Electrochemical energy storage heat abuse early warning method based on enhanced deep learning

Abstract

The invention discloses an electrochemical energy storage heat abuse early warning method based on enhanced deep learning, and relates to the technical field of energy storage. Comprising the following steps: acquiring charge time sequence data, equipment surface temperature time sequence data and external environment temperature time sequence data in a current time period; taking the data as input of a temperature prediction model to obtain equipment surface predicted temperature time sequence data of the next time period; on the basis of the current charge data, determining charge time sequence data in the next time period; based on the historical time sequence data and the predicted time sequence data of the equipment surface temperature in the charging and discharging process, the historical charging time sequence data in the charging and discharging process and the charging time sequence data in the next time period, establishing an equipment surface temperature curve changing along with the charging data; determining whether there is a risk of thermal abuse based on the curve; and when the thermal abuse risk exists, early warning information is generated. According to the method disclosed by the invention, early warning can be performed before thermal abuse, and unnecessary loss caused by thermal abuse is avoided.

Description

Technical Field

[0001] The present invention belongs to the field of energy storage technology, and specifically relates to an electrochemical energy storage thermal abuse early warning method based on enhanced deep learning. Background Art

[0002] With the rapid development of renewable energy, the role of energy storage systems in power systems has become increasingly prominent. As one of the core components of energy storage systems, the safety and stability of electrochemical energy storage devices are directly related to the reliable operation of the entire system. However, in the process of storing energy through electrochemical energy storage devices, various problems that may be encountered, especially thermal abuse, are one of the main factors threatening the safety of energy storage systems.

[0003] Thermal abuse is generally caused by external heat sources or excessive internal heat generation and poor heat dissipation. Thermal abuse can cause the internal temperature of the electrochemical energy storage device to rise sharply, which in turn causes the chemical reaction inside the battery to get out of control, generating a large amount of heat and possibly causing a fire or explosion. In particular, during the charging and discharging process, the heat generated by the internal reaction of the electrochemical energy storage device is more likely to cause thermal abuse. At present, thermal abuse during the charging and discharging process is mostly detected based on the surface temperature of the electrochemical energy storage device. This method can generally only detect thermal abuse of the electrochemical energy storage device after the thermal abuse occurs, and cannot provide early warning before the thermal abuse occurs, thus causing unnecessary losses.

[0004] Therefore, how to provide an effective solution to provide early warning before thermal abuse occurs has become a difficult problem to be solved in the prior art. Summary of the invention

[0005] The purpose of the present invention is to provide an electrochemical energy storage thermal abuse warning method based on enhanced deep learning to solve the above-mentioned problems existing in the prior art.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] In a first aspect, the present invention provides an electrochemical energy storage thermal abuse warning method based on enhanced deep learning, comprising:

[0008] Obtaining charging time series data, equipment surface temperature time series data and external environment temperature time series data of the electrochemical energy storage device being charged and discharged in the current period;

[0009] Based on the charge and discharge state of the electrochemical energy storage device, determining a temperature prediction model corresponding to the charge and discharge state and based on enhanced deep learning, wherein the charge and discharge state is a charge state or a discharge state;

[0010] The charging time series data, the device surface temperature time series data and the external environment temperature time series data are used as inputs of the temperature prediction model to perform calculations to obtain the device surface temperature time series data predicted for the electrochemical energy storage device in the next time period;

[0011] Based on the current charge data of the electrochemical energy storage device, determining the charge timing data of the electrochemical energy storage device in the next time period;

[0012] Based on the historical time series data of the device surface temperature of the electrochemical energy storage device during the current charge and discharge process and the predicted time series data of the device surface temperature, as well as the historical charge time series data of the electrochemical energy storage device during the current charge and discharge process and the charge time series data of the electrochemical energy storage device in the next time period, a device surface temperature curve of the electrochemical energy storage device that changes with the charge data from the start of the current charge and discharge to the next time period is established;

[0013] Determining whether the electrochemical energy storage device has a risk of thermal abuse based on the device surface temperature curve;

[0014] When the electrochemical energy storage device has a risk of thermal abuse, a warning message is generated.

