An intelligent charging control method, device and medium

Abstract

This invention relates to the field of charging control technology, specifically to an intelligent charging control method, device, and medium, applied to a public DC bus charging system to address the problems of low equipment utilization and long user waiting times in existing power allocation systems. The method includes: acquiring charging vehicle data based on the interaction between each charging pile and the charging vehicle; clustering the start timestamps of the charging vehicle data based on a preset time window to identify competing charging groups with shared DC bus resources; determining key charging indicators for each charging vehicle based on the charging vehicle data within each competing charging group; and obtaining an adaptive comprehensive priority for the corresponding charging vehicle based on the key charging indicators, the current system load status, and current time period characteristics, thereby dynamically scheduling the power modules of the public DC bus charging system based on the adaptive comprehensive priority to allocate charging power to each charging vehicle.

Description

Technical Field

[0001] This invention relates to an intelligent charging control method, device, and medium, belonging to the field of charging control technology. Background Technology

[0002] With the rapid popularization of new energy vehicles, the demand for high-power DC fast charging continues to grow. The common DC bus architecture, due to its flexible power sharing capabilities, has become one of the mainstream technical solutions for charging stations. In this architecture, multiple charging piles are connected to a set of dynamically switchable power modules via a common DC bus. Each terminal can borrow non-core power modules from the bus according to actual needs, achieving power sharing and theoretically significantly improving equipment utilization and user charging experience.

[0003] However, existing power allocation strategies for public DC bus charging systems still have significant shortcomings, especially during peak hours, often resulting in unreasonable vehicle power allocation, low equipment utilization, and excessively long user waiting times. Specifically, most current systems employ static or mechanical module switching logic, such as switching based solely on the integer multiple relationship between vehicle power demand and the rated power of a single power module. While this approach satisfies power supply requirements, it neglects the operating efficiency of power modules and the overall system energy efficiency. Furthermore, there is a lack of intelligent scheduling mechanisms for situations where multiple vehicles compete for bus resources. When faced with multiple high-power vehicles with short-term charging needs, this easily leads to a contradictory situation where long waiting times and equipment idleness coexist with congestion. In addition, although existing technologies have attempted to introduce user-side sequencing, they do not consider the multidimensional factors in charging decisions and the difficulty in adaptively adjusting intelligent charging control based on system status. Summary of the Invention

[0004] This invention addresses the shortcomings of existing technologies by providing an intelligent charging control method, device, and medium.

[0005] The technical solution of the present invention to solve the above-mentioned technical problems is as follows: How to provide an intelligent charging control method that considers multiple factors, supports multi-dimensional dynamic priority decision-making, and can adaptively adjust with the system state.

[0006] One or more embodiments of this application provide a smart charging control method, the method comprising:

[0007] The public DC bus charging system acquires charging vehicle data based on the interaction between the charging pile and the charging vehicle.

[0008] Clustering is performed on the start timestamps of the charging vehicle data based on a preset time window to identify whether there is competition for public DC bus resources, and the charging vehicle data is divided into multiple charging groups; wherein, the charging groups include: competing charging groups and non-competing charging groups;

[0009] Based on the charging vehicle data in each of the competing charging groups, the key charging indicators for the corresponding charging vehicles are determined; wherein, the key charging indicators include: theoretical charging time and power module matching degree.

[0010] Based on the key charging indicators, the current system load status, and the characteristics of the current time period, an adaptive comprehensive priority for the corresponding charging vehicle is obtained. The power modules of the public DC bus charging system are dynamically scheduled based on the adaptive comprehensive priority to allocate charging power to each charging vehicle.

[0011] Optionally, in one or more embodiments of this application, obtaining charging vehicle data based on the interaction between the charging pile and the charging vehicle specifically includes:

[0012] When the charging gun is inserted into the vehicle's charging port, the charging pile and the charging vehicle establish a communication connection based on a pre-set charging communication protocol.

[0013] The charging pile receives charging vehicle data from the charging vehicle via the communication connection; wherein the charging vehicle data includes: the charging vehicle's start timestamp, vehicle power demand, vehicle initial state of charge, and vehicle battery capacity.

[0014] Optionally, in one or more embodiments of this application, the charging vehicle data is clustered based on the start timestamps of a preset time window to identify whether there is competition for common DC bus resources, and the charging vehicle data is divided into multiple charging groups, specifically including:

[0015] The vehicles are sorted in ascending order based on their start timestamps to compare the start timestamps of adjacent charging vehicles and obtain the start time difference between the adjacent charging vehicles.

[0016] If the start-up time difference is less than or equal to the preset time window, it is determined that there is a concurrent demand for the use of the common DC bus resources among the adjacent charging vehicles, and the charging vehicles corresponding to the start-up time difference are assigned to the same competitive charging group.

[0017] If the start-up time difference is greater than the preset time window, it is determined that there is no concurrent demand for the use of the common DC bus resources among the charging vehicles, and the charging vehicles corresponding to the start-up time difference are assigned to non-competitive charging groups.

