Charging method, charging system, energy storage system and power consumption device

Abstract

The present application discloses a charging method, a charging system, an energy storage system and an electrical device, which belongs to the field of electrical devices. The charging method comprises: receiving target operating parameters sent by the charging system and the energy storage system during the process of the charging system charging the energy storage system; in the case of determining that the charging is abnormal based on the target operating parameters, adjusting the charging strategy, and sending the adjusted charging strategy to the charging system or the energy storage system, wherein the charging strategy is used to control the charging condition of the energy storage system. The charging method described in the present application can realize multi-party interactive adjustment and dynamic adaptive charging adjustment, has high real-time performance and flexibility, and has high control precision and accuracy, which significantly improves the charging efficiency.

Description

Technical Field

[0001] The present application belongs to the field of electrical devices, and in particular relates to a charging method, a charging system, an energy storage system and an electrical device. Background Art

[0002] As an important representative of green shipping, electric ships are increasingly widely used. In the related art, the independent and detachable box-type power supply in the electric ship is mainly charged by the charging system. However, the overall control logic of the existing charging regulation process is relatively simple, resulting in low charging efficiency. Summary of the invention

[0003] The present application aims to solve at least one of the technical problems existing in the prior art. To this end, the present application proposes a charging method, a charging system, an energy storage system and an electric device, which can realize multi-party interactive adjustment and dynamic adaptive charging adjustment, have high real-time performance and flexibility, and have high control precision and control accuracy, and significantly improve charging efficiency.

[0004] In a first aspect, the present application provides a charging method, which is applied to a cloud server, and the method includes:

[0005] Receiving target operating parameters sent by the charging system and the energy storage system during the process of the charging system charging the energy storage system;

[0006] In the case where charging abnormality is determined based on the target operating parameter, adjusting a charging strategy, and sending the adjusted charging strategy to the charging system or the energy storage system, wherein the charging strategy is used to control the charging condition of the energy storage system;

[0007] The determining of charging abnormality based on the target operating parameter includes:

[0008] Inputting the target operating parameters into a charging efficiency prediction model corresponding to the energy storage system to obtain a theoretical charging efficiency output by the charging efficiency prediction model;

[0009] When the theoretical charging efficiency is not within a preset charging efficiency range, determining that the charging is abnormal;

[0010] Wherein, the charging efficiency prediction model corresponding to the energy storage system is constructed based on the historical operating parameters of the charging system and the energy storage system;

[0011] The charging efficiency prediction model is constructed based on historical operating parameters of the charging system and the energy storage system, and includes:

[0012] Based on the device identification of the energy storage system in the target operating parameter, acquiring a historical operating parameter corresponding to the device identification;

[0013] Acquire a historical battery temperature, a historical SOC value, a historical charging power, and a historical charging efficiency corresponding to the energy storage system from the historical operating parameters corresponding to the device identifier;

[0014] The historical battery temperature, historical SOC value, historical charging power and historical charging efficiency are fitted by the least square method to construct a charging efficiency prediction model corresponding to the energy storage system.

[0015] According to the charging method of the present application, the target operating parameters of the energy storage system and the charging system are obtained in real time through the cloud server, so as to judge whether the charging is abnormal in real time based on the target operating parameters, and when the charging is abnormal, the adjusted charging strategy is promptly generated and sent to the energy storage system or the charging system to adjust the charging efficiency, which can realize multi-party interactive adjustment and dynamic adaptive charging adjustment, has high real-time and flexibility, and has high control precision and control accuracy, and significantly improves the charging efficiency.

[0016] According to an embodiment of the present application, the adjusting the charging strategy and sending the adjusted charging strategy to the charging system or the energy storage system includes:

[0017] generating at least one of a first instruction for reducing charging power, a second instruction for adjusting charging time, a target instruction for instructing fine-tuning of working parameters, and warning prompt information;

[0018] At least one of the first instruction, the second instruction, the target instruction, and the warning prompt information is sent to the charging system.

[0019] According to one embodiment of the present application, generating at least one of a first instruction for reducing charging power, a second instruction for adjusting charging time, a target instruction for indicating fine-tuning working parameters, and warning prompt information includes:

[0020] When it is determined that the charging efficiency is lower than a lower limit threshold, at least one of the first instruction, the second instruction and the warning prompt information is generated.

[0021] According to one embodiment of the present application, generating at least one of a first instruction for reducing charging power, a second instruction for adjusting charging time, a target instruction for indicating fine-tuning working parameters, and warning prompt information includes:

[0022] In the case where it is determined that the charging efficiency is higher than the upper limit threshold, at least one of verifying the charging efficiency prediction model and detecting the working status of the charging system and the energy storage system is performed; the charging efficiency prediction model is a model for performing charging abnormality detection;

[0023] When it is determined that the charging efficiency prediction model is abnormal and / or the working state is abnormal, outputting the alarm prompt information;

[0024] When it is determined that the charging efficiency prediction model is normal and the working state is normal, the target instruction is output.

[0025] According to an embodiment of the present application, the adjusting the charging strategy and sending the adjusted charging strategy to the charging system or the energy storage system includes:

[0026] Based on the charging characteristic curve corresponding to the energy storage system, obtaining a target charging temperature when a target SOC value in the target operating parameter is achieved;

[0027] generating a water cooling control instruction based on the target charging temperature;

[0028] The water cooling control instruction is issued to the energy storage system, and the water cooling control instruction is used to instruct to adjust the water cooling system of the energy storage system.

[0029] According to an embodiment of the present application, determining the charging abnormality based on the target operating parameter includes:

[0030] Obtaining a charging curve corresponding to the type of the energy storage system;

[0031] Determining optimal charging parameters based on the target SOC value in the target operating parameters and the charging curve;

[0032] In the case where the difference between the optimal charging parameter and the target charging parameter in the target operating parameters exceeds a target threshold, the charging abnormality is determined.

[0033] In a second aspect, the present application provides a charging method, which is applied to a charging system, and the method includes:

[0034] Sending target operating parameters corresponding to the charging system to a cloud server, wherein the target operating parameters are used by the cloud server to generate a charging strategy;

[0035] Upon receiving the adjusted charging strategy sent by the cloud server, charging the energy storage system in response to the adjusted charging strategy;

[0036] The cloud server generates a charging strategy, including: in the case of determining that charging is abnormal based on the target operating parameter, adjusting the charging strategy:

[0037] The determining of charging abnormality based on the target operating parameter includes:

[0038] Inputting the target operating parameters into a charging efficiency prediction model corresponding to the energy storage system to obtain a theoretical charging efficiency output by the charging efficiency prediction model;

[0039] When the theoretical charging efficiency is not within a preset charging efficiency range, determining that the charging is abnormal;

[0040] Wherein, the charging efficiency prediction model corresponding to the energy storage system is constructed based on the historical operating parameters of the charging system and the energy storage system;

[0041] The charging efficiency prediction model is constructed based on historical operating parameters of the charging system and the energy storage system, and includes:

[0042] Based on the device identification of the energy storage system in the target operating parameter, acquiring a historical operating parameter corresponding to the device identification;

[0043] Acquire a historical battery temperature, a historical SOC value, a historical charging power, and a historical charging efficiency corresponding to the energy storage system from the historical operating parameters corresponding to the device identifier;

[0044] The historical battery temperature, historical SOC value, historical charging power and historical charging efficiency are fitted by the least square method to construct a charging efficiency prediction model corresponding to the energy storage system.

[0045] In a third aspect, the present application provides a charging method, applied to an energy storage system, the method comprising:

[0046] Sending target operating parameters corresponding to the energy storage system to a cloud server, wherein the target operating parameters are used by the cloud server to generate a charging strategy;

[0047] Upon receiving the adjusted charging strategy sent by the cloud server, adjusting the target operating parameter in response to the adjusted charging strategy;

[0048] The cloud server generates a charging strategy, including: in the case of determining that charging is abnormal based on the target operating parameter, adjusting the charging strategy:

[0049] The determining of charging abnormality based on the target operating parameter includes:

[0050] Inputting the target operating parameters into a charging efficiency prediction model corresponding to the energy storage system to obtain a theoretical charging efficiency output by the charging efficiency prediction model;

[0051] When the theoretical charging efficiency is not within a preset charging efficiency range, determining that the charging is abnormal;

[0052] Wherein, the charging efficiency prediction model corresponding to the energy storage system is constructed based on historical operating parameters of the charging system and the energy storage system;

[0053] The charging efficiency prediction model is constructed based on historical operating parameters of the charging system and the energy storage system, and includes:

[0054] Based on the device identification of the energy storage system in the target operating parameter, acquiring a historical operating parameter corresponding to the device identification;

[0055] Acquire a historical battery temperature, a historical SOC value, a historical charging power, and a historical charging efficiency corresponding to the energy storage system from the historical operating parameters corresponding to the device identifier;

[0056] The historical battery temperature, historical SOC value, historical charging power and historical charging efficiency are fitted by the least square method to construct a charging efficiency prediction model corresponding to the energy storage system.

