Fault early warning method and device of energy storage battery, and computer readable storage medium

Abstract

The invention discloses a fault early warning method and device for an energy storage battery, and a computer readable storage medium. The method comprises the steps that battery operation parameters and the actual temperature of an energy storage battery are obtained in real time, and the battery operation parameters are used for reflecting the operation state of the energy storage battery; inputting the battery operation parameters into a temperature prediction model, and processing the battery operation parameters by using the temperature prediction model to obtain a predicted temperature of the energy storage battery; determining the actual temperature of the energy storage battery at the second moment as the target actual temperature; the temperature deviation ratio of the energy storage battery at the second moment is determined according to the target actual temperature and the predicted temperature; and when the temperature deviation ratio is not smaller than the deviation ratio threshold value, a fault early warning signal is sent out. According to the invention, the technical problem of low prediction accuracy caused by data redundancy and large calculation amount due to the fact that a large amount of data at the same moment is usually adopted for fitting when a traditional method is used for monitoring and safety early warning of the energy storage battery in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of battery failure warning, and in particular to a method and device for warning failure of an energy storage battery, and a computer-readable storage medium. Background Art

[0002] Energy storage batteries have safety shortcomings such as material aging, thermal runaway, and fire risks. Especially in large-scale applications of power energy storage, the safety of electrochemical energy storage is a system-level safety issue. The characteristic parameters of energy storage batteries in electrochemical energy storage systems mainly include temperature, voltage, current, etc., which are usually monitored in real time using sensors. There are problems such as low data acquisition accuracy, few dimensions, single safety monitoring and early warning mode, time lag, low accuracy, and high safety risks.

[0003] Existing prediction methods usually use a large amount of historical data such as voltage, current, temperature, etc. at the same time to fit the system model. The structure is computationally intensive and ignores the differences in battery fault triggering temperatures at different SOC states.

[0004] In the above-mentioned related technologies, traditional methods are used to monitor energy storage batteries and provide safety warnings. A large amount of data at the same time is usually used for fitting, which results in data redundancy and large amount of calculation, resulting in low prediction accuracy. No effective solution has been proposed yet. Summary of the invention

[0005] The embodiments of the present invention provide a method and device for early warning of a fault of an energy storage battery, and a computer-readable storage medium, so as to at least solve the technical problem in the related art that a large amount of data at the same time is usually used for fitting when using traditional methods to monitor and provide safety early warning for energy storage batteries, resulting in data redundancy and large amount of calculation, resulting in low prediction accuracy.

[0006] According to one aspect of an embodiment of the present invention, a fault warning method for an energy storage battery is provided, comprising: acquiring battery operating parameters and actual temperature of the energy storage battery in real time, wherein the battery operating parameters are used to reflect the operating state of the energy storage battery; inputting the battery operating parameters into a temperature prediction model, so as to process the battery operating parameters using the temperature prediction model to obtain a predicted temperature of the energy storage battery, wherein the temperature prediction model is fitted by using multiple sets of historical battery operating parameters through mathematical analysis modeling, the historical battery operating parameters are the battery operating parameters of the energy storage battery in a historical time period, the predicted temperature is the temperature predicted by the temperature prediction model based on the battery operating parameters at a first moment for the temperature at a second moment, the first moment being any moment in the process of monitoring the energy storage battery, and the second moment being a moment after the first moment; determining the actual temperature of the energy storage battery at the second moment as a target actual temperature; determining the temperature deviation ratio of the energy storage battery at the second moment according to the target actual temperature and the predicted temperature; and issuing a fault warning signal when the temperature deviation ratio is not less than a deviation ratio threshold.

[0007] Optionally, before the battery operating parameters are input into the temperature prediction model to process the battery operating parameters using the temperature prediction model to obtain the predicted temperature of the energy storage battery, the fault warning method for the energy storage battery further includes: obtaining multiple groups of historical battery operating parameters of the energy storage battery at multiple historical moments in a historical time period; determining the historical battery operating parameters during the charge and discharge period as target historical battery operating parameters, wherein the charge and discharge period refers to the time period when the energy storage battery is in a charging state or a discharging state; determining the historical battery operating parameters of the energy storage battery at a first historical target moment as target historical battery operating parameters, and obtaining the historical battery box internal environment temperature of the energy storage battery at the first target historical moment and the historical battery surface temperature at the second target historical moment, wherein, The first historical target moment is any moment among the multiple target historical moments, and the second target historical moment is the moment after the first target historical moment; the difference between the historical battery surface temperature and the historical battery box ambient temperature is determined as the heat generated by the energy storage battery during the historical temperature rise period, wherein the historical temperature rise period is the period between the second target historical moment and the first target historical moment; the binomial parabola model is solved according to the target historical battery operating parameters and the heat to obtain the model coefficients of the binomial parabola model, wherein the binomial parabola model is a model established by using the least squares method for nonlinear polynomial regression based on the thermal effect process of the energy storage battery during the historical temperature rise period; the temperature prediction model is constructed according to the model coefficients.

[0008] Optionally, solving a binomial parabola model according to the target historical battery operating parameter and the heat to obtain a model coefficient of the binomial parabola model includes: substituting the target historical battery operating parameter and the heat into a first expression of the binomial parabola model for solving to obtain the model coefficient of the binomial parabola model, wherein the first expression is: t m-1 represents the first target historical moment, represents the historical current in the target historical battery operating parameter, represents the heat generated by the energy storage battery during the historical temperature rise period, and a, b, and c are the model coefficients of different terms in the binomial parabola model.