[0015] Based on the above disclosed content, the present invention obtains the charging time series data, the device surface temperature time series data and the external environment temperature time series data of the electrochemical energy storage device in the current time period; based on the charging and discharging state of the electrochemical energy storage device, determines the temperature prediction model corresponding to the charging and discharging state and based on enhanced deep learning; uses the charging time series data, the device surface temperature time series data and the external environment temperature time series data as inputs of the temperature prediction model for calculation, and obtains the device surface predicted temperature time series data of the electrochemical energy storage device in the next time period; determines the electrochemical energy storage device based on the current charging data of the electrochemical energy storage device. The charging time series data of the energy storage device in the next time period; based on the historical time series data of the surface temperature of the electrochemical energy storage device in the current charging and discharging process and the predicted temperature time series data of the surface of the device, as well as the historical charging time series data of the electrochemical energy storage device in the current charging and discharging process and the charging time series data of the electrochemical energy storage device in the next time period, establish the device surface temperature curve of the electrochemical energy storage device that changes with the charging data from the beginning of this charging and discharging to the next time period; determine whether the electrochemical energy storage device has a risk of thermal abuse based on the device surface temperature curve; generate warning information when the electrochemical energy storage device has a risk of thermal abuse. In this way, it can be predicted whether the electrochemical energy storage device has a risk of thermal abuse in the next time period, and generate warning information when there is a risk of thermal abuse, so as to achieve early warning before thermal abuse occurs, so as to take corresponding measures in advance to prevent the occurrence of thermal abuse and avoid unnecessary losses caused by thermal abuse.

[0016] In a possible design, the charging time series data, the device surface temperature time series data, and the external environment temperature time series data are used as inputs of the temperature prediction model to perform operations to obtain the device surface predicted temperature time series data of the electrochemical energy storage device in the next time period, including:

[0017] The charging time series data, the device surface temperature time series data, and the external environment temperature time series data are standardized and combined to obtain a vector matrix, where each row in the vector matrix corresponds to data of one dimension;

[0018] The vector matrix is ​​used as an input of the temperature prediction model to perform calculations to obtain predicted device surface temperature time series data of the electrochemical energy storage device in the next time period.

[0019] In one possible design, determining the charging time series data of the electrochemical energy storage device in the next time period based on the current charging data of the electrochemical energy storage device includes:

[0020] Based on the current charge data of the electrochemical energy storage device and at least one recent charge and discharge record of the electrochemical energy storage device, the charge time series data of the electrochemical energy storage device in the next time period is estimated.

[0021] In one possible design, the device surface temperature curve of the electrochemical energy storage device that changes with the charge data from the start of the current charge and discharge to the next period is established based on the device surface temperature historical time series data of the electrochemical energy storage device during the current charge and discharge process and the device surface predicted temperature time series data, as well as the historical charge time series data of the electrochemical energy storage device during the current charge and discharge process and the charge time series data of the electrochemical energy storage device in the next period, including:

[0022] Determine the device surface temperature time series data of the electrochemical energy storage device from the start of the current charge and discharge to the next time period based on the device surface temperature history time series data of the electrochemical energy storage device during the current charge and discharge process and the device surface predicted temperature time series data;

[0023] Determine the charge time sequence data of the electrochemical energy storage device from the start of the current charge and discharge to the next time period based on the historical charge time sequence data of the electrochemical energy storage device during the current charge and discharge process and the charge time sequence data of the electrochemical energy storage device in the next time period;

[0024] Based on the device surface temperature time series data of the electrochemical energy storage device from the start of this charge and discharge to the next time period and the charge time series data of the electrochemical energy storage device from the start of this charge and discharge to the next time period, a device surface temperature curve of the electrochemical energy storage device that changes with the charge data from the start of this charge and discharge to the next time period is established.

[0025] In one possible design, determining whether the electrochemical energy storage device has a risk of thermal abuse based on the device surface temperature curve includes:

[0026] The device surface temperature curve is compared with a historical device surface temperature curve of the electrochemical energy storage device or a storage device of the same model as the electrochemical energy storage device when no thermal abuse risk occurs, to determine whether the electrochemical energy storage device has a thermal abuse risk.

[0027] In one possible design, comparing the device surface temperature curve with a historical device surface temperature curve of the electrochemical energy storage device or a device of the same model as the electrochemical energy storage device when no thermal abuse risk occurs includes:

[0028] The device surface temperature curve is compared with a historical device surface temperature curve when the initial curve of the electrochemical energy storage device or a storage device of the same model as the electrochemical energy storage device is similar and no thermal abuse risk occurs.

