Power pooling and flexible allocation method, architecture and megawatt liquid-cooled supercharging device

Abstract

The application relates to a power pooling and flexible distribution method, architecture and megawatt liquid-cooled supercharging device, and relates to the technical field of charging. On the basis of the power pooling and power flexible intelligent distribution technology, a multi-objective optimization algorithm is introduced, multi-dimensional information related to the megawatt liquid-cooled supercharging device is utilized, real-time power resources are distributed, and the optimization effect of power resource distribution is achieved. Therefore, the technical scheme can realize the optimization of power pooling and flexible distribution effect, improve the utilization rate of the power resource pool, improve the charging efficiency of the megawatt liquid-cooled supercharging device, meet the charging demands of more vehicles, and further improve the income of the charging station.

Description

Technical Field

[0001] The present application relates to the field of charging technology, and in particular, to a power pooling and flexible allocation method, architecture, and a megawatt-level liquid-cooled supercharging device. Background Art

[0002] As the penetration rate of new energy vehicles continues to rise, consumer demand for public charging stations is also growing. From a consumer perspective, difficulty and slow charging remain the biggest barriers to choosing new energy vehicles. From an operator's perspective, the high investment, long cycle times, high costs, and difficulty in generating profitability of charging stations are common pain points in the industry.

[0003] Consequently, power pooling and flexible intelligent power allocation technologies have evolved. These technologies split the charging module of a traditional charging station into two, adopting a two-tiered power-sharing architecture. At the first tier, power pooling streamlines the total power of the charging station into a resource pool, laying the foundation for unified intelligent allocation. At the second tier, flexible intelligent power allocation utilizes multi-tiered channels to dynamically and precisely allocate power resources to different charging stations, meeting the charging needs of vehicles with varying power requirements. Summary of the Invention

[0004] The purpose of this application is to provide a power pooling and flexible allocation method, architecture and megawatt-level liquid-cooled supercharging device. The power pooling and flexible allocation method, architecture and megawatt-level liquid-cooled supercharging device can optimize the power pooling and flexible allocation effects, improve the utilization rate of the power resource pool, and improve the charging efficiency of the megawatt-level liquid-cooled supercharging device, meet the charging needs of more vehicles, and thereby increase the revenue of the charging station.

[0005] In order to achieve the above-mentioned objectives, in the first aspect, the present application provides a power pooling and flexible allocation method, including: generating a power resource pool corresponding to a charging station based on power resources provided by at least one power supply, wherein the charging station includes multiple megawatt-level liquid-cooled supercharging devices; based on the power resource pool, allocating original power resources to the multiple megawatt-level liquid-cooled supercharging devices respectively; obtaining the real-time charging demand, real-time operation information and historical charging demand corresponding to the multiple megawatt-level liquid-cooled supercharging devices respectively; through a multi-objective optimization algorithm, based on the power resource pool and the real-time charging demand, real-time operation information and historical charging demand corresponding to the multiple megawatt-level liquid-cooled supercharging devices respectively, allocating real-time power resources to the multiple megawatt-level liquid-cooled supercharging devices respectively.

[0006] Optionally, the power resource pool corresponding to the charging station is generated based on the power resources provided by at least one power supply, including: generating a main power resource pool and a redundant power resource pool corresponding to the charging station based on the power resources provided by at least one power supply, wherein the power resources of the redundant power resource pool are less than the power resources of the main power resource pool; the original power resources are allocated to the multiple megawatt-class liquid-cooled supercharging devices based on the power resource pool, including: allocating original power resources to the multiple megawatt-class liquid-cooled supercharging devices based on the main power resource pool; the real-time power resources are allocated to the multiple megawatt-class liquid-cooled supercharging devices based on the real-time charging requirements, real-time operation information and historical charging requirements corresponding to the power resource pool and the multiple megawatt-class liquid-cooled supercharging devices respectively through a multi-objective optimization algorithm, including: allocating real-time power resources to the multiple megawatt-class liquid-cooled supercharging devices based on the real-time charging requirements, real-time operation information and historical charging requirements corresponding to the redundant power resource pool and the multiple megawatt-class liquid-cooled supercharging devices respectively through a multi-objective optimization algorithm.

[0007] Optionally, the allocating original power resources to the multiple megawatt-class liquid-cooled supercharging devices based on the power resource pool includes: when the number of the multiple megawatt-class liquid-cooled supercharging devices is lower than a preset number, allocating the same original power resources to the multiple megawatt-class liquid-cooled supercharging devices based on the power resource pool; when the number of the multiple megawatt-class liquid-cooled supercharging devices is higher than the preset number, determining the predicted charging demands corresponding to the multiple megawatt-class liquid-cooled supercharging devices within a preset future time based on the position information corresponding to the multiple megawatt-class liquid-cooled supercharging devices, wherein the position information is used to characterize the position relative to the entrance of the charging station; based on the power resource pool, the sliding time window mechanism and the predicted charging demands corresponding to the multiple megawatt-class liquid-cooled supercharging devices, allocating corresponding original power resources to the multiple megawatt-class liquid-cooled supercharging devices.

[0008] Optionally, the method of allocating corresponding original power resources to the multiple megawatt-class liquid-cooled supercharging devices based on the power resource pool, the sliding time window mechanism and the predicted charging demands corresponding to the multiple megawatt-class liquid-cooled supercharging devices respectively includes: determining a first megawatt-class liquid-cooled supercharging device and a second megawatt-class liquid-cooled supercharging device from the multiple megawatt-class liquid-cooled supercharging devices according to the predicted charging demands corresponding to the multiple megawatt-class liquid-cooled supercharging devices, the predicted charging demand corresponding to the first megawatt-class liquid-cooled supercharging device being higher than the preset charging demand, and the predicted charging demand corresponding to the second megawatt-class liquid-cooled supercharging device being lower than the preset charging demand; allocating the same original power resources to the first megawatt-class liquid-cooled supercharging device based on the power resource pool; determining the sliding time window size corresponding to the second megawatt-class liquid-cooled supercharging device according to the predicted charging demands corresponding to the second megawatt-class liquid-cooled supercharging device; and allocating original power resources to the second megawatt-class liquid-cooled supercharging device from the remaining power resources of the power resource pool based on the sliding time window size corresponding to the second megawatt-class liquid-cooled supercharging device.

