An Adaptive Energy Storage Device and Regulation System

Abstract

An embodiment of this specification provides an adaptive energy storage device and a control system. The device includes at least one energy storage battery module, an energy storage management subsystem, a monitoring and warning device, an environmental stability component, and a controller: The controller is communicatively connected to the energy storage battery module, the energy storage management subsystem, the monitoring and warning device, and the environmental stability component through communication lines; The energy storage battery module includes an energy storage interface and a discharge interface. The energy storage interface is configured to store electrical energy, and the discharge interface is configured to release electrical energy; The energy storage management subsystem is physically connected to the energy storage battery module through the energy storage interface and the discharge interface. The energy storage management subsystem is configured to control the operation of at least one energy storage battery module based on control parameters; The monitoring and warning device includes an environmental monitoring device and an alarm device. The environmental monitoring device is configured to obtain environmental monitoring data; The controller is configured to generate maintenance instructions, warning instructions, and adjustment instructions. The system includes a maintenance module, a warning module, and an adjustment module.

Description

Technical Field

[0001] This specification relates to the field of energy storage, and particularly to an adaptive energy storage device and a control system. Background Art

[0002] Most of the existing charge and discharge scheduling methods for energy storage devices mainly consider the comprehensive trade-off of time-of-use electricity price differences, peak and valley periods of electricity consumption, power loss, and management costs. Their scheduling efficiency is affected by the size of the energy storage capacity and does not consider the differences in actual electricity consumption. In addition, when the energy storage device is frequently charged and discharged or the working environment changes, there may be potential safety hazards.

[0003] The prior art CN110190597B proposes a distributed power management system, which can achieve power dispatch and energy storage management according to the actual electricity consumption and user demand. However, this system does not consider the potential safety hazards existing in the energy storage device during the charge and discharge process.

[0004] Therefore, there is provided an adaptive energy storage device and a control system, which can intelligently adjust the control parameters of the energy storage battery module to achieve charge and discharge management, and at the same time maintain the stability of the operating environment through the environment control module to improve the safety factor. Summary of the Invention

[0005] One embodiment of this specification provides an adaptive energy storage device, which includes at least one energy storage battery module, an energy storage management subsystem, a monitoring and warning device, an environmental stability component, and a controller, where: The controller is communicatively connected to the energy storage battery module, the energy storage management subsystem, the monitoring and warning device, and the environmental stability component through communication lines; The energy storage battery module includes an energy storage interface and a discharge interface, the energy storage interface is configured to store electrical energy, and the discharge interface is configured to release electrical energy; The energy storage management subsystem is physically connected to the energy storage battery module through the energy storage interface and the discharge interface, and the energy storage management subsystem is configured to control the operation of the at least one energy storage battery module based on the control parameters of the at least one energy storage battery module; The monitoring and warning device is deployed in the energy storage battery module, and the monitoring and warning device includes an environmental monitoring device and an alarm device. The environmental monitoring device is configured to obtain environmental monitoring data, and the environmental monitoring data includes the environmental temperature at at least one time point and the temperature of the at least one energy storage battery module; The operating environment stability component includes a stability module and an emergency avoidance module; The controller includes a processor, and the processor is configured to: generate a maintenance instruction, and the maintenance instruction is configured to determine the maintenance cycle of the operating environment stability component; generate a warning instruction, and the warning instruction is configured to control the alarm device to issue a warning notice; generate an adjustment instruction, and the adjustment instruction is configured to adjust the control parameters of the at least one energy storage battery module to obtain adjusted optimized control parameters.

[0006] One embodiment of this specification provides an adaptive energy storage regulation system. The system includes a maintenance module, a warning module, and an adjustment module: The maintenance module is configured to generate a maintenance instruction, and the maintenance instruction is configured to determine the maintenance cycle of the operating environment stability component; The warning module is configured to generate a warning instruction, and the warning instruction is configured to control the alarm device to issue a warning notice; The adjustment module is configured to generate an adjustment instruction, and the adjustment instruction is configured to adjust the control parameters of the at least one energy storage battery module to obtain adjusted optimized control parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] This specification will be further described by way of exemplary embodiments, and these exemplary embodiments will be described in detail through the drawings. These embodiments are not restrictive. In these embodiments, the same numbers represent the same structures, where:

[0008] Figure 1 is an exemplary module diagram of an adaptive energy storage regulation system shown according to some embodiments of this specification;

[0009] Figure 2is an exemplary schematic diagram of an adaptive energy storage device shown in some embodiments of this specification;

[0010] Figure 3 is an exemplary flowchart of generating maintenance instructions shown in some embodiments of this specification;

[0011] Figure 4 is an exemplary flowchart of generating adjustment instructions shown in some embodiments of this specification;

[0012] Figure 5 is an exemplary schematic diagram of a prediction model shown in some embodiments of this specification. Detailed implementation manners

[0013] To more clearly illustrate the technical solutions of the embodiments of this specification, the accompanying drawings required for the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some examples or embodiments of this specification. For those of ordinary skill in the art, without creative efforts, this specification can also be applied to other similar scenarios based on these drawings. Unless obvious from the language context or otherwise stated, the same reference numerals in the figures represent the same structure or operation.

[0014] It should be understood that the "system", "device", "unit" and / or "module" used herein is a method for distinguishing different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the said words can be replaced by other expressions.

[0015] As shown in this specification and the claims, unless the context clearly indicates an exception, words such as "a", "an", "one" and / or "the" are not specifically singular and may also include the plural. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0016] Flowcharts are used in this specification to illustrate the operations performed by the systems according to the embodiments of this specification. It should be understood that the operations before or after may not necessarily be executed precisely in sequence. On the contrary, the steps can be processed in reverse order or simultaneously. At the same time, other operations can also be added to these processes, or one or several steps can be removed from these processes.

[0017] In the prior art, the charge-discharge scheduling methods of energy storage devices are complex and diverse. Different regions and different users have different demands, resulting in different schedules. The prior art CN110190597B proposes a distributed power management system, which provides different power scheduling schemes according to the actual electricity consumption and power load. However, the energy storage capacity set based on the user's electricity consumption needs sometimes causes additional management costs, maintenance costs, and battery losses due to unnecessary scheduling. In addition, when the energy storage device is frequently charged and discharged, there are often safety hazards caused by the inability of the environmental control module to ensure the stable operation of the energy storage device because it does not consider this situation.

[0018] Therefore, this specification provides an adaptive energy storage device and control system, which can intelligently adjust the control parameters of the energy storage battery module according to environmental monitoring parameters and electricity consumption conditions, ensure the stable operation of the energy storage battery module, and at the same time can issue a warning notice through a warning device, which is beneficial for maintenance personnel to maintain the environmental stability components, prevent safety hazards, and reduce cost losses.

[0019] Figure 1 It is an exemplary block diagram of an adaptive energy storage control system shown according to some embodiments of this specification.

[0020] In some embodiments, as Figure 1 shown, the adaptive energy storage control system 100 includes a maintenance module 110, a warning module 120, and an adjustment module 130.

[0021] The maintenance module 110 refers to a module for maintaining the components that ensure the stability of the operating environment. In some embodiments, the maintenance module 110 is configured to generate a maintenance instruction, and the maintenance instruction is configured to determine the maintenance cycle of the components that ensure the stability of the operating environment.

