scheduling method and system. The method comprises: collecting the operating state parameters of a plurality of liquid-cooled charging piles; identifying a target pile body in a charging state or standby state; calculating the theoretical upper limit of energy supply based on an electro-thermal model; combining vehicle charging demand, priority, battery state and power grid load to construct a power scheduling model and perform distribution; adjusting and redistributing power when detecting cooling abnormalities or connection failures; and finally generating a scheduling control instruction and executing it in real time. The system comprises an information collection module, a state identification module, a model calculation module, a power distribution module, a dynamic adjustment module and an instruction issuing module. The present application realizes efficient, safe and dynamic power scheduling control of high-power liquid-cooled charging piles in a multi-device parallel scenario.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of charging pile thermoelectric management, and particularly relates to a high-power liquid-cooled charging pile charging power optimization scheduling method and system. BACKGROUND

[0002] With the continuous growth of the number of new energy vehicles, the construction and operation efficiency of charging infrastructure has become one of the key factors restricting the development of electric transportation systems. Especially in high-power application scenarios such as public transportation, logistics, and intercity fast charging, traditional air cooling or natural cooling methods are difficult to meet the thermal management needs in the process of fast charging. Therefore, liquid-cooled charging piles with higher heat dissipation efficiency gradually become an important technical route in the field of high-power fast charging. Liquid-cooled charging piles can take away the heat of charging modules and cables through liquid medium circulation, which can greatly improve the continuous output capacity, reduce the risk of equipment thermal decay, and have the advantages of high safety and high stability.

[0003] However, the existing liquid-cooled charging piles still have problems such as rough power scheduling, slow response of cooling capacity fluctuation in multi-pile parallel operation or centralized charging station scenarios. On the one hand, the cooling efficiency, thermal load capacity and equipment aging state of different piles are different, and if there is no targeted dynamic scheduling, it is easy to cause some equipment to overload, unbalanced heat dissipation or frequent current limiting; on the other hand, in the actual scheduling process, the complex boundary conditions such as vehicle charging priority, power grid load limitation, reservation period and multi-user concurrency are often not considered, resulting in a lack of real-time adaptability of power distribution results, affecting the overall energy efficiency and service quality of the system.

[0004] In view of the above problems, there is an urgent need for a charging power optimization scheduling scheme with refined cooling monitoring, which can accurately identify and model analyze the operating state and thermal management capacity of each charging pile, thereby improving the overall scheduling efficiency and operation reliability of the high-power liquid-cooled charging pile system under complex working conditions. SUMMARY

[0005] To achieve the purpose of the application, the following technical scheme is adopted: a high-power liquid-cooled charging pile charging power optimization scheduling method, comprising the following steps:

[0006] S1, collecting equipment state parameters: obtaining the operating state information of a plurality of high-power liquid-cooled charging piles, including the current output current, voltage, temperature rise change, cooling liquid flow rate and charging connection state;

[0007] S2, identifying scheduling target objects: identifying target charging piles in a charging state or standby state based on the collected operating state information;

[0008] S3, calculate the upper limit of the theoretical energy supply: analyze the electro-thermal model of the target charging pile, combine the current cooling efficiency, equipment health status and environmental temperature, and calculate the theoretical maximum power supply power of each charging pile;

[0009] S4, execute the power distribution strategy: according to the charging demand of the target vehicle, the state of charge of the battery, and the information of the scheduled charging period, construct a power scheduling strategy, and distribute the output power of each charging pile without exceeding the theoretical upper limit of energy supply;

[0010] S5, dynamic balance adjustment: when detecting that the cooling capacity of a charging pile decreases, the current is abnormal or the connection is interrupted, the output power of the pile is reduced in time, the remaining power is redistributed to other piles, and the overall power utilization rate and temperature control safety are ensured;

[0011] S6, generate scheduling control instructions: according to the above distribution results, generate and issue scheduling control instructions to adjust the output strategy of each target charging pile, implement phased charging power control, and update the scheduling parameters in real time.