[0009] Optionally, the multi-objective optimization algorithm is an NSGA-II algorithm, and the multi-objective optimization algorithm is used to allocate real-time power resources to the multiple megawatt-level liquid-cooled supercharging devices based on the real-time charging requirements, real-time operation information and historical charging requirements corresponding to the power resource pool and the multiple megawatt-level liquid-cooled supercharging devices respectively, including: using the NSGA-II algorithm, based on the real-time charging requirements, real-time operation information and historical charging requirements corresponding to the power resource pool and the multiple megawatt-level liquid-cooled supercharging devices respectively, calculating the objective function value of the NSGA-II algorithm; using the NSGA-II algorithm, based on the objective function value and preset constraints, solving the power resource allocation scheme, the preset constraints including: the minimum allocated power resource constraint of the megawatt-level liquid-cooled supercharging device and the matching relationship between the liquid cooling system flow and power resources of the megawatt-level liquid-cooled supercharging device; according to the power resource allocation scheme, real-time power resources are allocated to the multiple megawatt-level liquid-cooled supercharging devices respectively.

[0010] Optionally, the objective function of the NSGA-II algorithm includes: a power resource pool utilization function, a megawatt-level liquid-cooled supercharging device heat load function and a megawatt-level liquid-cooled supercharging device waiting time function. The NSGA-II algorithm is used to calculate the objective function value of the NSGA-II algorithm based on the real-time charging demand, real-time operation information and historical charging demand corresponding to the power resource pool and the multiple megawatt-level liquid-cooled supercharging devices, including: determining the power resource pool utilization function value based on the actual allocated power and available power of the power resource pool; determining the megawatt-level liquid-cooled supercharging device waiting time function value based on the real-time charging demand and the historical charging demand; and determining the megawatt-level liquid-cooled supercharging device heat load function value based on the real-time operation information.

[0011] Optionally, the NSGA-II algorithm is used to solve a power resource allocation scheme based on the objective function value and preset constraints, including: solving an initial power resource allocation scheme solution set based on the objective function value and preset constraints; determining a Pareto optimal solution set from the initial power resource allocation scheme solution set through an elite retention strategy; and determining a target power resource allocation scheme from the Pareto optimal solution set through a fuzzy decision algorithm; wherein the input variables of the fuzzy decision algorithm include: grid electricity price period, manual intervention instructions of operation and maintenance personnel, and emergency charging request signals, and the output variables of the fuzzy decision algorithm include: a dynamic adjustment range of the weight ratio of the objective function of the NSGA-II algorithm.

[0012] Optionally, the power pooling and flexible allocation method also includes: in response to a faulty megawatt-class liquid-cooled supercharging device among the multiple megawatt-class liquid-cooled supercharging devices, obtaining fault information; based on the fault information, determining a target megawatt-class liquid-cooled supercharging device from the megawatt-class liquid-cooled supercharging devices other than the faulty megawatt-class liquid-cooled supercharging device; and transferring the power resources allocated to the faulty megawatt-class liquid-cooled supercharging device to the target megawatt-class liquid-cooled supercharging device.

[0013] In the second aspect, the present application provides a power pooling flexible allocation architecture, including: at least one power supply; multiple megawatt-level liquid-cooled supercharging devices respectively connected to the at least one power supply; an intelligent scheduling end, respectively connected to the at least one power supply and the multiple megawatt-level liquid-cooled supercharging devices, and the intelligent scheduling end is used to execute the power pooling and flexible allocation method as described in the first aspect of this disclosure.

[0014] In the third aspect, the present application provides a megawatt-level liquid-cooled supercharging device, which allocates power resources through the power pooling and flexible allocation method as described in the first aspect of the present disclosure. The megawatt-level liquid-cooled supercharging device includes: a modular power unit, including: a liquid-cooled SiC charging module; a distributed liquid cooling system, including: a manifold-type diverter and a microchannel cold plate; a power pooling interface; wherein, the liquid cooling system adopts a graded temperature control strategy: primary cooling adopts ethylene glycol aqueous solution circulation; secondary cooling adopts a phase change material cold storage device; emergency cooling adopts a compressor refrigeration unit.

[0015] Through the above technical solution, based on the power resources provided by at least one power source, a power resource pool corresponding to the charging station is generated, and based on the power resource pool, original power resources are allocated to multiple megawatt-class liquid-cooled supercharging devices. Through a multi-objective optimization algorithm, combined with the real-time charging needs, real-time operation information and historical charging needs corresponding to the power resource pool and multiple megawatt-class liquid-cooled supercharging devices, real-time power resources are allocated to multiple megawatt-class liquid-cooled supercharging devices. This technical solution introduces a multi-objective optimization algorithm on the basis of power pooling and flexible power intelligent allocation technology, and utilizes multi-dimensional information related to megawatt-class liquid-cooled supercharging devices to allocate real-time power resources, thereby optimizing the allocation of power resources. Therefore, this technical solution can optimize the power pooling and flexible allocation effects, improve the utilization rate of the power resource pool, and improve the charging efficiency of megawatt-class liquid-cooled supercharging devices, meet the charging needs of more vehicles, and thereby increase the revenue of the charging station.

[0016] Other features and advantages of the present application will be described in detail in the subsequent detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings are used to provide a further understanding of the present application and constitute a part of the specification. Together with the following detailed description, they are used to explain the present application but do not constitute a limitation of the present application. In the accompanying drawings:

[0018] Figure 1 The figure is a schematic diagram showing a power pooling flexible allocation architecture according to an exemplary embodiment.

[0019] Figure 2 It is a structural block diagram of a megawatt-class liquid-cooled supercharging device according to an exemplary embodiment.

[0020] Figure 3 The figure is a flow chart showing a method for power pooling and flexible allocation according to an exemplary embodiment.