[0022] In some embodiments, the maintenance module is further configured to: for each of the energy storage battery modules, determine the heat dissipation characteristics of the energy storage battery module based on the environmental temperature at at least one time point and the temperature of at least one energy storage battery module of the adaptive energy storage device; determine the vibration characteristics of the energy storage battery module based on the vibration data of the energy storage battery module; generate the maintenance instruction based on the environmental dust data, the heat dissipation characteristics, and the vibration characteristics of the energy storage battery module.

[0023] The warning module 120 refers to a module with a warning function. In some embodiments, the warning module 120 is configured to generate a warning instruction, and the warning instruction is configured to control an alarm device to issue a warning notice.

[0024] The adjustment module 130 refers to a module for adjusting the energy storage battery module. In some embodiments, the adjustment module 130 is configured to generate an adjustment instruction, and the adjustment instruction is configured to adjust the control parameters of at least one energy storage battery module to obtain adjusted optimized control parameters.

[0025] In some embodiments, the adjustment module is further configured to: for each energy storage battery module, determine the usage characteristics of the energy storage battery module based on the current monitoring data; determine the charge and discharge risk value of the energy storage battery module during the target period based on the usage characteristics and the maintenance period; and generate an adjustment instruction based on the charge and discharge risk value.

[0026] In some embodiments, the adjustment module is further configured to: in response to the charge and discharge risk value of the energy storage battery module during the target period satisfying a first preset condition, generate a first adjustment instruction, and the first adjustment instruction is configured to adjust the charging parameters of the energy storage battery module during the target period to obtain adjusted optimized charging parameters.

[0027] In some embodiments, the adjustment module is further configured to: calculate the comprehensive risk value of the energy storage battery module based on a preset frequency; in response to the comprehensive risk value satisfying a third preset condition, generate a second adjustment instruction, and the second adjustment instruction is configured to adjust the discharge parameters of the energy storage battery module during the current period to obtain optimized discharge parameters.

[0028] For more details about the above embodiments, reference can be made to Figures 2 - 4 and related descriptions.

[0029] It should be noted that the above description of the adaptive energy storage regulation system and its modules is only for convenience of description, and does not limit this specification to the scope of the examples given. It can be understood that for those skilled in the art, after understanding the principle of the system, they may, without departing from this principle, make any combination of the various modules, or form a subsystem and connect it with other modules. In some embodiments, Figure 1 the maintenance module 110, the warning module 120, and the adjustment module 130 disclosed in

[0030] Figure 2 may be different modules in a system, or a module may implement the functions of two or more of the above modules. For example, the various modules may share a storage module, or each module may have its own storage module. Such variations are all within the protection scope of this specification.

[0031] In some embodiments, such as Figure 2As shown, the adaptive energy storage device 200 includes at least one energy storage battery module 210, an energy storage management subsystem 220, a monitoring and warning device 230, an environmental stability component 240, and a controller 250.

[0032] In some embodiments, the energy storage battery module 210 is a component for providing a power source for the adaptive energy storage device 200.

[0033] In some embodiments, the energy storage battery module 210 can be configured as one or more energy storage batteries. The energy storage battery can be a lithium iron phosphate battery, etc., and the energy storage battery can be of different specifications. One or more energy storage batteries can be connected in parallel and / or in series to form energy storage battery modules 210 with different capacities, so as to meet different power consumption requirements.

[0034] In some embodiments, the energy storage battery module 210 includes an energy storage interface and a discharge interface.

[0035] In some embodiments, the energy storage interface is configured to store electrical energy. In some embodiments, the discharge interface is configured to release electrical energy.

[0036] The energy storage management subsystem 220 is a system for managing and controlling the charging and discharging of the energy storage battery module 210. In some embodiments, the energy storage management subsystem 220 can be configured as a BMS (Battery Management System).

[0037] In some embodiments, the energy storage management subsystem 220 is physically connected to the energy storage battery module through the energy storage interface and the discharge interface. For example, the energy storage management subsystem 220 can be connected to the energy storage interface and the discharge interface of the energy storage battery module through a connecting wire.

[0038] In some embodiments, the energy storage management subsystem 220 is configured to control the operation of at least one energy storage battery module based on control parameters of at least one energy storage battery module.

[0039] The control parameter refers to a relevant parameter for controlling the operation of the energy storage battery module 210. For example, the control parameter can include the current form and current magnitude when the energy storage battery module operates, and the current form can include alternating current and direct current. The control parameter can be preset manually according to requirements and experience.

[0040] In some embodiments, the control parameter can include a charging parameter and a discharging parameter, and specific details can be referred to Figure 4 and related descriptions.

[0041] In some embodiments, the energy storage management subsystem 220 further includes a current monitoring component, and specific details can be referred to Figure 4 and related descriptions.

[0042] The monitoring and warning device 230 is a device for monitoring the energy storage battery module 210 and giving warnings. In some embodiments, the monitoring and warning device is deployed in the energy storage battery module 210. In some embodiments, the monitoring and warning device 230 includes an environmental monitoring device and an alarm device.

[0043] The environmental monitoring device is a device for monitoring the working environment of the energy storage battery module 210. For example, the environmental monitoring device can be configured as a temperature sensor, etc. In some embodiments, the environmental monitoring device is configured to obtain environmental monitoring data.

[0044] The environmental monitoring data is data obtained by monitoring environmental-related parameters. In some embodiments, the environmental monitoring data includes the environmental temperature at at least one time point and the temperature of at least one energy storage battery module. The environmental temperature refers to the temperature of the working environment of the energy storage battery module.

[0045] The alarm device is a device for sending warning notifications. The warning notifications can include voice information, visual information, etc. In some embodiments, the alarm device can be configured as a warning bell, a sound and light device, etc.

[0046] In some embodiments, the monitoring and warning device can include a dust monitoring device and a vibration sensor. For details, please refer to Figure 3 and related descriptions.

[0047] The environmental stability component 240 is a component for maintaining the stability of the working environment of the adaptive energy storage device 100. In some embodiments, the environmental stability component 240 includes a stability module and an emergency avoidance module.

[0048] The stability module is a module for stabilizing environmental factors. The environmental factors can include temperature, humidity, etc. In some embodiments, the stability module can be configured as an air conditioner, a fan, etc.

[0049] The emergency avoidance module is a module for coping with and preventing risk factors occurring in the adaptive energy storage device 100. In some embodiments, the emergency avoidance module can be configured as a fire extinguishing system, an emergency power-off device, etc.

[0050] In some embodiments, the environmental stability component 240 can control the stability module to turn on based on the environmental temperature monitored by the environmental monitoring device being greater than the first temperature threshold, such as controlling the air conditioner and the fan to turn on; and control the emergency avoidance module to turn on based on the environmental temperature being greater than the second temperature threshold, such as controlling the fire extinguishing system to start and controlling the emergency power-off device to cut off the power, etc. The second temperature threshold is greater than the first temperature threshold.

[0051] The controller 250 is a component for controlling and managing the operating parameters of the adaptive energy storage device 100. In some embodiments, the controller 250 is communicatively connected to the energy storage battery module 210, the energy storage management subsystem 220, the monitoring and warning device 230, and the environmental stability component 240 via a communication line.