[0057] In a fourth aspect, the present application provides a charging device, applied to a cloud server, the device comprising:

[0058] A first processing module is used to receive target operating parameters sent by the charging system and the energy storage system during the process of the charging system charging the energy storage system;

[0059] A second processing module is used to adjust the charging strategy when charging abnormality is determined based on the target operating parameter, and send the adjusted charging strategy to the charging system or the energy storage system, wherein the charging strategy is used to control the charging condition of the energy storage system;

[0060] The determining of charging abnormality based on the target operating parameter includes:

[0061] Inputting the target operating parameters into a charging efficiency prediction model corresponding to the energy storage system to obtain a theoretical charging efficiency output by the charging efficiency prediction model;

[0062] When the theoretical charging efficiency is not within a preset charging efficiency range, determining that the charging is abnormal;

[0063] Wherein, the charging efficiency prediction model corresponding to the energy storage system is constructed based on the historical operating parameters of the charging system and the energy storage system;

[0064] The charging efficiency prediction model is constructed based on historical operating parameters of the charging system and the energy storage system, and includes:

[0065] Based on the device identification of the energy storage system in the target operating parameter, acquiring a historical operating parameter corresponding to the device identification;

[0066] Acquire a historical battery temperature, a historical SOC value, a historical charging power, and a historical charging efficiency corresponding to the energy storage system from the historical operating parameters corresponding to the device identifier;

[0067] The historical battery temperature, historical SOC value, historical charging power and historical charging efficiency are fitted by the least square method to construct a charging efficiency prediction model corresponding to the energy storage system.

[0068] According to the charging device of the present application, the target operating parameters of the energy storage system and the charging system are obtained in real time through the cloud server, so as to judge whether the charging is abnormal in real time based on the target operating parameters, and when the charging is abnormal, the adjusted charging strategy is promptly generated and sent to the energy storage system or the charging system to adjust the charging efficiency, which can realize multi-party interactive adjustment and dynamic adaptive charging adjustment, has high real-time and flexibility, and has high control precision and control accuracy, and significantly improves the charging efficiency.

[0069] In a fifth aspect, the present application provides a charging device, applied to a charging system, the device comprising:

[0070] A third processing module, used for sending target operating parameters corresponding to the charging system to a cloud server, wherein the target operating parameters are used for the cloud server to generate a charging strategy;

[0071] a fourth processing module, configured to charge the energy storage system in response to the adjusted charging strategy upon receiving the adjusted charging strategy sent by the cloud server;

[0072] The cloud server generates a charging strategy, including: in the case of determining that charging is abnormal based on the target operating parameter, adjusting the charging strategy:

[0073] The determining of charging abnormality based on the target operating parameter includes:

[0074] Inputting the target operating parameters into a charging efficiency prediction model corresponding to the energy storage system to obtain a theoretical charging efficiency output by the charging efficiency prediction model;

[0075] When the theoretical charging efficiency is not within a preset charging efficiency range, determining that the charging is abnormal;

[0076] Wherein, the charging efficiency prediction model corresponding to the energy storage system is constructed based on the historical operating parameters of the charging system and the energy storage system;

[0077] The charging efficiency prediction model is constructed based on historical operating parameters of the charging system and the energy storage system, and includes:

[0078] Based on the device identification of the energy storage system in the target operating parameter, acquiring a historical operating parameter corresponding to the device identification;

[0079] Acquire a historical battery temperature, a historical SOC value, a historical charging power, and a historical charging efficiency corresponding to the energy storage system from the historical operating parameters corresponding to the device identifier;

[0080] The historical battery temperature, historical SOC value, historical charging power and historical charging efficiency are fitted by the least square method to construct a charging efficiency prediction model corresponding to the energy storage system.

[0081] In a sixth aspect, the present application provides a charging device, applied to an energy storage system, the device comprising:

[0082] A fifth processing module, configured to send target operating parameters corresponding to the energy storage system to a cloud server, wherein the target operating parameters are used by the cloud server to generate a charging strategy;

[0083] a sixth processing module, configured to adjust the target operating parameter in response to the adjusted charging strategy upon receiving the adjusted charging strategy sent by the cloud server;

[0084] The cloud server generates a charging strategy, including: in the case of determining that charging is abnormal based on the target operating parameter, adjusting the charging strategy:

[0085] The determining of charging abnormality based on the target operating parameter includes:

[0086] Inputting the target operating parameters into a charging efficiency prediction model corresponding to the energy storage system to obtain a theoretical charging efficiency output by the charging efficiency prediction model;

[0087] When the theoretical charging efficiency is not within a preset charging efficiency range, determining that the charging is abnormal;

[0088] Wherein, the charging efficiency prediction model corresponding to the energy storage system is constructed based on historical operating parameters of the charging system and the energy storage system;

[0089] The charging efficiency prediction model is constructed based on historical operating parameters of the charging system and the energy storage system, and includes:

[0090] Based on the device identification of the energy storage system in the target operating parameter, acquiring a historical operating parameter corresponding to the device identification;

[0091] Acquire a historical battery temperature, a historical SOC value, a historical charging power, and a historical charging efficiency corresponding to the energy storage system from the historical operating parameters corresponding to the device identifier;

[0092] The historical battery temperature, historical SOC value, historical charging power and historical charging efficiency are fitted by the least square method to construct a charging efficiency prediction model corresponding to the energy storage system.

[0093] In a seventh aspect, the present application provides a charging system for charging an energy storage system based on the charging method described in the first aspect, the second aspect or the third aspect.

[0094] In an eighth aspect, the present application provides an energy storage system that is charged based on the charging method described in the first aspect, the second aspect, or the third aspect.

[0095] In a ninth aspect, the present application provides an electrical device comprising the energy storage system as described in the eighth aspect.

[0096] In a tenth aspect, the present application provides a non-transitory computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the electricity usage method as described in the first aspect, the second aspect or the third aspect above is implemented.

[0097] In an eleventh aspect, the present application provides a computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the electricity usage method as described in the first aspect, the second aspect or the third aspect above is implemented.

[0098] The above one or more technical solutions in the embodiments of the present application have at least one of the following technical effects:

[0099] The target operating parameters of the energy storage system and the charging system are obtained in real time through the cloud server, so as to judge whether the charging is abnormal in real time based on the target operating parameters. When the charging is abnormal, the adjusted charging strategy is generated and sent to the energy storage system or the charging system in time to adjust the charging efficiency. It can realize multi-party interactive adjustment and dynamic adaptive charging adjustment, has high real-time and flexibility, and has high control precision and accuracy, which significantly improves the charging efficiency.

[0100] Furthermore, by using the charging efficiency prediction model corresponding to each energy storage system constructed by using the historical working parameters of each energy storage system to predict the theoretical charging efficiency of the energy storage system and the charging system under the target working parameters, whether the charging is abnormal is judged based on the predicted theoretical charging efficiency. This can reduce the error in the prediction result caused by individual differences of different energy storage systems under the same category, improve the precision and accuracy of the calculation results, thereby improving the accuracy of charging control and improving the charging effect.

[0101] Furthermore, by selecting the corresponding charging strategy adjustment method based on the charging efficiency in the case of abnormal charging, a variety of abnormal charging scenarios can be covered, which has a wide range of application scenarios.

[0102] Furthermore, after receiving the charging strategy sent by the cloud server, the charging system performs security verification on the charging strategy based on its own logic to determine whether to update the charging strategy, which can further ensure the stable operation of the charging system and improve the safety and reliability of the charging process.

[0103] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0104] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0105] Figure 1 This is one of the flow charts of the charging method provided in the embodiment of the present application;

[0106] Figure 2 This is the second flow chart of the charging method provided in the embodiment of the present application;

[0107] Figure 3 This is the third flow chart of the charging method provided in the embodiment of the present application;

[0108] Figure 4 This is the fourth flow chart of the charging method provided in the embodiment of the present application. DETAILED DESCRIPTION

[0109] The following will be combined with the drawings in the embodiments of the present application to clearly describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments in the present application belong to the scope of protection of this application.

[0110] The terms "first", "second", etc. in the specification and claims of the present application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described here, and the objects distinguished by "first", "second", etc. are generally of one type, and the number of objects is not limited. For example, the first object can be one or more. In addition, "and / or" in the specification and claims represents at least one of the connected objects, and the character " / " generally indicates that the objects associated with each other are in an "or" relationship.

[0111] The charging method, charging device, charging system, energy storage system, power consumption device and readable storage medium provided in the embodiments of the present application are described in detail below with reference to the accompanying drawings through specific embodiments and their application scenarios.

[0112] The charging method may be applied to a terminal, and may be specifically executed by hardware or software in the terminal.

[0113] The charging method provided in the embodiment of the present application is applied to charging scenarios, including but not limited to ship charging scenarios and vehicle charging scenarios.

[0114] In the charging scenario, it involves a cloud server, a charging system that communicates with the cloud server, and an energy storage system.

[0115] Among them, the energy storage system is used to provide power to electrical devices.

[0116] The charging system is used to charge the energy storage system.

[0117] Cloud servers are used to perform data collection and application.

[0118] In some embodiments, the charging system and the energy storage system are connected during the charging process.

[0119] The charging system and energy storage system are connected to the cloud server via wireless MQTT communication.