[0012] As a preferred technical solution, when acquiring the cooling liquid flow rate in step S1, further comprising:

[0013] A multi-point flow rate detection unit is arranged on the cooling pipeline, including an inlet section, an outlet section of the radiator and an outlet section of the main pump, and the instantaneous flow rates thereof are measured respectively. By comparing the flow rate gradient change trend between the three sections, it is judged whether there is a local blockage or flow resistance increase in the cooling system;

[0014] When detecting that the local flow rate decreases by more than a set threshold, the cooling efficiency of the charging pile is marked as a degradation state, and the cooling capacity loss coefficient is dynamically calculated;

[0015] In the power distribution link, the maximum allowed output power of the charging pile is limited by using the cooling capacity loss coefficient, and the limit value is not more than 80% of the initial rated output power, so as to ensure that the thermal control system can still maintain normal operation under limited flow conditions.

[0016] As a preferred technical solution, in step S3, the electro-thermal model analysis of the target charging pile specifically includes:

[0017] A multi-parameter fitting model is constructed based on the actual working curve of the charging pile. The model input includes: current output current value, environmental temperature, cooling liquid inlet temperature, outlet temperature, flow rate, radiator efficiency, etc.;

[0018] Taking the relationship between output power and temperature rise as the core feature, a nonlinear fitting curve is constructed, and a device aging factor is introduced to weight and correct the model output. The aging factor is estimated by the device historical use time, cumulative load running time and maintenance record;

[0019] The final output is a dynamic power supply capability curve, which represents the upper limit of stable power supply capability that the charging pile can maintain under the current environment and device state.

[0020] As a preferred technical solution, in step S4, the power scheduling strategy has the following constraints:

[0021] a) Under the condition that the total power supply capability remains unchanged, prioritize the charging requests of vehicles set as "high priority" by the user;

[0022] b) Calculate the acceptable maximum charging current per unit time for each target vehicle based on its current battery state of charge, and limit the allocated power to not exceed this value;

[0023] c) Introduce a grid load feedback parameter, if the regional grid load approaches the upper limit, the overall scheduled power will be compressed proportionally;

[0024] d) Consider the urgency of the vehicle charging reservation period, and increase the power allocation weight for vehicles with remaining reservation window less than a set threshold;

[0025] Under the premise of meeting the above constraints, linear programming is used to calculate the output power of each charging pile, and the allocation result is adjusted in real time to adapt to subsequent dynamic changes.

[0026] As a preferred technical solution, in step S5, when the cooling capacity of a charging pile is detected to decrease, a judgment mechanism is constructed based on the temperature difference between the inlet and outlet of the cooling liquid and the cooling load change rate, which specifically includes:

[0027] Set a standard cooling efficiency curve, monitor the temperature difference between the outlet temperature and the inlet temperature, and the temperature rise rate per unit power generated in real time; compare the temperature difference and temperature rise rate with the standard value, if both exceed the set interval, mark the pile state as "partially available" and trigger the load reduction strategy;

[0028] The load reduction ratio is set according to the temperature difference offset by a step function:

[0029] The temperature difference less than or equal to the first threshold is a mild offset, limiting the charging power to 90%;

[0030] The temperature difference greater than the first threshold and less than or equal to the second threshold is a moderate offset, limiting the charging power to 70%;

[0031] The temperature difference greater than the second threshold is a severe offset, limiting the charging power to 50%, and updating the global power allocation pool.

[0032] As a preferred technical solution, in step S6 when generating the scheduling control instruction, the following operations are further performed:

[0033] Encode the current power allocation result as a multi-parameter instruction set, including target power value, allowed error interval, execution period and effective period identifier, and write the instruction into the scheduling buffer of each pile control unit;

[0034] Synchronously generate a scheduling log to record the power change trajectory, electric heating parameter change and load response data information of each pile body, and store the log in the local cache and remote scheduling database in a time stamp manner;

[0035] When power execution fails or an abnormal alarm occurs, the log data is traced back and the parameters are corrected.