[0052] The communication line is a line for achieving a communication connection. The communication line may include a wired line and / or a wireless line. For example, a 4G wireless network communication line, an RJ45 interface, a router, or a switch, etc. In some embodiments, the communication line includes a general line and a backup line, thereby improving the stability of the communication line.

[0053] In some embodiments, the controller 250 includes a processor. The processor is a component for processing information and / or data related to the adaptive energy storage device 100. In some embodiments, the processor may include one or a combination of a microcontroller (MCU), an embedded processor, a graphics processing unit (GPU), etc.

[0054] In some embodiments, the processor is configured to: generate a maintenance instruction; generate a warning instruction; generate an adjustment instruction.

[0055] The maintenance instruction is a related instruction for maintaining the stable operation of the adaptive energy storage device 100. In some embodiments, the maintenance instruction is configured to determine the maintenance cycle of the environmental stability component.

[0056] The maintenance cycle refers to the cycle for maintaining the environmental stability component. For example, the maintenance cycle may include the dust cleaning cycle and the reliability inspection cycle of the environmental stability component. The reliability inspection refers to checking whether the environmental stability component has failed or malfunctioned.

[0057] In some embodiments, the processor may determine the maintenance instruction through various methods based on the environmental monitoring data satisfying a preset condition. The preset condition may include that the environmental temperature monitored by the environmental monitoring device or the temperature of the energy storage battery module is greater than a preset temperature threshold.

[0058] For example, the processor may determine the maintenance cycle of the environmental stability component based on the environmental monitoring data meeting a preset condition through a first preset correspondence. The first preset correspondence is a mapping relationship between the environmental monitoring data and the maintenance cycle, and the first preset correspondence can be set manually according to experience. For example, the first preset correspondence may include the average value of the environmental temperature at at least one time point, and the correspondence between the average value of the temperature of at least one energy storage battery module and the maintenance cycle. The greater the average value of the environmental temperature, the shorter the maintenance cycle; the greater the average value of the temperature of at least one energy storage battery module, the shorter the maintenance cycle.

[0059] In some embodiments, the processor may also generate a maintenance instruction based on the environmental temperature, the temperature of the energy storage battery module, the vibration data, and the environmental dust data. For specific content, please refer to Figure 3 and related descriptions.

[0060] The warning instruction is configured to control the alarm device to issue a warning notice. The warning notice may be a voice warning, a ringtone warning, a light warning, etc. The warning notice may include a maintenance reminder, a temperature - too - high reminder, etc.

[0061] For example, the processor may generate a warning instruction based on the environmental temperature or the temperature of the energy storage battery module monitored by the environmental monitoring device being greater than a preset temperature threshold, and control the alarm device to issue a temperature - too - high reminder. Also, for example, when the dust - cleaning cycle and / or the reliability - inspection cycle is approaching, the processor may control the alarm device to issue a maintenance reminder in advance. Maintenance personnel may perform maintenance on the environmental stability component based on the maintenance reminder.

[0062] The adjustment instruction is an instruction for adjusting relevant parameters of the energy storage battery module 210. In some embodiments, the adjustment instruction is configured to adjust the control parameters of at least one energy storage battery module to obtain adjusted optimized control parameters. The energy storage management subsystem 220 may control the energy storage battery module 210 to operate according to the optimized control parameters based on the adjustment instruction.

[0063] The optimized control parameters refer to the control parameters obtained after optimizing and adjusting the control parameters of at least one energy storage battery module.

[0064] In some embodiments, the processor may determine the optimized control parameters through various methods based on the environmental monitoring data meeting a preset condition. For example, the processor may determine the control - parameter adjustment amount through a second preset correspondence based on the environmental monitoring data, and determine the optimized control parameters based on the control - parameter adjustment amount. The control - parameter adjustment amount refers to the adjustment value for reducing the control parameters.

[0065] Among them, the second preset corresponding relationship refers to the mapping relationship between environmental monitoring data and the adjustment amount of control parameters, and the second preset corresponding relationship can be set manually according to experience. For example, the second preset corresponding relationship may include the average value of the environmental temperature at at least one time point, and the mapping relationship between the average value of the temperature of at least one energy storage battery module and the adjustment amount of control parameters. The greater the average value of the environmental temperature, the greater the adjustment amount of control parameters; the greater the average value of the temperature of at least one energy storage battery module, the greater the adjustment amount of control parameters.

[0066] In some embodiments, the processor may also generate an adjustment instruction based on the current monitoring data and the maintenance cycle. For details, please refer to Figure 4 and related descriptions.

[0067] For more information about the processor, please refer to Figures 3 - 5 and related descriptions.

[0068] In some embodiments, the controller 250 further includes a touch screen and a memory. The touch screen is configured to present warning notifications, etc. The memory is configured to store the operating parameters of the adaptive energy storage device.

[0069] In some embodiments of the present specification, through the adaptive energy storage device, environmental monitoring data can be collected and maintenance instructions, warning instructions, and adjustment instructions can be generated, so as to uniformly manage and maintain the energy storage battery module. Through the monitoring and warning device, warning information can be sent in a timely manner, and the control parameters of the energy storage battery module can be adjusted through the energy storage management subsystem to ensure the stable operation of the energy storage battery module. Through the environmental stability component, the working environment can be cooled to further control the environmental temperature and avoid safety risks caused by excessive temperature; in the event of a safety risk, it can also be processed in a timely manner to prevent the risk from further expanding.

[0070] Figure 3 It is an exemplary flowchart for generating a maintenance instruction according to some embodiments of the present specification.

[0071] In some embodiments, the monitoring and warning device 230 includes a dust monitoring device and a vibration sensor.

[0072] The dust monitoring device is a device for monitoring dust-related parameters in the working environment of the energy storage battery module 210. For example, a particulate matter monitor, a light scattering sensor, etc.

[0073] The vibration sensor is a device for monitoring the vibration condition of the energy storage battery module 210. For example, an acceleration sensor, a vibration sensor, etc.

[0074] In some embodiments, the environmental monitoring data further includes environmental dust data at at least one time point and vibration data of the energy storage battery module at at least one time point. The environmental dust data is data reflecting the dust situation in the working environment of the energy storage battery module 210. For example, the dust concentration. The vibration data is data reflecting the vibration situation of the monitored energy storage battery module. For example, the vibration amplitude, the number of vibrations, etc.

[0075] In some embodiments, the dust monitoring device is configured to acquire environmental dust data at at least one time point; the vibration sensor is configured to acquire vibration data of the energy storage battery module at at least one time point.

[0076] Process 300 is an exemplary process for generating a maintenance instruction based on the environmental temperature, the temperature of the energy storage battery module, the environmental dust data, and the vibration data. In some embodiments, as Figure 3 shown, process 300 includes the following steps 310-step 330. The processor is further configured to execute steps 310-step 330.

[0077] Step 310, for each energy storage battery module, determine the heat dissipation characteristics of the energy storage battery module based on the environmental temperature and the temperature of the energy storage battery module.

[0078] The heat dissipation characteristics are characteristic parameters reflecting the actual heat dissipation ability of the energy storage battery module. Ideally, the heat dissipation ability of the energy storage battery module is extremely strong, and the temperature of the energy storage battery module is the same as the environmental temperature, which is equivalent to the heat generated by the charging and discharging of the energy storage battery module being immediately dissipated into the surrounding environment. In actual situations, the heat dissipation ability of the energy storage battery module is affected by various factors, and there is a difference between the temperature of the energy storage battery module and the environmental temperature.