[0120] Taking the ship charging scenario as an example, the charging system on the shore charges the ship's energy storage system (such as the ship's power supply). The charging system can be a fixed device, and the ship's power supply can be a mobile device. The ship's power supply is detachable. After the ship docks, the power supply can be removed by lifting and then transported to the battery swap station for charging.

[0121] The charging system charges the ship's power supply, and the ship's power supply provides power to the ship. The data of the charging system and the ship's power supply are connected to the cloud server for unified scheduling.

[0122] The charging method of the present application is described below with the cloud server, charging system and energy storage system as the executing entities.

[0123] like Figure 1 As shown, the charging method is applied to a cloud server, and the charging method includes: step 110 and step 120.

[0124] Step 110: receiving target operating parameters sent by the charging system and the energy storage system during the process of the charging system charging the energy storage system;

[0125] In this step, the charging system may include a power station or a charging pile, etc.

[0126] The energy storage system may include a battery or a power supply, such as a box power supply.

[0127] In some embodiments, the energy storage system may also be a vehicle or a ship.

[0128] Cloud servers can be deployed on cloud platforms.

[0129] The target operating parameters are used to characterize the real-time operating parameters and performance parameters of the charging system and energy storage system.

[0130] The target operating parameters may include static data and dynamic data.

[0131] Among them, static data may include: equipment identification of the charging system (such as number or ID, etc.), charging gun number, equipment model, charging voltage range, input grid voltage, rated power, maximum output current, whole machine charging efficiency, charging gun type, manufacturer and region, etc.

[0132] Static data may also include fixed information such as the serial number and number of battery clusters, packs and cells of the energy storage system, as well as rated power.

[0133] Dynamic data may include: the status of the charging system (such as "online" or "offline", etc.), the equipment identification of the energy storage system (such as the ID of the energy storage system), data collection time, charging self-test results (pass / fail), operating condition data (such as "normal operating condition"), gun tip temperature, real-time output voltage, real-time output current, real-time power, real-time charge amount, current real-time charging efficiency, the remaining power of the current energy storage system (State Of Charge, SOC), battery health (State Of Health, SOH), peak power, peak power, valley power and average power classified according to electricity price periods, etc.

[0134] Dynamic data can also include: real-time charging current, charging voltage, battery (or cell) temperature, charging progress, and alarm signals of the energy storage system.

[0135] like Figure 2 As shown, in the actual implementation process, before the charging system charges the energy storage system, the charging system first sends basic information to the cloud server.

[0136] The basic information includes but is not limited to: a unique device identifier of the energy storage system, such as a code or ID, etc.; the power of the charging system, the charging voltage and the charging current of the charging system, etc.

[0137] During the charging process, the charging system and energy storage system can respectively send their own target operating parameters to the cloud server.

[0138] For example, the charging system can be securely connected to the cloud server via the MQTT+TLS communication protocol.

[0139] When the charging system is successfully connected to the energy storage system and starts to charge the energy storage system, the charging system will send dynamic data and static data to the cloud server at a preset frequency (such as once per minute, once every 30 seconds, or any other time interval).

[0140] The above data includes but is not limited to: equipment identification of the charging system (such as the ID of the charging system), clear charging system status (such as "online" or "offline", etc.), charging gun number, equipment identification of the energy storage system (such as the ID of the energy storage system), charging self-test results (pass / fail), working condition description (such as "normal working condition"), gun tip temperature, output voltage, output current, charging start time, current energy storage system SOC value, current battery voltage, charging status feedback, real-time power of the charging system, real-time charging amount, and peak power, peak power, valley power and average power classified according to electricity price time periods, etc.

[0141] The energy storage system establishes a secure connection with the cloud server through the MQTT+TLS communication protocol, and reports data to the cloud server at a set frequency (such as once a minute, once every 30 seconds, or other arbitrary time intervals).

[0142] The reported data includes static data, such as fixed information about the battery cluster, pack and battery cell number and number, as well as rated capacity; and dynamic data, such as real-time charging current, charging voltage, battery (or battery cell) temperature, charging progress and alarm signals.

[0143] The cloud server receives and processes this data for monitoring, analysis and optimization of the charging process.

[0144] In some embodiments, after step 110, the method may further include:

[0145] Store target operating parameters.

[0146] In this embodiment, the cloud server receives the real-time operating parameters sent by the charging system and the energy storage system at any collection time, and stores the acquired real-time operating parameters for subsequent retrieval when needed or queried.

[0147] By sending the target operating parameters corresponding to the charging system and the energy storage system to the cloud server for storage, unified management is performed on the cloud server to facilitate subsequent data tracing during problem troubleshooting, effectively solving the technical problem of difficulty in tracing historical data.

[0148] Step 120: When charging abnormality is determined based on the target operating parameters, the charging strategy is adjusted, and the adjusted charging strategy is sent to the charging system or the energy storage system. The charging strategy is used to control the charging status of the energy storage system.

[0149] In this step, the charging abnormality may be manifested as charging not being performed based on a better charging efficiency.

[0150] During the actual execution process, the cloud server can analyze and process the received target working parameters in real time to determine whether there is any charging abnormality.

[0151] The cloud server can store multi-dimensional data such as target operating parameters, historical operating parameters of the charging system and energy storage system, charging curves of various types of energy storage systems, and charging characteristic curves.

[0152] Among them, the charging curve is used to characterize the changes in working parameters of a certain type of energy storage system with charging time, etc. at a certain temperature during the charging process when the system is in a good charging state.

[0153] The charging characteristic curve is used to characterize the charging characteristics of the energy storage system.

[0154] In some embodiments, determining whether charging is abnormal based on the target operating parameter may include: determining whether charging is abnormal based on the target operating parameter in combination with at least one of the historical operating parameter and the charging curve. The charging strategy may include relevant parameters for adjusting the charging situation.

[0155] In some embodiments, the charging strategy may include, but is not limited to: SOC value, charging power, battery temperature, charging voltage, charging current, charging efficiency, and water cooling adjustment instructions for controlling the water cooling system.

[0156] In some embodiments, when charging is determined to be normal based on the target operating parameters, the current charging strategy is maintained.

[0157] In this embodiment, if Figure 2 As shown, when it is determined that charging is normal, it can be considered that the condition is not met, and the process ends.

[0158] In some embodiments, when charging abnormality is determined based on the target operating parameters, the cloud server adjusts the charging strategy and sends the adjusted charging strategy to the charging system or the energy storage system to adjust the charging status of the energy storage system.

[0159] In some embodiments, adjusting the charging strategy may include: adjusting the charging strategy in combination with a charging characteristic curve.

[0160] In this application, by setting up a cloud server as a third-party processing platform for comprehensive data processing, it is possible to combine the target operating parameters and other multi-dimensional data, such as historical operating parameters, to perform real-time charging anomaly detection and charging strategy adjustment, with high detection accuracy and precision, which helps to improve charging efficiency.

[0161] During the actual execution process, the cloud server may send the charging strategy to the corresponding adjustment object based on the adjustment object corresponding to the specific charging strategy.

[0162] For example, when the charging strategy is used to instruct adjustment of operating parameters of the charging system, the cloud server may send the charging strategy to the charging system.

[0163] For another example, when the charging strategy is used to indicate adjustment of the operating parameters of the energy storage system, the cloud server may directly send the charging strategy to the energy storage system.

[0164] During the actual implementation process, when the cloud server sends the adjusted charging strategy to the charging system, the charging system receives the adjusted charging strategy, and in response to the adjusted charging strategy, adjusts its own operating parameters to adjust the relevant parameters for charging the energy storage system.

[0165] When the cloud server sends the adjusted charging strategy to the energy storage system, the energy storage system receives the adjusted charging strategy and adjusts its own operating parameters in response to the adjusted charging strategy.

[0166] Continuing with the above embodiment as an example, the cloud server can act as an MQTT server, and the charging system and the energy storage system can act as MQTT clients. The target working parameters reported by the two are stored in the database of the cloud server.

[0167] When charging system A starts to charge box-type power supply B, the cloud server receives a new charging request and determines whether box-type power supply B is currently being charged based on the value of DeviceState in the dynamic data reported in real time by the charging system.

[0168] When DeviceState is equal to 10, the cloud server recognizes that the charging system A is charging the box-type power supply B, and then finds the device identification (such as ID, etc.) of the box-type power supply in the dynamic data reported by the charging system, and finds the static data and dynamic data of the box-type power supply B in the database of the cloud server. Based on the static data and dynamic data, it is determined whether there is a charging abnormality, and the charging strategy is adjusted in the case of charging abnormality.

[0169] In some embodiments, continue to refer to Figure 2 ,When the cloud server cannot query the target operating parameters corresponding to the energy storage system, the confirmation conditions are not met and the process ends.

[0170] Continue to refer Figure 2 In some embodiments, after step 120, the method may further include:

[0171] The cloud server sends feedback information to the charging system and stores the feedback information.

[0172] The cloud server stores feedback information to facilitate subsequent data tracing.

[0173] In this application, by maintaining the charging system and the energy storage system in the form of IoT devices on a cloud server and maintaining the factory and static data of the devices, the data of the devices can be managed in the cloud server, and data can be referenced when adjusting the charging strategy.