[0036] The application also provides a high-power liquid-cooled charging pile charging power optimization scheduling system for implementing the method, comprising:

[0037] An information acquisition module is configured to acquire the current, voltage, temperature rise and cooling liquid flow rate and other operating state parameters of each charging pile;

[0038] A state identification module is configured to identify the target charging pile and judge its cooling capacity and working state;

[0039] A model calculation module is configured to calculate the theoretical energy supply upper limit of each charging pile based on an electric heating model;

[0040] A power allocation module is configured to construct a scheduling strategy and allocate power;

[0041] A dynamic adjustment module is configured to respond to operating state changes and adjust the power allocation result;

[0042] An instruction issuing module is configured to generate scheduling control instructions and control each charging pile to execute the charging strategy in real time.

[0043] As a preferred technical solution, the information acquisition module is connected with a temperature sensor, a current sensor and a flow monitoring unit, and has data preprocessing and abnormal marking functions.

[0044] As a preferred technical solution, the model calculation module is preloaded with energy supply curve templates under multiple environmental conditions, and has the ability to dynamically switch corresponding working conditions based on real-time data.

[0045] As a preferred technical solution, the power allocation module supports a user charging reservation information interface, can receive priority parameters set by the scheduling background, and is used to guide the scheduling model to sort resource allocation.

[0046] The application has the following beneficial effects:

[0047] The application realizes real-time monitoring and dynamic determination of the thermal management state of the high-power liquid-cooled charging pile by introducing a cooling liquid flow rate multi-point detection mechanism and a cooling efficiency degradation identification model. On this basis, the system can timely adjust the output power upper limit of the corresponding pile body according to the flow rate anomaly or temperature difference deviation degree, avoid thermal runaway of the equipment caused by local degradation of the cooling system, and thus improve the overall thermal stability and safety under high-power operating conditions.

[0048] In the power distribution link, the application constructs a scheduling optimization algorithm based on the electric heating model correction and multi-dimensional weight factor, which can realize fine dynamic power allocation under the consideration of multiple constraint conditions such as vehicle charging priority, battery state, power grid load capacity and reservation window urgency. Especially in the multi-pile parallel and high-load working condition, the charging pile energy supply capability curve can be fitted in real time, the on-site changes can be flexibly responded, the accuracy and adaptability of the scheduling strategy can be effectively improved, and the resource waste or overload phenomenon can be avoided.

[0049] In addition, the application introduces a structured instruction set and a synchronous log mechanism in the process of generating the scheduling control instruction, so as to ensure that each power adjustment process has a clear execution boundary and feedback link. The system supports real-time backtracking analysis of the execution state and power response process of each charging pile, provides data support for subsequent operation optimization and abnormality troubleshooting, and thus enhances the control closed-loop capability and intelligent operation and maintenance level of the whole system. BRIEF DESCRIPTION OF DRAWINGS

[0050] Figure 1 The figure is a method flowchart of the application;

[0051] Figure 2 The figure is a system structure diagram of the application. DETAILED DESCRIPTION

[0052] In order to deepen the understanding of the application, the application will be further described in combination with the embodiments below, and the embodiments are only used to explain the application and do not constitute a limitation on the protection scope of the application.

[0053] Embodiment one

[0054] According to Figure 1 As shown in the figure, the embodiment is intended to illustrate a specific implementation process of a "high-power liquid-cooled charging pile charging power optimization scheduling method" suitable for urban large electric vehicle energy supply stations.

[0055] The method is mainly used for managing the energy output scheduling and temperature control safety management of high-power liquid-cooled DC charging piles, ensuring that the charging pile resources are reasonably distributed under the condition of high load and simultaneous charging of multiple vehicles, and avoiding problems such as local thermal runaway or system power redundancy. Specifically, it includes:

[0056] S1, collect device state parameters:

[0057] In the system initialization phase, the dispatch control center establishes real-time data connection with 30 charging piles through the communication interface, and obtains the running state information including but not limited to the following:

[0058] The current output current and output voltage, with a sampling period of 1 second;

[0059] The temperature rise data of each pile, which is derived from the thermocouple array arranged inside the device;

[0060] The cooling liquid flow rate is monitored by flow rate detection units arranged at different sections of the cooling circuit;

[0061] The charging connection state, including the gun mouth insertion confirmation signal, vehicle VIN identification state, whether to start charging identification, etc.