[0018] Optionally, in one or more embodiments of this application, key charging indicators for corresponding charging vehicles are determined based on charging vehicle data within each competing charging group, specifically including:

[0019] The difference in state of charge is obtained based on the initial state of charge of the vehicle and the preset full-charge state of charge.

[0020] Based on the state of charge difference and the vehicle battery capacity, the amount of charge to be applied to the corresponding charging vehicle is obtained, and the theoretical charging time is obtained based on the amount of charge to be applied and the vehicle power demand of the corresponding charging vehicle.

[0021] Obtain the rated power of each power module in the public DC bus charging system, and based on the rated power and the vehicle's required power, obtain the required number of power modules for the corresponding charging vehicle.

[0022] By comparing the difference between the total power of the required number of power modules and the power demand of the vehicle, the power utilization efficiency is obtained, and the matching degree value corresponding to the power utilization efficiency is matched to obtain the power matching degree.

[0023] Optionally, in one or more embodiments of this application, an adaptive comprehensive priority for the corresponding charging vehicle is obtained based on the key charging indicators, the current system load state, and the characteristics of the current time period, specifically including:

[0024] The power matching degree is obtained as the first priority factor, and the reciprocal of the theoretical charging time is obtained as the second priority factor.

[0025] The load dimension weight is determined based on the current system load status, and the time period dimension weight is determined based on the current time period characteristics.

[0026] The load dimension weight and the time period dimension weight are weighted and fused according to a preset ratio to obtain a first weight coefficient;

[0027] The first priority factor is weighted based on the first weight coefficient, and the second priority factor is weighted based on the complement of the first weight coefficient to obtain the sum of the weighted priority factors, which serves as the adaptive comprehensive priority of the corresponding charging vehicle.

[0028] Optionally, in one or more embodiments of this application, determining the load dimension weight based on the current system load state and determining the time period dimension weight based on the current time period characteristics specifically includes:

[0029] Obtain the current load status of the system, and calculate the ratio of the number of power modules currently in operation to the total number of power modules in the system based on the current load status, and determine the corresponding load dimension weight;

[0030] Obtain the current system time and determine the time period dimension weight based on the preset time period interval in which the current system time is located; wherein, different time period intervals correspond to different weight benchmark values, and the charging demand intensity of different time period intervals is positively correlated with the corresponding time period dimension weight.

[0031] Optionally, in one or more embodiments of this application, the power modules of the public DC bus charging system are dynamically scheduled based on the adaptive comprehensive priority to allocate charging power to each charging vehicle, specifically including:

[0032] Obtain the adaptive comprehensive priority of each corresponding charging vehicle in the competitive charging group, sort the corresponding charging vehicles in descending order based on the adaptive comprehensive priority, and generate a power allocation queue.

[0033] Monitor the release events or new available power events of the power modules in the public DC bus charging system;

[0034] In response to the release event or the new available power event, a power module combination that meets the power requirements of the corresponding charging vehicle is allocated according to the order of the power allocation queue; wherein, the power module combination consists of a primary power module and a non-primary power module of the common DC bus;

[0035] When a new charging vehicle connects or an existing charging vehicle finishes charging, the adaptive comprehensive priority of the current charging vehicle is re-acquired, and the power allocation queue is updated to achieve real-time dynamic scheduling of the power module.

[0036] Optionally, in one or more embodiments of this application, the method further includes:

[0037] The charging vehicles in the non-competitive charging group are sorted according to the start timestamp to obtain a power allocation queue;

[0038] Based on the order of the power allocation queue, the vehicle power requirement of each corresponding charging vehicle is obtained, and a corresponding power module combination is allocated to the corresponding charging vehicle according to the vehicle power requirement and the rated power of each power module.

[0039] One or more embodiments of this application provide an intelligent charging control device, the device comprising:

[0040] At least one processor; and,

[0041] A memory communicatively connected to the at least one processor; wherein,

[0042] The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform any of the methods described above.

[0043] One or more embodiments of this application provide a non-volatile computer storage medium storing computer-executable instructions, wherein the computer-executable instructions are configured to execute any of the methods described above.

[0044] The beneficial effects of this invention are as follows: By clustering the start timestamps based on a preset time window, the system can group charging vehicles competing for common DC bus resources into competitive charging groups. Adaptive comprehensive priority calculation and power module scheduling are only performed on charging vehicles in these competitive groups, reducing the analysis of unnecessary charging vehicles and lowering the system's computational burden. For charging vehicles in competitive charging groups, the system comprehensively considers theoretical charging time and power module matching, and integrates system load status and time period characteristics to determine adaptive comprehensive priority. This allows for dynamic and intelligent adjustment of the priority of power module borrowing by each charging vehicle based on different times and vehicle charging needs, improving the utilization rate of charging equipment, shortening user waiting time, and enhancing the overall quality of charging services and user experience. Attached Figure Description

[0045] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings:

[0046] Figure 1 This is a schematic flowchart of an intelligent charging control method provided in an embodiment of this application;

[0047] Figure 2 A logical diagram illustrating an intelligent charging control process in an application scenario provided by an embodiment of this application;

[0048] Figure 3 This application provides a schematic diagram of a full-matrix group control charging system in an application scenario.