[0079] In some embodiments, the heat dissipation characteristics include the maximum difference value and the heating rate between the temperature of the energy storage battery module and the environmental temperature within a first preset period.

[0080] The first preset period is a time period reflecting the significant change in the difference value between the temperature of the energy storage battery module and the environmental temperature. In some embodiments, the processor can determine the first preset period based on the environmental temperature and the energy storage battery module within a second preset period; use the maximum value within the first preset period as the maximum difference value. The second preset period can be preset according to requirements, such as half a month, one week, etc.

[0081] In some embodiments, the processor may obtain the ambient temperature at multiple time points collected by the environmental monitoring device within a second preset period and the temperature of the energy storage battery module, and calculate the temperature differences between multiple adjacent time points; draw a heat dissipation change diagram based on the temperature differences between multiple adjacent time points; select the maximum value point or the maximum value point closest to the current time (when there are multiple maximum value points) in the heat dissipation change diagram, and use the time point corresponding to the maximum value point as the reference time point; use the preset time range before and after the reference time point as the first preset period. The preset time range can be set according to experience, such as half an hour before and after the reference time point.

[0082] In some embodiments, the processor may calculate a heating rate based on the maximum value, the reference time point, and the starting time point of the first preset period. The starting time point is the starting time point of the first preset period. The heating rate is positively correlated with the maximum value point and negatively correlated with the difference between the starting time point and the reference time point.

[0083] For example, the processor may calculate the heating rate based on formula (1). Formula (1) is shown as follows:

[0084] Heating rate = maximum value ÷ (starting time point - reference time point) × 100% (1)

[0085] Step 320, determine the vibration characteristics of the energy storage battery module based on the vibration data of the energy storage battery module.

[0086] The vibration characteristics are characteristic parameters used to reflect the vibration situation, and the vibration characteristics can reflect the reliability of the energy storage battery module.

[0087] In some embodiments, the vibration characteristics may include the average vibration amplitude, vibration stability, and vibration frequency, etc. An overly large average vibration amplitude or frequency may affect the normal operation of the energy storage battery module and increase the operation risk of the energy storage battery module. The vibration stability reflects the stability of the vibration of the energy storage battery module. The vibration frequency refers to the number of vibrations of the energy storage battery module per unit time.

[0088] In some embodiments, the processor may determine the vibration characteristics of the energy storage battery module based on the vibration data of the energy storage battery module through various methods.

[0089] For example, the processor may calculate the average value of the vibration amplitudes collected at multiple time points within the first preset period and use this average value as the average vibration amplitude. For the first preset period, refer to the relevant description above.

[0090] For another example, the processor may calculate the standard deviation of the vibration amplitudes collected at multiple time points within the first preset period and use the standard deviation as the vibration stability. It can be understood that the greater the vibration stability, the greater the change in the vibration amplitude and the more unstable the vibration.

[0091] For another example, the processor may determine the average number of vibrations within a first preset time period as the vibration frequency.

[0092] Step 330: Generate a maintenance instruction based on the environmental dust data, heat dissipation characteristics, and vibration characteristics of the energy storage battery module.

[0093] For the environmental dust data, reference can be made to the relevant description above; for the maintenance instruction, reference can be made to Figure 2 the relevant description.

[0094] In some embodiments, the processor may determine the maintenance cycle through various methods based on the environmental dust data, heat dissipation characteristics, and vibration characteristics of the energy storage battery module; and generate a maintenance instruction based on the maintenance cycle.

[0095] For example, the processor may calculate the average value of the dust concentrations at multiple time points within a first preset time period as the average dust concentration; in response to the average dust concentration being greater than the concentration threshold, calculate the concentration difference between the average dust concentration and the concentration threshold, and look up the adjustment amount of the dust cleaning cycle in a first preset table based on the concentration difference.

[0096] When the dust concentration is high, the maintenance cycle needs to be shortened. Therefore, the adjustment amount of the dust cleaning cycle refers to the adjustment value for shortening the dust cleaning cycle. The first preset table includes the mapping relationship between the concentration difference and the adjustment amount of the dust cleaning cycle. The mapping relationship can be determined based on experience. Among them, the adjustment amount of the dust cleaning cycle is positively correlated with the concentration difference, and the greater the concentration difference, the greater the adjustment amount of the dust cleaning cycle.

[0097] For another example, the processor may evaluate the charge-discharge risk value of the energy storage battery module during a target time period based on the heat dissipation characteristics and vibration characteristics of the energy storage battery module; and determine the adjustment amount of the reliability inspection cycle through a second preset table based on the charge-discharge risk value.

[0098] The second preset table includes the mapping relationship between the charge-discharge risk value and the adjustment amount of the reliability inspection cycle. The mapping relationship of the second preset table is similar to that of the first preset table. For the charge-discharge risk value and the target time period, reference can be made to Figure 4 and the relevant description.

[0099] In some embodiments, the processor may construct a first feature vector based on the heat dissipation characteristics, vibration characteristics of the energy storage battery module, and the reliability inspection cycle of the environmental stability component, and look up at least one first reference vector that meets the preset conditions in a first vector database as the first target vector based on the first feature vector; calculate the charge-discharge risk value during the target time period based on the number of first target vectors and the first reference results corresponding to the first target vectors.

[0100] The first vector database includes a plurality of first reference vectors and corresponding first reference results. The first reference vectors can be constructed based on the historical heat dissipation characteristics, historical vibration characteristics of the energy storage battery module, and the historical reliability inspection period of the environmental stability components. The corresponding first reference results are the historical inspection results of the environmental stability components, and the inspection results can be scored by inspectors based on experience. Exemplarily, the inspection result can be the degree of failure, and the scoring range can be 0 to 10, where 0 points indicate that the environmental stability components are free of faults. The preset condition can be that the vector distance between the first feature vector and the first reference vector is less than the distance threshold, and the vector distance can be the Euclidean distance, cosine distance, etc.

[0101] In some embodiments, the charge and discharge risk value is positively correlated with the sum of the first reference results and negatively correlated with the number of the first target vectors. For example, the processor can calculate the charge and discharge risk value based on formula (2). Formula (2) is shown as follows:

[0102]

[0103] where A i is the first reference result corresponding to the i-th first target vector, and n is the number of the first target vectors.

[0104] In some embodiments of the present specification, the dust concentration in the environment and the vibration condition of the energy storage battery module can be monitored by a dust monitoring device and a vibration sensor, so as to adjust the maintenance period of the environmental stability components when the dust concentration is too high or the vibration amplitude of the energy storage battery module is too high, shorten the maintenance period, so as to facilitate the inspectors to maintain the environmental stability components in time, thereby ensuring the normal operation of the energy storage battery module. During the actual operation of the energy storage battery module, the heat dissipation capacity may be affected by external environmental factors. By analyzing the heat dissipation characteristics of the energy storage battery module, the energy storage battery modules with abnormal heat dissipation can be detected in time, so as to adjust the maintenance period of the environmental stability components, thereby maintaining the stability of the operating environment of the energy storage battery module.

[0105] It should be noted that the above description of process 300 is only for illustration and explanation, and does not limit the scope of application of the present specification. Those skilled in the art can make various corrections and changes to process 300 under the guidance of the present specification. However, these corrections and changes are still within the scope of the present specification.