[0174] During the research and development process, the inventors discovered that in the related technology, during the process of charging the energy storage system, charging control is mainly performed through direct information interaction between the charging system and the energy storage system, resulting in a low utilization rate of the charging efficiency.

[0175] In this application, by setting up a cloud server as a three-party processing platform, the target operating parameters of the energy storage system and the charging system are obtained in real time. In the process of charging anomaly detection and charging strategy adjustment, the cloud server, the charging system, the energy storage system and other parties can be mutually restricted. The charging strategy is dynamically optimized by combining the target operating parameters and other multi-dimensional data stored in the cloud server, thereby significantly improving the charging efficiency.

[0176] According to the charging method provided in the embodiment of the present application, the target operating parameters of the energy storage system and the charging system are obtained in real time through the cloud server, so as to judge whether the charging is abnormal in real time based on the target operating parameters, and when the charging is abnormal, the adjusted charging strategy is promptly generated and sent to the energy storage system or the charging system to adjust the charging efficiency, which can realize multi-party interactive adjustment and dynamic adaptive charging adjustment, has high real-time and flexibility, and has high control precision and control accuracy, and significantly improves the charging efficiency.

[0177] The specific method for determining whether charging is abnormal is described below.

[0178] In some embodiments, determining charging abnormality based on the target operating parameter may include:

[0179] Input the target operating parameters into the charging efficiency prediction model corresponding to the energy storage system to obtain the theoretical charging efficiency output by the charging efficiency prediction model;

[0180] In the case where the theoretical charging efficiency is not within the preset charging efficiency range, it is determined that the charging is abnormal.

[0181] In this embodiment, one energy storage system may correspond to one charging efficiency prediction model.

[0182] The charging efficiency prediction model corresponding to the energy storage system is constructed based on the historical operating parameters of the charging system and the energy storage system, and is used to predict the theoretical charging efficiency under the real-time operating parameters based on the real-time operating parameters corresponding to the energy storage system and the charging system.

[0183] It should be noted that the charging efficiency prediction model can be pre-built or built in real time after the cloud server receives the charging request.

[0184] Theoretical charging efficiency can be expressed as a percentage.

[0185] The theoretical charging efficiency is the charging efficiency that the charging system is most likely to achieve under the predicted current target operating parameters.

[0186] The preset charging efficiency range is used to represent a range of better charging efficiency of the charging system.

[0187] In some embodiments, the preset charging efficiency range can be obtained by collecting statistics and processing historical operating parameters.

[0188] For example, by collecting statistics on historical working parameters, the maximum charging efficiency and the minimum charging efficiency under optimal charging efficiency are obtained, and a preset charging efficiency range is constructed with the maximum charging efficiency and the minimum charging efficiency as upper and lower limit thresholds.

[0189] In some embodiments, the preset charging efficiency range may also be user-defined, which is not limited in this application.

[0190] It can be understood that when the theoretical charging efficiency is within the preset charging efficiency range, it can be approximately considered that a better charging efficiency can be achieved under the current target operating parameters. If the current charging status is good, it is considered that the charging is normal and there is no need to adjust the charging strategy. Charging can continue according to the current strategy.

[0191] The theoretical charging efficiency is not within the preset charging efficiency range, which may include: the theoretical charging efficiency is less than a lower threshold of the preset charging efficiency range, or the theoretical charging efficiency is greater than an upper threshold of the preset charging efficiency range.

[0192] When the theoretical charging efficiency is not within the preset charging efficiency range, it is determined that charging is abnormal, and the charging strategy needs to be adjusted to adjust the charging efficiency to within the preset charging efficiency range.

[0193] In some embodiments, when the theoretical charging efficiency is not within the preset charging efficiency range, different charging strategies may be adjusted based on the difference between the theoretical charging efficiency and the preset charging efficiency range.

[0194] The specific adjustment method will be described in the following embodiments, and will not be elaborated in detail in this application.

[0195] During the research and development process, the inventors also discovered that in the related art, there is a method for SOH estimation based on the same type of battery data source. This method ignores the differences between individual energy storage systems, affects the precision and accuracy of the SOH estimation results, and thus affects the accuracy of the control.

[0196] In the present application, a charging efficiency prediction model corresponding to each energy storage system is constructed based on the historical operating parameters of each energy storage system, so as to predict the theoretical charging efficiency of the energy storage system and the charging system under the target operating parameters based on the charging efficiency prediction model. This can reduce the error in the prediction result caused by individual differences of different energy storage systems under the same category, improve the precision and accuracy of the predicted theoretical charging efficiency, thereby improving the accuracy of subsequent charging abnormality judgment and the accuracy of the charging strategy.

[0197] According to the charging method provided in the embodiment of the present application, the theoretical charging efficiency of the energy storage system and the charging system under the target working parameters is predicted by using the charging efficiency prediction model corresponding to each energy storage system constructed by using the historical working parameters of each energy storage system, so as to judge whether the charging is abnormal based on the predicted theoretical charging efficiency. This can reduce the error caused by individual differences of different energy storage systems under the same category to the prediction result, improve the precision and accuracy of the calculation result, thereby improving the accuracy of charging control and improving the charging efficiency.

[0198] In some embodiments, the charging efficiency prediction model is constructed based on historical operating parameters of the charging system and the energy storage system, and may include:

[0199] Based on the device identification of the energy storage system in the target operating parameters, acquiring the historical operating parameters corresponding to the device identification;

[0200] Obtaining historical battery temperature, historical SOC value, historical charging power and historical charging efficiency corresponding to the energy storage system from historical operating parameters corresponding to the device identifier;

[0201] The historical battery temperature, historical SOC value, historical charging power and historical charging efficiency are fitted by the least squares method to build a charging efficiency prediction model corresponding to the energy storage system.

[0202] In this embodiment, the device identifier of the energy storage system is a unique identity identifier of the energy storage system.

[0203] The historical operating parameters are target operating parameters sent by each charging system and related energy storage system and received by the cloud server during the historical charging process.

[0204] Based on the device identification, the historical operating parameters related to the device identification can be screened from all the historical operating parameters stored in the cloud server.

[0205] For example, for a certain charging station, key parameters and corresponding charging efficiencies during multiple charging processes in a historical period of time can be obtained.

[0206] Among them, key parameters may include: battery temperature (x1, unit: °C), remaining battery power (x2, unit: %), and charging power (x3, unit: kW).

[0207] Thus, the following historical operating parameters and the historical charging efficiency corresponding to the historical operating parameters can be obtained:

[0208] (historical battery temperature = 20°C, historical SOC value = 30%, historical charging power = 30kW), historical charging efficiency = 85%;

[0209] (historical battery temperature = 25°C, historical SOC value = 40%, historical charging power = 25kW), historical charging efficiency = 88%;

[0210] (historical battery temperature = 30°C, historical SOC value = 50%, historical charging power = 35kW), historical charging efficiency = 90%;

[0211] …

[0212] After receiving a new charging request, the cloud server identifies the device identification of the energy storage system based on the received target operating parameters, and obtains the historical operating parameters corresponding to the energy storage system in the database based on the device identification; taking the historical battery temperature, historical SOC value and historical charging power as input and the historical charging efficiency as output target, the historical battery temperature, historical SOC value, historical charging power and historical charging efficiency (x1, x2, x3 and the corresponding historical charging efficiency f) are fitted by the least squares method to obtain a multivariate linear regression model or a more complex nonlinear model, thereby constructing a charging efficiency prediction model corresponding to the energy storage system.

[0213] Taking the multivariate linear model as an example, the charging efficiency prediction model can be expressed as follows:

[0214]

[0215] in, , , and are coefficients calculated by the least squares method, x1, x2, x3 are the historical battery temperature, historical SOC value and historical charging power respectively, and f is the historical charging efficiency.

[0216] In actual application, the cloud server only needs to input the target working parameters such as target battery temperature, target SOC value and target charging power obtained in real time into the charging efficiency prediction model. , you can get the theoretical charging efficiency output by the charging efficiency prediction model.

[0217] According to the charging method provided in the embodiment of the present application, a charging efficiency prediction model corresponding to the energy storage system is constructed by performing least squares fitting on the historical battery temperature, historical SOC value, historical charging power and historical charging efficiency. The parameters are easy to obtain, the modeling method is simple and has high calculation precision and accuracy.

[0218] In some embodiments, determining charging abnormality based on the target operating parameter may include:

[0219] Obtain a charging curve corresponding to the type of energy storage system;

[0220] Determine optimal charging parameters based on the target SOC value and the charging curve in the target operating parameters;

[0221] In the case where the degree of difference between the optimal charging parameter and the target charging parameter among the target operating parameters exceeds a target threshold value, charging abnormality is determined.

[0222] In this embodiment, the charging curve is used to characterize the changes in operating parameters of a certain type of energy storage system with charging time, etc. at a certain temperature during a charging process in a good charging state.

[0223] The operating parameters may include but are not limited to: charging voltage, charging current, and SOC value.

[0224] The target operating parameters may include: target charging temperature, target SOC value, target charging voltage, target charging current, etc.