[0029] In one possible design, the electrochemical energy storage device is a lithium-ion battery or a sodium-sulfur battery.

[0030] In a second aspect, the present invention provides an electrochemical energy storage thermal abuse warning device based on enhanced deep learning, comprising:

[0031] An acquisition unit, used to acquire charge time series data, device surface temperature time series data, and external environment temperature time series data of a charge-discharge electrochemical energy storage device in a current period;

[0032] A first determining unit is used to determine, based on the charge and discharge state of the electrochemical energy storage device, a temperature prediction model corresponding to the charge and discharge state and based on enhanced deep learning, wherein the charge and discharge state is a charging state or a discharging state;

[0033] A calculation unit, used to calculate the charging time series data, the device surface temperature time series data and the external environment temperature time series data as inputs of the temperature prediction model to obtain the device surface predicted temperature time series data of the electrochemical energy storage device in the next time period;

[0034] A second determination unit, configured to determine the charge timing data of the electrochemical energy storage device in the next time period based on the current charge data of the electrochemical energy storage device;

[0035] An establishing unit, for establishing a device surface temperature curve of the electrochemical energy storage device that changes with the charge data from the start of the current charge and discharge to the next period based on the device surface temperature history time series data of the electrochemical energy storage device during the current charge and discharge process and the device surface predicted temperature time series data, as well as the historical charge time series data of the electrochemical energy storage device during the current charge and discharge process and the charge time series data of the electrochemical energy storage device in the next period;

[0036] A third determination unit, configured to determine whether the electrochemical energy storage device has a risk of thermal abuse based on the device surface temperature curve;

[0037] The early warning unit is used to generate early warning information when there is a risk of thermal abuse of the electrochemical energy storage device.

[0038] In a third aspect, the present invention provides an electronic device comprising a memory, a processor and a transceiver which are communicatively connected in sequence, wherein the memory is used to store a computer program, the transceiver is used to send and receive messages, and the processor is used to read the computer program to execute the electrochemical energy storage thermal abuse warning method based on reinforced deep learning as described in the first aspect or any possible design of the first aspect.

[0039] In a fourth aspect, the present invention provides a computer-readable storage medium having instructions stored thereon, which, when executed on a computer, executes the electrochemical energy storage thermal abuse warning method based on enhanced deep learning as described in the first aspect or any possible design of the first aspect.

[0040] In a fifth aspect, the present invention provides a computer program product comprising instructions, which, when executed on a computer, causes the computer to execute the electrochemical energy storage thermal abuse warning method based on enhanced deep learning as described in the first aspect or any possible design of the first aspect.

[0041] Beneficial effects:

[0042] The electrochemical energy storage thermal abuse early warning method based on enhanced deep learning provided by the present invention can predict whether there is a risk of thermal abuse of the electrochemical energy storage device in the next period of time, and generate early warning information when there is a risk of thermal abuse, thereby achieving early warning before the thermal abuse occurs, so as to take corresponding measures in advance to prevent the occurrence of thermal abuse, avoid unnecessary losses caused by thermal abuse, and facilitate practical application and promotion. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 A flow chart of an electrochemical energy storage thermal abuse warning method based on enhanced deep learning provided in an embodiment of the present application;

[0044] Figure 2A schematic block diagram of an electrochemical energy storage thermal abuse warning device based on enhanced deep learning provided in an embodiment of the present application;

[0045] Figure 3 A schematic block diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the present invention will be briefly introduced below in combination with the drawings and the description of the embodiments or the prior art. Obviously, the following description of the structure of the drawings is only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. It should be noted that the description of these embodiments is used to help understand the present invention, but does not constitute a limitation of the present invention.

[0047] In order to provide early warning before thermal abuse occurs, an embodiment of the present application provides an electrochemical energy storage thermal abuse warning method based on enhanced deep learning. The electrochemical energy storage thermal abuse warning method based on enhanced deep learning can provide early warning before thermal abuse occurs, so as to take corresponding measures in advance to prevent the occurrence of thermal abuse and avoid unnecessary losses caused by thermal abuse.

[0048] The electrochemical energy storage thermal abuse warning method based on enhanced deep learning provided in the embodiment of the present application can be applied to a background terminal device or server for detecting and managing electrochemical energy storage equipment. It can be understood that the execution subject does not constitute a limitation on the embodiment of the present application.