[0106] Figure 4 is an exemplary flowchart of generating an adjustment instruction shown in some embodiments of the present specification.

[0107] In some embodiments, the energy storage management subsystem 220 includes a current monitoring component.

[0108] The current monitoring component refers to a component that can monitor the current of the energy storage battery module. For example, a current detection sensor, a voltmeter, etc.

[0109] The current monitoring component is configured to obtain current monitoring data at at least one time point of at least one energy storage battery module.

[0110] The current monitoring data refers to the data obtained by monitoring the current of the energy storage battery module. For example, the current monitoring data may include the magnitude and type of current at at least one time point. The current type may include charging current and discharging current.

[0111] Process 400 is an exemplary process for generating an adjustment instruction based on current monitoring data and a maintenance cycle. In some embodiments, as Figure 4 shown, process 400 includes the following steps 410-step 430. The processor is further configured to execute steps 410-step 430.

[0112] Step 410, for each energy storage battery module, determine the usage characteristics of the energy storage battery module based on the current monitoring data.

[0113] The usage characteristics are characteristic parameters that reflect the user's power consumption situation and / or the charge and discharge situation of the energy storage battery module. For example, the usage characteristics may include the total discharge duration and total charge duration of the energy storage battery module per unit time, the range of discharge current and the range of charging current, etc. The unit time can be set manually according to the actual situation. For example, the unit time can be one day.

[0114] In some embodiments, the processor can determine the usage characteristics of the energy storage battery module based on the current monitoring data through various methods.

[0115] For example, the processor can obtain the current monitoring data at multiple time points collected by the current monitoring component within a third preset period, and determine the usage characteristics based on the current monitoring data within the third preset period. The third preset period can be preset according to requirements. For example, the third preset period can be the past three days of the current time point.

[0116] In some embodiments, the processor can use the average discharge duration within the third preset period as the total discharge duration per unit time. Exemplarily, when the third preset period is three days and the unit time is one day, the total discharge duration per unit time is the average daily discharge duration within three days.

[0117] In some embodiments, the processor can use the average charge duration within the third preset period as the total charge duration per unit time.

[0118] In some embodiments, the processor can use the difference between the maximum discharge current and the minimum discharge current within the third preset period as the range of the discharge current.

[0119] In some embodiments, the processor may use the difference between the maximum charging current and the minimum charging current within a third preset period as the range of the charging current.

[0120] Step 420: Determine the charge-discharge risk value of the energy storage battery module during the target period based on the usage characteristics and the maintenance cycle.

[0121] The charge-discharge risk value is a parameter value reflecting the risk level when the energy storage battery module is charged and discharged. In some embodiments, the charge-discharge risk value includes a charging risk value and a discharging risk value. The charging risk value refers to the risk when charging the energy storage battery module, and the discharging risk value refers to the risk when the energy storage battery module releases the stored electric energy for power supply.

[0122] The target period refers to the period from the current moment to the next maintenance of the environmental stability component.

[0123] In some embodiments, the processor may determine the charge-discharge risk value of the energy storage battery module during the target period based on the usage characteristics and the maintenance cycle through multiple methods.

[0124] For example, the processor may construct a second feature vector based on the usage characteristics and the maintenance cycle, and search for at least one second reference vector that meets the preset conditions in the second vector database based on the second feature vector as the second target vector; calculate the charge-discharge risk value during the target period based on the number of second target vectors and the second reference results corresponding to the second target vectors.

[0125] The second vector database includes multiple second reference vectors and corresponding second reference results. The second reference vectors may be constructed based on historical usage characteristics and historical maintenance cycles. The second reference results are the historical inspection results of the environmental stability components, and the determination method of the second reference results is similar to that of the first reference results. For details, please refer to the foregoing and related descriptions.

[0126] Among them, the second reference results include failure behaviors and failure degrees. The failure behaviors include charging behaviors and discharging behaviors. The failure degrees include the failure degree during the charging process and the failure degree during the discharging process. The preset condition may be that the vector distance between the second feature vector and the second reference vector is less than the distance threshold. For the failure degree, please refer to Figure 3 and related descriptions.

[0127] In some embodiments, the charge-discharge risk value is positively correlated with the sum of the second reference results and negatively correlated with the number of second target vectors. For example, the processor may calculate the charge-discharge risk value based on formula (3). Formula (3) is as follows:

[0128]

[0129] Among them, e is the amplification factor, and e can be set manually according to experience. For example, e can be set to 10. When determining the charging risk value, m is the total number of second target vectors corresponding to the charging behavior as the fault behavior, and B j is the second reference result corresponding to the j-th second target vector among them; when determining the discharging risk value, m is the total number of second target vectors corresponding to the discharging behavior as the fault behavior, and Bj is the second reference result corresponding to the j-th second target vector among them.

[0130] In some embodiments, the processor can also determine the charge and discharge risk values of the target period through a prediction model based on the current control parameters and usage characteristics. For specific content, see Figure 5 and related descriptions.

[0131] Step 430, generate an adjustment instruction based on the charge and discharge risk values.

[0132] The adjustment instruction is configured to adjust the control parameters of at least one energy storage battery module to obtain optimized control parameters. For the adjustment instruction, control parameters, and optimized control parameters, see Figure 2 and related descriptions.

[0133] In some embodiments, the control parameters include charging parameters and discharging parameters. The charging parameters refer to the relevant parameters for controlling the energy storage battery module to charge, such as the magnitude of the charging current, charging cycle, charging type, etc. The charging type can include continuous charging and intermittent charging. The discharging parameters refer to the relevant parameters for controlling the energy storage battery module to discharge, such as the magnitude of the discharging current, discharging cycle, etc.

[0134] Exemplarily, it is possible to control the energy storage battery module to charge or discharge for 3 hours, then stop charging or discharging for 1 hour, and then continue charging or discharging, so as to control the temperature of the energy storage battery module and reduce the risk during the charging or discharging process of the energy storage battery module.

[0135] In some embodiments, the optimized control parameters include optimized charging parameters and optimized discharging parameters. The optimized charging parameters are the adjusted charging parameters, and the optimized discharging parameters are the adjusted discharging parameters.

[0136] In some embodiments, the processor can generate an adjustment instruction through various methods based on the charge and discharge risk values.

[0137] For example, the processor may adjust the charging parameters based on the charging risk value. In some embodiments, the processor may, in response to the charging risk value being greater than the charging risk threshold, based on the first difference between the charging risk value and the charging risk threshold, look up a third preset table to use the corresponding reference charging current magnitude and / or reference charging period as the optimized charging parameters. The third preset table includes the mapping relationship between the first difference and the reference charging current magnitude and / or reference charging period, and the reference charging current magnitude and the reference charging period are negatively correlated with the first difference.

[0138] In some embodiments, the processor may, in response to the discharge risk value being greater than the discharge risk threshold, based on the second difference between the discharge risk value and the discharge risk threshold, look up a fourth preset table to use the corresponding reference discharge current magnitude and / or reference discharge period as the optimized discharge parameters. The fourth preset table includes the mapping relationship between the second difference and the reference discharge current magnitude and / or reference discharge period, and the reference discharge current magnitude and the reference discharge period are negatively correlated with the second difference.