[0009] Optionally, constructing the temperature prediction model according to the model coefficients includes: constructing the temperature prediction model according to the model coefficients, wherein a second standard expression of the temperature prediction model is: t n represents the second moment, t n-1 represents the first moment, represents the predicted temperature, represents the current in the battery operating parameters, Indicates the ambient temperature inside the battery box of the energy storage battery at the first moment.

[0010] Optionally, determining the temperature deviation ratio of the energy storage battery at the second moment according to the target actual temperature and the predicted temperature includes: determining the temperature deviation ratio of the energy storage battery at the second moment according to the target actual temperature and the predicted temperature using a first formula, wherein the first formula is: σ represents the temperature deviation ratio, t n represents the second moment, represents the target actual temperature at the second moment, represents the predicted temperature at the second moment.

[0011] Optionally, when the temperature deviation ratio is not less than the deviation ratio threshold, a fault warning signal is issued, including at least one of the following: when the temperature deviation ratio is not less than the deviation ratio threshold, controlling the voice component to issue a voice warning signal; when the temperature deviation ratio is not less than the deviation ratio threshold, controlling the sound and light component to issue a sound and light warning signal; when the temperature deviation ratio is not less than the deviation ratio threshold, controlling the communication component to issue a pop-up warning signal.

[0012] Optionally, the fault warning method for the energy storage battery also includes: in the process of processing the battery operating parameters using the temperature prediction model to obtain the predicted temperature of the energy storage battery, adaptively optimizing the temperature prediction model using the battery operating parameters and the predicted temperature corresponding to the battery operating parameters, and updating the temperature prediction model to the optimized temperature prediction model.

[0013] According to another aspect of an embodiment of the present invention, a fault warning device for an energy storage battery is further provided, comprising: a first acquisition unit, used for acquiring battery operating parameters and actual temperature of the energy storage battery in real time, wherein the battery operating parameters are used to reflect the operating state of the energy storage battery; a second acquisition unit, used for inputting the battery operating parameters into a temperature prediction model, so as to process the battery operating parameters using the temperature prediction model to obtain the predicted temperature of the energy storage battery, wherein the temperature prediction model is obtained by fitting multiple groups of historical battery operating parameters through mathematical analysis modeling, and the historical battery operating parameters are all the historical conditions of the energy storage battery in a historical time period. the battery operating parameters, the predicted temperature is the temperature obtained by the temperature prediction model predicting the temperature at a second moment according to the battery operating parameters at a first moment, the first moment being any moment in the process of monitoring the energy storage battery, and the second moment being a moment after the first moment; a first determining unit, used to determine that the actual temperature of the energy storage battery at the second moment is a target actual temperature; a second determining unit, used to determine the temperature deviation ratio of the energy storage battery at the second moment according to the target actual temperature and the predicted temperature; and an early warning unit, used to issue a fault early warning signal when the temperature deviation ratio is not less than a deviation ratio threshold.

[0014] Optionally, the fault warning device for the energy storage battery further includes: a third acquisition unit, used to obtain multiple groups of historical battery operating parameters of the energy storage battery at multiple historical moments in a historical time period before inputting the battery operating parameters into the temperature prediction model to process the battery operating parameters using the temperature prediction model to obtain the predicted temperature of the energy storage battery; a third determination unit, used to determine the historical battery operating parameters during the charging and discharging period as the target historical battery operating parameters, wherein the charging and discharging period refers to the time period when the energy storage battery is in a charging state or a discharging state; a fourth determination unit, used to determine the historical battery operating parameters of the energy storage battery at a first historical target moment as the target historical battery operating parameters, and obtain the historical battery box internal ambient temperature of the energy storage battery at the first target historical moment and the historical battery surface temperature at the second target historical moment, wherein , the first historical target moment is any moment among the multiple target historical moments, and the second target historical moment is the moment after the first target historical moment; a fifth determination unit is used to determine that the difference between the historical battery surface temperature and the historical battery box internal ambient temperature is the heat generated by the energy storage battery during the historical temperature rise period, wherein the historical temperature rise period is the period between the second target historical moment and the first target historical moment; a fourth acquisition unit is used to solve the binomial parabola model according to the target historical battery operating parameters and the heat to obtain the model coefficient of the binomial parabola model, wherein the binomial parabola model is a model established based on the thermal effect process of the energy storage battery in the historical temperature rise period using the least squares method for nonlinear polynomial regression; a construction unit is used to construct the temperature prediction model according to the model coefficients.

[0015] Optionally, the fourth acquisition unit includes: an acquisition module, used to substitute the target historical battery operating parameter and the heat into the first expression of the binomial parabola model for solution to obtain the model coefficient of the binomial parabola model, wherein the first expression is: t m-1 represents the first target historical moment, represents the historical current in the target historical battery operating parameter, represents the heat generated by the energy storage battery during the historical temperature rise period, and a, b, and c are the model coefficients of different terms in the binomial parabola model.

[0016] Optionally, the construction unit includes: a construction module, configured to construct the temperature prediction model according to the model coefficients, wherein the second standard expression of the temperature prediction model is: t n represents the second moment, tn-1 represents the first moment, represents the predicted temperature, represents the current in the battery operating parameters, Indicates the ambient temperature inside the battery box of the energy storage battery at the first moment.