[0062] Among them, three-stage detection structure is specially set up for monitoring the cooling liquid flow rate:

[0063] The first group of flow rate sensors is installed at the cooling liquid inlet section, which is used to monitor the flow rate of the fluid before it enters the device;

[0064] The second group of sensors is arranged at the radiator outlet section, which is used to detect the output flow rate of the cooling liquid after heat exchange;

[0065] The third group of sensors is set at the main pump outlet section, which monitors the flow efficiency of the fluid at the core part of the cooling system.

[0066] The system analyzes the flow stability through three-point flow rate gradient. If the flow rate of a node abnormally decreases, the system determines that there is a cooling obstacle, such as local blockage, pump failure, etc., and generates a preliminary diagnosis label "cooling efficiency degradation".

[0067] S2, identify the dispatch target object:

[0068] In each dispatch cycle, the system first eliminates the charging piles in offline state, fault state or maintenance mode, and then identifies all the piles in "standby but connected vehicle" or "charging" as the target objects of this round of dispatch.

[0069] For example, at a certain time point, among the 30 charging piles, 20 are connected to vehicles, of which 12 are charging and 8 are reserved by users but have not started; among the remaining 10, 3 are in maintenance state, 2 are in limited power mode due to cooling system alarm, and 5 are in complete idle state. The system selects these 20 as the target piles.

[0070] S3, calculate the theoretical upper limit of energy supply:

[0071] The system performs electro-thermal coupling analysis modeling based on the actual data of each target pile, which includes:

[0072] Output current, ambient temperature, cooling liquid inlet and outlet temperature difference, flow rate;

[0073] Real-time estimation of radiator efficiency (fitted by heat conduction model);

[0074] Aging factor generated by device service life and historical failure conditions;

[0075] The electro-thermal model adopts a nonlinear fitting structure;

[0076] The aging factor is calculated by the following indicators: cumulative working time (e.g. 5000 hours as the critical value), single continuous high load running time, three-month maintenance frequency and replacement component record.

[0077] The final output is the "upper limit of stable power supply capacity under current environment" for each device, such as a 250kW pile, whose maximum safe output is adjusted to 220kW due to cooling efficiency decline and aging factor influence.

[0078] S4, execute power distribution strategy:

[0079] The dispatching center constructs an optimization scheduling model based on user reservation information, battery SOC (state of charge), remaining reservation time, and current grid load:

[0080] High-priority vehicles are identified as the main constraint to ensure their power demand;

[0081] Evaluate the "maximum acceptable charging current per unit time" for each vehicle to prevent battery overload;

[0082] Collect the instantaneous load feedback from the substation gateway (e.g. 90% of the rated capacity has been reached), and the overall power upper limit needs to be compressed;

[0083] If the remaining reservation time is less than 15 minutes, increase the power scheduling weight of the vehicle.

[0084] Based on the above constraints, the scheduling module constructs a target function (maximize power utilization rate), and uses a linear programming algorithm to output the optimal distribution solution in real time, such as Table 1:

[0085]

[0086] The system also records the remaining power pool for subsequent dynamic adjustment.

[0087] S5, dynamic balance adjustment:

[0088] During the execution of the scheduling, if the cooling liquid outlet temperature of a certain pile body such as No. 05 is detected to be significantly increased (more than 15℃ higher than the inlet temperature), and the cooling efficiency is lower than the set standard curve, the system immediately marks its state as "partially available" and starts the load shedding mechanism.

[0089] According to the temperature difference ladder mechanism:

[0090] Temperature difference 12℃: moderate deviation, limit power to 70% of the rated value (240kW originally, adjusted to 168kW);

[0091] If it rises to 18℃ again: severe deviation, limit power to 50% (120kW), and recover the load shedding difference (48kW) to the power pool and distribute it to well-conditioned piles such as No. 12 and No. 17.

[0092] This mechanism can prevent thermal runaway caused by local overheating of a device, while improving the utilization rate of overall power resources.