[0225] The optimal charging parameters may include: optimal charging voltage and optimal charging current, etc.

[0226] The difference can be expressed as the absolute value of the difference or the absolute value of the ratio.

[0227] Target thresholds can be customized based on user needs.

[0228] In the actual implementation process, the charging curves corresponding to each category of charging systems at multiple charging temperatures can be obtained in advance, so that in the subsequent application process, the charging curves under the category corresponding to the target charging temperature and the target SOC value can be directly obtained based on the category of the energy storage system to be detected and the target charging temperature; then, based on the charging curve query, the optimal charging voltage and / or optimal charging current under the target SOC value are obtained, and then the size relationship between the target charging voltage and / or target charging current and the optimal charging voltage and / or optimal charging current is compared.

[0229] When the difference does not exceed the target threshold, the current charging state is considered to be good and the charging is determined to be normal.

[0230] When the difference exceeds the target threshold, it is considered that the current charging state is not good and charging is determined to be abnormal.

[0231] According to the charging method provided in the embodiment of the present application, by providing a variety of judgment methods for charging abnormalities, the best judgment method can be selected based on actual needs, which has high flexibility and universality.

[0232] The following describes how to adjust the charging strategy.

[0233] 1. Charging strategy for sending to the charging system

[0234] In some embodiments, in step 120, adjusting the charging strategy and sending the adjusted charging strategy to the charging system or the energy storage system may include:

[0235] generating at least one of a first instruction for reducing charging power, a second instruction for adjusting charging time, a target instruction for instructing fine-tuning of working parameters, and warning prompt information;

[0236] At least one of a first instruction, a second instruction, a target instruction, and an alarm prompt message is sent to the charging system.

[0237] In this embodiment, the first instruction for reducing the charging power may include, but is not limited to: an instruction for reducing the charging current and / or an instruction for reducing the charging voltage, etc.

[0238] By instructing to reduce the charging power, the internal temperature rise of the energy storage system can be reduced, reducing the risk of overheating affecting efficiency.

[0239] The second instruction for adjusting the charging time is used to instruct charging to avoid a battery temperature peak period or a grid load peak period.

[0240] During the actual execution process, the temperature of the energy storage system and the load of the power grid station and other data can be monitored in real time to set the time period when the temperature of the energy storage system exceeds the temperature threshold as the battery temperature peak period, and the time when the load of the power grid station exceeds the load threshold as the power grid load peak period; when it is judged that the current charging time is within the battery temperature peak period or the power grid load peak period, a second instruction for adjusting the charging time is generated to control the charging system to stop charging the energy storage system.

[0241] When it is determined that the current charging time is not within the peak period of battery temperature and grid load, it is considered that the current temperature of the energy storage system is at a suitable charging temperature or the site load is low, in which case the charging time does not need to be adjusted.

[0242] Warning information is used to warn users.

[0243] The warning prompt information may include: information for notifying possible causes of charging abnormalities, prompt information for notifying hardware failures or software errors, and information for notifying abnormalities of charging efficiency prediction models.

[0244] Among them, the charging efficiency prediction model is a model used to detect whether charging is abnormal.

[0245] In the actual execution process, different warning prompt information can be output based on different detection results.

[0246] It should be noted that, in the present application, the first instruction, the second instruction, the target instruction and the warning prompt information can be output one by one, or multiple of them can be output at the same time, and the specific selection can be made based on the actual detection results.

[0247] For example, when charging is abnormal and the charging efficiency is low, relevant instructions for instructing to improve the charging efficiency can be output; when charging is abnormal and the charging efficiency is high, relevant instructions for instructing to reduce the charging efficiency can be output.

[0248] In some embodiments, generating at least one of a first instruction for reducing charging power, a second instruction for adjusting charging time, a target instruction for instructing fine-tuning of working parameters, and warning prompt information may include:

[0249] When it is determined that the charging efficiency is lower than a lower limit threshold, at least one of a first instruction, a second instruction and an alarm prompt information is generated.

[0250] In this embodiment, the lower limit threshold may be user-defined or determined based on historical operating parameters, such as setting the lower limit threshold to a lower limit threshold of a preset charging efficiency range.

[0251] During the actual implementation process, the theoretical charging efficiency under the target operating parameters can be predicted based on the charging efficiency prediction model, and the theoretical charging efficiency can be compared with the lower limit threshold of the preset charging efficiency range. When the theoretical charging efficiency is less than the lower limit threshold of the preset charging efficiency range, the charging efficiency is considered to be low.

[0252] The method for determining the theoretical charging efficiency and the preset charging efficiency range has been described in the above embodiments, and will not be repeated in this application.

[0253] When the charging efficiency is lower than the lower limit threshold, it is considered that the charging efficiency under the current target operating parameters is low. The cloud server can send relevant instructions to the charging system to instruct the charging efficiency to be improved, such as a first instruction for reducing the charging power and a second instruction for adjusting the charging time.

[0254] When the charging system receives the first instruction and / or the second instruction, in response to the received first instruction, the charging voltage or charging current of the energy storage system is reduced; and / or, in response to the received second instruction, when it is determined that the current charging time is at the peak of the battery temperature or the peak of the grid load, the charging of the energy storage system is suspended; and after the peak of the battery temperature or the peak of the grid load ends, the energy storage system is recharged.

[0255] In some embodiments, when the theoretical charging efficiency is lower than a preset charging efficiency range, the cloud server may also send an alarm message to the charging system to inform the possible cause of the charging abnormality so as to send a notification to the user, informing the cause of the reduced charging efficiency and the possible impact.

[0256] According to the charging method provided in the embodiment of the present application, the charging efficiency is improved from different dimensions by outputting a first instruction for reducing the charging power and a second instruction for adjusting the charging time when the charging efficiency is lower than the lower limit threshold, and the reliability is high.

[0257] In some embodiments, generating at least one of a first instruction for reducing charging power, a second instruction for adjusting charging time, a target instruction for instructing fine-tuning of working parameters, and warning prompt information may include:

[0258] When it is determined that the charging efficiency is higher than the upper limit threshold, at least one of verifying the charging efficiency prediction model and detecting the working state of the charging system and the energy storage system is executed; the charging efficiency prediction model is a model for performing charging abnormality detection;

[0259] When it is determined that the charging efficiency prediction model is abnormal and / or the working state is abnormal, an alarm prompt message is output;

[0260] When it is determined that the charging efficiency prediction model is normal and the working state is normal, the target instruction is output.

[0261] In this embodiment, the charging efficiency prediction model is used to predict the theoretical charging efficiency, and the theoretical charging efficiency is used to determine whether the charging is abnormal.

[0262] The upper limit threshold may be user-defined or determined based on historical operating parameters, such as setting the upper limit threshold to an upper limit threshold of a preset charging efficiency range.

[0263] During the actual execution process, the theoretical charging efficiency under the target operating parameters can be predicted based on the charging efficiency prediction model, and the theoretical charging efficiency can be compared with the upper limit threshold of the preset charging efficiency range. When the theoretical charging efficiency is greater than the upper limit threshold of the preset charging efficiency range, the charging efficiency is considered to be low.

[0264] When the charging efficiency is higher than the upper threshold, it is considered that the charging efficiency is high under the current target operating parameters. There may be measurement errors or special operating conditions. The data accuracy should be verified and whether adjustments are needed should be considered.

[0265] During the actual implementation process, when it is determined that the charging efficiency is higher than the upper limit threshold, the cloud server can first verify the charging efficiency prediction model, and detect the working status of the charging system and the energy storage system, so as to verify the accuracy of the current charging efficiency prediction model and whether there are any abnormalities in the charging system and the energy storage system.

[0266] For example, it is possible to detect whether the "status" fields of the charging system and the energy storage system are abnormal. If the working status is abnormal, it is considered that there may be a hardware failure or software error.

[0267] When it is determined that the charging efficiency prediction model is abnormal, the cloud server can send an alarm prompt message to the charging system to inform the user that the charging efficiency prediction model is abnormal and the charging efficiency prediction model needs to be adjusted and optimized.

[0268] When it is determined that the charging system and / or energy storage system is abnormal, the cloud server can send an alarm prompt message to the charging system to inform the user that the high theoretical charging efficiency may be caused by hardware failure or software error, so as to assist the user to troubleshoot the hardware or software in time.

[0269] When the charging efficiency prediction model is normal and the working status is normal, it can be roughly considered that the high charging efficiency may be caused by special operating conditions. In this case, the cloud server can send target instructions to the charging system for adjusting the working parameters of the charging system and the energy storage system to fine-tune the operating condition data within a reasonable range, so as to adjust the charging efficiency to within the preset charging efficiency range.

[0270] During the actual implementation process, a preset range can be set for each operating condition parameter, and the charging strategy can be adjusted by adjusting the operating condition parameter within the preset range.

[0271] According to the charging method provided in the embodiment of the present application, by selecting the corresponding charging strategy adjustment method based on the charging efficiency in the case of abnormal charging, it can cover a variety of abnormal charging scenarios and has a wide range of application scenarios.

