Flexible charging system and method

Abstract

The application discloses a flexible charging system and method, wherein the system comprises a charging detection module, a charging processing module and a charging execution module; the charging detection module is used for acquiring at least one charging state data when detecting that a device to be charged is in a charging state; the charging processing module is used for receiving the at least one charging state data, determining corresponding target charging data, processing the at least one target charging data based on a pre-trained charging law prediction model, obtaining a charging law prediction result, and updating a pre-determined initial charging strategy based on the charging law prediction result; and the charging execution module is used for receiving the updated initial charging strategy and charging the device to be charged based on the updated initial charging strategy. The technical scheme provided in the embodiment realizes the effect that the charging power is intelligently variable during the charging process, and improves the charging efficiency and the utilization rate of the charging equipment.

Description

Technical Field

[0001] This invention relates to the field of automotive charging technology, and more particularly to a flexible charging system and method. Background Technology

[0002] With the gradual implementation of carbon neutrality policies, the production and sales of pure electric vehicles have been steadily increasing. However, the industry still faces the challenge of slow charging. To address this issue, it is necessary to increase either the charging voltage or the charging current.

[0003] Currently, some companies address the slow charging issue by increasing the charging current. However, this approach has a drawback: for vehicle battery packs, high-current charging may pose a higher risk of overheating, thus affecting charging safety. Summary of the Invention

[0004] This invention provides a flexible charging system and method to achieve intelligent variable charging power during the charging process, and improves charging efficiency and utilization of charging equipment, thereby enabling vehicles to be charged based on the optimal charging strategy.

[0005] According to one aspect of the present invention, a flexible charging system is provided, the system comprising: a charging detection module, a charging processing module, and a charging execution module; the charging detection module is electrically connected to the charging processing module, the charging detection module is disposed in the charging gun and / or the device to be charged, and the charging processing module is disposed in the charging gun and / or the device to be charged;

[0006] A charging detection module is used to acquire at least one charging status data corresponding to the charging processing module when it detects that the device to be charged is in a charging state, and to transmit at least one charging status data to the charging processing module; wherein, at least one charging status data is used to characterize the charging process status of the charging gun and / or the device to be charged;

[0007] The charging processing module is used to receive at least one charging status data and determine the corresponding target charging data based on the at least one charging status data. When the flexible charging system is detected to be offline, the module processes the at least one target charging data based on a pre-trained charging pattern prediction model to obtain a charging pattern prediction result. Based on the charging pattern prediction result, the module updates the pre-determined initial charging strategy and sends the updated initial charging strategy to the charging execution module.

[0008] The charging execution module is used to receive the updated initial charging strategy and charge the device to be charged based on the updated initial charging strategy.

[0009] Optionally, the charging processing module is also used to send at least one target charging data to the charging cloud platform when the flexible charging system is detected to be in a network state;

[0010] The charging cloud platform is used to receive at least one target charging data, process at least one target charging data, obtain a target charging strategy, and send it to the charging processing module.

[0011] The charging processing module is also used to send the target charging strategy to the charging execution module;

[0012] The charging execution module is used to receive the target charging strategy and charge the device to be charged based on the target charging strategy.

[0013] Optionally, the charging cloud platform and the charging processing module can communicate with each other via Ethernet, WIFI, 4G, 5G, LoRa, or NB-IoT.

[0014] Optionally, the charging processing module includes a processing unit, an edge computing unit, and a communication unit, wherein,

[0015] The processing unit is used to receive at least one charging status data, process the at least one charging status data according to a preset data processing method, obtain the corresponding target charging data, and send it to the edge computing unit.

[0016] The edge computing unit is used to process at least one target charging data based on the charging pattern prediction model when the flexible charging system is detected to be offline, to obtain the charging pattern prediction result, and to update the initial charging strategy based on the charging pattern prediction result and send it to the communication unit.

[0017] The communication unit is used to send the updated initial charging strategy to the charging execution module.

[0018] Optionally, the processing unit is also configured to send at least one target charging data to the communication unit;

[0019] The communication unit is also used to receive at least one target charging data and, when the flexible charging system is detected to be in a network state, to send at least one target charging data to the charging cloud platform based on a pre-set standard interface protocol.

[0020] Optionally, the flexible charging system also includes: a temperature control module, wherein,

[0021] The charging detection module is also used to collect temperature information from the charging processing module and send it to the charging processing module when the device to be charged is in a charging state or a non-charging state.

[0022] The charging processing module is also used to receive temperature information and generate a temperature control command and send it to the temperature control module when the temperature information is detected to reach a preset temperature threshold.