[0093] S6, generate scheduling control instructions:

[0094] After the scheduling result is generated, the system automatically constructs the control instruction package in the following format:

[0095] {

[0096] "target_pile_id": "No. 01",

[0097] "assigned_power": "200kW",

[0098] "allowable_deviation": "±2%",

[0099] "execution_cycle": "30s",

[0100] "validity": "T+120s"

[0101] }

[0102] The instructions are sent to the pile control unit through the CAN bus, written to the local scheduling buffer, and control the conversion parameters of the PWM module to accurately adjust the power.

[0103] Synchronous generation of scheduling logs, including: allocation history of each pile body; real-time temperature rise, flow rate, aging factor and other parameters; dynamic power change and load response rate.

[0104] The log is written in JSON structure to the local Flash cache and synchronized to the dispatch center database daily. If there are problems such as abnormal disconnection of the pile body, failure of control execution, etc., the system will perform dispatch rollback, abnormal alarm and start the standby dispatch process according to the log.

[0105] This embodiment comprehensively presents the application process of "a high-power liquid-cooled charging pile charging power optimization scheduling method" in actual operation, covering the whole process from state acquisition, energy supply analysis, distribution strategy execution, dynamic adjustment, to control instruction generation and log storage. This method improves the efficiency and thermal control of existing high-power liquid-cooled charging infrastructure without increasing physical devices, providing a safe, efficient and intelligent scheduling means for urban high-density charging scenarios.

[0106] Embodiment two

[0107] As shown in Figure 2 , the embodiment provides a high-power liquid-cooled charging pile charging power optimization scheduling system for intelligent management of urban large public charging stations.

[0108] The system is suitable for parallel operation scenarios of high-power direct-current liquid-cooled charging piles, and cooperates with multiple modules such as information acquisition, state identification, electric-thermal model calculation, scheduling distribution, dynamic adjustment and control instruction issuing to realize safe, stable and efficient charging power scheduling control.

[0109] The system mainly includes the following six core modules: information acquisition module, state identification module, model calculation module, power distribution module, dynamic adjustment module and instruction issuing module.

[0110] The overall architecture of the system is based on a distributed edge computing architecture, and the modules interact with each other through high-speed Ethernet or CAN bus. All modules are centrally controlled on the dispatch center server, and can be connected to the user management platform and power grid load feedback channel to realize multi-dimensional decision optimization.

[0111] 1. Information acquisition module:

[0112] The information acquisition module is the basic module of the system operation, responsible for real-time acquisition of the following operating parameters of each high-power liquid-cooled charging pile:

[0113] Output current and voltage values: millisecond-level sampling is performed through integrated current sensors and voltage sensors to reflect the energy supply state of the pile body in real time;

[0114] Temperature rise data: multiple temperature sensors are arranged in the system to cover the power module, radiator outlet, cooling pipe inlet and pile body shell to ensure full coverage of thermal control;

[0115] Coolant flow rate: 3 flow rate monitoring units are set for each cooling pipeline, installed at the inlet section, main pump outlet section, and radiator outlet section, to measure the instantaneous flow rate;

[0116] Connection status: including muzzle insertion detection, vehicle VIN reading status, charging start, reservation confirmation, etc.

[0117] The information acquisition module has edge preprocessing capability and embedded micro data processing unit, supporting data filtering, abnormality rejection, data compression, and labeling operations. For example, if the coolant flow rate at a certain measuring point fluctuates more than ±20% within 3 seconds, the system can automatically mark this point as "potential abnormality" and report it to the state recognition module.

[0118] In addition, the information acquisition module has an event recording mechanism that generates standard event logs when power surges, communication interruptions, thermal sensor drifts, and other situations occur, supporting subsequent scheduling analysis and control tracing.