[0272] 2. Charging strategy for sending to energy storage system

[0273] In some embodiments, in step 120, adjusting the charging strategy and sending the adjusted charging strategy to the charging system or the energy storage system may include:

[0274] Based on the charging characteristic curve corresponding to the energy storage system, a target charging temperature is obtained when the target SOC value in the target operating parameter is achieved;

[0275] Generate water cooling control instructions based on target charging temperature;

[0276] A water cooling control instruction is issued to the energy storage system, where the water cooling control instruction is used to instruct and adjust the water cooling system of the energy storage system.

[0277] In this embodiment, the charging characteristic curve is used to characterize the charging characteristics of the energy storage system.

[0278] It is understandable that an energy storage system may correspond to a charging characteristic curve.

[0279] In the actual implementation process, the charging characteristic curve corresponding to the category can be obtained based on the category of the energy storage system;

[0280] Based on the charging characteristic curve corresponding to the optimization category of the historical working parameters of the energy storage system, a charging characteristic curve corresponding to the energy storage system is obtained.

[0281] The target charging temperature is a more suitable charging temperature.

[0282] During the actual execution process, the cloud server can obtain the charging characteristic curve corresponding to the energy storage system based on the identification of the energy storage system, and obtain the target charging temperature under the target SOC value based on the charging characteristic curve, and then send a water cooling control instruction to the water cooling system of the energy storage system based on the target charging temperature, so as to control the energy storage system at a better charging temperature by adjusting the working parameters of the water cooling system to improve the charging efficiency.

[0283] According to the charging method provided in the embodiment of the present application, the target charging temperature under the current SOC is obtained through the charging characteristic curve corresponding to the energy storage system, so as to directly issue a water cooling control instruction to the energy storage system based on the target charging temperature. This method can take into account the differences between individual energy storage systems and has higher adjustment precision, accuracy and higher response rate.

[0284] The embodiment of the present application also provides a charging method.

[0285] like Figure 3 As shown, the charging method is applied to a charging system, and the charging method includes: step 310 and step 320.

[0286] Step 310: Send target operating parameters corresponding to the charging system to the cloud server, where the target operating parameters are used by the cloud server to generate a charging strategy;

[0287] In this step, the target operating parameters are used by the cloud server to generate a charging strategy.

[0288] During the actual execution process, the cloud server can determine whether the charging is abnormal based on the target operating parameters corresponding to the charging system and the target operating parameters corresponding to the energy storage system. If the charging is normal, the current charging strategy remains unchanged; if the charging is abnormal, the current charging strategy is adjusted.

[0289] Step 320: Upon receiving the adjusted charging strategy sent by the cloud server, charge the energy storage system in response to the adjusted charging strategy.

[0290] In this step, after the charging system receives the adjusted charging strategy sent by the cloud server, it can respond to the charging strategy and control the charging parameters based on the sent charging strategy to charge the charging system.

[0291] In some embodiments, in response to the adjusted charging strategy, charging the energy storage system may include:

[0292] In case the charging strategy includes a first instruction for reducing the charging power, reducing the charging current and / or the charging voltage;

[0293] In case the charging strategy includes the second instruction for adjusting the charging time, the energy storage system is controlled to stop charging during the battery temperature peak period or the grid load peak period.

[0294] In this embodiment, by reducing the charging voltage or charging current of the energy storage system, the internal temperature rise of the energy storage system is reduced, the risk of overheating affecting efficiency is reduced, and the charging efficiency is improved.

[0295] When it is determined that the current charging time is at the peak period of battery temperature or the peak period of grid load, the charging of the energy storage system is suspended; and after the peak period of battery temperature or the peak period of grid load ends, the energy storage system is charged again to improve the charging efficiency.

[0296] In some embodiments, when a charging strategy sent by a cloud server is received, the method may further include:

[0297] Output at least one of a first instruction, a second instruction, a target instruction, and an alarm prompt message.

[0298] In this embodiment, the output may include, but is not limited to, image output, text output, voice output, indicator light output, character output, and any other achievable output methods.

[0299] According to the charging method provided in the embodiment of the present application, the target operating parameters of the energy storage system and the charging system are obtained in real time through the cloud server, so as to judge whether the charging is abnormal in real time based on the target operating parameters, and when the charging is abnormal, the adjusted charging strategy is promptly generated and sent to the energy storage system or the charging system to adjust the charging efficiency, which can realize multi-party interactive adjustment and dynamic adaptive charging adjustment, has high real-time and flexibility, and has high control precision and control accuracy, and significantly improves the charging efficiency.

[0300] Continue to refer Figure 2 In some embodiments, charging the energy storage system in response to the adjusted charging strategy may include:

[0301] Conduct safety verification on the adjusted charging strategy;

[0302] When the safety verification is passed, the target operating parameters are adjusted based on the adjusted charging strategy to charge the energy storage system.

[0303] In this embodiment, the safety verification of the adjusted charging strategy may include:

[0304] Obtain key operating parameters included in the charging strategy;

[0305] Determine whether key operating parameters are within the safe range.

[0306] In this embodiment, the key operating parameters may include: SOC value, charging efficiency, charging voltage, charging power, and charging current, etc.

[0307] Key working parameters are sent by the cloud server.

[0308] The safety range can be set based on the reasonable range of each parameter and whether it exceeds the specifications of the energy storage system.

[0309] When it is determined that the key operating parameters are within a safe range, it is considered that the charging strategy sent by the cloud server can ensure the safe operation of the system and will not cause damage to the energy storage system, and the safety verification is considered to have passed.

[0310] If the safety verification is passed, it is considered to be in compliance with the modification, and the charging system adjusts the target operating parameters based on the received charging strategy to improve the charging efficiency, such as adjusting the charging voltage or charging current.

[0311] In some embodiments, if the safety verification fails, it is considered that the modification is not met, the current process is terminated, and the charging system charges the energy storage system based on the initial charging strategy.

[0312] The initial charging strategy is the original charging strategy that is being run before receiving the adjusted charging strategy sent by the cloud server.

[0313] According to the charging method provided in the embodiment of the present application, after receiving the charging strategy sent by the cloud server, the charging system performs security verification on the charging strategy based on its own logic to determine whether to choose to update the charging strategy, which can further ensure the stable operation of the charging system and improve the safety and reliability of the charging process.

[0314] In some embodiments, the method may further include:

[0315] Receive and display the historical operating parameters of the charging system and / or energy storage system sent by the cloud server.

[0316] In this embodiment, the historical operating parameters may be displayed on a display screen of the charging system for the user to review.

[0317] In some embodiments, after step 320, the method may further include:

[0318] Send feedback information to the cloud server.

[0319] In this embodiment, the feedback information is used to characterize the adjustment history of the charging system.

[0320] The feedback information may include: target operating parameters before and after the charging strategy is adjusted.

[0321] The cloud server stores the feedback information after receiving it, which facilitates subsequent data tracing.

[0322] The embodiment of the present application also provides a charging method.

[0323] like Figure 4 As shown, the charging method is applied to an energy storage system, and the charging method includes: step 410 and step 420.

[0324] Step 410: Send target operating parameters corresponding to the energy storage system to the cloud server, where the target operating parameters are used by the cloud server to generate a charging strategy;

[0325] In this step, the target operating parameters are used by the cloud server to generate a charging strategy.

[0326] During the actual execution process, the cloud server can determine whether the charging is abnormal based on the target operating parameters corresponding to the charging system and the target operating parameters corresponding to the energy storage system. If the charging is normal, the current charging strategy remains unchanged; if the charging is abnormal, the current charging strategy is adjusted.

[0327] Step 420: upon receiving the adjusted charging strategy sent by the cloud server, adjust the target operating parameters in response to the adjusted charging strategy.

[0328] In this step, the charging strategy may include a water cooling control instruction for controlling a water cooling system of the energy storage system.

[0329] After the energy storage system receives the charging strategy sent by the cloud server, the operating parameters of the water cooling system can be adjusted based on the water cooling control instruction to adjust the temperature of the energy storage system to a better temperature range, thereby improving the charging efficiency.

[0330] According to the charging method provided in the embodiment of the present application, the theoretical charging efficiency of the energy storage system and the charging system under the target working parameters is predicted by using the charging efficiency prediction model corresponding to each energy storage system constructed by using the historical working parameters of each energy storage system, so as to judge whether the charging is abnormal based on the predicted theoretical charging efficiency. This can reduce the error in the prediction result caused by the differences between different individuals under the same category, improve the precision and accuracy of the calculation results, thereby improving the accuracy of the charging control and the charging efficiency.

[0331] The charging method provided in the embodiment of the present application can be executed by a charging device. In the embodiment of the present application, the charging method is executed by a charging device as an example to illustrate the charging device provided in the embodiment of the present application.

[0332] The embodiment of the present application also provides a charging device.

[0333] The charging device is applied to a cloud server, and comprises: a first processing module and a second processing module.

[0334] A first processing module is used to receive target operating parameters sent by the charging system and the energy storage system during the process of the charging system charging the energy storage system;

[0335] The second processing module is used to adjust the charging strategy when charging abnormality is determined based on the target working parameters, and send the adjusted charging strategy to the charging system or the energy storage system, where the charging strategy is used to control the charging status of the energy storage system.