[0017] Optionally, the second determination unit includes: a determination module, configured to determine the temperature deviation ratio of the energy storage battery at the second moment according to the target actual temperature and the predicted temperature using a first formula, wherein the first formula is: σ represents the temperature deviation ratio, t n represents the second moment, represents the target actual temperature at the second moment, represents the predicted temperature at the second moment.

[0018] Optionally, the early warning unit comprises at least one of the following: a first early warning module, used to control the voice component to issue a voice early warning signal when the temperature deviation ratio is not less than the deviation ratio threshold; a second early warning module, used to control the sound and light component to issue an sound and light early warning signal when the temperature deviation ratio is not less than the deviation ratio threshold; a third early warning module, used to control the communication component to issue a pop-up warning signal when the temperature deviation ratio is not less than the deviation ratio threshold.

[0019] Optionally, the fault warning device of the energy storage battery also includes: an optimization unit, which is used to adaptively optimize the temperature prediction model using the battery operating parameters and the predicted temperature corresponding to the battery operating parameters in the process of processing the battery operating parameters using the temperature prediction model to obtain the predicted temperature of the energy storage battery, and update the temperature prediction model to the optimized temperature prediction model.

[0020] According to another aspect of an embodiment of the present invention, a fault warning system for an energy storage battery is further provided. The fault warning system for an energy storage battery uses any one of the above-mentioned fault warning methods for an energy storage battery.

[0021] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is further provided, wherein the computer-readable storage medium includes a stored program, wherein the program executes any one of the above-mentioned energy storage battery fault warning methods.

[0022] According to another aspect of an embodiment of the present invention, a processor is further provided, the processor being used to run a program, wherein the program executes any one of the above-mentioned energy storage battery fault warning methods when running.

[0023] According to another aspect of an embodiment of the present invention, a computer program product is further provided, comprising computer instructions, wherein when the computer instructions are executed by a processor, any one of the above-mentioned energy storage battery fault warning methods is executed.

[0024] In an embodiment of the present invention, the battery operating parameters and actual temperature of the energy storage battery can be obtained in real time, wherein the battery operating parameters are used to reflect the operating state of the energy storage battery; the battery operating parameters are input into a temperature prediction model, so as to process the battery operating parameters using the temperature prediction model to obtain the predicted temperature of the energy storage battery, wherein the temperature prediction model is fitted by using multiple sets of historical battery operating parameters through mathematical analysis modeling, the historical battery operating parameters are the battery operating parameters of the energy storage battery in a historical time period, the predicted temperature is the temperature predicted by the temperature prediction model based on the battery operating parameters at the first moment for the temperature at the second moment, the first moment is any moment in the process of monitoring the energy storage battery, and the second moment is the moment after the first moment; the actual temperature of the energy storage battery at the second moment is determined as the target actual temperature; the temperature deviation ratio of the energy storage battery at the second moment is determined according to the target actual temperature and the predicted temperature; and a fault warning signal is issued when the temperature deviation ratio is not less than the deviation ratio threshold. Through the above technical scheme, the purpose of using the temperature prediction model to process the operating parameters of the energy storage battery obtained in real time to predict the battery temperature is achieved, and a warning signal is issued when the deviation ratio between the predicted result and the actual result exceeds a certain threshold. The technical effect of using a small amount of data for fitting to accurately predict the battery temperature is achieved, which improves the efficiency and accuracy of battery fault prediction, and further solves the technical problem in related technologies that when using traditional methods to monitor energy storage batteries and provide safety warnings, a large amount of data at the same time is usually used for fitting, data redundancy and large amount of calculation, resulting in low prediction accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0026] Figure 1 It is a hardware structure block diagram of a mobile terminal of a method for early warning of a failure of an energy storage battery according to an embodiment of the present invention;

[0027] Figure 2 is a flow chart of a fault warning method for an energy storage battery according to an embodiment of the present invention;

[0028] Figure 3 is a flow chart of an optional energy storage battery failure warning method according to an embodiment of the present invention;

[0029] Figure 4 is a flow chart of a training temperature prediction model according to an embodiment of the present invention;

[0030] Figure 5 is a schematic diagram of a fault warning device for an energy storage battery according to an embodiment of the present invention. DETAILED DESCRIPTION

[0031] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0032] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0033] As introduced in the background technology, in the related art, when using traditional methods to monitor and warn of energy storage batteries, a large amount of data at the same time is usually used for fitting, which results in data redundancy and large amount of calculation, resulting in low prediction accuracy. In view of the above defects, a method and device for early warning of energy storage battery faults and a computer-readable storage medium are provided in the embodiments of the present invention.

[0034] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0035] The method embodiments provided in the embodiments of the present invention can be executed in a mobile terminal, a computer terminal or a similar computing device. Taking running on a mobile terminal as an example, Figure 1 FIG. 1 is a hardware structure block diagram of a mobile terminal of a method for early warning of a failure of an energy storage battery according to an embodiment of the present invention. Figure 1 As shown, the mobile terminal may include one or more ( Figure 1Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data, wherein the mobile terminal may also include a transmission device 106 and an input / output device 108 for communication functions. It can be understood by those skilled in the art that Figure 1 The structure shown is for illustration only and does not limit the structure of the mobile terminal. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations shown.