[0023] The temperature control module is used to receive temperature information and, when the temperature information is detected to reach a preset temperature threshold, to cool the charging processing module based on a pre-set cooling strategy.

[0024] Optionally, the processing unit includes: a charging alarm subunit, wherein,

[0025] The charging alarm subunit is used to send an alarm message and control the charging power supply to disconnect when at least one charging status data reaches a preset charging alarm threshold. The at least one charging status data includes leakage information or attitude information of the charging processing module.

[0026] Optionally, the charging detection module is also used to acquire lifecycle data corresponding to the charging processing module and send it to the charging processing module when the charging of the device to be charged is detected to be completed.

[0027] The charging processing module is also used to receive lifecycle data and process the lifecycle data based on a pre-trained health prediction model to determine the health prediction result of the charging processing module.

[0028] According to another aspect of the present invention, a flexible charging method is provided, the method comprising:

[0029] When the device to be charged is detected to be in a charging state, the charging detection module acquires at least one charging status data corresponding to the charging processing module and sends it to the charging processing module; wherein, the at least one charging status data is used to characterize the charging process status of the charging gun and / or the device to be charged.

[0030] The charging processing module receives at least one charging status data and determines the corresponding target charging data based on the at least one charging status data. When the flexible charging system is detected to be offline, the at least one target charging data is processed based on the pre-trained charging pattern prediction model to obtain the charging pattern prediction result. Based on the charging pattern prediction result, the target charging strategy is determined and sent to the charging execution module.

[0031] The charging execution module receives the target charging strategy and charges the device to be charged based on the target charging strategy.

[0032] Optionally, the flexible charging system also includes: a charging cloud platform, a flexible charging method, and further includes:

[0033] When the flexible charging system is detected to be connected to the network, at least one target charging data item is sent to the charging cloud platform through the charging processing module.

[0034] The system receives at least one target charging data point through the charging cloud platform, processes the target charging data point to obtain a target charging strategy, and sends it to the charging processing module.

[0035] The target charging strategy is sent to the charging execution module through the charging processing module;

[0036] The charging execution module receives the target charging strategy and charges the device to be charged based on the target charging strategy.

[0037] This invention provides a flexible charging system, comprising: a charging detection module, a charging processing module, and a charging execution module; the charging detection module is electrically connected to the charging processing module, the charging detection module being disposed in the charging gun and / or the device to be charged, and the charging processing module being disposed in the charging gun and / or the device to be charged; the charging detection module is used to acquire at least one charging status data corresponding to the charging processing module when it detects that the device to be charged is in a charging state, and transmit the at least one charging status data to the charging processing module; wherein, the at least one charging status data is used to characterize the charging process conditions of the charging gun and / or the device to be charged; the charging processing module is used to receive the at least one charging status data, and determine corresponding target charging data based on the at least one charging status data, so that when it detects that the flexible charging system is in an offline state, it processes the at least one target charging data based on a pre-trained charging pattern prediction model to obtain a charging pattern prediction result, and updates a pre-determined initial charging strategy based on the charging pattern prediction result, and sends the updated initial charging strategy to the charging execution module; the charging execution module is used to receive the updated initial charging strategy, and charge the device to be charged based on the updated initial charging strategy. The technical solution provided by this invention solves the problems of high voltage platform charging of vehicles, which requires high voltage levels of power devices and control devices of vehicle electrical equipment, making it difficult to effectively solve vehicle charging problems, and the high risk of overheating caused by high current charging, thus affecting charging safety. It realizes the effect of intelligent variable charging power during the charging process, and improves charging efficiency and utilization rate of charging equipment, so as to enable vehicles to be charged based on the optimal charging strategy.

[0038] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0039] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0040] Figure 1 This is a schematic diagram of a flexible charging system according to Embodiment 1 of the present invention;

[0041] Figure 2 This is a schematic diagram of the structure of a charging processing module in a flexible charging system according to Embodiment 1 of the present invention;

[0042] Figure 3 This is a schematic diagram of the structure of a charging processing module and a temperature control module according to Embodiment 1 of the present invention;

[0043] Figure 4 This is a flowchart of a flexible charging method provided according to Embodiment 2 of the present invention. Detailed Implementation

[0044] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0045] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0046] Example 1

[0047] Figure 1 This is a schematic diagram of a flexible charging system provided in Embodiment 1 of the present invention, for reference. Figure 1The flexible charging system includes: a charging detection module 1, a charging processing module 2, and a charging execution module 3; the charging detection module 1 is electrically connected to the charging processing module 2, the charging detection module 1 is installed in the charging gun and / or the device to be charged, and the charging processing module 2 is installed in the charging gun and / or the device to be charged.