[0049] The electrochemical energy storage thermal abuse warning method based on enhanced deep learning provided in an embodiment of the present application will be described in detail below.

[0050] like Figure 1 As shown, it is a flow chart of the electrochemical energy storage thermal abuse warning method based on enhanced deep learning provided in the first aspect of the embodiment of the present application. The electrochemical energy storage thermal abuse warning method based on enhanced deep learning may include but is not limited to the following steps S101-S107.

[0051] Step S101. Obtaining charging time series data, device surface temperature time series data, and external environment temperature time series data of a charging and discharging electrochemical energy storage device in a current time period.

[0052] Among them, the electrochemical energy storage device can be, but is not limited to, a lithium-ion battery or a sodium-sulfur battery, etc., which converts electrical energy into chemical energy, and then converts chemical energy into electrical energy when needed.

[0053] In an embodiment of the present application, the charge timing data of the charged and discharged electrochemical energy storage device in the current time period can be collected through the battery management system (BMS), and temperature sensors can be set on the surface and around the electrochemical energy storage device, and the device surface temperature timing data and the external environment temperature timing data of the electrochemical energy storage device in the current time period can be obtained through the temperature sensor.

[0054] Among them, the charging timing data includes multiple charging data (State of ChargeS, SOC) and the time corresponding to each charging data, the equipment surface temperature timing data includes multiple equipment surface temperature data and the time corresponding to each equipment surface temperature data, and the external environment temperature timing data includes multiple external environment temperature data and the time corresponding to each external environment temperature data. The time may refer to the time difference between the start of charging and discharging and the acquisition of the corresponding data.

[0055] The charging time series data, device surface temperature time series data and external environment temperature time series data of the electrochemical energy storage device during the current time period. It can be the charging time series data, device surface temperature time series data and external environment temperature time series data of the chemical energy storage device during the current time period, or it can be the charging time series data, device surface temperature time series data and external environment temperature time series data of the chemical energy storage device during the current time period.

[0056] Step S102. Based on the charge and discharge state of the electrochemical energy storage device, determine a temperature prediction model corresponding to the charge and discharge state and based on enhanced deep learning.

[0057] The charge and discharge state is a charge state or a discharge state.

[0058] In the embodiment of the present application, two temperature prediction models based on enhanced deep learning are pre-trained, and the two temperature prediction models based on enhanced deep learning are used for temperature prediction of the electrochemical energy storage device during the charging state and the discharging state. The training process of the temperature prediction model will be described later.

[0059] If the electrochemical energy storage device is in a charging state, a temperature prediction model corresponding to the charging state is selected; if the chemical energy storage device is in a discharging state, a temperature prediction model corresponding to the discharging state is selected.

[0060] Step S103: Using the charging time series data, the device surface temperature time series data and the external environment temperature time series data as inputs of the temperature prediction model for calculation, and obtaining the device surface temperature time series data predicted for the electrochemical energy storage device in the next time period.

[0061] In the embodiment of the present application, the charging time series data, the device surface temperature time series data and the external environment temperature time series data can be combined after being standardized to obtain a vector matrix, in which each row of the vector matrix corresponds to data of one dimension. The vector matrix is ​​then used as the input of the temperature prediction model for operation to obtain the predicted device surface temperature time series data of the electrochemical energy storage device in the next period.

[0062] It should be noted that, during the standardization process, the charge data in the charging time series data, the device surface temperature data in the device surface temperature time series data, and the external ambient temperature data in the external ambient temperature time series data are mainly standardized, and the time data is not processed.

[0063] For example, the data sequence obtained by standardizing the charging time series data is (0.2, 0.22, 0.25, 0.27, 0.28), the data sequence obtained by standardizing the equipment surface temperature time series data is (0.5, 0.51, 0.51, 0.52, 0.53), and the data sequence obtained by standardizing the external environment temperature time series data is (0.61, 0.6, 0.6, 0.61, 0.62). Then, the charging time series data, the equipment surface temperature time series data, and the external environment temperature time series data are standardized and combined, and the obtained vector matrix can be expressed as

[0064] In an embodiment of the present application, when training a temperature prediction model, the historical vector matrix corresponding to the electrochemical energy storage device and the energy storage device of the same model as the electrochemical energy storage device (corresponding to the historical charging time series data, the historical device surface temperature time series data, and the historical external environment temperature time series data) can be used as sample input, and the device surface temperature time series data of the subsequent time period corresponding to the historical vector matrix can be used as sample output for calculation.