[0049] Figure 4 This is a schematic diagram of the structure of an intelligent charging control device provided in an embodiment of this application;

[0050] Figure 5 This is a schematic diagram of the structure of a non-volatile storage medium provided in an embodiment of this application. Detailed Implementation

[0051] This application provides an intelligent charging control method, device, and medium.

[0052] To enable those skilled in the art to better understand the technical solutions in this application, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this application.

[0053] As described in the background section, with the rapid popularization of new energy vehicles, the demand for high-power DC fast charging continues to grow. The common DC bus architecture, due to its flexible power sharing capabilities, has become one of the mainstream technical solutions for charging stations. In this architecture, multiple charging piles are connected to a set of dynamically switchable power modules via a common DC bus. Each terminal can borrow non-core power modules from the bus according to actual needs, achieving power sharing and theoretically significantly improving equipment utilization and user charging experience.

[0054] However, the power allocation strategies of existing public DC bus charging systems still have significant shortcomings, especially during peak hours, often resulting in unreasonable vehicle power allocation, low equipment utilization, and excessively long user waiting times. Specifically, most systems employ static, mechanical module switching logic. For example, switching is based solely on the relationship between the vehicle's power demand and the rated power of a single power module, such as 30kW. When the demand is 35kW, two 30kW modules are forcibly activated, totaling 60kW. This causes the second module to operate in an inefficient low-load range for an extended period, resulting in energy waste and equipment damage. While this approach satisfies the power supply requirement, it neglects the operating efficiency of the power modules and the overall system energy efficiency.

[0055] Furthermore, some existing public DC bus charging systems employ a first-come, first-served or fixed-sequence charging approach. For example, they prioritize charging the first vehicle at full power until it is nearly fully charged before releasing resources to subsequent vehicles. This approach easily leads to excessively long waiting times and a coexistence of idle equipment and congestion when multiple vehicles with high-power, short-term charging needs compete for bus resources. Especially during peak charging periods, public DC bus charging systems cannot differentiate priority based on the actual charging time and power matching of vehicles, resulting in equipment utilization rates far below the theoretical upper limit and low operational efficiency.

[0056] In addition, although existing technologies have attempted to introduce user-side sorting, such as sorting by insertion time or by paying a premium to jump the queue, they have not solved the problem of dynamic optimization allocation of power resources within the system.

[0057] To address the aforementioned issues, this application proposes an intelligent charging control method. This method dynamically and intelligently adjusts the charging priority of each vehicle based on their charging needs at different times and for different vehicles when the public DC bus is occupied. This maximizes the utilization rate of charging equipment, shortens user waiting time, and improves the overall quality of charging services and user experience. Figure 1 As shown, this application provides a schematic flowchart of an intelligent charging control method. Figure 1 As can be seen, in one or more embodiments of this application, an intelligent charging control method is applied to a public DC bus charging system, and the method includes:

[0058] S101: The public DC bus charging system acquires charging vehicle data based on the interaction between the charging pile and the charging vehicle.

[0059] To provide a data foundation for subsequent intelligent power scheduling, the public DC bus charging system in this embodiment first acquires charging vehicle data based on the interaction between each charging pile and the charging vehicle. Specifically, after the charging gun is inserted into the vehicle's charging port, each charging pile and the charging vehicle establishes a communication connection based on a pre-set charging communication protocol. At this time, the public DC bus charging system can acquire the charging vehicle data received by the charging pile based on the communication connection. This charging vehicle data includes: the charging vehicle's start timestamp. Vehicle power requirements Initial state of charge of the vehicle and vehicle battery capacity The start-up timestamp is automatically generated by the charging station's local high-precision clock when the charging session is confirmed to have started effectively. It identifies the precise moment when the vehicle initiated the charging request, accurate to the second. Vehicle power demand refers to the maximum charging power the vehicle is currently allowed to receive, measured in kilowatts. This can be dynamically calculated and reported by the charging station and the vehicle's Battery Management System (BMS) based on battery temperature, state of charge, voltage platform, and safety policies. The vehicle's initial state of charge indicates the percentage of remaining battery charge at the start of charging, provided by the BMS. The vehicle battery capacity refers to the rated total energy storage capacity of the power battery, which can be directly reported by the BMS or obtained by matching vehicle identification information through the charging station's internal vehicle model database.

[0060] S102: Cluster the start timestamps of the charging vehicle data based on a preset time window to identify whether there is competition for public DC bus resources, and divide the charging vehicle data into multiple charging groups; wherein, the charging groups include: competing charging groups and non-competing charging groups.