[0048] The charging detection module 1 is used to acquire at least one charging status data corresponding to the charging processing module 2 and send it to the charging processing module 2 when the device to be charged is detected to be in a charging state. The charging processing module 2 is used to receive at least one charging status data and determine the corresponding target charging data based on the at least one charging status data. When the flexible charging system is detected to be in an offline state, the at least one target charging data is processed based on a pre-trained charging pattern prediction model to obtain a charging pattern prediction result. Based on the charging pattern prediction result, a target charging strategy is determined and sent to the charging execution module 3. The charging execution module 3 is used to receive the target charging strategy and charge the device to be charged based on the target charging strategy.

[0049] In this embodiment, the charging detection module 1 may include a temperature sensor, a humidity sensor, a leakage detection sensor, a power sensor, a current acquisition device, and a voltage acquisition device. The charging detection module 1 can monitor the charging status of the charging processing module 2 in real time during the charging process. Simultaneously, it can collect lifecycle data (i.e., charging status data) of the charging processing module 2 upon completion of the charging process, allowing for prediction of the health of the charging processing module 2 based on this lifecycle data. The charging detection module 1 can be installed in the charging gun, the device to be charged, or both. The charging gun can be a device installed on a charging pile to execute the charging process. The device to be charged can be a vehicle requiring charging, such as an electric vehicle. The charging processing module 2 can be a component that acts as a connector during the charging process of the device to be charged. The charging processing module 2 and the charging detection module 1 are connected by a circuit. The charging processing module 2 can be installed in the charging gun, the device to be charged, or both. The charging execution module 3 can be a device that executes the charging process, i.e., charges the device to be charged. Optionally, the charging execution module 3 can be a charging pile.

[0050] The charging status data can be data characterizing the charging conditions of the charging gun and / or the device to be charged during the charging process. Charging conditions may include temperature or leakage status. Optionally, the charging status data includes, but is not limited to, temperature data, humidity data, voltage data, and current data. It should be noted that the charging status data can include charging data of the charging gun and / or the device to be charged while charging, as well as charging data of the charging gun and / or the device to be charged while not charging.

[0051] In practical applications, in order to monitor the charging process of the device to be charged so that it can be charged with high charging efficiency and low charging risk, charging status data of the charging processing module located at the device to be charged and / or the charging gun can be collected during the charging process. This charging status data can then be sent to the charging processing module for processing based on multiple data processing units located in the charging processing module.

[0052] It should be noted that the charging status data in this embodiment includes charging data of the charging gun and / or the device to be charged in both the non-charging and / or charging states. Furthermore, the charging processing module can determine two initial charging strategies based on the state of the charging gun and / or the device to be charged before charging (i.e., the non-charging state) and the state of the charging gun and / or the device to be charged during charging (i.e., the charging state). The initial charging strategy determined based on the charging data of the charging gun and / or the device to be charged in the non-charging state may be relatively coarse. It can be adjusted again during the charging process based on the charging process data. It should also be noted that the advantage of selecting charging data in the charging state as the charging status data is that it can improve the accuracy of the charging pattern prediction results, helping the charging execution module 3 to charge the device to be charged based on the optimal charging strategy.

[0053] Figure 2 This is a schematic diagram of the structure of a charging processing module in a flexible charging system provided by an embodiment of the present invention. Optionally, the charging processing module 2 includes a processing unit 21, an edge computing unit 22, and a communication unit 23. The processing unit 21 is used to receive at least one charging status data, process the at least one charging status data according to a preset data processing method to obtain corresponding target charging data, and send it to the edge computing unit 22. The edge computing unit 22 is used to, when detecting that the flexible charging system is in an offline state, process the at least one target charging data based on a charging pattern prediction model to obtain a charging pattern prediction result, update the initial charging strategy based on the charging pattern prediction result, and send it to the communication unit 23. The communication unit 23 is used to send the updated initial charging strategy to the charging execution module 3.

[0054] In this embodiment, the processing unit 21 can be a unit for receiving data sent by the charging detection module and processing the received data. The preset data processing method can be a pre-set method for processing the received charging status data so that the processed data can be recognized by the charging processing module 2. Optionally, the preset data processing method may include at least one of data signal conversion, abnormal data cleaning, and data synchronization. Offline state is the state without network connection. The charging pattern prediction model can be a pre-trained deep learning neural network model used to process the charging status data process to obtain the corresponding charging trend. The charging pattern prediction model can be a neural network model with any model structure. The charging pattern prediction result can predict the charging efficiency change trend of the charging processing module 2 during the charging process.