[0021] Figure 4 The figure is a block diagram showing a power pooling and flexible allocation device according to an exemplary embodiment.

[0022] Figure 5 It is a block diagram of an electronic device according to an exemplary embodiment. DETAILED DESCRIPTION

[0023] The following describes the specific embodiments of the present application in detail with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only used to illustrate and explain the present application and are not intended to limit the present application.

[0024] As the penetration rate of new energy vehicles continues to rise, consumer demand for public charging stations is also growing. From a consumer perspective, difficulty and slow charging remain the biggest barriers to choosing new energy vehicles. From an operator's perspective, the high investment, long cycle times, high costs, and difficulty in generating profitability of charging stations are common pain points in the industry.

[0025] Consequently, power pooling and flexible intelligent power allocation technologies have evolved. These technologies split the charging module of a traditional charging station into two, adopting a two-tiered power-sharing architecture. At the first tier, power pooling streamlines the total power of the charging station into a resource pool, laying the foundation for unified intelligent allocation. At the second tier, flexible intelligent power allocation utilizes multi-tiered channels to dynamically and precisely allocate power resources to different charging stations, meeting the charging needs of vehicles with varying power requirements.

[0026] In related technologies, during the second-level power distribution process, distribution is based on vehicle charging needs. This distribution method is too simple, resulting in poor power pooling and flexible distribution effects, and thus low utilization of the power resource pool. The charging efficiency of the megawatt-level liquid-cooled supercharging device is low, which cannot meet the charging needs of more vehicles, and the revenue of the charging station is difficult to be significantly improved.

[0027] Based on this, an embodiment of the present application provides a technical solution, which generates a power resource pool corresponding to the charging station based on the power resources provided by at least one power supply, and allocates original power resources to multiple megawatt-level liquid-cooled supercharging devices based on the power resource pool. Through a multi-objective optimization algorithm, combined with the power resource pool, the real-time charging needs, real-time operation information and historical charging needs corresponding to the multiple megawatt-level liquid-cooled supercharging devices, real-time power resources are allocated to multiple megawatt-level liquid-cooled supercharging devices.

[0028] Based on power pooling and flexible intelligent power allocation technology, this technical solution introduces a multi-objective optimization algorithm, utilizes multi-dimensional information related to megawatt-level liquid-cooled supercharging devices, allocates real-time power resources, and optimizes power resource allocation.

[0029] Therefore, this technical solution can optimize power pooling and flexible allocation effects, improve the utilization rate of the power resource pool, and improve the charging efficiency of megawatt-level liquid-cooled supercharging devices, meet the charging needs of more vehicles, and thus increase the revenue of charging stations.

[0030] Figure 1 is a schematic diagram showing a power pooling flexible allocation architecture according to an exemplary embodiment. Figure 1 As shown, the power pooling flexible allocation architecture includes: at least one power supply; multiple megawatt-class liquid-cooled supercharging devices respectively connected to at least one power supply; and an intelligent scheduling end, respectively connected to at least one power supply and multiple megawatt-class liquid-cooled supercharging devices.

[0031] In some embodiments, the at least one power source may include: a power grid, photovoltaics, energy storage batteries, and generators.

[0032] In some embodiments, the intelligent scheduling end may be a computing gateway, such as an edge computing gateway.

[0033] Figure 2 FIG. 1 is a block diagram of a megawatt-class liquid-cooled supercharging device according to an exemplary embodiment. Figure 2 As shown, the megawatt-class liquid-cooled supercharging device includes: a modular power unit, a distributed liquid cooling system and a power pooling interface.

[0034] In some embodiments, the modular power unit may include a liquid-cooled SiC charging module, through which the charging function of a megawatt-class liquid-cooled supercharging device may be realized.

[0035] In some embodiments, the distributed liquid cooling system may include: a manifold-type diverter and a microchannel cold plate, through which distributed liquid cooling temperature control can be achieved.

[0036] In some embodiments, the liquid cooling system adopts a hierarchical temperature control strategy: primary cooling adopts ethylene glycol aqueous solution circulation; secondary cooling adopts a phase change material cold storage device; emergency cooling adopts a compressor refrigeration unit.

[0037] Through the hierarchical temperature control strategy, the stability and reliability of temperature control can be improved, thereby ensuring the reliability and stability of the distributed liquid cooling system.

[0038] In some embodiments, the power pooling interface may support N+1 redundant parallel expansion, and may enable connection with external objects, such as connection with an intelligent scheduling terminal and at least one power supply.

[0039] In some embodiments, the megawatt-class liquid-cooled supercharging device may also include a controller / processor that integrates V2G (Vehicle to Grid) communication protocol and load forecasting functions, which is not limited here.

[0040] In some embodiments, the SiC charging module is adjustable between 1C-4C charging rates and has a charging curve learning function that can automatically optimize the charging strategy based on battery health.

[0041] Figure 3 This is a flow chart of a power pooling and flexible allocation method according to an exemplary embodiment. The method can be applied to Figure 1 The intelligent scheduling terminal shown is used to Figure 2 The megawatt-class liquid-cooled supercharger shown in the figure performs power resource allocation, as shown in the figure. Figure 3 As shown, the method includes the following steps:

[0042] Step S31: generating a power resource pool corresponding to the charging station based on power resources provided by at least one power source, wherein the charging station includes multiple megawatt-class liquid-cooled supercharging devices.

[0043] Step S32: Based on the power resource pool, allocate original power resources to multiple megawatt-class liquid-cooled supercharging devices respectively.

[0044] Step S33: Obtain the real-time charging demand, real-time operation information, and historical charging demand corresponding to multiple megawatt-class liquid-cooled supercharging devices.

[0045] In step S34, real-time power resources are allocated to multiple megawatt-class liquid-cooled supercharging devices through a multi-objective optimization algorithm based on the power resource pool, the real-time charging demands, real-time operation information and historical charging demands corresponding to the multiple megawatt-class liquid-cooled supercharging devices.