[0139] Regarding the charging risk threshold and the discharge risk threshold, reference may be made to the relevant descriptions hereinafter.

[0140] In some embodiments, the control parameters include charging parameters. Regarding the charging parameters, reference may be made to the relevant descriptions above.

[0141] In some embodiments, the processor is further configured to: generate a first adjustment instruction in response to the charge-discharge risk value of the energy storage battery module in the target period satisfying a first preset condition.

[0142] In some embodiments, the first preset condition may be that the charging risk value is greater than the charging risk threshold. The charging risk threshold may be set according to requirements.

[0143] In some embodiments, the charging risk threshold is also related to the frequency of parameter adjustment in the past preset time period and / or the external environment. For specific details, reference may be made to the relevant descriptions hereinafter.

[0144] The first adjustment instruction is configured to adjust the charging parameters of the energy storage battery module in the target period to obtain the adjusted optimized charging parameters.

[0145] When the charging risk value of the energy storage battery module in the target period is greater than the charging risk threshold, it indicates that the current charging parameters pose a relatively high risk of causing a failure of the energy storage battery module. Adjusting the charging parameters can timely avoid the possible risks and thus reduce the losses.

[0146] In some embodiments, the processor may be configured to: generate a plurality of candidate charging parameters; determine the charge-discharge risk value of each candidate charging parameter in the target period; and use the candidate charging parameters whose charge-discharge risk values satisfy a second preset condition as the optimized charging parameters.

[0147] In some embodiments, the processor may adjust at least one of the current charging cycle and the current charging current magnitude within a preset range to obtain multiple charging cycles and / or multiple charging current magnitudes as candidate charging parameters. The preset range is a range in which the control parameter is adjusted downward.

[0148] In some embodiments, the processor may determine the charge-discharge risk value of the target time period corresponding to each candidate charging parameter based on a prediction model. For the description of the prediction model, reference can be made to Figure 5 and related descriptions.

[0149] In some embodiments, the second preset condition may be that the charge risk value is lower than the charge risk threshold and the discharge risk value is lower than the discharge risk threshold. If there are multiple candidate charging parameters that meet the second preset condition, the candidate charging parameter with the lowest corresponding charge risk value is used as the optimized charging parameter.

[0150] Determining the optimized charging parameter based on multiple candidate charging parameters can obtain a charging parameter with a lower charge-discharge risk value, thereby making the charging process of the energy storage battery module safer.

[0151] In some embodiments, the target time period includes a peak period, a flat period, and a valley period. The charge-discharge risk values of the target time period include the charge-discharge risk values of the peak period, the flat period, and the valley period.

[0152] During a day, the electricity prices are different at different time periods. The electricity price corresponding to the peak period is relatively high, the electricity price corresponding to the flat period is medium, and the electricity price corresponding to the valley period is relatively low. The division standard of the electricity price can be set according to experience.

[0153] In some embodiments, the charge risk thresholds corresponding to the peak period, the flat period, and the valley period are different. For example, the charge risk thresholds corresponding to the peak period, the flat period, and the valley period increase in sequence. The electricity load during the peak period is relatively large and the risk is higher, so a lower charge risk threshold can be set; the electricity load during the valley period is relatively small and the risk is lower, so a higher charge risk threshold can be set.

[0154] By dividing the target time period into a peak period, a flat period, and a valley period, the charge risk threshold can be made more in line with the actual electricity consumption situation.

[0155] In some embodiments, the control parameter includes a discharge parameter. For the discharge parameter, reference can be made to the related description above.

[0156] In some embodiments, the processor is further configured to: calculate the comprehensive risk value of the energy storage battery module based on a preset frequency; and generate a second adjustment instruction in response to the comprehensive risk value meeting the third preset condition.

[0157] The preset frequency can be preset manually. For example, the preset frequency is positively correlated with the user's needs.

[0158] The comprehensive risk value reflects the overall risk value when at least one energy storage battery module discharges.

[0159] In some embodiments, the processor may obtain the comprehensive risk value through weighted summation based on the average discharge current magnitude of at least one energy storage battery module over a preset period.

[0160] The preset period is a period from the past to the present, which can be set according to experience. For example, the preset period can be a two-hour period from the past to the present.

[0161] In some embodiments, the weight of the average discharge current magnitude is related to the discharge risk value of at least one energy storage battery module during the target period. The discharge risk value of the energy storage battery module during the target period can be determined based on a prediction model. For details about the prediction model, refer to Figure 5 and related descriptions.

[0162] In some embodiments, the weight of the average discharge current magnitude is positively correlated with the discharge risk value. The processor may determine the weight of the average discharge current magnitude corresponding to at least one energy storage battery module based on the ratio of the discharge risk values of at least one energy storage battery module during the target period. For example, the processor may calculate different weights based on formula (4). Formula (4) is as follows:

[0163] a u =b u ÷ (b1 + b2 +...... + b v ) × 100% (4)

[0164] where the total number of energy storage battery modules is v, the ratio of the discharge risk values of the v energy storage battery modules during the target period is [b1: b2: ……: bv], and b u represents the discharge risk value of the u-th energy storage battery module. a u is the weight of the average discharge current magnitude of the u-th energy storage battery module.

[0165] In some embodiments, the processor may calculate the comprehensive risk value based on formula (5). Formula (5) is as follows:

[0166] Comprehensive risk value = a1 × c1 + a2 × c2 +...... + a v × c v (5)

[0167] where c v represents the average discharge current magnitude of the v-th energy storage battery module.

[0168] The average discharge currents of different energy storage battery modules are different during a preset time period, and the corresponding weights are also different. By performing weighted summation to obtain a comprehensive risk value, the obtained comprehensive risk value can better conform to the actual working conditions of the energy storage battery module, thereby making the subsequent adjustment of the discharge parameters more accurate.

[0169] The second adjustment instruction is configured to adjust the discharge parameters of the energy storage battery module during the current time period to obtain optimized discharge parameters. The current time period is a preset time period from the current time to the future, and the current time period is shorter and closer to the current time than the target time period. For example, if the target time period is 10 days in the future, the current time period can be 2 hours in the future.

[0170] The third preset condition is a condition satisfied by the comprehensive risk value. The third preset condition may include that the comprehensive risk value is greater than the discharge risk threshold.

[0171] In some embodiments, the third preset condition changes with time. For example, the discharge risk threshold in the third preset condition may gradually decrease with time.

[0172] In some embodiments, the processor may calculate a control parameter based on the comprehensive risk value and the discharge risk threshold. For example, the processor may calculate a current adjustment amount based on the comprehensive risk value and the discharge risk threshold; and determine the magnitude of the optimized discharge current based on the current adjustment amount. The current adjustment amount is positively correlated with the comprehensive risk value and negatively correlated with the discharge risk threshold. The processor may calculate the current adjustment amount based on formula (6):

[0173] Current reduction amount = (Comprehensive risk value - Discharge risk threshold) ÷ (b1 + b2 +...... + b v ) (6)

[0174] Regarding b1, b2......b v Please refer to the relevant description above.

[0175] The closer the time is to the maintenance period, the more dust accumulates, and the greater the impact on the performance and safety of the energy storage battery module. As time goes by, reducing the discharge risk threshold can timely adjust the parameters to reduce the risk.