[0036] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the fault warning method of the energy storage battery in the embodiment of the present invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, the above method is implemented. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories can be connected to the mobile terminal via a network. Examples of the above network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof. The transmission device 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the mobile terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0037] According to an embodiment of the present invention, a method embodiment of a fault warning method for an energy storage battery is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0038] Figure 2 is a flow chart of a fault warning method for an energy storage battery according to an embodiment of the present invention. Figure 2 As shown, the method comprises the following steps:

[0039] Step S202, obtaining the battery operating parameters and actual temperature of the energy storage battery in real time, wherein the battery operating parameters are used to reflect the operating status of the energy storage battery.

[0040] Optionally, the above battery operating parameters may include but are not limited to: voltage, current, battery rack temperature, battery cluster temperature, single cell surface temperature, SOC (state of charge), SOH (state of health), etc.

[0041] Combine the following Figure 3 The above embodiments of the present invention are described in detail. Figure 3 is a flow chart of an optional energy storage battery fault warning method according to an embodiment of the present invention; Figure 3 As shown, the energy storage power station is equipped with a BMS (battery management system), which can measure the state quantities of the battery in operation online, such as voltage, current, battery rack temperature, battery cluster temperature, single cell surface temperature, SOC (state of charge), SOH (state of health), etc.; the battery operation parameters and current actual temperature of the energy storage battery can be obtained in real time through the battery management system.

[0042] Step S204, inputting the battery operating parameters into the temperature prediction model, so as to process the battery operating parameters using the temperature prediction model to obtain the predicted temperature of the energy storage battery, wherein the temperature prediction model is fitted by using multiple sets of historical battery operating parameters through mathematical analysis modeling, the historical battery operating parameters are the battery operating parameters of the energy storage battery in a historical time period, and the predicted temperature is the temperature predicted by the temperature prediction model based on the battery operating parameters at the first moment for the temperature at the second moment, the first moment being any moment in the process of monitoring the energy storage battery, and the second moment being the moment after the first moment.

[0043] In this embodiment, the temperature prediction model can be used to process the battery operating parameters obtained in real time to predict the temperature of the energy storage battery. It should be noted that the temperature prediction model here predicts the temperature at the next moment based on the battery operating parameters at a certain moment. For example, if the battery operating parameters of the energy storage battery at the first moment are input into the temperature prediction model, the result output by the temperature prediction model is the predicted temperature of the energy storage battery at the second moment. The second moment here is the next moment of the first moment (i.e., the next moment), so the temperature prediction model is also a real-time prediction model. By inputting the battery operating parameters obtained in real time into the model, the predicted temperature at the next moment of the moment where the battery operating parameters are located can be output in real time.

[0044] Step S206, determining that the actual temperature of the energy storage battery at the second moment is the target actual temperature.

[0045] In this embodiment, the actual temperature of the energy storage battery at the second moment may be determined as the target actual temperature, so as to serve as a basis for subsequent determination of whether the battery has a fault.

[0046] Step S208, determining the temperature deviation ratio of the energy storage battery at the second moment according to the target actual temperature and the predicted temperature.

[0047] In this embodiment, the temperature deviation ratio of the energy storage battery at the second moment can be determined according to the predicted temperature and the actual temperature of the energy storage battery at the second moment to determine whether there is a fault.

[0048] Step S210: When the temperature deviation ratio is not less than the deviation ratio threshold, a fault warning signal is issued.

[0049] In this embodiment, as above Figure 3 As shown, if the temperature deviation ratio of the energy storage battery at a certain moment exceeds a certain threshold, it is considered that the battery is faulty and a fault warning signal needs to be issued. The warning signal can be issued by lighting an alarm light, emitting an automatic buzzer, etc.

[0050] It should be noted that the predicted temperature of the temperature prediction model is the temperature that the energy storage battery should reach. If the deviation ratio between the actual temperature of the energy storage model and the predicted temperature at that moment exceeds a certain threshold, it is considered that the battery has a fault and a fault warning signal needs to be issued.

[0051] As can be seen from the above, through the technical solution provided by the above embodiment of the present invention, the battery operating parameters and actual temperature of the energy storage battery can be obtained in real time, wherein the battery operating parameters are used to reflect the operating state of the energy storage battery; the battery operating parameters are input into the temperature prediction model, so as to process the battery operating parameters using the temperature prediction model to obtain the predicted temperature of the energy storage battery, wherein the temperature prediction model is obtained by fitting multiple groups of historical battery operating parameters through mathematical analysis modeling, the historical battery operating parameters are the battery operating parameters of the energy storage battery in the historical time period, and the predicted temperature is the temperature predicted by the temperature prediction model according to the battery operating parameters at the first moment for the temperature at the second moment, and the first moment is the temperature of the energy storage battery. At any moment in the process of monitoring the battery, the second moment is the moment after the first moment; the actual temperature of the energy storage battery at the second moment is determined as the target actual temperature; the temperature deviation ratio of the energy storage battery at the second moment is determined according to the target actual temperature and the predicted temperature; when the temperature deviation ratio is not less than the deviation ratio threshold, a fault warning signal is issued, so that the operating parameters of the energy storage battery obtained in real time are processed by the temperature prediction model to predict the battery temperature, and a warning signal is issued when the deviation ratio between the predicted result and the actual result exceeds a certain threshold, and the technical effect of accurately predicting the battery temperature by fitting with a small amount of data is achieved, thereby improving the efficiency and accuracy of battery fault prediction.