[0046] In some embodiments, the power resource pool may be understood as a virtual resource pool, which may analyze power resources that can be provided by at least one power source, and then allocate power resources based on the resource pool.

[0047] In some embodiments, power resources provided by at least one power source may be integrated into an aggregated power resource pool, such that the power resource pool covers the available power capacity that can be provided by the at least one power source.

[0048] In some embodiments, step S31 may include: generating a main power resource pool and a redundant power resource pool corresponding to the charging station based on power resources provided by at least one power source, wherein the power resources of the redundant power resource pool are less than the power resources of the main power resource pool.

[0049] In this embodiment, the power resource pool is divided into two resource pools to achieve fine division of power resources, which can improve the flexibility and redundancy of subsequent power resource allocation.

[0050] Furthermore, in step S32, raw power resources are allocated to each of the multiple megawatt-class liquid-cooled supercharging devices based on the primary power resource pool. Furthermore, in step S34, real-time power resources are allocated to each of the multiple megawatt-class liquid-cooled supercharging devices based on the redundant resource pool, the real-time charging demand, real-time operating information, and historical charging demand corresponding to each of the multiple megawatt-class liquid-cooled supercharging devices, using a multi-objective optimization algorithm.

[0051] In this implementation, raw power resources are allocated based on the primary power resource pool. Since raw power resources are the foundation for the operation of each megawatt-class liquid-cooled supercharger, allocating raw power resources based on the primary power resource pool, which has more power resources, ensures that each megawatt-class liquid-cooled supercharger can provide stable charging capabilities. Furthermore, allocating real-time power resources based on the redundant power resource pool allows for flexible allocation of redundant power resources based on existing raw power resources, improving power resource utilization.

[0052] It can be understood that the original power resources are allocated based on the main power resource pool, representing the total power resources that can be allocated, which is the power resources in the main power resource pool. The real-time power resources are allocated based on the redundant resource pool, representing the total power resources that can be allocated, which is the power resources in the redundant resource pool.

[0053] As an optional implementation, step S32 includes: when the number of multiple megawatt-class liquid-cooled supercharging devices is lower than a preset number, based on the power resource pool, allocating the same original power resources to the multiple megawatt-class liquid-cooled supercharging devices respectively.

[0054] In some embodiments, the preset number can be a value within the range of 2 to 5. If the number of multiple megawatt-class liquid-cooled supercharging devices is less than the preset number, it indicates that the number of megawatt-class liquid-cooled supercharging devices is small. Therefore, there is no need to differentiate the allocation of raw power resources, and the same raw power resources can be allocated to multiple megawatt-class liquid-cooled supercharging devices.

[0055] Therefore, taking the main power resource pool as an example, the power resources in the main power resource pool can be evenly distributed to each megawatt-class liquid-cooled supercharger. For example, if there are three megawatt-class liquid-cooled superchargers and the power resources in the main power resource pool are 300 kW, each megawatt-class liquid-cooled supercharger can be allocated 100 kW of raw power resources.

[0056] In some embodiments, when the number of multiple megawatt-class liquid-cooled supercharging devices is higher than a preset number, based on the location information corresponding to the multiple megawatt-class liquid-cooled supercharging devices, the predicted charging demands corresponding to the multiple megawatt-class liquid-cooled supercharging devices within a preset future time are determined, wherein the location information is used to characterize the location relative to the entrance of the charging station; based on the power resource pool, the sliding time window mechanism and the predicted charging demands corresponding to the multiple megawatt-class liquid-cooled supercharging devices, the corresponding original power resources are allocated to the multiple megawatt-class liquid-cooled supercharging devices respectively.

[0057] In some embodiments, the preset future time may be a time between 15 minutes and 30 minutes.

[0058] In some embodiments, the closer the megawatt-class liquid-cooled Supercharger is to the charging station entrance, the higher the predicted charging demand. Therefore, location information can be represented by a distance value, and a relationship between the distance value and the charging demand can be preconfigured. Based on this relationship and the corresponding location information, the charging demand can be predicted.

[0059] As an example, the relationship between distance and charging demand can be expressed as P = I × D + P0, where P0 can be a fixed value representing the basic charging demand, I is a preset coefficient, and D is the distance. The preset coefficient can be determined by analyzing charging demand data from a large number of megawatt-class liquid-cooled supercharging devices and can be understood as a conversion coefficient between distance and charging demand. The value is not limited here.

[0060] Furthermore, after determining the predicted charging demand, corresponding original power resources can be allocated to multiple megawatt-class liquid-cooled supercharging devices based on the power resource pool, the sliding time window mechanism and the predicted charging demands corresponding to multiple megawatt-class liquid-cooled supercharging devices.

[0061] As an optional implementation manner, based on the power resource pool, the sliding time window mechanism and the predicted charging demands corresponding to multiple megawatt-level liquid-cooled supercharging devices, corresponding original power resources are allocated to multiple megawatt-level liquid-cooled supercharging devices respectively, including: according to the predicted charging demands corresponding to the multiple megawatt-level liquid-cooled supercharging devices, a first megawatt-level liquid-cooled supercharging device and a second megawatt-level liquid-cooled supercharging device are determined from the multiple megawatt-level liquid-cooled supercharging devices, the predicted charging demand corresponding to the first megawatt-level liquid-cooled supercharging device is higher than the preset charging demand, and the predicted charging demand corresponding to the second megawatt-level liquid-cooled supercharging device is lower than the preset charging demand; based on the power resource pool, the same original power resources are allocated to the first megawatt-level liquid-cooled supercharging device respectively; according to the predicted charging demands corresponding to the second megawatt-level liquid-cooled supercharging device, the sliding time window size corresponding to the second megawatt-level liquid-cooled supercharging device is determined; based on the sliding time window size corresponding to the second megawatt-level liquid-cooled supercharging device, the original power resources are allocated to the second megawatt-level liquid-cooled supercharging device from the remaining power resources in the power resource pool.