[0336] According to the charging device provided in the embodiment of the present application, the target operating parameters of the energy storage system and the charging system are obtained in real time through the cloud server, so as to judge whether the charging is abnormal in real time based on the target operating parameters, and when the charging is abnormal, the adjusted charging strategy is promptly generated and sent to the energy storage system or the charging system to adjust the charging efficiency, which can realize multi-party interactive adjustment and dynamic adaptive charging adjustment, has high real-time and flexibility, and has high control precision and control accuracy, and significantly improves the charging efficiency.

[0337] In some embodiments, the second processing module may also be used to:

[0338] generating at least one of a first instruction for reducing charging power, a second instruction for adjusting charging time, a target instruction for instructing fine-tuning of working parameters, and warning prompt information;

[0339] At least one of a first instruction, a second instruction, a target instruction, and an alarm prompt message is sent to the charging system.

[0340] In some embodiments, the second processing module may also be used to:

[0341] When it is determined that the charging efficiency is lower than a lower limit threshold, at least one of a first instruction, a second instruction and an alarm prompt information is generated.

[0342] In some embodiments, the second processing module may also be used to:

[0343] When it is determined that the charging efficiency is higher than the upper limit threshold, at least one of verifying the charging efficiency prediction model and detecting the working state of the charging system and the energy storage system is executed; the charging efficiency prediction model is a model for performing charging abnormality detection;

[0344] When it is determined that the charging efficiency prediction model is abnormal and / or the working state is abnormal, an alarm prompt message is output;

[0345] When it is determined that the charging efficiency prediction model is normal and the working state is normal, the target instruction is output.

[0346] In some embodiments, the second processing module may also be used to:

[0347] Based on the charging characteristic curve corresponding to the energy storage system, a target charging temperature is obtained when the target SOC value in the target operating parameter is achieved;

[0348] Generate water cooling control instructions based on target charging temperature;

[0349] A water cooling control instruction is issued to the energy storage system, where the water cooling control instruction is used to instruct and adjust the water cooling system of the energy storage system.

[0350] In some embodiments, the second processing module may also be used to:

[0351] Input the target operating parameters into the charging efficiency prediction model corresponding to the energy storage system to obtain the theoretical charging efficiency output by the charging efficiency prediction model;

[0352] When the theoretical charging efficiency is not within a preset charging efficiency range, determining that charging is abnormal;

[0353] Among them, the charging efficiency prediction model corresponding to the energy storage system is constructed based on the historical operating parameters of the charging system and the energy storage system.

[0354] In some embodiments, the second processing module may also be used to:

[0355] Based on the device identification of the energy storage system in the target operating parameters, acquiring the historical operating parameters corresponding to the device identification;

[0356] Obtaining historical battery temperature, historical SOC value, historical charging power and historical charging efficiency corresponding to the energy storage system from historical operating parameters corresponding to the device identifier;

[0357] The historical battery temperature, historical SOC value, historical charging power and historical charging efficiency are fitted by the least squares method to build a charging efficiency prediction model corresponding to the energy storage system.

[0358] In some embodiments, the second processing module may also be used to:

[0359] Obtain a charging curve corresponding to the type of energy storage system;

[0360] Determine optimal charging parameters based on the target SOC value and the charging curve in the target operating parameters;

[0361] In the case where the degree of difference between the optimal charging parameter and the target charging parameter among the target operating parameters exceeds a target threshold value, charging abnormality is determined.

[0362] The embodiment of the present application also provides a charging device.

[0363] The charging device is applied to a charging system, and comprises: a third processing module and a fourth processing module.

[0364] A third processing module is used to send target operating parameters corresponding to the charging system to the cloud server, and the target operating parameters are used for the cloud server to generate a charging strategy;

[0365] The fourth processing module is used to charge the energy storage system in response to the adjusted charging strategy when the adjusted charging strategy sent by the cloud server is received.

[0366] According to the charging device provided in the embodiment of the present application, the target operating parameters of the energy storage system and the charging system are obtained in real time through the cloud server, so as to judge whether the charging is abnormal in real time based on the target operating parameters, and when the charging is abnormal, the adjusted charging strategy is promptly generated and sent to the energy storage system or the charging system to adjust the charging efficiency, which can realize multi-party interactive adjustment and dynamic adaptive charging adjustment, has high real-time and flexibility, and has high control precision and control accuracy, and significantly improves the charging efficiency.

[0367] In some embodiments, the fourth processing module may also be used to:

[0368] Conduct safety verification on the adjusted charging strategy;

[0369] When the safety verification is passed, the target operating parameters are adjusted based on the adjusted charging strategy to charge the energy storage system.

[0370] The embodiment of the present application also provides a charging device.

[0371] The charging device is applied to a charging system, and comprises: a fifth processing module and a sixth processing module.

[0372] A fifth processing module, used for sending target operating parameters corresponding to the energy storage system to the cloud server, where the target operating parameters are used for the cloud server to generate a charging strategy;

[0373] The sixth processing module is used to adjust the target operating parameters in response to the adjusted charging strategy when the adjusted charging strategy sent by the cloud server is received.

[0374] The charging device in the embodiment of the present application may be a cloud server, a charging system, an energy storage system, or a component in the charging system or the energy storage system, such as an integrated circuit or a chip.

[0375] The charging device in the embodiment of the present application may be a device having an operating system. The operating system may be an Android operating system, an IOS operating system, or other possible operating systems, which are not specifically limited in the embodiment of the present application.

[0376] The charging device provided in the embodiment of the present application can achieve Figures 1 to 4 To avoid repetition, the various processes implemented by the method embodiment are not described here.

[0377] An embodiment of the present application also provides a charging system, which charges the energy storage system based on the charging method described in any of the above embodiments.

[0378] Among them, the charging system is electrically connected to the cloud controller and the energy storage system respectively.

[0379] According to the charging system provided in the embodiment of the present application, the target operating parameters of the energy storage system and the charging system are obtained in real time through the cloud server, so as to judge whether the charging is abnormal in real time based on the target operating parameters, and when the charging is abnormal, the adjusted charging strategy is promptly generated and sent to the energy storage system or the charging system to adjust the charging efficiency, which can realize multi-party interactive adjustment and dynamic adaptive charging adjustment, has high real-time and flexibility, and has high control precision and control accuracy, and significantly improves the charging efficiency.

[0380] An embodiment of the present application also provides an energy storage system, which is charged based on the charging method described in any of the above embodiments.

[0381] Among them, the energy storage system is electrically connected to the cloud controller and the charging system respectively.

[0382] The energy storage system can be used in energy storage power stations, wind power generation systems, solar power generation systems, mobile power systems or temporary power supply systems.

[0383] The energy storage system can store electrical energy as needed and output it at the appropriate time.

[0384] For example, the energy storage system can store electricity when electricity consumption is low, and provide electricity to relevant users or electrical devices when electricity consumption is peak.

[0385] The energy storage system provided in the embodiment of the present application can be any power system that requires an energy storage system.

[0386] In some embodiments, the energy storage system is an energy storage container or an energy storage cabinet.

[0387] In some embodiments, the energy storage system may be a vehicle or a ship, etc.

[0388] According to the energy storage system provided in the embodiment of the present application, the target operating parameters of the energy storage system and the charging system are obtained in real time through the cloud server, so as to judge whether the charging is abnormal in real time based on the target operating parameters, and when the charging is abnormal, the adjusted charging strategy is promptly generated and sent to the energy storage system or the charging system to adjust the charging efficiency, which can realize multi-party interactive adjustment and dynamic adaptive charging adjustment, has high real-time performance and flexibility, and has high control precision and control accuracy, and significantly improves the charging efficiency.

[0389] The embodiment of the present application also provides an electrical device.

[0390] The electrical device includes the energy storage system as described in any of the above embodiments.

[0391] The energy storage system is charged based on the charging method described in any of the above embodiments.

[0392] In some embodiments, the electrical devices may include electric vehicles and electric ships, etc.

[0393] According to the power-consuming device provided in the embodiment of the present application, the target operating parameters of the energy storage system and the charging system are obtained in real time through the cloud server, so as to judge whether the charging is abnormal in real time based on the target operating parameters, and when the charging is abnormal, the adjusted charging strategy is promptly generated and sent to the energy storage system or the charging system to adjust the charging efficiency, which can realize multi-party interactive adjustment and dynamic adaptive charging adjustment, has high real-time and flexibility, and has high control precision and control accuracy, significantly improves the charging efficiency, and reduces carbon emissions.

[0394] The embodiment of the present application also provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the various processes of the above-mentioned charging method embodiment are implemented and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0395] The processor is the processor in the electronic device described in the above embodiment. The readable storage medium includes a computer readable storage medium, such as a computer read-only memory ROM, a random access memory RAM, a magnetic disk or an optical disk.

[0396] An embodiment of the present application also provides a computer program product, including a computer program, which implements the above-mentioned charging method when executed by a processor.

[0397] The processor is the processor in the electronic device described in the above embodiment. The readable storage medium includes a computer readable storage medium, such as a computer read-only memory ROM, a random access memory RAM, a magnetic disk or an optical disk.