[0061] To effectively identify whether multiple charging vehicles are simultaneously competing for limited public DC bus power resources, this embodiment of the application clusters the start timestamps of charging vehicle data based on a preset time window to identify whether charging vehicles awaiting scheduling are competing for public DC bus resources. The preset time window defines the time range of concurrent charging requests from charging vehicles, such as 5 minutes. This means that concurrent charging requests within this preset time window may simultaneously compete for the power modules of the public DC bus. This preset time window can be determined based on expert experience through a comprehensive analysis of the charging system's hardware response characteristics and typical user behavior features, or based on historical operating data and domain knowledge.

[0062] Specifically, in one or more embodiments of this application, the charging vehicle data is clustered based on the start timestamp of a preset time window to identify whether there is competition for common DC bus resources, and the charging vehicle data is divided into multiple charging groups. The specific process includes the following:

[0063] like Figure 2 The above shows an ascending queue obtained by sorting the data in ascending order based on the startup timestamp. To avoid the situation where multiple charging guns start simultaneously and compete for the bus, separate measures are taken for each charging gun. By comparing the start timestamps of adjacent charging vehicles i and j, the start time difference between adjacent charging vehicles can be obtained. If there is a time difference in startup time If the time difference between the two vehicles' starts is greater than the preset time window T, it indicates a significant gap in their startup times. Therefore, it is determined that there is no concurrent demand for the shared DC bus resources between the charging vehicles. The charging vehicle corresponding to this startup time difference is then assigned to a non-competitive charging group. The earlier the time, the higher the vehicle's priority. The earlier the position in the middle, the better. And if the startup time difference... If the time difference is less than or equal to the preset time window T, it means that the two vehicles start at roughly the same time and can be considered to start at the same time. It is determined that there is a concurrent demand for the use of the common DC bus resources between the adjacent charging vehicles, and the charging vehicles corresponding to the start time difference are assigned to the same competitive charging group.

[0064] During this process, charging vehicles that are significantly separated in time are filtered out by a preset time window, and only charging vehicles that compete for common DC bus resources are classified into a competition group, which improves the targeting of subsequent dynamic scheduling in response to conflict situations.

[0065] Furthermore, in one or more embodiments of this application, the method further includes:

[0066] Based on the above, if the start-up time difference between the charging vehicles in the non-contested charging group exceeds a preset time window, the system determines that there is no concurrent demand for the power resources of the common DC bus. In other words, these vehicles are independent in time; the preceding vehicle has sufficient time to occupy the power module and complete initial charging after starting, and will not cause immediate resource conflicts with subsequent vehicles. Therefore, in this embodiment, the corresponding charging vehicles in the non-contested charging group are sorted in ascending order according to their start-up timestamps to obtain a power allocation queue. Based on the first-come, first-served principle, the power allocation queue is arranged according to its order as follows: Figure 2 The startup timestamp sequence shown indicates that the power demand of each corresponding charging vehicle is obtained sequentially. Based on the vehicle's power demand and the rated power of each power module, a corresponding power module combination is allocated to the corresponding charging vehicle. The power module combination consists of the charging gun's dedicated core power module and non-core power modules scheduled via the common DC bus to fill power gaps.

[0067] For example, if a vehicle in a non-competitive charging group requires 75kW of power, while a single power module has a rated power of 30kW, the system will allocate three modules (totaling 90kW), with the actual output limited to 75kW. Since the vehicle is in a non-competitive state, the system can complete the allocation immediately without waiting for other vehicles to release resources. This process eliminates the need to calculate theoretical charging time, power matching, or adaptive comprehensive priority, significantly simplifying the meaningless scheduling logic of non-competitive charging groups, reducing control latency, and making it suitable for low-load or off-peak periods such as nighttime. It effectively improves system response speed and user satisfaction, and complements the complex intelligent scheduling of subsequent competing charging groups, jointly constructing a dynamic power management system covering all scenarios.

[0068] S103: Based on the charging vehicle data in each of the competing charging groups, determine the key charging indicators for the corresponding charging vehicles; wherein, the key charging indicators include: theoretical charging time and power module matching degree.

[0069] For the vehicles in each competing charging group, their start times are almost identical, meaning they can be considered to start simultaneously and have the same time priority. To achieve charging scheduling, other dimensions need to be considered, and the theoretical charging time needs to be calculated based on vehicle information. It's understandable that vehicles with shorter theoretical charging times should be prioritized to avoid excessive waiting times for users. Then, the matching degree between the system's charging demand and the charging module power is calculated based on the rated power of each individual power module, reflecting the compatibility between vehicle demand and the power module group. Therefore, in this embodiment, after dividing the competing charging groups based on the above steps, key charging indicators for the corresponding vehicles are determined based on the charging vehicle data within each group. These key charging indicators include theoretical charging time and power module matching degree. By simultaneously introducing theoretical charging time and power module matching degree as key charging indicators, a foundation is provided for constructing a multi-objective balanced adaptive comprehensive priority system. This enables the public DC bus charging system to intelligently balance user experience and equipment efficiency under different loads and time periods, achieving globally optimized charging control.