[0062] In some embodiments, the preset charging demand may be a charging demand between 0 and 50 kW. If the predicted charging demand is lower than the preset charging demand, it indicates a low charging demand. If the predicted charging demand is higher than the preset charging demand, it indicates a high charging demand.

[0063] Therefore, for the first megawatt-class liquid-cooled supercharging device, due to the lower charging demand, the same raw power resources can be allocated. Specifically, 50KW of raw power resources can be uniformly allocated.

[0064] For the second megawatt-class liquid-cooled supercharger, due to its higher charging demand, different raw power resources can be allocated. Therefore, the sliding time window size corresponding to each second megawatt-class liquid-cooled supercharger can be determined based on the predicted charging demand corresponding to each second megawatt-class liquid-cooled supercharger.

[0065] In some embodiments, the higher the predicted charging demand, the larger the sliding time window size. As an example, for every 1KW increase in the predicted charging demand, the sliding time window size increases by 1 minute.

[0066] Furthermore, based on the sliding time window size corresponding to the second megawatt liquid-cooled supercharging device, original power resources are allocated to the second megawatt liquid-cooled supercharging device from the remaining power resources in the power resource pool.

[0067] In some embodiments, the larger the sliding time window size, the more raw power resources are allocated.

[0068] As an example, for every 1 minute that the sliding time window size increases, the allocated original power resources increase by 10 KW.

[0069] Furthermore, after allocating original power resources to multiple megawatt-class liquid-cooled supercharging devices respectively, the charging station can be considered to have started operation, and subsequently vehicles will use the corresponding megawatt-class liquid-cooled supercharging devices for charging according to demand.

[0070] In some embodiments, the allocation of power resources can be understood as generating a PWM (Pulse Width Modulation) duty cycle instruction for the SiC driver of the power module of the megawatt-class liquid-cooled supercharging device, and synchronously adjusting the liquid cooling pump speed to achieve charging power adjustment of the megawatt-class liquid-cooled supercharging device.

[0071] That is, the allocation of power resources can be understood as first determining the power resource allocation plan, and then converting it into a PWM duty cycle instruction for the SiC driver of the power module of the megawatt-level liquid-cooled supercharging device, and synchronously adjusting the liquid cooling pump speed.

[0072] Furthermore, in step S33, the real-time charging demand, real-time operation information and historical charging demand corresponding to multiple megawatt-class liquid-cooled supercharging devices are obtained.

[0073] Regarding real-time charging demand, it can be related to the charging demand of vehicles currently connected to the megawatt-class liquid-cooled supercharging device.

[0074] Real-time operating information may include operating parameters of the liquid cooling system of the megawatt-class liquid-cooled supercharging device, as well as the operating temperature, operating load, etc. of the megawatt-class liquid-cooled supercharging device.

[0075] The historical charging demand may be related to the charging demand of vehicles connected to the megawatt-class liquid-cooled supercharging device in a past time period, and the historical charging demand may be recorded by the megawatt-class liquid-cooled supercharging device.

[0076] Furthermore, in step S34, through a multi-objective optimization algorithm, real-time power resources are allocated to multiple megawatt-class liquid-cooled supercharging devices based on the power resource pool, the real-time charging needs, real-time operation information and historical charging needs corresponding to the multiple megawatt-class liquid-cooled supercharging devices respectively.

[0077] In some embodiments, the multi-objective optimization algorithm is the NSGA-II algorithm. The NSGA-II algorithm is a multi-objective genetic algorithm that reduces the complexity of the non-inferior sorting genetic algorithm and has the advantages of fast execution speed and good convergence of the solution set, becoming a benchmark for the performance of other multi-objective optimization algorithms.

[0078] Therefore, step S34 may include: calculating the objective function value of the NSGA-II algorithm based on the power resource pool and the real-time charging demands, real-time operation information and historical charging demands corresponding to multiple megawatt-level liquid-cooled supercharging devices through the NSGA-II algorithm; solving the power resource allocation plan through the NSGA-II algorithm based on the objective function value and preset constraints, the preset constraints including: the minimum allocated power resource constraint of the megawatt-level liquid-cooled supercharging device and the matching relationship between the liquid cooling system flow and power resources of the megawatt-level liquid-cooled supercharging device; and allocating real-time power resources to multiple megawatt-level liquid-cooled supercharging devices according to the power resource allocation plan.

[0079] In some embodiments, the NSGA-II algorithm is configured with an objective function and preset constraints, which can be used to find a solution.

[0080] In some embodiments, the real-time charging demand, the real-time operating information, and the historical charging demand may be related to the objective function and thus may be used to calculate the objective function value.

[0081] In some embodiments, the preset constraints include: a minimum allocated power resource constraint of a megawatt-class liquid-cooled supercharging device and a matching relationship between the liquid cooling system flow and power resources of the megawatt-class liquid-cooled supercharging device.

[0082] As an example, the minimum allocated power resource constraint is: allocated power ≥ its minimum operating threshold (≥50 kW); the matching relationship between the liquid cooling system flow and power resources is: Q ≥ 0.2 × P_alloc (L / min / kW), where Q represents the flow and P_alloc represents the allocated power resources.

[0083] Furthermore, after obtaining the power resource allocation scheme, the allocation result can be converted into a SiC driver PWM duty cycle instruction of the power module with reference to the implementation method in the aforementioned embodiment, and the liquid cooling pump speed can be adjusted synchronously to achieve power resource allocation.

[0084] In some embodiments, the objective function of the NSGA-II algorithm includes: a power resource pool utilization function, a megawatt-class liquid-cooled supercharger heat load function, and a megawatt-class liquid-cooled supercharger waiting time function.

[0085] Accordingly, based on the real-time charging demand, real-time operation information and historical charging demand corresponding to the power resource pool and multiple megawatt-class liquid-cooled supercharging devices, the objective function value of the NSGA-II algorithm is calculated, including: determining the power resource pool utilization function value based on the actual allocated power and available power of the power resource pool; determining the waiting time function value of the megawatt-class liquid-cooled supercharging device based on the real-time charging demand and historical charging demand; and determining the heat load function value of the megawatt-class liquid-cooled supercharging device based on the real-time operation information.