[0176] In some embodiments, the discharge risk threshold is also related to the frequency of parameter adjustment and / or environmental parameters during a past preset time period.

[0177] In some embodiments, the processor may determine the number of times of adjusting the control parameter per unit time during a past preset time period as the frequency. In some embodiments, the processor may increase the discharge risk threshold based on the frequency being greater than a preset threshold, thereby reducing the number of times of adjusting the control parameter.

[0178] Environmental parameters refer to the relevant parameters of the working environment of the energy storage battery module. For example, environmental temperature, etc.

[0179] In some implementations, the processor can determine the charging risk threshold based on the frequency and / or environmental parameters through a preset corresponding relationship. The preset corresponding relationship can include the mapping relationship between the discharge risk threshold and the frequency and / or environmental parameters, and the preset corresponding relationship can be set manually according to experience. Among them, the discharge risk threshold is positively correlated with the frequency and negatively correlated with the environmental parameters.

[0180] In some embodiments, the charging risk threshold is related to the frequency of parameter adjustment and / or environmental parameters within a preset past time period. The processor can determine the charging risk threshold based on the frequency and / or environmental parameters, and the determination method is similar to that of the discharge risk threshold.

[0181] Frequent adjustment of control parameters may also increase the likelihood of failures in the energy storage battery module. Timely adjustment of the charge / discharge risk threshold can reduce the number of control parameters, thereby reducing the risk of failures in the energy storage battery module. When the weather is hotter, the working environmental temperature of the energy storage battery module is higher and it is more prone to failures. Lowering the charge / discharge risk threshold can make the adjustment instructions more accurate, thereby reducing the risk of failures in the energy storage battery module.

[0182] The factors affecting the discharge behavior of the energy storage battery module are mainly the needs of users, such as the type of load that the user needs to charge, the amount of electricity required for different loads to charge, etc. During a relatively long target period, the needs of users may vary greatly. Therefore, adjusting the discharge parameters for a relatively short current period can make the optimized discharge parameters after adjustment more in line with the user's needs.

[0183] In some embodiments of this specification, by determining the usage characteristics through current monitoring data, the characteristics of user power consumption and the charge / discharge characteristics of the energy storage battery module can be analyzed, so that when the user's power consumption is large or the current of the energy storage battery module during charge / discharge is unstable, the control parameters of the energy storage battery module can be adjusted in a timely manner, thereby avoiding possible risks.

[0184] It should be noted that the above description of process 400 is only for illustration and explanation, and does not limit the scope of application of this specification. For those skilled in the art, various modifications and changes can be made to process 400 under the guidance of this specification. However, these modifications and changes are still within the scope of this specification.

[0185] Figure 5 It is an exemplary schematic diagram of a prediction model shown in some embodiments of this specification.

[0186] In some embodiments, as shown in the figure, the processor is further configured to determine a charge-discharge risk value 530 for a target period through a prediction model 520 based on the current control parameters 511 and usage characteristics 512 of at least one energy storage battery module.

[0187] The current control parameters 511 refer to the control parameters of the energy storage battery module at the current moment. For the control parameters, usage characteristics 512, and charge-discharge risk value 530, reference can be made to Figure 4 and the relevant descriptions.

[0188] The prediction model is a model used to predict the charge-discharge risk value. In some embodiments, the prediction model is a machine learning model, for example, a Neural Networks (NN) model.

[0189] In some embodiments, the prediction model can be trained by a large number of first training samples and first labels corresponding to the first training samples. In some embodiments, multiple first training samples with first labels can be input into an initial prediction model, a loss function can be constructed based on the first labels and the results of the initial prediction model, and the parameters of the initial prediction model can be iteratively updated based on the loss function through gradient descent or other methods. When the preset conditions are met, the model training is completed, and a trained prediction model is obtained. Among them, the preset conditions can be that the loss function converges, the number of iterations reaches a threshold, etc.

[0190] Each set of training samples in the first training samples can include the sample control parameters and sample usage characteristics in the sample data. The sample data can be obtained from historical data. The first labels corresponding to the first training samples can be determined based on the subsequent failure situations of the energy storage battery modules corresponding to each group of training samples. For example, if the energy storage battery module fails during the historical target period and fails during both the charging process and the discharging process, the label is (1, 1); if it fails during the charging process and does not fail during the discharging process, the label is (1, 0); if it does not fail during both the charging process and the discharging process, the label is (0, 0). The first labels can be obtained through manual annotation or automatic annotation.

[0191] In some implementations, the target period includes peak periods, flat-rate periods, and off-peak periods, and the prediction model output includes the charge-discharge risk values for peak periods, flat-rate periods, and off-peak periods. For peak periods, flat-rate periods, and off-peak periods, reference can be made to Figure 4 the relevant descriptions.

[0192] In some embodiments, the first tag can be represented as [t1, t2, t3, t4, t5, t6], where t1 represents the charging risk value during peak hours, t2 represents the discharging risk value during peak hours, t3 represents the charging risk value during off-peak hours, t4 represents the discharging risk value during off-peak hours, t5 represents the charging risk value during valley hours, and t6 represents the discharging risk value during valley hours). Exemplarily, if a failure occurs when the training sample discharges during the peak hours of the target period and no failure occurs during other periods, the first tag is [0, 1, 0, 0, 0, 0].

[0193] By separately predicting the charging and discharging risk values of the energy storage battery module during peak hours, off-peak hours, and valley hours of a day, the prediction model can estimate the charging and discharging risk values more accurately.

[0194] In some embodiments, as Figure 5 shown, the input of the prediction model further includes the heat dissipation feature 513 and the vibration feature 514 of the energy storage battery module. For the heat dissipation feature and the vibration feature, reference can be made to Figure 3 and the relevant descriptions.

[0195] In some embodiments, each group of training samples in the first training sample can include the sample control parameters, sample usage features, sample heat dissipation features, and sample vibration features in the sample data. The sample data can be obtained from historical data.

[0196] By adding the heat dissipation feature and the vibration feature to the input of the prediction model, the stability and accuracy of the prediction model can be improved.

[0197] In some embodiments of this specification, by predicting the charging and discharging risk values during the target period through the prediction model, misjudgment caused by human judgment can be avoided, the prediction accuracy of the charging and discharging risk values of the energy storage battery module can be improved, so that the subsequent adjustment of the control parameters can be more accurate, and thus the stable operation of the energy storage battery module can be maintained.

[0198] The basic concepts have been described above. Obviously, for those skilled in the art, the above detailed disclosure is only an example and does not constitute a limitation to this specification. Although not explicitly stated here, those skilled in the art may make various modifications, improvements, and corrections to this specification. Such modifications, improvements, and corrections are suggested in this specification, so such modifications, improvements, and corrections still fall within the spirit and scope of the exemplary embodiments of this specification.

[0199] At the same time, this specification uses specific terms to describe the embodiments of this specification. For example, "some embodiments" means a certain feature, structure, or characteristic related to at least one embodiment of this specification. Therefore, it should be emphasized and noted that "some embodiments" mentioned twice or more at different positions in this specification do not necessarily refer to the same embodiment. In addition, certain features, structures, or characteristics in one or more embodiments of this specification can be appropriately combined.