[0055] It should be noted that the charging pattern prediction model is trained based on historical charging state data. The training process of the charging pattern prediction model will be explained in detail below.

[0056] In practical applications, the historical charging status data of the charging processing module 2 within a first preset time period before the current moment, and the charging efficiency data of the charging processing module 2 within a second preset time period are obtained. Training samples are constructed based on the historical charging status data and the charging efficiency data. Furthermore, the training samples are input into the model to be trained to obtain the actual output results. The actual output results are compared with the charging efficiency data to determine the model loss. Based on the model loss, the model parameters of the model to be trained are adjusted, and finally, the trained charging pattern prediction model is obtained.

[0057] In specific implementation, after acquiring at least one charging status data corresponding to the charging processing module 2, this charging status data can be sent to the processing unit 21 in the charging processing module 2. Upon receiving this charging status data, the processing unit 21 can process the charging status data based on a preset data processing method, and send the processed data as target charging data to the edge computing unit 22. Further, when the edge computing unit 22 receives at least one target charging data and detects that the flexible charging system is offline, it processes the at least one target charging data based on the charging pattern prediction model pre-deployed in the edge computing unit 22 to obtain the charging pattern prediction result, and parses the charging prediction result. Based on the parsed result, it updates the pre-determined initial charging strategy and sends the updated initial charging strategy to the communication unit 23, so that the communication unit 23 sends the updated initial charging strategy to the charging execution module 3.

[0058] The initial charging strategy can be a charging strategy determined before the charging process begins, based on the performance parameters of the device to be charged and the charging terminal connected to it. The initial charging strategy can limit the charging current and / or charging voltage at each charging stage during the charging process. The communication unit 23 can be a unit with interactive functions, capable of data interaction and logic control with the external temperature control module, and also capable of data interaction and logic control with the charging execution module 3 and the charging cloud platform 4.

[0059] Furthermore, when the charging execution module 3 receives the updated initial charging strategy, it can adjust its charging parameters based on the updated strategy, so that the charging execution module 3 can charge the device to be charged based on the adjusted charging parameters. These charging parameters may include, but are not limited to, temperature parameters, current parameters, voltage parameters, and humidity parameters.

[0060] It should be noted that the process of collecting, analyzing, and updating charging status data and the charging strategy can continue throughout the entire charging process. Specifically, while charging the device to be charged based on the updated initial charging strategy, charging status data can continue to be collected and analyzed to obtain charging pattern prediction results. If the charging pattern prediction results obtained this time determine that the updated initial charging strategy still needs to be updated, it can be updated again, and the device to be charged can be charged based on the updated charging strategy. The steps of collecting and analyzing charging status data are repeated until the device to be charged is finished charging.

[0061] It should also be noted that, based on the technical solution provided in this embodiment, during the charging process, both the charging gun and the device to be charged will dynamically adjust the charging parameters according to the charging status data during the charging process, so that the charging response can be performed according to the best performance of both the charging gun and the device to be charged before each charging, or, during the charging process, the charging strategy can be dynamically adjusted according to the changes in the charging status data, so that the entire charging process is always in the optimal state.

[0062] It should be noted that analyzing the target charging data based on the charging pattern prediction model deployed in the edge computing unit 22 can be an emergency handling method in the charging status data analysis process. That is, it can also monitor the charging process when the flexible charging system is offline. Under normal circumstances, the flexible charging system can always be connected to the network. In this case, the target charging data can be sent to the cloud for specific data analysis based on the deep learning neural network model deployed in the cloud.

[0063] Based on this, continue to refer to Figure 1The flexible charging system also includes a charging cloud platform 4, wherein the charging processing module 2 is further configured to send at least one target charging data to the charging cloud platform 4 when the flexible charging system is detected to be in a network state; the charging cloud platform 4 is configured to receive at least one target charging data, process the at least one target charging data to obtain a target charging strategy and send it to the charging processing module 2; the charging processing module 2 is further configured to send the target charging strategy to the charging execution module 3; the charging execution module 3 is configured to receive the target charging strategy and charge the device to be charged based on the target charging strategy.

[0064] In this embodiment, the charging cloud platform 4 is connected to the charging processing module 2 and is a cloud platform capable of real-time monitoring and data analysis of the charging process. The charging cloud platform 4 and the charging processing module 2 can communicate via Ethernet, WIFI, 4G, 5G, LoRa, or NB-IoT. LoRa (Long Range Radio) is a wireless communication technology specifically designed for long-range, low-power communication. Its modulation method significantly increases the communication distance compared to other communication methods, and it can be widely used in various long-range, low-speed IoT wireless communication applications. NB-IoT (Narrow Band Internet of Things) is a low-power wide-area cellular communication technology that allows for the creation of a network connecting devices over a wide area in a low-cost and low-power environment. It should be noted that the above communication connection methods can be selected as wired or wireless connections according to actual needs. The target charging strategy can be a charging strategy determined by the charging cloud platform 4 after analyzing the received target charging data.