[0086] In this embodiment, the actual allocated power of the power resource pool may be the power resources currently allocated by the power resource pool, and the available power may be the power resources currently not allocated by the power resource pool.

[0087] As an example, the power resource pool utilization function value η=Σ(actual allocated power / available power).

[0088] In some embodiments, the waiting time function value is: T_wait = Σ(demanded power - allocated power) / rated power. The demanded power can be determined based on real-time charging demand and historical charging demand. For example, the demanded power is the weighted sum of real-time charging demand and historical charging demand, where the weight of historical charging demand is lower than that of real-time charging demand. The rated power is an inherent parameter of the megawatt-class liquid-cooled supercharging device and can be directly obtained.

[0089] In some embodiments, the waiting time function value may also consider the priority of each faulty megawatt-class liquid-cooled supercharging device. The higher the priority, the smaller the waiting time function value needs to be.

[0090] In some embodiments, the heat load function value H=Σ(ΔT×L), where ΔT is the temperature difference between the inlet and outlet of the liquid cooling system, and L is the flow rate of the liquid cooling system, both of which can be obtained through real-time operation information.

[0091] Furthermore, a power resource allocation scheme is solved based on the objective function value and preset constraints through the NSGA-II algorithm, including: solving an initial power resource allocation scheme solution set based on the objective function value and preset constraints; determining a Pareto optimal solution set from the initial power resource allocation scheme solution set through an elite retention strategy; and determining a target power resource allocation scheme from the Pareto optimal solution set through a fuzzy decision algorithm; wherein the input variables of the fuzzy decision algorithm include: grid electricity price period, manual intervention instructions of operation and maintenance personnel, and emergency charging request signals; and the output variables of the fuzzy decision algorithm include: a dynamic adjustment range of the weight ratio of the objective function of the NSGA-II algorithm.

[0092] In this embodiment, the NSGA-II algorithm can solve the multi-objective optimization problem based on the objective function value and preset constraints to obtain an initial power resource allocation solution set.

[0093] Then, the elite retention strategy is used to determine the Pareto optimal solution set from the initial power resource allocation solution set.

[0094] Furthermore, the target power resource allocation scheme is determined from the Pareto optimal solution set through a fuzzy decision algorithm.

[0095] In some embodiments, the elite retention strategy is an existing strategy of the NSGA-II algorithm, and can refer to mature technologies in the field, which will not be introduced here.

[0096] In some embodiments, the fuzzy decision algorithm can be understood as a further improvement based on the elite retention strategy. It can add the control of three input variables: grid electricity price period, manual intervention instructions of operation and maintenance personnel, and emergency charging request signal, to obtain the dynamic adjustment range of the weight ratio of the objective function of the NSGA-II algorithm, thereby updating the objective function value to solve the target (optimal) power resource allocation plan from the Pareto optimal solution set.

[0097] The dynamic adjustment range of the weight ratio of the objective function can be understood as adjusting the weight ratio of the three objective functions in solving the allocation solution to achieve the effect of dynamically adjusting the key influencing factors of power resource allocation.

[0098] In some embodiments, the NSGA-II algorithm can employ dual-dimensional encoding in space and time: The spatial dimension gene represents the power allocation ratio from each power source to the supercharger (encoded as a real number 0-1). The temporal dimension gene represents the adjustment coefficient for predicted future demand changes (fitted with sliding window historical data). This dual-dimensional encoding can be used to determine the power allocation solution.

[0099] In some embodiments, a battery health compensation factor can also be introduced into the objective function: BSoH_correction=1+0.5×(1-BSoH). When it is detected that the SOH of the charging vehicle battery is less than 80%, its allocated power weight is automatically reduced by 20%~30%.

[0100] By adding a fuzzy decision-making algorithm on the basis of the NSGA-II algorithm, the flexibility and applicability of the NSGA-II algorithm are improved, thereby improving the adaptability of the final power resource allocation scheme to the current charging scenario, improving charging efficiency, and reducing the waste of power resources.

[0101] In some embodiments, the power pooling and flexible allocation method may also include: obtaining fault information in response to a faulty megawatt-class liquid-cooled supercharging device among multiple megawatt-class liquid-cooled supercharging devices; determining a target megawatt-class liquid-cooled supercharging device from megawatt-class liquid-cooled supercharging devices other than the faulty megawatt-class liquid-cooled supercharging device based on the fault information; and transferring the power resources allocated to the faulty megawatt-class liquid-cooled supercharging device to the target megawatt-class liquid-cooled supercharging device.

[0102] In this embodiment, in the event of a faulty megawatt-class liquid-cooled supercharger, a power resource transfer operation can be performed. Therefore, a target megawatt-class liquid-cooled supercharger can be determined based on the fault information.

[0103] In some embodiments, the fault information may include: fault duration, fault cause (reported by the corresponding fault detection unit) and charging requirements before the fault.

[0104] Then, the real-time charging demand of the target megawatt-class liquid-cooled supercharging device can be matched with the fault duration, fault cause (reported by the corresponding fault detection unit) and the charging demand before the fault.

[0105] As an example, the fault impact range (represented by the distance from the faulty megawatt-class liquid-cooled supercharging device) is determined based on the fault duration and fault cause. The target megawatt-class liquid-cooled supercharging device must be located within this fault impact range. The longer the fault duration, the larger the fault impact range, while the simpler the fault cause, the smaller the fault impact range.

[0106] Furthermore, based on the charging demand before the fault, an adapted charging demand range is determined. The charging demand of the target megawatt-class liquid-cooled supercharger must fall within this adapted charging demand range. The adapted charging demand range may include the charging demand before the fault. For example, if the charging demand before the fault was 60 kW, the adapted charging demand range may be 50-70 kW.

[0107] Through the above constraints, the target megawatt-level liquid-cooled supercharging device can be determined, and then the power resources allocated to the faulty megawatt-level liquid-cooled supercharging device can be transferred and allocated to the target megawatt-level liquid-cooled supercharging device.