[0065] Step S104: Based on the current charge data of the electrochemical energy storage device, determine the charge timing data of the electrochemical energy storage device in the next time period.

[0066] In the embodiment of the present application, the charge timing data of the electrochemical energy storage device in the next time period can be estimated based on the current charge data of the electrochemical energy storage device and at least one recent charge and discharge record of the electrochemical energy storage device.

[0067] For example, the electrochemical energy storage device is currently in a charging state, the current charge data of the electrochemical energy storage device is 50%, and the time difference between the charge sequence data is 1 minute. According to the recent multiple charge and discharge records, the electrochemical energy storage device is in the charging process. For a period of time after the charge data is 50%, it has maintained an average charging speed of 1.5% per minute. Assuming that the duration of the next time period is 5 minutes, the charge sequence data of the next time period (the charge data in) can be expressed as 51.5%, 53%, 54.5%, 56% and 57.5% respectively.

[0068] Step S105. Based on the historical time series data of the device surface temperature of the electrochemical energy storage device during the current charge and discharge process and the predicted time series data of the device surface temperature, as well as the historical charge time series data of the electrochemical energy storage device during the current charge and discharge process and the charge time series data of the electrochemical energy storage device in the next time period, establish a device surface temperature curve of the electrochemical energy storage device that changes with the charge data from the start of the current charge and discharge to the next time period.

[0069] Specifically, the device surface temperature time series data of the electrochemical energy storage device from the start of this charge and discharge to the next time period can be determined based on the device surface temperature historical time series data of the electrochemical energy storage device during this charge and discharge process and the device surface predicted temperature time series data. And the charge time series data of the electrochemical energy storage device from the start of this charge and discharge to the next time period can be determined based on the historical charge time series data of the electrochemical energy storage device during this charge and discharge process and the charge time series data of the electrochemical energy storage device in the next time period. Then, based on the device surface temperature time series data of the electrochemical energy storage device from the start of this charge and discharge to the next time period and the charge time series data of the electrochemical energy storage device from the start of this charge and discharge to the next time period, a device surface temperature curve of the electrochemical energy storage device that changes with the charge data from the start of this charge and discharge to the next time period is established.

[0070] Step S106: Determine whether the electrochemical energy storage device has a risk of thermal abuse based on the device surface temperature curve.

[0071] In the embodiment of the present application, the device surface temperature curve can be compared with the historical surface temperature curve of the electrochemical energy storage device or the same type of energy storage device when there is no risk of thermal abuse, to determine whether the electrochemical energy storage device has a risk of thermal abuse. Specifically, the device surface temperature curve can be compared with the historical surface temperature curve of the electrochemical energy storage device or the same type of energy storage device when the initial curve is similar to the electrochemical energy storage device and there is no risk of thermal abuse, to determine whether the electrochemical energy storage device has a risk of thermal abuse.

[0072] More specifically, the temperature value at the last moment in the surface temperature curve of the device can be compared with the temperature value at the corresponding moment in the historical surface temperature curve of the electrochemical energy storage device or the same type of energy storage device that has no thermal abuse risk and has the same or similar initial charge data and initial temperature. If the temperature value at the last moment in the surface temperature curve of the device is higher than the temperature value at the corresponding moment in the historical surface temperature curve of the device, and the difference exceeds a preset threshold or exceeds a preset ratio, it is determined that the electrochemical energy storage device has a thermal abuse risk; otherwise, it is determined that the electrochemical energy storage device does not have a thermal abuse risk.

[0073] Step S107: When there is a risk of thermal abuse of the electrochemical energy storage device, generate warning information.

[0074] When the judgment result is that the electrochemical energy storage device has a risk of thermal abuse, an early warning message can be generated to remind relevant personnel to take corresponding measures, such as stopping charging and discharging and cooling the electrochemical energy storage device through water cooling. In this way, early warning and corresponding measures can be taken before thermal abuse occurs to prevent thermal abuse and avoid unnecessary losses caused by thermal abuse.