[0065] In specific implementation, after obtaining at least one target charging data, the network status of the flexible charging system can be detected. When the flexible charging system is detected to be in a network state, at least one target charging data can be sent to the charging cloud platform 4. Further, the received target charging data is processed by the data analysis system pre-deployed in the charging cloud platform 4 to obtain the target charging strategy, and the target charging strategy is sent to the charging processing module 2. When the charging processing module 2 receives the target charging strategy, it can send the target charging strategy to the charging execution module 3, so that when the charging execution module 3 receives the target charging strategy, it can charge the device to be charged based on the target charging strategy.

[0066] Optionally, the processing unit 21 is further configured to send at least one target charging data to the communication unit 23; the communication unit 23 is further configured to receive at least one target charging data, and when the flexible charging system is detected to be in a network state, send at least one target charging data to the charging cloud platform 4 based on a pre-set standard interface protocol.

[0067] The standard interface protocol can be defined as the communication method and requirements that need to be followed between interfaces that need to exchange data. In this embodiment, the standard interface protocol can be defined as the communication method and requirements that need to be followed when the communication unit 23 and the charging cloud platform 4 interact with each other.

[0068] In practice, after receiving the target charging data and detecting that the flexible charging system is connected to the network, the target charging data can be filtered according to the pre-set standard interface protocol, and the target charging data that conforms to the standard interface protocol can be sent to the charging cloud platform 4.

[0069] It should be noted that temperature is one of the important factors affecting charging efficiency. When charging the device with a large current to improve charging efficiency, there may be a high risk of overheating. In this case, a temperature control device can be set up to make the charging temperature controllable.

[0070] Based on this, continue to refer to Figure 1 The flexible charging system also includes a temperature control module 5. The charging detection module 1 is used to collect temperature information from the charging processing module 2 and send it to the charging processing module 2 when the device to be charged is in a charging state or a non-charging state. The charging processing module 2 is also used to receive temperature information and generate a temperature control command and send it to the temperature control module 5 when the temperature information reaches a preset temperature threshold. The temperature control module 5 is used to cool the charging processing module 2 based on the temperature control command when it receives the temperature control command.

[0071] The temperature control module 5 is a device for detecting and adjusting the temperature of the charging processing module 2. The preset temperature threshold can be a pre-set maximum temperature value used to limit the charging temperature. The temperature control command can be a pre-written program code that can be used to trigger the temperature control process.

[0072] In specific implementation, when the device to be charged is in a charging state, the temperature information of the charging processing module 2 can be collected by the temperature sensor in the charging detection module 1, and the collected temperature information can be sent to the charging processing module 2. Furthermore, when the charging processing module 2 detects that the temperature information reaches the preset temperature threshold, it can generate a temperature control command and send it to the temperature control module 5, so that when the temperature control module 5 receives the temperature control command, it can parse the temperature control command and perform cooling treatment on the charging processing module 2 based on the parsed result.

[0073] It should also be noted that during the charging process, in addition to real-time monitoring of the charging processing module 2, a charging alarm function can also be implemented, which can effectively predict the occurrence of faults and nip potential charging hazards in the bud, thus effectively preventing spontaneous combustion accidents of the device being charged.

[0074] Based on this, the processing unit includes a charging alarm subunit, wherein the charging alarm subunit is used to send an alarm message and control the charging power supply to disconnect when at least one charging status data reaches a preset charging alarm threshold.

[0075] The charging status data also includes leakage information and attitude information of the charging processing module.

[0076] In this embodiment, the preset charging alarm threshold can be a pre-set value used to monitor whether a charging alarm occurs during the charging process. It should be noted that separate preset charging alarm thresholds can be set for each charging status data point.

[0077] In specific implementation, when the charging alarm module detects that the data corresponding to the leakage information and / or attitude information of the charging processing module 2 reaches the preset charging alarm threshold, it can immediately disconnect the charging power supply and send alarm information to relevant maintenance personnel and the user of the device to be charged. This allows the relevant maintenance personnel to troubleshoot the charging processing module 2 after receiving the alarm information, and the user of the device to be charged to check the performance of the device after receiving the alarm information.