[0108] Figure 4 is a block diagram of a power pooling and flexible allocation device 400 according to an exemplary embodiment. Figure 4 As shown, the power pooling and flexible allocation device includes:

[0109] The generation module 401 is used to generate a power resource pool corresponding to the charging station based on the power resources provided by at least one power source, wherein the charging station includes multiple megawatt-class liquid-cooled supercharging devices.

[0110] The allocation module 401 is used to allocate original power resources to the multiple megawatt-class liquid-cooled supercharging devices based on the power resource pool.

[0111] Acquisition module 403, for obtaining the real-time charging demand, real-time operation information and historical charging demand corresponding to the multiple megawatt-class liquid-cooled supercharging devices.

[0112] The allocation module 402 is also used to allocate real-time power resources to the multiple megawatt-class liquid-cooled supercharging devices based on the real-time charging requirements, real-time operation information and historical charging requirements corresponding to the power resource pool and the multiple megawatt-class liquid-cooled supercharging devices through a multi-objective optimization algorithm.

[0113] Regarding the apparatus in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0114] Figure 5 FIG. 5 is a block diagram of an electronic device 500 according to an exemplary embodiment. Figure 5 As shown, the electronic device 500 may include: a processor 501 , a memory 502 , and may further include one or more of a multimedia component 503 , an input / output (I / O) interface 504 , and a communication component 505 .

[0115] The processor 501 is used to control the overall operation of the electronic device 500 to complete all or part of the steps in the above-mentioned power pooling and flexible allocation method. The memory 502 is used to store various types of data to support the operation of the electronic device 500. This data may include, for example, instructions for any application or method operating on the electronic device 500, as well as application-related data such as contact information, sent and received messages, images, audio, video, etc. The memory 502 can be implemented by any type of volatile or non-volatile storage device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The multimedia component 503 may include a screen and an audio component. The screen may be, for example, a touch screen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signal may be further stored in the memory 502 or sent through the communication component 505. The audio component also includes at least one speaker for outputting audio signals. The I / O interface 504 provides an interface between the processor 501 and other interface modules, and the above-mentioned other interface modules may be a keyboard, a mouse, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 505 is used for wired or wireless communication between the electronic device 500 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G or 4G, or a combination of one or more of them, so the corresponding communication component 505 may include: a Wi-Fi module, a Bluetooth module, an NFC module.

[0116] In an exemplary embodiment, the electronic device 500 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above-mentioned power pooling and flexible allocation method.

[0117] In another exemplary embodiment, a computer-readable storage medium including program instructions is further provided. When executed by a processor, the program instructions implement the steps of the above-described power pooling and flexible allocation method. For example, the computer-readable storage medium may be the aforementioned memory 502 including the program instructions. The program instructions may be executed by the processor 501 of the electronic device 500 to implement the above-described power pooling and flexible allocation method.

[0118] In another exemplary embodiment, a computer program product is further provided. The computer program product includes a computer program executable by a processor. When the computer program is executed by the processor, the steps of the above-mentioned power pooling and flexible allocation method are implemented.

[0119] The preferred embodiments of the present application are described in detail above in conjunction with the accompanying drawings. However, the present application is not limited to the specific details in the above embodiments. Within the technical concept of the present application, various simple modifications can be made to the technical solution of the present application, and these simple modifications all fall within the scope of protection of the present application.

[0120] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any appropriate manner unless there is any contradiction. In order to avoid unnecessary repetition, this application will not further describe various possible combinations.

[0121] In addition, the various implementation methods of the present application may be arbitrarily combined, and as long as they do not violate the concept of the present application, they should also be regarded as the contents disclosed in the present application.

Claims

1. A power pooling and flexible allocation method, characterized in that: include: generating a power resource pool corresponding to a charging station based on power resources provided by at least one power source, wherein the charging station includes a plurality of megawatt-class liquid-cooled supercharging devices; Based on the power resource pool, allocating original power resources to each of the multiple megawatt-class liquid-cooled supercharging devices; Obtaining real-time charging demand, real-time operation information, and historical charging demand corresponding to each of the multiple megawatt-class liquid-cooled supercharging devices; Allocate real-time power resources to the multiple megawatt-class liquid-cooled supercharging devices based on the real-time charging demand, real-time operation information, and historical charging demand corresponding to the power resource pool and the multiple megawatt-class liquid-cooled supercharging devices, respectively, using a multi-objective optimization algorithm; Allocating original power resources to the plurality of megawatt-class liquid-cooled supercharging devices based on the power resource pool includes: When the number of the plurality of megawatt-class liquid-cooled supercharging devices is less than a preset number, allocating the same original power resource to each of the plurality of megawatt-class liquid-cooled supercharging devices based on the power resource pool; In a case where the number of the multiple megawatt-class liquid-cooled supercharging devices is higher than a preset number, based on the position information corresponding to the multiple megawatt-class liquid-cooled supercharging devices, the predicted charging demands corresponding to the multiple megawatt-class liquid-cooled supercharging devices within a preset future time are determined, wherein the position information is used to characterize the position relative to the entrance of the charging station; based on the power resource pool, the sliding time window mechanism and the predicted charging demands corresponding to the multiple megawatt-class liquid-cooled supercharging devices, corresponding original power resources are allocated to the multiple megawatt-class liquid-cooled supercharging devices respectively.

2. The power pooling and flexible allocation method according to claim 1, characterized in that: The generating a power resource pool corresponding to the charging station based on the power resources provided by at least one power source includes: generating a main power resource pool and a redundant power resource pool corresponding to the charging station based on power resources provided by at least one power source, wherein the power resources of the redundant power resource pool are less than the power resources of the main power resource pool; Allocating original power resources to the plurality of megawatt-class liquid-cooled supercharging devices based on the power resource pool includes: Based on the main power resource pool, allocating original power resources to the multiple megawatt-class liquid-cooled supercharging devices respectively; The multi-objective optimization algorithm allocates real-time power resources to the multiple megawatt-class liquid-cooled supercharging devices based on the real-time charging demands, real-time operation information, and historical charging demands corresponding to the power resource pool and the multiple megawatt-class liquid-cooled supercharging devices, respectively, including: Through a multi-objective optimization algorithm, real-time power resources are allocated to the multiple megawatt-class liquid-cooled supercharging devices based on the real-time charging demands, real-time operation information and historical charging demands corresponding to the redundant power resource pool and the multiple megawatt-class liquid-cooled supercharging devices respectively.