[0070] Specifically, in one or more embodiments of this application, the key charging indicators of the corresponding charging vehicles are determined based on the charging vehicle data in each competing charging group, which specifically includes the following steps:

[0071] Based on the vehicle's initial state of charge (SBC) and preset full-charge state of charge (SBC), a SBC difference is obtained. This SBC difference reflects the proportion of electricity required to charge the vehicle from its current charge level to full charge. Then, the SBC difference is multiplied by the vehicle's battery capacity to obtain the amount of charge to be applied to the corresponding vehicle. For example, if a vehicle has an 80kWh battery and a current SBC of 30%, the amount of charge to be applied is approximately 56kWh. Dividing this amount of charge by the reported power demand of the corresponding vehicle yields the theoretical charging time. It can be understood that a shorter theoretical charging time indicates a greater likelihood that the vehicle will quickly complete charging and release its occupied power resources. Specifically, the theoretical charging time is:

[0072] ;

[0073] in, Theoretical charging time, For the power required by the vehicle, This represents the initial state of charge of the vehicle. This refers to the vehicle's battery capacity.

[0074] Simultaneously, the rated power of each power module in the public DC bus charging system is obtained. Based on the rated power and the vehicle's required power, the minimum number of power modules required for the corresponding charging vehicle is determined. This means selecting the minimum number of modules so that the total output capacity after combination is not less than the vehicle's required power. Based on this, the total power of the required number of power modules is calculated (e.g., 90kW from three 30kW modules). The difference between the total power of the required number of power modules and the vehicle's required power is compared. The smaller the difference, the closer the allocated power is to the vehicle's actual needs, and the higher the degree of effective utilization of the module output. Conversely, if the difference is large (e.g., allocating 90kW to a 40kW requirement), there is significant power redundancy and resource waste. Based on the power utilization efficiency reflected by this power difference, a standardized matching degree value is mapped as the power matching degree. A higher power matching degree indicates that the vehicle can more fully utilize the allocated power modules, helping to improve the overall operating efficiency and energy utilization of the station equipment. Specifically, the power matching degree is:

[0075] ;

[0076] In the formula, The rated power of a single power module, k is the number of modules in operation (positive integer). ), , A higher value indicates a higher power group utilization rate. Priority should be given to vehicles with high utilization rates to prevent the power module from being in a low utilization state for a long time.

[0077] S104: Based on the key charging indicators, the current load status of the system, and the characteristics of the current time period, obtain the adaptive comprehensive priority of the corresponding charging vehicle, and dynamically schedule the power modules of the public DC bus charging system based on the adaptive comprehensive priority to allocate charging power to each charging vehicle.

[0078] In order to be able to use the vehicle start timestamp Theoretical charging time value Power matching In this embodiment, a multi-dimensional sorting method is employed. Based on the key charging indicators obtained in S101-S103, the current system load status, and current time period characteristics, an adaptive comprehensive priority is acquired for the corresponding charging vehicle. This adaptive comprehensive priority then dynamically schedules the power modules of the public DC bus charging system to allocate charging power to each charging vehicle. This process considers multiple factors, moving beyond reliance on a single factor such as the order of plugging in the charging guns. Instead, it comprehensively considers the charging characteristics of each charging vehicle, the current system load status, and typical electricity consumption characteristics at different times of the day, dynamically scheduling the power modules to allocate charging power.

[0079] Specifically, in one or more embodiments of this application, an adaptive comprehensive priority for the corresponding charging vehicle is obtained based on key charging indicators, the current system load state, and current time period characteristics, including:

[0080] Key indicators extracted from the data of each charging vehicle in the competitive charging group are used as priority factors, namely, power matching degree as the first priority factor and the reciprocal of the theoretical charging time as the second priority factor. Load dimension weights are determined based on the current system load state, and time period dimension weights are determined based on the characteristics of the current time period. Specifically, the current system load state is obtained, and the ratio of the number of currently operational power modules to the total number of power modules in the system is calculated to obtain the system load rate. Based on this load rate, the corresponding load dimension weight is determined according to a preset mapping rule. The system reads the local clock to obtain the current system time, and the time period dimension weight is determined based on the preset time period interval in which the current system time is located. Different time period intervals correspond to different weight benchmark values, and the charging demand intensity of different time period intervals is positively correlated with the corresponding time period dimension weight. The load dimension weight and the time period dimension weight are weighted and fused according to a preset ratio to obtain a first weight coefficient. The first priority factor is weighted using the first weight coefficient, and the second priority factor is weighted based on the complement of the first weight coefficient, obtaining the sum of weighted priority factors, which serves as the adaptive comprehensive priority of the corresponding charging vehicle.