[0078] It should be noted that, in addition to real-time monitoring of the charging process of the charging processing module 2, the charging detection module 1 can also collect the lifecycle data of the charging processing module 2 during this charging process after the charging is completed, so as to analyze and predict the health of the charging processing module 2 based on the collected lifecycle data.

[0079] Optionally, the charging detection module 1 is further configured to acquire lifecycle data corresponding to the charging processing module 2 and send it to the charging processing module 2 when the charging of the device to be charged is detected to be completed; the charging processing module 2 is further configured to receive the lifecycle data and process the lifecycle data based on the pre-trained health prediction model to determine the health prediction result of the charging processing module 2.

[0080] In this embodiment, the lifecycle data can be charging efficiency data representing each stage of the charging process, that is, the charging efficiency data of the charging processing module 2 from the start to the end of charging. The health prediction model can be a pre-trained deep learning neural network model used to process the lifecycle data to obtain the corresponding health change trend. The health prediction model can be a neural network model with any model structure. The health prediction result can be a result representing the maximum usable time of the charging processing module 2. It should be noted that the health prediction model is trained based on historical lifecycle data and health data, and its model training process is consistent with the model training process of the charging pattern prediction model, which will not be described in detail here.

[0081] It should be noted that the lifecycle data corresponding to the charging processing module 2 can be obtained when the flexible charging system is not connected to the network or when the flexible charging system is connected to the network. This embodiment does not make specific limitations on this.

[0082] In specific implementation, when the charging detection module 1 detects that the charging device has completed charging, it can collect the life cycle data of the charging processing module 2 during this charging process and send the collected life cycle data to the charging processing module 2. Furthermore, based on the health prediction model pre-deployed in the charging processing module 2, the life cycle data is processed to obtain the health prediction result corresponding to the charging processing module 2, so that the charging processing module 2 can be maintained according to the health prediction result.

[0083] For example, it can be combined Figure 3 The charging processing module 2 and the temperature control module 5 are described as follows: The device to be charged is the vehicle to be charged. The charging processing module 2 may include a temperature acquisition unit and a charging socket in the vehicle, as well as a temperature acquisition unit, a charging plug and a high-power cable in the charging end. The temperature control module 5 may include a temperature control unit and a cooling device.

[0084] This invention provides a flexible charging system comprising: a charging detection module, a charging processing module, and a charging execution module; the charging detection module is electrically connected to the charging processing module, the charging detection module being disposed in the charging gun and / or the device to be charged, and the charging processing module being disposed in the charging gun and / or the device to be charged; the charging detection module is used to acquire at least one charging status data corresponding to the charging processing module when it detects that the device to be charged is in a charging state, and transmit the at least one charging status data to the charging processing module; wherein, the at least one charging status data is used to characterize the charging process conditions of the charging gun and / or the device to be charged; the charging processing module is used to receive the at least one charging status data, and determine corresponding target charging data based on the at least one charging status data, so that when it detects that the flexible charging system is in an offline state, it processes the at least one target charging data based on a pre-trained charging pattern prediction model to obtain a charging pattern prediction result, and updates a pre-determined initial charging strategy based on the charging pattern prediction result, and sends the updated initial charging strategy to the charging execution module; the charging execution module is used to receive the updated initial charging strategy, and charge the device to be charged based on the updated initial charging strategy. The technical solution provided in this embodiment solves the problems of high voltage requirements for the power devices and control devices of vehicle electrical equipment when using a high voltage platform to charge vehicles, which cannot effectively solve the vehicle charging problem, and the high risk of overheating that may occur with high current charging, thus affecting charging safety. It achieves the effect of intelligent variable charging power during the charging process, and improves charging efficiency and utilization of charging equipment, so as to enable vehicles to be charged based on the optimal charging strategy.

[0085] Example 2

[0086] Figure 4 This is a flowchart of a flexible charging method provided in Embodiment 2 of the present invention. This method can be applied to the flexible charging system provided in the above embodiments. See also... Figure 4 As shown, the method may include the following steps:

[0087] S210. When the device to be charged is detected to be in a charging state, at least one charging state data corresponding to the charging processing module is obtained through the charging detection module and sent to the charging processing module.

[0088] Among them, at least one charging status data includes charging data of the device to be charged in an uncharged state / charging state, and at least one charging status data is used to characterize the charging process conditions of the charging gun and / or the device to be charged.

[0089] S220. Receive at least one charging status data through the charging processing module, and determine the corresponding target charging data based on the at least one charging status data. When the flexible charging system is detected to be offline, process the at least one target charging data based on the pre-trained charging pattern prediction model to obtain the charging pattern prediction result. Based on the charging pattern prediction result, update the pre-determined initial charging strategy and send the updated initial charging strategy to the charging execution module.