[0052] Therefore, the technical solution provided by the above-mentioned embodiment of the present invention solves the technical problem that when using traditional methods in the related art to monitor energy storage batteries and provide safety warnings, a large amount of data at the same time is usually used for fitting, data redundancy and large amount of calculation are used, resulting in low prediction accuracy.

[0053] According to the above embodiment of the present invention, before the battery operating parameters are input into the temperature prediction model to process the battery operating parameters using the temperature prediction model to obtain the predicted temperature of the energy storage battery, the fault warning method of the energy storage battery further includes: obtaining multiple groups of historical battery operating parameters of the energy storage battery at multiple historical moments in the historical time period; determining the historical battery operating parameters during the charging and discharging period as the target historical battery operating parameters, wherein the charging and discharging period refers to the time period when the energy storage battery is in the charging state or the discharging state; determining the historical battery operating parameters of the energy storage battery at the first historical target moment as the target historical battery operating parameters, and obtaining the historical battery box internal environment temperature of the energy storage battery at the first target historical moment and the historical battery box internal environment temperature at the second target historical moment; Battery surface temperature, wherein the first historical target moment is any moment among multiple target historical moments, and the second target historical moment is the moment after the first target historical moment; determining the difference between the historical battery surface temperature and the historical battery box ambient temperature as the heat generated by the energy storage battery during the historical temperature rise period, wherein the historical temperature rise period is the period between the second target historical moment and the first target historical moment; solving the binomial parabola model according to the target historical battery operating parameters and heat to obtain the model coefficients of the binomial parabola model, wherein the binomial parabola model is a model established based on the thermal effect process of the energy storage battery during the historical temperature rise period using the least squares method for nonlinear polynomial regression; and constructing a temperature prediction model according to the model coefficients.

[0054] Combine the following Figure 4 The above embodiments of the present invention are described in detail. Figure 4 is a flowchart of a training temperature prediction model according to an embodiment of the present invention.

[0055] like Figure 4As shown, the historical data such as battery SOC, current, voltage, operation time, charge and discharge times, single cell surface temperature, and ambient temperature in the battery cluster collected by the energy storage station monitoring system BMS can be collected at a predetermined time interval and the data can be exported; then the data is screened, and the data during the battery charge and discharge period (such as current, voltage, and parameters with obvious temperature changes) are selected as the target data for the subsequent training model; then the temperature rise time interval △T (historical temperature rise period) can be confirmed. Since the embodiment provided by the present invention is mainly based on a simple temperature prediction model, that is, more complex influencing factors are ignored, it is considered that the surface temperature of the single cell at this moment is the result of the thermal effect of the battery operation at the previous moment and the ambient temperature in the battery cluster accumulated within the time interval △T; after confirming △T, the temperature calculation can be performed, and the surface temperature of the single cell after △T time is calculated by the temperature before △T time. The heat generated by the thermal effect of battery operation is obtained by adding the ambient temperature in the original battery cluster; this temperature data is matched with the voltage, current value, and SOC before △T time, that is, the historical battery operation parameters and temperature data at the same historical moment are matched, and a binomial parabola model of the heat generated during the battery thermal effect is established using the least squares method for nonlinear polynomial regression, and the model coefficients in the binomial parabola model are solved based on the acquired historical battery operation parameters and the temperature difference between two historical moments (that is, the difference between the historical battery surface temperature and the ambient temperature in the battery box), and finally a temperature prediction model is constructed based on the solved model coefficients; after the temperature prediction model is trained, the model can be used to predict the temperature of the energy storage battery, and a warning signal is issued when the deviation ratio between the predicted result and the actual temperature at the corresponding moment exceeds a certain threshold.

[0056] It should be noted that the battery operating parameters, historical battery operating parameters, target historical battery operating parameters, historical battery surface temperature, battery surface temperature, battery box internal ambient temperature and historical battery box internal ambient temperature involved in the above embodiments of the present invention are all related parameters of the energy storage battery. They are just at different times. In the specific description process, they will not be distinguished in detail.

[0057] According to the above embodiment of the present invention, the binomial parabola model is solved according to the target historical battery operating parameters and heat to obtain the model coefficients of the binomial parabola model, including: substituting the target historical battery operating parameters and heat into the first expression of the binomial parabola model for solving, to obtain the model coefficients of the binomial parabola model, wherein the first expression is: t m-1 Indicates the first target historical moment, Represents the historical current in the target historical battery operating parameters, It represents the heat generated by the energy storage battery during the historical temperature rise period. a, b, and c are the model coefficients of different terms in the binomial parabola model.

[0058] Specifically, the expression of the binomial parabola model is as follows: In the formula, Represents the historical current in the target historical battery operating parameters, It represents the heat generated by the energy storage battery during the historical temperature rise period. a, b, and c are the model coefficients of different items in the binomial parabola model. The model coefficients a, b, and c in the binomial parabola model can be solved based on the acquired historical battery operating parameters and the temperature difference between two historical moments (i.e., the difference between the historical battery surface temperature and the ambient temperature in the battery box) to obtain the specific values ​​of a, b, and c.