[0119] 2. State recognition module:

[0120] The state recognition module is used to analyze collected data and determine the operating state of the charging pile, mainly divided into the following two levels:

[0121] Charging pile state recognition: determine whether each pile is in running, standby, offline, or fault state;

[0122] Cooling capacity evaluation: judge the cooling efficiency based on flow rate gradient, temperature difference change, equipment running period, etc., including:

[0123] If the main pump outlet flow rate is significantly higher than the radiator outlet, it is judged as "local blockage";

[0124] If the temperature difference is long-term high (more than 10°C above the inlet and outlet), it is judged as "heat exchange capacity degradation";

[0125] If the coolant circulation period is too long (exceeding the set threshold), it is judged as "pump efficiency decline" or "aging degradation".

[0126] The recognition result will be input into the model calculation module as input data, labeling "normal", "usable but limited", or "unusable" three working state labels for subsequent scheduling strategy reference.

[0127] 3. Model calculation module:

[0128] The model calculation module is used to estimate the theoretical upper limit of the power supply capacity of each charging pile, using an electro-thermal coupling model, and considering the following factors in the calculation process:

[0129] Current output power and temperature rise relationship;

[0130] Cooling liquid flow rate on the influence of heat dissipation capacity;

[0131] Device historical aging parameters (working time, load proportion, fault records);

[0132] The interference of external environment temperature on heat dissipation performance.

[0133] Multiple electrical heating model templates under different environmental conditions are preset in the system, such as:

[0134] High temperature and high humidity model (35℃, humidity 90%);

[0135] Winter low temperature model (-5℃, dry);

[0136] Standard working condition model (25℃, humidity 50%);

[0137] The model calculation module can dynamically switch the working condition template based on real-time data, select the model that best fits the current external environment, and generate a "maximum power supply capacity dynamic curve" for the pile.

[0138] The curve is nonlinear, for example:

[0139] After the output power exceeds 180kW, for every 10kW increase, the required cooling capacity increases exponentially, and if the flow rate decreases by 5%, the maximum maintainable power decreases by 15%.

[0140] The final output data will be passed to the power distribution module as a scheduling constraint.

[0141] 4. Power distribution module:

[0142] The power distribution module is the core of the scheduling system, responsible for developing a reasonable charging power distribution scheme based on the target vehicle and device status. The main functions are as follows:

[0143] Receive vehicle reservation information: including priority, reservation time period, current battery SOC, target SOC, etc.

[0144] Receive grid feedback parameters: such as substation load rate, grid frequency disturbance, electricity price peak valley section, etc.

[0145] Guiding scheduling sequence: according to the set weight, such as user VIP level, current state of charge (lower than 20% priority), reservation time urgency, etc.

[0146] The system uses linear programming or heuristic multi-objective optimization algorithm to output the recommended power value of each device, with "shortest charging completion time + most balanced energy consumption + smallest device operation risk" as the objective function.

[0147] The system also supports hierarchical scheduling strategies, such as:

[0148] Guaranteed power scheduling: Ensure that all vehicles at least meet the minimum charging demand;

[0149] Differentiated power boost: Allocate peak power to high-priority vehicles in advance;

[0150] Grid protection strategy: Reduce overall scheduling capacity by 15% during peak hours.

[0151] 5. Dynamic adjustment module:

[0152] During the charging process, the system continuously monitors the operating status of each pile. If any of the following events occurs, the dynamic adjustment logic will be triggered: abnormal temperature rise of the power module of a certain pile; Abnormal decrease in cooling fluid flow rate; Frequent current fluctuations; User ends charging in advance; New vehicles access the system.

[0153] The system dynamically adjusts the power distribution results according to the remaining capacity of the power pool. For example, when No. 05 pile is marked as "available limited", the power is limited to 70% after the power is limited, the excess power will be redistributed in No. 06, No. 12 and other well-conditioned piles to ensure overall power utilization.

[0154] At the same time, the system generates a "state change log" and records the adjustment history in real time to prevent system oscillation or frequent fluctuations in resource scheduling.

[0155] 6. Instruction issuing module:

[0156] This module converts the scheduling results into control instructions and issues them to each charging pile. The format includes: target output power value; allowable error range; execution duration (such as 30 seconds refresh); scheduling command validity period.

[0157] The instructions enter the power control logic module through the communication interface in the pile controller, and real-time adjust the PWM controller or switching power supply module to achieve fine charging control.