[0075] In summary, the electrochemical energy storage thermal abuse warning method based on enhanced deep learning provided by the present invention obtains the charging time series data, the equipment surface temperature time series data and the external environment temperature time series data of the electrochemical energy storage device being charged and discharged in the current time period; based on the charging and discharging state of the electrochemical energy storage device, determines the temperature prediction model corresponding to the charging and discharging state and based on enhanced deep learning; uses the charging time series data, the equipment surface temperature time series data and the external environment temperature time series data as inputs of the temperature prediction model for calculation, and obtains the predicted equipment surface temperature time series data of the electrochemical energy storage device in the next time period; based on the current charging time series data of the electrochemical energy storage device Data, determine the charging time series data of the electrochemical energy storage device in the next time period; based on the historical time series data of the surface temperature of the electrochemical energy storage device during this charging and discharging process and the predicted temperature time series data of the device surface, as well as the historical charging time series data of the electrochemical energy storage device during this charging and discharging process and the charging time series data of the electrochemical energy storage device in the next time period, establish the device surface temperature curve of the electrochemical energy storage device that changes with the charging data from the beginning of this charging and discharging to the next time period; determine whether the electrochemical energy storage device has a risk of thermal abuse based on the device surface temperature curve; when the electrochemical energy storage device has a risk of thermal abuse, generate warning information. In this way, it can be predicted whether the electrochemical energy storage device has a risk of thermal abuse in the next time period, and generate warning information when there is a risk of thermal abuse, so as to achieve early warning before thermal abuse occurs, so as to take corresponding measures in advance to prevent the occurrence of thermal abuse, avoid unnecessary losses caused by thermal abuse, and facilitate practical application and promotion.

[0076] See also Figure 2 In a second aspect, an embodiment of the present application provides an electrochemical energy storage thermal abuse warning device based on enhanced deep learning, and the electrochemical energy storage thermal abuse warning device based on enhanced deep learning includes:

[0077] An acquisition unit, used to acquire charge time series data, device surface temperature time series data, and external environment temperature time series data of a charge-discharge electrochemical energy storage device in a current period;

[0078] A first determining unit is used to determine, based on the charge and discharge state of the electrochemical energy storage device, a temperature prediction model corresponding to the charge and discharge state and based on enhanced deep learning, wherein the charge and discharge state is a charging state or a discharging state;

[0079] A calculation unit, used to calculate the charging time series data, the device surface temperature time series data and the external environment temperature time series data as inputs of the temperature prediction model to obtain the device surface predicted temperature time series data of the electrochemical energy storage device in the next time period;

[0080] A second determination unit, configured to determine the charge timing data of the electrochemical energy storage device in the next time period based on the current charge data of the electrochemical energy storage device;

[0081] An establishing unit, for establishing a device surface temperature curve of the electrochemical energy storage device that changes with the charge data from the start of the current charge and discharge to the next period based on the device surface temperature history time series data of the electrochemical energy storage device during the current charge and discharge process and the device surface predicted temperature time series data, as well as the historical charge time series data of the electrochemical energy storage device during the current charge and discharge process and the charge time series data of the electrochemical energy storage device in the next period;

[0082] A third determination unit, configured to determine whether the electrochemical energy storage device has a risk of thermal abuse based on the device surface temperature curve;

[0083] The early warning unit is used to generate early warning information when there is a risk of thermal abuse of the electrochemical energy storage device.

[0084] The working process, working details and technical effects of the electrochemical energy storage thermal abuse warning device based on enhanced deep learning provided in the second aspect of this embodiment can be found in the first aspect of the embodiment and will not be repeated here.

[0085] like Figure 3As shown, the third aspect of an embodiment of the present application provides an electronic device, comprising a memory, a processor and a transceiver that are communicatively connected in sequence, wherein the memory is used to store computer programs, the transceiver is used to send and receive messages, and the processor is used to read the computer program to execute the electrochemical energy storage thermal abuse warning method based on enhanced deep learning as described in the first aspect of the embodiment.

[0086] For specific examples, the memory may include but is not limited to random access memory (RAM), read-only memory (ROM), flash memory, first-in-first-out memory (FIFO) and / or first-in-last-out memory (FILO), etc.; the processor may be but is not limited to a microprocessor of the STM32F105 series, an ARM (Advanced RISC-Machines), an X86 or other architecture processor, or a processor with an integrated NPU (neural-network processing units); the transceiver may be but is not limited to a WiFi (Wireless Fidelity) wireless transceiver, a Bluetooth wireless transceiver, a General Packet Radio Service (GPRS) wireless transceiver, a ZigBee protocol (a low-power local area network protocol based on the IEEE802.15.4 standard, ZigBee) wireless transceiver, a 3G transceiver, a 4G transceiver and / or a 5G transceiver, etc.