[0090] S230: Receive the updated initial charging strategy through the charging execution module, and charge the device to be charged based on the updated initial charging strategy.

[0091] The technical solution provided by this invention, when the device to be charged is detected to be in a charging state, acquires at least one charging state data corresponding to the charging processing module through the charging detection module and sends it to the charging processing module. Further, the charging processing module receives the at least one charging state data and determines the corresponding target charging data based on it. When the flexible charging system is detected to be offline, the at least one target charging data is processed based on a pre-trained charging pattern prediction model to obtain a charging pattern prediction result. Based on the charging pattern prediction result, the pre-determined initial charging strategy is updated and sent to the charging execution module. Finally, the charging execution module receives the updated initial charging strategy and charges the device to be charged based on it. This solves the problems in the prior art where high-voltage platforms are used to charge vehicles, which place high demands on the voltage levels of the power devices and control devices of the vehicle's electrical equipment, making it difficult to effectively solve vehicle charging problems. It also addresses the issue that high-current charging may pose a high risk of overheating, thus affecting charging safety. This solution achieves intelligent variable charging power during the charging process and improves charging efficiency and the utilization rate of the charging equipment, enabling vehicles to be charged based on the optimal charging strategy.

[0092] Optionally, when the flexible charging system is detected to be connected to the network, at least one target charging data is sent to the charging cloud platform through the charging processing module;

[0093] The system receives at least one target charging data point through the charging cloud platform, processes the target charging data point to obtain a target charging strategy, and sends it to the charging processing module.

[0094] The target charging strategy is sent to the charging execution module through the charging processing module;

[0095] The charging execution module receives the target charging strategy and charges the device to be charged based on the target charging strategy.

[0096] Optionally, the charging cloud platform and the charging processing module can communicate via Ethernet, WIFI, 4G, 5G, LoRa or NB-IoT.

[0097] Optionally, the processing unit receives at least one charging status data and processes the at least one charging status data according to a preset data processing method to obtain the corresponding target charging data and send it to the edge computing unit.

[0098] When the edge computing unit detects that the flexible charging system is offline, it processes at least one target charging data based on the charging pattern prediction model to obtain the charging pattern prediction result. Based on the charging pattern prediction result, the initial charging strategy is updated and sent to the communication unit.

[0099] The updated initial charging strategy is sent to the charging execution module via the communication unit.

[0100] Optionally, at least one target charging data item may be sent to the communication unit via the processing unit;

[0101] The system receives at least one target charging data through the communication unit, and when it detects that the flexible charging system is connected to the network, it sends at least one target charging data to the charging cloud platform based on the preset standard interface protocol.

[0102] Optionally, when the device to be charged is in a charging state or a non-charging state, the temperature information of the charging connection device is collected by the charging detection module and sent to the charging processing module.

[0103] The charging processing module receives temperature information and generates a temperature control command when the detected temperature reaches a preset temperature threshold, and sends it to the temperature control module.

[0104] When the temperature control module receives a temperature control command, it cools the charging processing module based on the command.

[0105] Optionally, when the charging alarm subunit detects that at least one charging status data has reached a preset charging alarm threshold, it sends an alarm message and controls the charging power supply to disconnect. The at least one charging status data includes leakage information or attitude information of the charging processing module.

[0106] Optionally, when the charging detection module detects that the device to be charged has completed charging, it acquires the lifecycle data corresponding to the charging processing module and sends it to the charging processing module.

[0107] The charging processing module receives lifecycle data and processes it based on a pre-trained health prediction model to determine the health prediction result of the charging processing module.

[0108] The flexible charging method provided in this embodiment has the same beneficial effects as the flexible charging system, and will not be described again in this embodiment.

[0109] Note that the above description is merely a preferred embodiment of the present invention and the technical principles employed. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments, and may include many other equivalent embodiments without departing from the concept of the present invention, the scope of which is determined by the scope of the appended claims.

Claims

1. A flexible charging system, characterized in that, include: The system includes a charging detection module, a charging processing module, and a charging execution module; the charging detection module is electrically connected to the charging processing module, the charging detection module is disposed in the charging gun and / or the device to be charged, and the charging processing module is disposed in the charging gun and / or the device to be charged. The charging detection module is used to acquire at least one charging status data corresponding to the charging processing module when the charging device is detected to be in a charging state, and transmit the at least one charging status data to the charging processing module, wherein the at least one charging status data is used to characterize the charging process status of the charging gun and / or the charging device. The charging processing module is used to receive the at least one charging status data and determine the corresponding target charging data based on the at least one charging status data. When the flexible charging system is detected to be offline, the module processes the at least one target charging data based on a pre-trained charging pattern prediction model to obtain a charging pattern prediction result. Based on the charging pattern prediction result, the module updates the pre-determined initial charging strategy and sends the updated initial charging strategy to the charging execution module. The charging execution module is used to receive the updated initial charging strategy and charge the device to be charged based on the updated initial charging strategy.