[0059] According to the above embodiment of the present invention, constructing a temperature prediction model according to the model coefficients includes: constructing a temperature prediction model according to the model coefficients, wherein the second standard expression of the temperature prediction model is: t n represents the second moment, t n-1 Indicates the first moment, represents the predicted temperature, Indicates the current in the battery operating parameters, Indicates the ambient temperature inside the battery box of the energy storage battery at the first moment.

[0060] Specifically, after solving the specific values ​​of the model coefficients a, b, and c in the binomial parabola model, the temperature prediction model can be constructed based on the principle that the surface temperature of a single cell at a certain moment is the result of the thermal effect of the battery operation at the previous moment and the accumulation of the ambient temperature in the battery cluster within the time interval △T. The specific expression is as follows: In the formula, t n represents the second moment, t n-1 Indicates the first moment, represents the predicted temperature, It represents the current in the battery operating parameters and the ambient temperature inside the battery box of the energy storage battery at the first moment.

[0061] According to the above embodiment of the present invention, determining the temperature deviation ratio of the energy storage battery at the second moment according to the target actual temperature and the predicted temperature includes: determining the temperature deviation ratio of the energy storage battery at the second moment according to the target actual temperature and the predicted temperature using a first formula, wherein the first formula is: σ represents the temperature deviation ratio, t n Indicates the second moment, represents the target actual temperature at the second moment, Indicates the predicted temperature at the second moment.

[0062] Specifically, the formula can be used based on the actual temperature of the energy storage battery at a certain moment and the predicted temperature predicted by the temperature prediction model: Determine the temperature deviation ratio of the energy storage battery at this moment, where σ represents the temperature deviation ratio, t n Indicates the second moment, represents the target actual temperature at the second moment, Indicates the predicted temperature at the second moment.

[0063] According to the above-mentioned embodiment of the present invention, when the temperature deviation ratio is not less than the deviation ratio threshold, a fault warning signal is issued, including at least one of the following: when the temperature deviation ratio is not less than the deviation ratio threshold, controlling the voice component to issue a voice warning signal; when the temperature deviation ratio is not less than the deviation ratio threshold, controlling the sound and light component to issue a sound and light warning signal; when the temperature deviation ratio is not less than the deviation ratio threshold, controlling the communication component to issue a pop-up warning signal.

[0064] Specifically, a variety of methods can be used for fault warning, for example, warning can be carried out through sound and light alarms, automatic sounds, voice broadcasts, etc., and warnings can also be carried out through pop-up windows on the display screen of the monitoring device, or directly sending warning information to the terminal devices of relevant staff (which may include but are not limited to: mobile phones, smart watches, etc.), so as to prompt the staff to take corresponding maintenance measures.

[0065] According to the above embodiment of the present invention, the fault warning method of the energy storage battery also includes: in the process of processing the battery operating parameters using the temperature prediction model to obtain the predicted temperature of the energy storage battery, the temperature prediction model is adaptively optimized using the battery operating parameters and the predicted temperature corresponding to the battery operating parameters, and the temperature prediction model is updated to the optimized temperature prediction model.

[0066] Specifically, in the process of using the temperature prediction model to process the battery operating parameters and obtain the predicted temperature of the energy storage battery, the temperature prediction model can be continuously optimized using new data, and the optimized model can continue to be put into use. In this way, iterative optimization is continuously performed to improve the performance and prediction accuracy of the temperature prediction model.

[0067] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0068] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0069] According to an embodiment of the present invention, there is also provided a fault warning device for an energy storage battery for implementing the above-mentioned fault warning method for an energy storage battery. Figure 5 is a schematic diagram of a fault warning device for an energy storage battery according to an embodiment of the present invention. Figure 5 As shown, the device includes: a first acquisition unit 51, a second acquisition unit 53, a first determination unit 55, a second determination unit 57 and an early warning unit 59. The fault early warning device of the energy storage battery is described in detail below.

[0070] The first acquisition unit 51 is used to acquire the battery operating parameters and actual temperature of the energy storage battery in real time, wherein the battery operating parameters are used to reflect the operating state of the energy storage battery.

[0071] The second acquisition unit 53 is used to input the battery operating parameters into the temperature prediction model, so as to process the battery operating parameters using the temperature prediction model to obtain the predicted temperature of the energy storage battery, wherein the temperature prediction model is fitted by using multiple sets of historical battery operating parameters through mathematical analysis modeling, the historical battery operating parameters are the battery operating parameters of the energy storage battery in a historical time period, and the predicted temperature is the temperature predicted by the temperature prediction model based on the battery operating parameters at the first moment for the temperature at the second moment, the first moment is any moment in the process of monitoring the energy storage battery, and the second moment is the moment after the first moment.

[0072] The first determining unit 55 is used to determine the actual temperature of the energy storage battery at the second moment as the target actual temperature.

[0073] The second determining unit 57 is used to determine the temperature deviation ratio of the energy storage battery at the second moment according to the target actual temperature and the predicted temperature.

[0074] The early warning unit 59 is used to send out a fault early warning signal when the temperature deviation ratio is not less than the deviation ratio threshold.

[0075] It should be noted here that the above-mentioned first acquisition unit 51, second acquisition unit 53, first determination unit 55, second determination unit 57 and early warning unit 59 correspond to steps S202 to S210 in the above-mentioned embodiments, and the five units are the same as the instances and application scenarios implemented by the corresponding steps, but are not limited to the contents disclosed in the above-mentioned embodiments.