[0081] To facilitate understanding, the process of obtaining the adaptive synthesis priority is further explained below in conjunction with the formula principle. The adaptive synthesis priority is as follows:

[0082] ;

[0083] in The higher the value, the higher the vehicle priority. , Let be the weighting coefficient, satisfying To achieve the goals of ensuring high utilization rates even when system resources are scarce and maintaining a good user experience when system resources are abundant, the following two scenarios are weighted by their respective influencing factors. =0.5, =0.5, calculate the weight contribution value for each scenario. and To avoid extreme weighting, set , Finally, the weighted average yields an adaptive satisfaction:

[0084] ;

[0085] Among them, system load dimension Based on system load rate matching, a parameter load rate λ is introduced in a certain scenario. λ is the ratio of the number of currently operating charging modules to the total number of charging modules in the system, reflecting the system's workload. Load levels are classified according to the load rate λ, and a correlation between load rate λ and... Correspondence:

[0086] When λ∈[0, 0.3), the system is determined to be in an extremely low load state, indicating that the charging module is largely idle and the equipment resources are abundant. The system should therefore focus on minimizing user waiting time. Take the lower limit value of 0.3;

[0087] When λ∈[0.3,0.8], a linear correspondence is achieved where higher load ensures higher equipment utilization. =λ;

[0088] When λ∈(0.8,1], the system is determined to be in an extremely high load state, indicating that the charging module resources are strained, and every effort should be made to ensure the full utilization of each device. Take the upper limit of 0.8.

[0089] Time period feature dimension Charging stations are categorized into different levels based on usage patterns at different times, with each level corresponding to different... Baseline values ​​for a given scenario:

[0090] 18:00-22:00 is the peak charging period at night, which is the time of day with the most people charging. Efforts must be made to ensure the equipment is used efficiently and to avoid wasting equipment. The value is 0.8;

[0091] The period from 11:00 AM to 2:00 PM is the peak charging time during lunchtime, the second highest number of people charging during the day. Priority should be given to ensuring equipment utilization. The value is 0.7;

[0092] 7:00-9:00 is the off-peak charging period in the morning, with a large number of people charging. It's necessary to ensure equipment utilization while also considering charging speed. The value is 0.6;

[0093] The period from 9:00 to 11:00 is the off-peak charging time in the morning, with a moderate number of people charging. It's necessary to balance equipment utilization and charging speed, and strike a good balance between the two. The value is 0.5;

[0094] The period from 2:00 PM to 6:00 PM is the off-peak charging period in the afternoon. There are fewer people charging, more charging modules are idle, and equipment resources are abundant. Therefore, minimizing users' charging wait times should be prioritized. The value is 0.4;

[0095] The period from 10:00 PM to 7:00 AM the next day is a low-charging time at night, with very few people charging. Charging modules are largely idle, and equipment resources are plentiful. It is necessary to ensure sufficient charging time for users. The value is 0.3;

[0096] according to and The conclusion is Afterwards, according to get The value of Pi is then calculated using the formula, and the value of Pi is updated based on the size of all Pi values. queue.

[0097] Once established, the system is continuously updated based on real-time vehicle information. When a new vehicle plugs in to charge, the process of acquiring and updating this data is repeated. , calculate, The process involves steps such as generation, comparison with adjacent vehicles, and insertion waiting.

[0098] It should be understood that the above-described adaptive comprehensive priority acquisition method is only a preferred embodiment of the present invention. In other embodiments, rule-based conditional judgment, lightweight historical feedback learning, or economic benefit models can also be used, as long as they can comprehensively reflect vehicle charging characteristics, system load status, and time period characteristics, and generate a priority sequence that can be used for dynamic scheduling.

[0099] Specifically, in one or more embodiments of this application, the power modules of the public DC bus charging system are dynamically scheduled based on adaptive comprehensive priority to allocate charging power to each charging vehicle, specifically including:

[0100] The system acquires the adaptive comprehensive priority of each charging vehicle within the competing charging group, and sorts these vehicles in descending order based on this priority to generate a power allocation queue. This queue represents the order in which vehicles acquire power resources under the current system state; higher priority vehicles receive the required power first. The system continuously monitors power module release events and newly available power events in the public DC bus charging system. Power module release events occur when a vehicle completes charging, disconnects its charging gun early, or reduces its power demand, releasing some or all of its occupied power modules back to the common pool. Newly available power events occur when a new power module comes online, a faulty module recovers, or the system expands its capacity, leading to an increase in the total available power.

[0101] Once any of the above events is detected, in response to the detected release event or the addition of available power event, starting from the head of the current power allocation queue, the power demand of each corresponding charging vehicle is checked sequentially, and a power module combination that meets its power demand is allocated; wherein, the power module combination consists of the primary power module and the non-primary power module of the common DC bus. When a new charging vehicle joins or when an existing vehicle finishes charging or leaves, the adaptive comprehensive priority of the current charging vehicle is re-obtained based on the process of steps S101-S104 above, and the power allocation queue is updated to achieve real-time dynamic scheduling of power modules.

[0102] This process utilizes adaptive comprehensive prioritization to allocate power to vehicles with high matching accuracy and short charging times, ensuring each module operates within its most efficient range and significantly improving charging equipment utilization. Furthermore, by employing descending order sorting and dynamic reordering, vehicles that can quickly complete charging and release resources receive power first, accelerating queue turnover and effectively reducing waiting times for subsequent users. In addition, the combination of dedicated power modules and non-dedicated power modules on the common DC bus enables power pooling and sharing across the entire station. Even if a particular charging station's dedicated module is insufficient, resources can be borrowed globally, fully leveraging the advantages of the common bus architecture.