2. The flexible charging system according to claim 1, characterized in that, The flexible charging system also includes: a charging cloud platform, wherein... The charging processing module is also used to send at least one of the target charging data to the charging cloud platform when the flexible charging system is detected to be in a network state; The charging cloud platform is used to receive at least one of the target charging data, process the at least one of the target charging data to obtain a target charging strategy, and send it to the charging processing module. The charging processing module is further configured to send the target charging strategy to the charging execution module; The charging execution module is used to receive the target charging strategy and charge the device to be charged based on the target charging strategy.

3. The flexible charging system according to claim 2, characterized in that, The charging cloud platform and the charging processing module communicate with each other via Ethernet, WIFI, 4G, 5G, LoRa or NB-IoT.

4. The flexible charging system according to any one of claims 2 or 3, characterized in that, The charging processing module includes a processing unit, an edge computing unit, and a communication unit, wherein... The processing unit is configured to receive at least one of the charging status data, process the at least one of the charging status data according to a preset data processing method, obtain the corresponding target charging data, and send it to the edge computing unit. The edge computing unit is used to process at least one of the target charging data based on the charging pattern prediction model when the flexible charging system is detected to be offline, to obtain the charging pattern prediction result, and to update the initial charging strategy based on the charging pattern prediction result and send it to the communication unit. The communication unit is used to send the updated initial charging strategy to the charging execution module.

5. The system according to claim 4, characterized in that, The processing unit is further configured to send at least one of the target charging data to the communication unit; The communication unit is also configured to receive at least one of the target charging data, and when the flexible charging system is detected to be in a network state, to send at least one of the target charging data to the charging cloud platform based on a pre-set standard interface protocol.

6. The flexible charging system according to claim 1, characterized in that, The system also includes a temperature control module, wherein... The charging detection module is also used to collect the temperature information of the charging processing module and send it to the charging processing module when the device to be charged is in a charging state or a non-charging state. The charging processing module is also used to receive the temperature information, and when the temperature information is detected to reach a preset temperature threshold, generate a temperature control command and send it to the temperature control module. The temperature control module is used to cool the charging processing module based on the temperature control command when it receives the temperature control command.

7. The flexible charging system according to claim 4, characterized in that, The processing unit includes: a charging alarm subunit, wherein... The charging alarm subunit is used to send an alarm message and control the charging power supply to disconnect when the at least one charging status data reaches a preset charging alarm threshold. The at least one charging status data includes leakage information or attitude information of the charging processing module.

8. The flexible charging system according to claim 1, characterized in that, The charging detection module is also used to acquire lifecycle data corresponding to the charging processing module and send it to the charging processing module when the charging device is detected to be fully charged. The charging processing module is further configured to receive the lifecycle data, process the lifecycle data based on a pre-trained health prediction model, and determine the health prediction result of the charging processing module.

9. A flexible charging method, characterized in that, include: When the device to be charged is detected to be in a charging state, the charging detection module acquires at least one charging status data corresponding to the charging processing module and sends it to the charging processing module; wherein, the at least one charging status data is used to characterize the charging process status of the charging gun and / or the device to be charged; The charging processing module receives at least one charging status data and determines corresponding target charging data based on the at least one charging status data. When the flexible charging system is detected to be offline, the at least one target charging data is processed based on a pre-trained charging pattern prediction model to obtain a charging pattern prediction result. Based on the charging pattern prediction result, a target charging strategy is determined and sent to the charging execution module. The charging execution module receives the target charging strategy and charges the device to be charged based on the target charging strategy.

10. The method according to claim 9, characterized in that, The flexible charging system also includes a charging cloud platform, and the flexible charging method further includes: When the flexible charging system is detected to be connected to the network, at least one of the target charging data is sent to the charging cloud platform through the charging processing module; The system receives at least one target charging data item through the charging cloud platform, processes the at least one target charging data item to obtain a target charging strategy, and sends it to the charging processing module. The target charging strategy is sent to the charging execution module through the charging processing module; The charging execution module receives the target charging strategy and charges the device to be charged based on the target charging strategy.

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