[0398] It should be noted that, in this article, the terms "comprise", "include" or any other variant thereof are intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise one..." do not exclude the presence of other identical elements in the process, method, article or device including the element. In addition, it should be pointed out that the scope of the methods and devices in the embodiments of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in reverse order according to the functions involved, for example, the described method may be performed in an order different from that described, and various steps may also be added, omitted, or combined. In addition, the features described with reference to certain examples may be combined in other examples.

[0399] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a computer software product, which is stored in a storage medium (such as ROM / RAM, a disk, or an optical disk), and includes a number of instructions for a terminal (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present application.

[0400] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present application, ordinary technicians in this field can also make many forms without departing from the purpose of the present application and the scope of protection of the claims, all of which are within the protection of the present application.

[0401] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "illustrative embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials, or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0402] Although the embodiments of the present application have been shown and described, those skilled in the art will appreciate that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present application, and that the scope of the present application is defined by the claims and their equivalents.

Claims

1. A charging method, characterized in that: Applied to a cloud server, the method includes: Receiving target operating parameters sent by the charging system and the energy storage system during the process of the charging system charging the energy storage system; In the case where charging abnormality is determined based on the target operating parameter, adjusting a charging strategy, and sending the adjusted charging strategy to the charging system or the energy storage system, wherein the charging strategy is used to control the charging condition of the energy storage system; The determining of charging abnormality based on the target operating parameter includes: Inputting the target operating parameters into a charging efficiency prediction model corresponding to the energy storage system to obtain a theoretical charging efficiency output by the charging efficiency prediction model; When the theoretical charging efficiency is not within a preset charging efficiency range, determining that the charging is abnormal; Wherein, the charging efficiency prediction model corresponding to the energy storage system is constructed based on the historical operating parameters of the charging system and the energy storage system; The charging efficiency prediction model is constructed based on historical operating parameters of the charging system and the energy storage system, and includes: Based on the device identification of the energy storage system in the target operating parameter, acquiring a historical operating parameter corresponding to the device identification; Acquire a historical battery temperature, a historical SOC value, a historical charging power, and a historical charging efficiency corresponding to the energy storage system from the historical operating parameters corresponding to the device identifier; Performing least squares fitting on the historical battery temperature, historical SOC value, historical charging power and historical charging efficiency to construct a charging efficiency prediction model corresponding to the energy storage system; When it is determined that the charging efficiency is higher than the upper limit threshold, at least one of a target instruction for instructing fine-tuning the working parameters and an alarm prompt message is issued; the alarm prompt message includes: information for informing of possible causes of charging abnormalities, prompt information for informing of hardware failures or software errors, and at least one of information for informing of abnormalities in a charging efficiency prediction model.

2. The charging method according to claim 1, characterized in that: The adjusting the charging strategy and sending the adjusted charging strategy to the charging system or the energy storage system includes: generating at least one of a first instruction for reducing charging power, a second instruction for adjusting charging time, a target instruction for instructing fine-tuning of working parameters, and warning prompt information; At least one of the first instruction, the second instruction, the target instruction, and the warning prompt information is sent to the charging system.

3. The charging method according to claim 2, characterized in that: The generating at least one of a first instruction for reducing charging power, a second instruction for adjusting charging time, a target instruction for instructing fine-tuning of working parameters, and warning prompt information includes: When it is determined that the charging efficiency is lower than a lower limit threshold, at least one of the first instruction, the second instruction and the warning prompt information is generated.

4. The charging method according to claim 2, characterized in that: The generating at least one of a first instruction for reducing charging power, a second instruction for adjusting charging time, a target instruction for instructing fine-tuning of working parameters, and warning prompt information includes: In the case where it is determined that the charging efficiency is higher than the upper limit threshold, at least one of verifying the charging efficiency prediction model and detecting the working status of the charging system and the energy storage system is executed; the charging efficiency prediction model is a model for performing charging abnormality detection; When it is determined that the charging efficiency prediction model is abnormal and / or the working state is abnormal, outputting the alarm prompt information; When it is determined that the charging efficiency prediction model is normal and the working state is normal, the target instruction is output.

5. The charging method according to claim 1, characterized in that: The adjusting the charging strategy and sending the adjusted charging strategy to the charging system or the energy storage system includes: Based on the charging characteristic curve corresponding to the energy storage system, obtaining a target charging temperature when a target SOC value in the target operating parameter is achieved; generating a water cooling control instruction based on the target charging temperature; The water cooling control instruction is issued to the energy storage system, and the water cooling control instruction is used to instruct to adjust the water cooling system of the energy storage system.

6. The charging method according to any one of claims 1 to 5, characterized in that: The determining of charging abnormality based on the target operating parameter includes: Obtaining a charging curve corresponding to the type of the energy storage system; Determining optimal charging parameters based on the target SOC value in the target operating parameters and the charging curve; In the case where the difference between the optimal charging parameter and the target charging parameter in the target operating parameters exceeds a target threshold, the charging abnormality is determined.

7. A charging method, characterized in that: Applied to a charging system, the method comprises: Sending target operating parameters corresponding to the charging system to a cloud server, wherein the target operating parameters are used by the cloud server to generate a charging strategy; Upon receiving the adjusted charging strategy sent by the cloud server, charging the energy storage system in response to the adjusted charging strategy; The cloud server generates a charging strategy, including: in the case of determining that charging is abnormal based on the target operating parameter, adjusting the charging strategy: The determining of charging abnormality based on the target operating parameter includes: Inputting the target operating parameters into a charging efficiency prediction model corresponding to the energy storage system to obtain a theoretical charging efficiency output by the charging efficiency prediction model; When the theoretical charging efficiency is not within a preset charging efficiency range, determining that the charging is abnormal; Wherein, the charging efficiency prediction model corresponding to the energy storage system is constructed based on the historical operating parameters of the charging system and the energy storage system; The charging efficiency prediction model is constructed based on historical operating parameters of the charging system and the energy storage system, and includes: Based on the device identification of the energy storage system in the target operating parameter, acquiring a historical operating parameter corresponding to the device identification; Acquire a historical battery temperature, a historical SOC value, a historical charging power, and a historical charging efficiency corresponding to the energy storage system from the historical operating parameters corresponding to the device identifier; Performing least squares fitting on the historical battery temperature, historical SOC value, historical charging power and historical charging efficiency to construct a charging efficiency prediction model corresponding to the energy storage system; Used for the cloud server to issue at least one of a target instruction for instructing fine-tuning of working parameters and an alarm prompt message when it is determined that the charging efficiency is higher than an upper limit threshold; the alarm prompt message includes: information for informing of possible causes of charging abnormalities, prompt information for informing of hardware failures or software errors, and information for informing of at least one of an abnormality in a charging efficiency prediction model.

8. The charging method according to claim 7, characterized in that: The step of charging the energy storage system in response to the adjusted charging strategy includes: Performing safety verification on the adjusted charging strategy; When the safety verification is passed, the target operating parameters are adjusted based on the adjusted charging strategy to charge the energy storage system.

9. A charging method, characterized in that: Applied to an energy storage system, the method comprises: Sending target operating parameters corresponding to the energy storage system to a cloud server, wherein the target operating parameters are used by the cloud server to generate a charging strategy; Upon receiving the adjusted charging strategy sent by the cloud server, adjusting the target operating parameter in response to the adjusted charging strategy; The cloud server generates a charging strategy, including: in the case of determining that charging is abnormal based on the target operating parameter, adjusting the charging strategy: The determining of charging abnormality based on the target operating parameter includes: Inputting the target operating parameters into a charging efficiency prediction model corresponding to the energy storage system to obtain a theoretical charging efficiency output by the charging efficiency prediction model; When the theoretical charging efficiency is not within a preset charging efficiency range, determining that the charging is abnormal; Wherein, the charging efficiency prediction model corresponding to the energy storage system is constructed based on historical operating parameters of the charging system and the energy storage system; The charging efficiency prediction model is constructed based on historical operating parameters of the charging system and the energy storage system, and includes: Based on the device identification of the energy storage system in the target operating parameter, acquiring a historical operating parameter corresponding to the device identification; Acquire a historical battery temperature, a historical SOC value, a historical charging power, and a historical charging efficiency corresponding to the energy storage system from the historical operating parameters corresponding to the device identifier; Performing least squares fitting on the historical battery temperature, historical SOC value, historical charging power and historical charging efficiency to construct a charging efficiency prediction model corresponding to the energy storage system; Used for the cloud server to issue at least one of a target instruction for instructing fine-tuning of working parameters and an alarm prompt message when it is determined that the charging efficiency is higher than an upper limit threshold; the alarm prompt message includes: information for informing of possible causes of charging abnormalities, prompt information for informing of hardware failures or software errors, and information for informing of at least one of an abnormality in a charging efficiency prediction model.

10. A charging system, characterized in that: The energy storage system is charged based on the charging method according to any one of claims 1 to 9.

11. An energy storage system, characterized in that: Charging is performed based on the charging method according to any one of claims 1 to 9.

12. An electrical device, characterized in that: Comprising the energy storage system as claimed in claim 11.

13. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the charging method according to any one of claims 1 to 9 is implemented.

14. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the charging method according to any one of claims 1 to 9 is implemented.

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