[0076] As can be seen from the above, in the scheme recorded in the above embodiment of the present invention, the first acquisition unit can be used to acquire the battery operating parameters and actual temperature of the energy storage battery in real time, wherein the battery operating parameters are used to reflect the operating state of the energy storage battery; then the battery operating parameters are input into the temperature prediction model by the second acquisition unit, so as to process the battery operating parameters by the temperature prediction model to obtain the predicted temperature of the energy storage battery, wherein the temperature prediction model is obtained by fitting multiple groups of historical battery operating parameters through mathematical analysis modeling, the historical battery operating parameters are the battery operating parameters of the energy storage battery in the historical time period, and the predicted temperature is the temperature predicted by the temperature prediction model according to the battery operating parameters at the first moment for the temperature at the second moment, and the first moment is the temperature during the monitoring process of the energy storage battery. at any moment of the first moment, the second moment is the moment after the first moment; then the first determination unit is used to determine that the actual temperature of the energy storage battery at the second moment is the target actual temperature; then the second determination unit is used to determine the temperature deviation ratio of the energy storage battery at the second moment according to the target actual temperature and the predicted temperature; finally, the early warning unit is used to issue a fault early warning signal when the temperature deviation ratio is not less than the deviation ratio threshold, thereby achieving the purpose of processing the operating parameters of the energy storage battery obtained in real time by using the temperature prediction model to predict the battery temperature, and issuing a warning signal when the deviation ratio between the predicted result and the actual result exceeds a certain threshold, achieving the technical effect of using a small amount of data for fitting to accurately predict the battery temperature, and improving the efficiency and accuracy of battery fault prediction.

[0077] Therefore, the technical solution provided by the above-mentioned embodiment of the present invention solves the technical problem that when using traditional methods in the related art to monitor energy storage batteries and provide safety warnings, a large amount of data at the same time is usually used for fitting, data redundancy and large amount of calculation are used, resulting in low prediction accuracy.

[0078] Optionally, the fault warning device for the energy storage battery further includes: a third acquisition unit, used to obtain multiple groups of historical battery operating parameters of the energy storage battery at multiple historical moments in a historical time period before inputting the battery operating parameters into the temperature prediction model to process the battery operating parameters using the temperature prediction model to obtain the predicted temperature of the energy storage battery; a third determination unit, used to determine the historical battery operating parameters during the charging and discharging period as the target historical battery operating parameters, wherein the charging and discharging period refers to the time period when the energy storage battery is in a charging state or a discharging state; a fourth determination unit, used to determine the historical battery operating parameters of the energy storage battery at a first historical target moment as the target historical battery operating parameters, and obtain the historical battery box ambient temperature of the energy storage battery at the first target historical moment and the historical battery box ambient temperature of the energy storage battery at the second target historical moment surface temperature, wherein the first historical target moment is any moment among multiple target historical moments, and the second target historical moment is the moment after the first target historical moment; a fifth determination unit is used to determine that the difference between the historical battery surface temperature and the historical ambient temperature in the battery box is the heat generated by the energy storage battery during the historical temperature rise period, wherein the historical temperature rise period is the period between the second target historical moment and the first target historical moment; a fourth acquisition unit is used to solve the binomial parabola model according to the target historical battery operating parameters and heat to obtain the model coefficients of the binomial parabola model, wherein the binomial parabola model is based on the thermal effect process of the energy storage battery in the historical temperature rise period. The model is established by using the least squares method for nonlinear polynomial regression; a construction unit is used to construct a temperature prediction model according to the model coefficients.

[0079] Optionally, the fourth acquisition unit includes: an acquisition module, which is used to substitute the target historical battery operating parameters and heat into the first expression of the binomial parabola model for solution to obtain the model coefficient of the binomial parabola model, wherein the first expression is: t m-1 Indicates the first target historical moment, Represents the historical current in the target historical battery operating parameters, It represents the heat generated by the energy storage battery during the historical temperature rise period. a, b, and c are the model coefficients of different terms in the binomial parabola model.

[0080] Optionally, the construction unit includes: a construction module, which is used to construct a temperature prediction model according to the model coefficients, wherein the second standard expression of the temperature prediction model is: t n represents the second moment, t n-1 Indicates the first moment, represents the predicted temperature, Indicates the current in the battery operating parameters, Indicates the ambient temperature inside the battery box of the energy storage battery at the first moment.

[0081] Optionally, the second determination unit includes: a determination module, configured to determine the temperature deviation ratio of the energy storage battery at the second moment according to the target actual temperature and the predicted temperature using a first formula, wherein the first formula is: Indicates the temperature deviation ratio, t n Indicates the second moment, represents the target actual temperature at the second moment, Indicates the predicted temperature at the second moment.

[0082] Optionally, the early warning unit includes at least one of the following: a first early warning module, used to control the voice component to issue a voice early warning signal when the temperature deviation ratio is not less than the deviation ratio threshold; a second early warning module, used to control the sound and light component to issue an sound and light early warning signal when the temperature deviation ratio is not less than the deviation ratio threshold; a third early warning module, used to control the communication component to issue a pop-up warning signal when the temperature deviation ratio is not less than the deviation ratio threshold.