[0103] To more intuitively understand the intelligent charging control method provided in the embodiments of this application, Figure 3 The scenario is illustrated with specific examples. Figure 3 In the corresponding scenario, a standard 1-to-6 full-matrix group control charging system was used for charging testing. The system contains six 30kW charging modules, providing a total of six charging guns that can charge simultaneously. Each charging gun corresponds to one 30kW native power group, and all other non-native power groups can be arbitrarily switched to any of the charging guns via a common DC bus. The system... The charging time was set to 5 minutes, the test period was 15:37 (afternoon off-peak period), and one vehicle M had already occupied two power groups for charging for 20 minutes. At this time, four vehicles A, B, C, and D simultaneously plugged in the charging gun to start charging. The vehicle charging information obtained by the charging system is shown in Table 1 below:

[0104] Table 1. Vehicle Charging Information Table

[0105]

[0106] The system detected that the inherent power groups (30kW) of vehicles A, B, C, and D could not meet the vehicle's demand, and it was necessary to borrow power from other power groups through the common DC bus. Therefore, the borrowing priority of the common DC bus was sorted and related data was calculated:

[0107] Based on vehicle start timestamp Sort and form a vehicle queue Calculate the time difference =30s, =42s, =48s, and the maximum interval =120s, all less than 5min, then the 4 cars are classified as having the same priority.

[0108] calculate for:

[0109] ;

[0110] ;

[0111] ;

[0112] ;

[0113] calculate for:

[0114] ;

[0115] ;

[0116] ;

[0117] ;

[0118] calculate The adaptive synthesis priority calculation process is as follows:

[0119] The load dimension is calculated as follows: all charging modules are currently operating (λ=1), and the system is under extremely high load. =0.8; the calculation time period is: 15:37 is the afternoon off-peak period. ;final =0.5×0.8+0.5×0.4=0.6, =1- =0.4.

[0120] ;

[0121] ;

[0122] ;

[0123] ;

[0124] The final power borrowing order should be B (1.509), A (0.875), D (0.801), and C (0.7202). Therefore, when a charging gun finishes borrowing or charging is completed, the system prioritizes allocating the idle power module to charging gun B. When charging gun B finishes borrowing or another charging gun finishes charging and an idle power module becomes available, the idle power module is then allocated to charging gun A, and so on, until charging gun D, ​​and finally charging gun C. This ordering minimizes equipment waste and shortens the overall charging time.

[0125] like Figure 4 As shown in the diagram, this application provides a structural schematic of an intelligent charging control device. Figure 4 As can be seen, in one or more embodiments of this application, a smart charging control device includes:

[0126] At least one processor; and,

[0127] A memory communicatively connected to the at least one processor; wherein,

[0128] The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform any of the methods described above.

[0129] like Figure 5 As shown in the diagram, this application provides a schematic diagram of a non-volatile storage medium structure. Figure 5 As can be seen, in one or more embodiments of this application, a non-volatile storage medium stores computer-executable instructions, which are capable of executing any of the methods described above.

[0130] The various embodiments in this application are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments of apparatus, devices, and non-volatile computer storage media are basically similar to the method embodiments, and therefore described more simply; relevant parts can be referred to the descriptions of the method embodiments.

[0131] The foregoing has described specific embodiments of this application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired results. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0132] The above description is merely one or more embodiments of this application and is not intended to limit this application. For those skilled in the art, various modifications and variations can be made to one or more embodiments of this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of one or more embodiments of this application should be included within the scope of the claims of this application.

Claims

1. An intelligent charging control method, applied to a public DC bus charging system, characterized in that, The method includes: The public DC bus charging system acquires charging vehicle data based on the interaction between the charging pile and the charging vehicle. Clustering of the charging vehicle data based on the start timestamps of the preset time windows is used to identify whether there is competition for public DC bus resources, and the charging vehicle data is divided into multiple charging groups; wherein, the preset time window is used to define the time range of concurrent charging requests of charging vehicles, the start timestamp is used to identify the precise time when the vehicle initiates a charging request, and the charging groups include: competing charging groups and non-competitive charging groups. Based on the charging vehicle data in each of the competing charging groups, the key charging indicators for the corresponding charging vehicles are determined; wherein, the key charging indicators include: theoretical charging time and power module matching degree; the theoretical charging time is the remaining charging time required for the vehicle to be fully charged, and the power module matching degree is used to reflect the compatibility between the vehicle's needs and the power module group. Based on the key charging indicators, the current load status of the system, and the characteristics of the current time period, the adaptive comprehensive priority of the corresponding charging vehicle is obtained, and the power modules of the public DC bus charging system are dynamically scheduled based on the adaptive comprehensive priority to allocate charging power to each charging vehicle. Based on the key charging indicators, the current system load status, and the characteristics of the current time period, an adaptive comprehensive priority for the corresponding charging vehicles is obtained, specifically including: The matching degree of the power module is obtained as the first priority factor, and the reciprocal of the theoretical charging time is obtained as the second priority factor. The load dimension weight is determined based on the current system load status, and the time period dimension weight is determined based on the current time period characteristics. The load dimension weight and the time period dimension weight are weighted and fused according to a preset ratio to obtain a first weight coefficient; The first priority factor is weighted based on the first weight coefficient, and the second priority factor is weighted based on the complement of the first weight coefficient to obtain the sum of the weighted priority factors, which serves as the adaptive comprehensive priority of the corresponding charging vehicle. The load dimension weight is determined based on the current system load status, and the time period dimension weight is determined based on the current time period characteristics, specifically including: Obtain the current load status of the system, and calculate the ratio of the number of power modules currently in operation to the total number of power modules in the system based on the current load status, and determine the corresponding load dimension weight; Obtain the current system time and determine the time period dimension weight based on the preset time period interval in which the current system time is located; wherein, different time period intervals correspond to different weight benchmark values, and the charging demand intensity of different time period intervals is positively correlated with the corresponding time period dimension weight.