3. The power pooling and flexible allocation method according to claim 1, characterized in that: The allocating corresponding original power resources to the multiple megawatt-class liquid-cooled supercharging devices based on the power resource pool, the sliding time window mechanism, and the predicted charging demands corresponding to the multiple megawatt-class liquid-cooled supercharging devices respectively includes: Determining, based on the predicted charging demands respectively corresponding to the multiple megawatt-class liquid-cooled supercharging devices, a first megawatt-class liquid-cooled supercharging device and a second megawatt-class liquid-cooled supercharging device from the multiple megawatt-class liquid-cooled supercharging devices, the predicted charging demand corresponding to the first megawatt-class liquid-cooled supercharging device being higher than the preset charging demand, and the predicted charging demand corresponding to the second megawatt-class liquid-cooled supercharging device being lower than the preset charging demand; Based on the power resource pool, allocating the same original power resource to each of the first megawatt liquid-cooled supercharging devices; Determining the sliding time window size corresponding to each of the second megawatt-class liquid-cooled supercharging devices based on the predicted charging demand corresponding to each of the second megawatt-class liquid-cooled supercharging devices; Based on the sliding time window size corresponding to the second megawatt liquid-cooled supercharging device, original power resources are allocated to the second megawatt liquid-cooled supercharging device from the remaining power resources in the power resource pool.

4. The power pooling and flexible allocation method according to claim 1, characterized in that: The multi-objective optimization algorithm is an NSGA-II algorithm. The multi-objective optimization algorithm allocates real-time power resources to the multiple megawatt-class liquid-cooled supercharging devices based on the real-time charging demand, real-time operation information, and historical charging demand corresponding to the power resource pool and the multiple megawatt-class liquid-cooled supercharging devices, respectively, including: Calculating, by the NSGA-II algorithm, an objective function value of the NSGA-II algorithm based on the real-time charging demand, real-time operation information, and historical charging demand corresponding to the power resource pool and the plurality of megawatt-class liquid-cooled supercharging devices; Solving a power resource allocation scheme using the NSGA-II algorithm based on the objective function value and preset constraints, wherein the preset constraints include: a minimum allocated power resource constraint for a megawatt-class liquid-cooled supercharger and a matching relationship between a liquid cooling system flow rate and power resources for the megawatt-class liquid-cooled supercharger; According to the power resource allocation scheme, real-time power resources are allocated to the multiple megawatt-class liquid-cooled supercharging devices respectively.

5. The power pooling and flexible allocation method according to claim 4, characterized in that: The objective function of the NSGA-II algorithm includes: a power resource pool utilization function, a megawatt-class liquid-cooled supercharging device heat load function, and a megawatt-class liquid-cooled supercharging device waiting time function. The objective function value of the NSGA-II algorithm is calculated by the NSGA-II algorithm based on the real-time charging demand, real-time operation information, and historical charging demand corresponding to the power resource pool and the multiple megawatt-class liquid-cooled supercharging devices, including: Determining a power resource pool utilization function value based on actual allocated power and available power of the power resource pool; Determining a waiting time function value of a megawatt-class liquid-cooled supercharging device based on the real-time charging demand and the historical charging demand; Based on the real-time operating information, a thermal load function value of a megawatt-class liquid-cooled supercharging device is determined.

6. The power pooling and flexible allocation method according to claim 4, characterized in that: Solving a power resource allocation scheme based on the objective function value and preset constraints using the NSGA-II algorithm includes: Solving the initial power resource allocation solution set based on the objective function value and preset constraints; Through the elite retention strategy, the Pareto optimal solution set is determined from the initial power resource allocation solution set; A target power resource allocation scheme is determined from the Pareto optimal solution set using a fuzzy decision-making algorithm. The input variables of the fuzzy decision-making algorithm include: grid electricity price period, manual intervention instructions from operators, and emergency charging request signals. The output variables of the fuzzy decision-making algorithm include: the dynamic adjustment range of the weight ratio of the objective function of the NSGA-II algorithm.

7. The power pooling and flexible allocation method according to claim 1, characterized in that: The power pooling and flexible allocation method further includes: In response to a faulty megawatt-class liquid-cooled supercharge device among the plurality of megawatt-class liquid-cooled supercharge devices, obtaining fault information; Determining a target megawatt-class liquid-cooled supercharging device from among the megawatt-class liquid-cooled supercharging devices other than the faulty megawatt-class liquid-cooled supercharging device according to the fault information; The power resources allocated to the faulty megawatt-class liquid-cooled supercharging device are transferred to the target megawatt-class liquid-cooled supercharging device.

8. A power pooling flexible allocation architecture, characterized in that: include: at least one power supply; a plurality of megawatt-class liquid-cooled supercharging devices respectively connected to the at least one power source; An intelligent scheduling end is connected to the at least one power supply and the multiple megawatt-class liquid-cooled supercharging devices respectively, and the intelligent scheduling end is used to execute the power pooling and flexible allocation method as described in any one of claims 1 to 7.

9. A megawatt-class liquid-cooled supercharging device, characterized in that: The megawatt-class liquid-cooled supercharging device allocates power resources by the power pooling and flexible allocation method according to any one of claims 1 to 7, and the megawatt-class liquid-cooled supercharging device includes: Modular power unit, including: liquid-cooled SiC charging module; Distributed liquid cooling system, including: manifold splitter and microchannel cold plate; Power pooling interface; Among them, the liquid cooling system adopts a hierarchical temperature control strategy: primary cooling adopts ethylene glycol aqueous solution circulation; secondary cooling adopts phase change material cold storage device; emergency cooling adopts compressor refrigeration unit.

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