[0158] The instruction issuing module also has an abnormal response mechanism. If a pile feedback instruction is not executed or an abnormal alarm is triggered, it will automatically backtrack, modify parameters or trigger a backup scheduling scheme.

[0159] Sensor integration: The information collection module supports integration with high-precision thermocouples, current transformers, and magnetic flow meters to improve data collection accuracy.

[0160] Model automatic switching: The model calculation module supports embedded AI analyzers that automatically adapt to the optimal working condition template after training based on past data.

[0161] Reservation system access: The power distribution module supports interfacing with mobile APPs, mini-programs, BMS systems, etc. to enable user self-reservation and strategy configuration.

[0162] Safety redundancy design: Each pile body has local simple control logic, which can automatically maintain the minimum safe output power according to the last dispatch cache when the system is abnormal.

[0163] The embodiment describes the overall architecture and six module functions of the "high-power liquid-cooled charging pile charging power optimization scheduling system" in detail, covers the whole process of real-time monitoring, energy supply capacity analysis, intelligent scheduling and execution control, has high adaptability, safety and expansibility, and is suitable for smart parks, urban fast charging stations, highway service areas and other complex scenes, and is an important part of the new generation of intelligent energy infrastructure.

[0164] The basic principles, main features and advantages of the present application are shown and described. Those skilled in the art should understand that the present application is not limited to the above examples, and the above examples and descriptions in the specification are only to illustrate the principles of the present application. Without departing from the spirit and scope of the present application, various changes and improvements can be made to the present application, and these changes and improvements all fall within the scope of the claimed present application. The scope of protection of the present application is defined by the appended claims and their equivalents.

Claims

1. A high-power liquid-cooled charging pile charging power optimization scheduling method, characterized in that, The method comprises the following steps: S1, collecting device state parameters: obtaining the operating state information of a plurality of high-power liquid-cooled charging piles, including the current output current, voltage, temperature rise change, cooling liquid flow rate and charging connection state; S2, identifying scheduling target objects: based on the collected operating state information, identifying target charging piles in a charging state or standby state; S3, calculating the theoretical upper limit of energy supply: performing an electrothermal model analysis on the target charging piles, combining the current cooling efficiency, device health state and environmental temperature, and calculating the theoretical maximum energy supply power of each charging pile; S4, executing a power distribution strategy: based on the charging demand of the target vehicle, the battery state of charge, and the pre-charge time period information, constructing a power scheduling strategy, and distributing the output power of each charging pile on the premise of not exceeding the theoretical upper limit of energy supply; S5, dynamic balance adjustment: when detecting that the cooling capacity of a certain charging pile decreases, the current is abnormal or the connection is interrupted, the output power of the pile is reduced in time, and the remaining power is redistributed to other piles, ensuring the overall power utilization rate and temperature control safety; S6, generating a scheduling control instruction: based on the above distribution results, generating and issuing a scheduling control instruction to adjust the output strategy of each target charging pile, implement phased charging power control, and update the scheduling parameters in real time, Wherein: when obtaining the cooling liquid flow rate in step S1, the method further comprises: A plurality of flow rate detection units are arranged on the cooling pipeline, including an inlet section, an outlet section of the radiator and an outlet section of the main pump, and the instantaneous flow rates thereof are measured respectively, and by comparing the flow rate gradient change trend between the three sections, it is judged whether there is local blockage or flow resistance increase in the cooling system; When detecting that the local flow rate decreases by more than a set threshold, mark the cooling efficiency of the charging pile as degraded state, and dynamically calculate the cooling capacity loss coefficient thereof; In the power distribution link, the maximum allowable output power of the charging pile is limited by using the cooling capacity loss coefficient, and the limit value is not more than 80% of the initial rated output power, so as to ensure that the thermal control system can still maintain normal operation under limited flow conditions, Wherein: in step S4, the power scheduling strategy has the following constraint conditions: a) On the premise that the total power supply capacity is unchanged, the charging request of the vehicle set as "high priority" is preferentially guaranteed; b) According to the current battery state of charge of each target vehicle, calculate the acceptable maximum charging current per unit time, and limit the allocated power not to exceed the value; c) Introduce the grid load feedback parameter, if the regional grid load is close to the upper limit, the overall scheduling power will be compressed by a certain proportion; d) Considering the urgency of the vehicle charging reservation time period, the power distribution weight of the vehicle with a remaining reservation window less than a set threshold is increased; Under the premise of meeting the above constraint conditions, the output power of each charging pile is calculated by linear programming, and the distribution result is adjusted in real time to adapt to subsequent dynamic changes, Wherein: in step S5, when detecting that the cooling capacity of a certain charging pile decreases, a judgment mechanism is constructed based on the cooling liquid inlet and outlet temperature difference and the cooling load change rate, specifically including: Set the standard cooling efficiency curve, monitor the temperature difference between the outlet temperature and the inlet temperature in real time, and the temperature rise rate per unit power generated; compare the temperature difference and temperature rise rate with the standard value, if both exceed the set interval, mark the pile state as "partially available", and trigger the load shedding strategy; The load shedding ratio is set according to the temperature difference offset by a step function: The temperature difference is less than or equal to the first threshold value, which is a mild offset, and the charging power is limited to 90%; The temperature difference is greater than the first threshold value and less than or equal to the second threshold value, which is a moderate offset, and the charging power is limited to 70%; The temperature difference is greater than the second threshold value, which is a severe offset, and the charging power is limited to 50%, and the global power allocatable pool is updated.