[0087] The fourth aspect of this embodiment provides a computer-readable storage medium storing instructions for the electrochemical energy storage thermal abuse warning method based on enhanced deep learning as described in the first aspect of the embodiment, that is, the computer-readable storage medium stores instructions, and when the instructions are run on a computer, the electrochemical energy storage thermal abuse warning method based on enhanced deep learning as described in the first aspect is executed. Wherein, the computer-readable storage medium refers to a carrier for storing data, which may include but is not limited to a floppy disk, an optical disk, a hard disk, a flash memory, a USB flash drive and / or a memory stick, etc., and the computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices.

[0088] The fifth aspect of this embodiment provides a computer program product comprising instructions, which, when executed on a computer, enables the computer to execute the electrochemical energy storage thermal abuse warning method based on enhanced deep learning as described in the first aspect of the embodiment, wherein the computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices.

[0089] It should be understood that certain details are provided in the following description to facilitate a complete understanding of the example embodiments. However, it should be understood by those of ordinary skill in the art that the example embodiments can be implemented without these certain details. For example, the system can be shown in a block diagram to avoid obscuring the example with unnecessary details. In other examples, well-known processes, structures, and techniques may not be shown in unnecessary detail to avoid obscuring the example embodiments.

[0090] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the protection scope of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for early warning of electrochemical energy storage thermal abuse based on enhanced deep learning, characterized in that: include: Obtaining charging time series data, equipment surface temperature time series data and external environment temperature time series data of the electrochemical energy storage device being charged and discharged in the current period; Based on the charge and discharge state of the electrochemical energy storage device, determining a temperature prediction model corresponding to the charge and discharge state and based on enhanced deep learning, wherein the charge and discharge state is a charge state or a discharge state; The charging time series data, the device surface temperature time series data and the external environment temperature time series data are used as inputs of the temperature prediction model to perform calculations to obtain the device surface temperature time series data predicted for the electrochemical energy storage device in the next time period; Based on the current charge data of the electrochemical energy storage device, determining the charge timing data of the electrochemical energy storage device in the next time period; Based on the historical time series data of the device surface temperature of the electrochemical energy storage device during the current charge and discharge process and the predicted time series data of the device surface temperature, as well as the historical charge time series data of the electrochemical energy storage device during the current charge and discharge process and the charge time series data of the electrochemical energy storage device in the next time period, a device surface temperature curve of the electrochemical energy storage device that changes with the charge data from the start of the current charge and discharge to the next time period is established; Determining whether the electrochemical energy storage device has a risk of thermal abuse based on the device surface temperature curve; When the electrochemical energy storage device has a risk of thermal abuse, a warning message is generated.

2. The electrochemical energy storage thermal abuse early warning method based on enhanced deep learning according to claim 1 is characterized in that: The step of using the charging time series data, the device surface temperature time series data and the external environment temperature time series data as inputs of the temperature prediction model to perform calculations to obtain the device surface temperature time series data predicted for the electrochemical energy storage device in the next time period includes: The charging time series data, the device surface temperature time series data, and the external environment temperature time series data are standardized and combined to obtain a vector matrix, where each row in the vector matrix corresponds to data of one dimension; The vector matrix is ​​used as an input of the temperature prediction model to perform calculations to obtain predicted device surface temperature time series data of the electrochemical energy storage device in the next time period.

3. The electrochemical energy storage thermal abuse early warning method based on enhanced deep learning according to claim 1 is characterized in that: The step of determining charging time series data of the electrochemical energy storage device in the next time period based on the current charging data of the electrochemical energy storage device includes: Based on the current charge data of the electrochemical energy storage device and at least one recent charge and discharge record of the electrochemical energy storage device, the charge time series data of the electrochemical energy storage device in the next time period is estimated.