2. The intelligent charging control method according to claim 1, characterized in that, Based on the interaction between charging piles and charging vehicles, data on charging vehicles is obtained, specifically including: When the charging gun is inserted into the vehicle's charging port, the charging pile and the charging vehicle establish a communication connection based on a pre-set charging communication protocol. The charging pile receives charging vehicle data from the charging vehicle via the communication connection; wherein the charging vehicle data includes: the charging vehicle's start timestamp, vehicle power demand, vehicle initial state of charge, and vehicle battery capacity.

3. The intelligent charging control method according to claim 1, characterized in that, Clustering the charging vehicle data based on the start timestamps of a preset time window to identify whether there is competition for common DC bus resources, and dividing the charging vehicle data into multiple charging groups, specifically including: The vehicles are sorted in ascending order based on their start timestamps to compare the start timestamps of adjacent charging vehicles and obtain the start time difference between the adjacent charging vehicles. If the start-up time difference is less than or equal to the preset time window, it is determined that there is a concurrent demand for the use of the common DC bus resources among the adjacent charging vehicles, and the charging vehicles corresponding to the start-up time difference are assigned to the same competitive charging group. If the start-up time difference is greater than the preset time window, it is determined that there is no concurrent demand for the use of the common DC bus resources among the charging vehicles, and the charging vehicles corresponding to the start-up time difference are assigned to non-competitive charging groups.

4. The intelligent charging control method according to claim 2, characterized in that, Based on the charging vehicle data within each of the competing charging groups, the key charging indicators for the corresponding charging vehicles are determined, specifically including: The difference in state of charge is obtained based on the initial state of charge of the vehicle and the preset full-charge state of charge. Based on the state of charge difference and the vehicle battery capacity, the amount of charge to be applied to the corresponding charging vehicle is obtained, and the theoretical charging time is obtained based on the amount of charge to be applied and the vehicle power demand of the corresponding charging vehicle. Obtain the rated power of each power module in the public DC bus charging system, and based on the rated power and the vehicle's required power, obtain the required number of power modules for the corresponding charging vehicle. By comparing the difference between the total power of the required number of power modules and the power demand of the vehicle, the power utilization efficiency is obtained. The matching degree value corresponding to the power utilization efficiency is then used to obtain the power module matching degree.

5. The intelligent charging control method according to claim 1, characterized in that, Based on the adaptive comprehensive priority, the power modules of the public DC bus charging system are dynamically scheduled to allocate charging power to each charging vehicle, specifically including: Obtain the adaptive comprehensive priority of each corresponding charging vehicle in the competitive charging group, sort the corresponding charging vehicles in descending order based on the adaptive comprehensive priority, and generate a power allocation queue. Monitor the release events or new available power events of the power modules in the public DC bus charging system; In response to the release event or the new available power event, a power module combination that meets the power requirements of the corresponding charging vehicle is allocated according to the order of the power allocation queue; wherein, the power module combination consists of a dedicated power module for the charging gun and a non-dedicated power module for the common DC bus to make up for the power gap; When a new charging vehicle connects or an existing charging vehicle finishes charging, the adaptive comprehensive priority of the current charging vehicle is re-acquired, and the power allocation queue is updated to achieve real-time dynamic scheduling of the power module.

6. The intelligent charging control method according to claim 2, characterized in that, The method further includes: The charging vehicles in the non-competitive charging group are sorted according to the start timestamp to obtain a power allocation queue; Based on the order of the power allocation queue, the vehicle power requirement of each corresponding charging vehicle is obtained, and a corresponding power module combination is allocated to the corresponding charging vehicle according to the vehicle power requirement and the rated power of each power module.

7. An intelligent charging control device, characterized in that, The device includes: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method described in any one of claims 1-6.

8. A non-volatile storage medium storing computer-executable instructions, characterized in that, The computer-executable instructions are capable of performing the method described in any one of claims 1-6.

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