2. The method of claim 1, wherein the method further comprises: In step S3, the electro-thermal model analysis of the target charging pile specifically includes: Based on the actual working curve of the charging pile, a multi-parameter fitting model is constructed, and the model input includes: current output current value, environmental temperature, cooling liquid inlet temperature, outlet temperature, flow rate, radiator efficiency; Taking the relationship between output power and temperature rise as the core feature, a nonlinear fitting curve is constructed, and a device aging factor is introduced to weight and correct the model output; the aging factor is estimated from the device historical use time, cumulative load running time and maintenance record; The final output is a dynamic power supply capability curve, which is used to represent the upper limit of the stable power supply capability that the charging pile can maintain under the current environment and device state.

3. The method of claim 1, wherein: When generating the scheduling control instruction in step S6, the following operations are further performed: Encode the current power allocation result into a multi-parameter instruction set, including target power value, allowed error interval, execution period and validity period identifier, and write the instruction into the scheduling buffer of each pile control unit; Synchronously generate a scheduling log to record the power change trajectory, electro-thermal parameter change, and load response data information of each pile body, and store the log in the local cache and remote scheduling database in time stamp mode; When power execution fails or an abnormal alarm occurs, backtrack and correct the parameters based on the log data.

4. A high-power liquid-cooled charging pile charging power optimization scheduling system for implementing the method of any one of claims 1 to 3, characterized in that, It includes: An information acquisition module for acquiring current, voltage, temperature rise, and cooling liquid flow rate operating state parameters of each charging pile; A state recognition module for recognizing the target charging pile and judging its cooling capacity and working state; A model calculation module for calculating the theoretical power supply upper limit of each charging pile based on the electro-thermal model; A power allocation module for building a scheduling strategy and allocating power; A dynamic adjustment module for responding to changes in operating state and adjusting the power allocation result; An instruction issuing module for generating a scheduling control instruction and controlling each charging pile to execute the charging strategy in real time.

5. The high-power liquid-cooled charging pile charging power optimization scheduling system according to claim 4, characterized in that: The information acquisition module is connected to temperature sensors, current sensors, and flow monitoring units, and has data preprocessing and abnormality marking functions.

6. The high-power liquid-cooled charging pile charging power optimization scheduling system according to claim 4, characterized in that: The model calculation module has a power supply curve template under multiple environmental conditions, and has the ability to dynamically switch to the corresponding working condition based on real-time data.

7. The high-power liquid-cooled charging pile charging power optimization scheduling system according to claim 4, characterized in that: The power allocation module supports a user charging reservation information interface, can receive priority parameters set by the scheduling background, and is used to guide the scheduling model to sort resource allocation.

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