4. The electrochemical energy storage thermal abuse early warning method based on enhanced deep learning according to claim 1 is characterized in that: The device surface temperature curve of the electrochemical energy storage device that changes with the charge data from the start of the current charge and discharge to the next time period is established based on the device surface temperature historical time series data of the electrochemical energy storage device during the current charge and discharge process and the device surface predicted temperature time series data, as well as the historical charge time series data of the electrochemical energy storage device during the current charge and discharge process and the charge time series data of the electrochemical energy storage device in the next time period, including: Determine the device surface temperature time series data of the electrochemical energy storage device from the start of the current charge and discharge to the next time period based on the device surface temperature history time series data of the electrochemical energy storage device during the current charge and discharge process and the device surface predicted temperature time series data; Determine the charge time sequence data of the electrochemical energy storage device from the start of the current charge and discharge to the next time period based on the historical charge time sequence data of the electrochemical energy storage device during the current charge and discharge process and the charge time sequence data of the electrochemical energy storage device in the next time period; Based on the device surface temperature time series data of the electrochemical energy storage device from the start of this charge and discharge to the next time period and the charge time series data of the electrochemical energy storage device from the start of this charge and discharge to the next time period, a device surface temperature curve of the electrochemical energy storage device that changes with the charge data from the start of this charge and discharge to the next time period is established.

5. The electrochemical energy storage thermal abuse early warning method based on enhanced deep learning according to claim 1 is characterized in that: The determining whether the electrochemical energy storage device has a risk of thermal abuse based on the device surface temperature curve includes: The device surface temperature curve is compared with a historical device surface temperature curve of the electrochemical energy storage device or a storage device of the same model as the electrochemical energy storage device when no thermal abuse risk occurs, to determine whether the electrochemical energy storage device has a thermal abuse risk.

6. The electrochemical energy storage thermal abuse early warning method based on enhanced deep learning according to claim 5 is characterized in that: The comparing the device surface temperature curve with the device historical surface temperature curve of the electrochemical energy storage device or the same type of energy storage device as the electrochemical energy storage device when no thermal abuse risk occurs includes: The device surface temperature curve is compared with a historical device surface temperature curve when the initial curve of the electrochemical energy storage device or a storage device of the same model as the electrochemical energy storage device is similar and no thermal abuse risk occurs.

7. The electrochemical energy storage thermal abuse early warning method based on enhanced deep learning according to claim 1 is characterized in that: The electrochemical energy storage device is a lithium ion battery or a sodium sulfur battery.

8. An electrochemical energy storage thermal abuse warning device based on enhanced deep learning, characterized in that: include: An acquisition unit, used to acquire charge time series data, device surface temperature time series data, and external environment temperature time series data of a charge-discharge electrochemical energy storage device in a current period; A first determining unit is used to determine, based on the charge and discharge state of the electrochemical energy storage device, a temperature prediction model corresponding to the charge and discharge state and based on enhanced deep learning, wherein the charge and discharge state is a charging state or a discharging state; A calculation unit, used to calculate the charging time series data, the device surface temperature time series data and the external environment temperature time series data as inputs of the temperature prediction model to obtain the device surface predicted temperature time series data of the electrochemical energy storage device in the next time period; A second determination unit, configured to determine the charge timing data of the electrochemical energy storage device in the next time period based on the current charge data of the electrochemical energy storage device; An establishing unit, for establishing a device surface temperature curve of the electrochemical energy storage device that changes with the charge data from the start of the current charge and discharge to the next period based on the device surface temperature history time series data of the electrochemical energy storage device during the current charge and discharge process and the device surface predicted temperature time series data, as well as the historical charge time series data of the electrochemical energy storage device during the current charge and discharge process and the charge time series data of the electrochemical energy storage device in the next period; A third determination unit, configured to determine whether the electrochemical energy storage device has a risk of thermal abuse based on the device surface temperature curve; The early warning unit is used to generate early warning information when there is a risk of thermal abuse of the electrochemical energy storage device.

9. An electronic device, characterized in that: It includes a memory, a processor and a transceiver which are communicatively connected in sequence, wherein the memory is used to store a computer program, the transceiver is used to send and receive messages, and the processor is used to read the computer program to execute the electrochemical energy storage thermal abuse warning method based on enhanced deep learning as described in any one of claims 1 to 7.

10. A computer program product comprising a computer program or instructions, characterized in that When the computer program or the instruction is executed by a computer, the electrochemical energy storage thermal abuse warning method based on enhanced deep learning as described in any one of claims 1 to 7 is implemented.

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