[0200] In addition, the order of the processing elements and sequences described in this specification, the use of numerical and alphabetical characters, or the use of other names are not used to limit the order of the processes and methods of this specification. Although some currently considered useful embodiments of the invention are discussed through various examples in the above disclosure, it should be understood that such details only serve the purpose of illustration. For example, although the system components described above can be implemented by hardware devices, they can also be implemented only through software solutions, such as installing the described system on existing servers or mobile devices.

[0201] Similarly, it should be noted that, in order to simplify the expression of the disclosure of this specification and thus help the understanding of one or more embodiments of the invention, in the foregoing description of the embodiments of this specification, sometimes multiple features are grouped into one embodiment, drawing, or description thereof. However, this disclosure method does not mean that the features required by the subject matter of this specification are more than those mentioned in the claims. In fact, the features of the embodiments are fewer than all the features of the individual embodiments disclosed above.

[0202] Finally, it should be understood that the embodiments described in this specification are only used to illustrate the principles of the embodiments of this specification. Other variations may also fall within the scope of this specification. Therefore, by way of example and not limitation, alternative configurations of the embodiments of this specification can be considered to be in accordance with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to the embodiments explicitly introduced and described in this specification.

Claims

1. An adaptive energy storage device, characterized in that, Comprising at least one energy storage battery module, an energy storage management subsystem, a monitoring and warning device, an environmental stability component, and a controller, wherein: The controller is communicatively connected to the energy storage battery module, the energy storage management subsystem, the monitoring and warning device, and the environmental stability component through communication lines; The energy storage battery module includes an energy storage interface and a discharge interface, the energy storage interface is configured to store electrical energy, and the discharge interface is configured to release electrical energy; The energy storage management subsystem is physically connected to the energy storage battery module through the energy storage interface and the discharge interface, and the energy storage management subsystem is configured to control the operation of the at least one energy storage battery module based on control parameters of the at least one energy storage battery module, and the control parameters include charging parameters and discharging parameters; The energy storage management subsystem includes a current monitoring component, and the current monitoring component is configured to obtain current monitoring data of the at least one energy storage battery module at at least one time point; The monitoring and warning device is deployed in the energy storage battery module, and the monitoring and warning device includes an environmental monitoring device and an alarm device. The environmental monitoring device is configured to obtain environmental monitoring data, and the environmental monitoring data includes the environmental temperature at at least one time point and the temperature of the at least one energy storage battery module; The environmental stability component includes a stability module and an emergency avoidance module; The controller includes a processor, and the processor is configured to: Generate a maintenance instruction, and the maintenance instruction is configured to determine the maintenance cycle of the environmental stability component; Generate a warning instruction, and the warning instruction is configured to control the alarm device to issue a warning notice; For each of the energy storage battery modules, determine the usage characteristics of the energy storage battery module based on the current monitoring data; Based on the usage characteristics, the maintenance cycle, and the heat dissipation characteristics and vibration characteristics of the energy storage battery module, determine the charge and discharge risk value of the energy storage battery module in a target period through a prediction model. The target period includes a peak period, a parity period, and a valley period, and the charge risk thresholds corresponding to the peak period, the parity period, and the valley period are different; In response to the charge and discharge risk value of the energy storage battery module in the target period satisfying a first preset condition, generate a first adjustment instruction, and the first adjustment instruction is configured to adjust the charging parameter of the energy storage battery module in the target period; Generate a plurality of adjusted candidate charging parameters, and determine the charge and discharge risk value of each candidate charging parameter in the target period through the prediction model; Use the candidate charging parameters whose charge and discharge risk values satisfy a second preset condition as optimized charging parameters; The processor is further configured to: Calculate the comprehensive risk value of the energy storage battery module based on a preset frequency; the comprehensive risk value is obtained by weighted summation based on the average discharge current magnitude of the at least one energy storage battery module in a preset period, and the weight of the average discharge current magnitude is determined based on the discharge risk values of the plurality of energy storage battery modules predicted by the prediction model in the target period; In response to the comprehensive risk value being greater than the discharge risk threshold, a second adjustment instruction is generated, and the second adjustment instruction is configured to adjust the discharge parameters of the energy storage battery module in the current period to obtain optimized discharge parameters; the discharge risk threshold gradually decreases over time, and the discharge risk threshold is related to the frequency of parameter adjustment and / or environmental parameters within a preset past period.

2. The device according to claim 1, characterized in that, The monitoring and warning device includes a dust monitoring device and a vibration sensor, and the environmental monitoring data further includes environmental dust data at the at least one time point and vibration data of the energy storage battery module at the at least one time point; The processor is further configured to: For each energy storage battery module, determine the heat dissipation characteristics of the energy storage battery module based on the environmental temperature and the temperature of the energy storage battery module; Determine the vibration characteristics of the energy storage battery module based on the vibration data of the energy storage battery module; Generate the maintenance instruction based on the environmental dust data, the heat dissipation characteristics, and the vibration characteristics of the energy storage battery module.

3. An adaptive energy storage regulation system, characterized in that, The system includes a maintenance module, a warning module, and an adjustment module: The maintenance module is configured to generate a maintenance instruction, and the maintenance instruction is configured to determine the maintenance period of the environmental stability component; The warning module is configured to generate a warning instruction, and the warning instruction is configured to control an alarm device to issue a warning notice; The adjustment module is configured to: For each energy storage battery module, determine the usage characteristics of the energy storage battery module based on the current monitoring data; Based on the usage characteristics, the maintenance period, and the heat dissipation characteristics and vibration characteristics of the energy storage battery module, determine the charge and discharge risk value of the energy storage battery module in the target period through a prediction model, where the target period includes a peak period, a parity period, and a valley period, and the charge risk thresholds corresponding to the peak period, the parity period, and the valley period are different; In response to the charge and discharge risk value of the energy storage battery module in the target period satisfying a first preset condition, generate a first adjustment instruction, and the first adjustment instruction is configured to adjust the charging parameters of the energy storage battery module in the target period; Generate a plurality of adjusted candidate charging parameters, and determine the charge and discharge risk value of the target period corresponding to each candidate charging parameter through the prediction model; Use the candidate charging parameter whose charge and discharge risk value satisfies a second preset condition as the optimized charging parameter; The adjustment module is further configured to: Calculate the comprehensive risk value of the energy storage battery module based on a preset frequency; the comprehensive risk value is obtained by weighted summation based on the average discharge current magnitude of at least one energy storage battery module in a preset period, and the weight of the average discharge current magnitude is determined based on the discharge risk values of a plurality of energy storage battery modules predicted by the prediction model in the target period; In response to the integrated risk value being greater than the discharge risk threshold, a second adjustment instruction is generated, and the second adjustment instruction is configured to adjust the discharge parameters of the energy storage battery module in the current period to obtain optimized discharge parameters; the discharge risk threshold gradually decreases over time, and the discharge risk threshold is related to the frequency of parameter adjustment and / or environmental parameters within a preset past period.

4. The system according to claim 3, wherein The maintenance module is further configured to: For each of the energy storage battery modules, based on the ambient temperature at at least one time point and the temperature of the at least one energy storage battery module of the adaptive energy storage device, determine the heat dissipation characteristics of the energy storage battery module; Based on the vibration data of the energy storage battery module, determine the vibration characteristics of the energy storage battery module; Generate the maintenance instruction based on the environmental dust data, the heat dissipation characteristics, and the vibration characteristics of the energy storage battery module.

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