[0083] Optionally, the fault warning device of the energy storage battery also includes: an optimization unit, which is used to adaptively optimize the temperature prediction model using the battery operating parameters and the predicted temperature corresponding to the battery operating parameters in the process of processing the battery operating parameters using the temperature prediction model to obtain the predicted temperature of the energy storage battery, and update the temperature prediction model to an optimized temperature prediction model.

[0084] According to another aspect of an embodiment of the present invention, a fault warning system for an energy storage battery is further provided. The fault warning system for an energy storage battery uses any of the above-mentioned fault warning methods for an energy storage battery.

[0085] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is further provided, the computer-readable storage medium comprising a stored program, wherein the program executes any one of the above-mentioned energy storage battery fault warning methods.

[0086] Optionally, in this embodiment, the computer-readable storage medium may be located in any one of the computer terminals in a computer terminal group in a computer network, or in any one of the communication devices in a communication device group.

[0087] Optionally, in this embodiment, the computer-readable storage medium is configured to store a program code for executing the following steps: acquiring battery operating parameters and actual temperature of the energy storage battery in real time, wherein the battery operating parameters are used to reflect the operating state of the energy storage battery; inputting the battery operating parameters into a temperature prediction model, so as to process the battery operating parameters using the temperature prediction model to obtain a predicted temperature of the energy storage battery, wherein the temperature prediction model is fitted by mathematical analysis modeling using multiple sets of historical battery operating parameters, the historical battery operating parameters are the battery operating parameters of the energy storage battery in a historical time period, and the predicted temperature is a temperature predicted by the temperature prediction model based on the battery operating parameters at the first moment for the temperature at the second moment, the first moment being any moment in the process of monitoring the energy storage battery, and the second moment being a moment after the first moment; determining the actual temperature of the energy storage battery at the second moment as the target actual temperature; determining the temperature deviation ratio of the energy storage battery at the second moment according to the target actual temperature and the predicted temperature; and issuing a fault warning signal when the temperature deviation ratio is not less than the deviation ratio threshold.

[0088] Optionally, in this embodiment, the computer-readable storage medium is configured to store program codes for executing the following steps: obtaining multiple groups of historical battery operating parameters of the energy storage battery at multiple historical moments within a historical time period; determining the historical battery operating parameters during the charging and discharging period as the target historical battery operating parameters, wherein the charging and discharging period refers to the time period when the energy storage battery is in a charging state or a discharging state; determining the historical battery operating parameters of the energy storage battery at a first historical target moment as the target historical battery operating parameters, and obtaining the historical battery box internal ambient temperature of the energy storage battery at the first target historical moment and the historical battery surface temperature at a second target historical moment, wherein the first historical target moment is multiple any moment among the target historical moments, the second target historical moment is the moment after the first target historical moment; determine the difference between the historical battery surface temperature and the historical battery box ambient temperature as the heat generated by the energy storage battery during the historical temperature rise period, wherein the historical temperature rise period is the period between the second target historical moment and the first target historical moment; solve the binomial parabola model according to the target historical battery operating parameters and heat to obtain the model coefficients of the binomial parabola model, wherein the binomial parabola model is a model established by using the least squares method for nonlinear polynomial regression based on the thermal effect process of the energy storage battery during the historical temperature rise period; and construct a temperature prediction model according to the model coefficients.

[0089] Optionally, in this embodiment, the computer-readable storage medium is configured to store a program code for executing the following steps: substituting the target historical battery operating parameters and heat into a first expression of a binomial parabola model for solving, to obtain a model coefficient of the binomial parabola model, wherein the first expression is: t m-1 Indicates the first target historical moment, Represents the historical current in the target historical battery operating parameters, It represents the heat generated by the energy storage battery during the historical temperature rise period. a, b, and c are the model coefficients of different terms in the binomial parabola model.

[0090] Optionally, in this embodiment, the computer-readable storage medium is configured to store a program code for executing the following steps: constructing a temperature prediction model according to the model coefficients, wherein the second standard expression of the temperature prediction model is: t n represents the second moment, t n-1 Indicates the first moment, represents the predicted temperature, Indicates the current in the battery operating parameters, Indicates the ambient temperature inside the battery box of the energy storage battery at the first moment.

[0091] Optionally, in this embodiment, the computer-readable storage medium is configured to store a program code for executing the following steps: determining a temperature deviation ratio of the energy storage battery at the second moment using a first formula according to the target actual temperature and the predicted temperature, wherein the first formula is: σ represents the temperature deviation ratio, t n Indicates the second moment, represents the target actual temperature at the second moment, Indicates the predicted temperature at the second moment.

[0092] Optionally, in this embodiment, the computer-readable storage medium is configured to store program codes for executing the following steps: when the temperature deviation ratio is not less than the deviation ratio threshold, controlling the voice component to issue a voice warning signal; when the temperature deviation ratio is not less than the deviation ratio threshold, controlling the sound and light component to issue a sound and light warning signal; when the temperature deviation ratio is not less than the deviation ratio threshold, controlling the communication component to issue a pop-up warning signal.

[0093] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for executing the following steps: in the process of processing the battery operating parameters using the temperature prediction model to obtain the predicted temperature of the energy storage battery, the temperature prediction model is adaptively optimized using the battery operating parameters and the predicted temperature corresponding to the battery operating parameters, and the temperature prediction model is updated to the optimized temperature prediction model.

[0094] According to another aspect of an embodiment of the present invention, a processor is further provided, and the processor is used to run a program, wherein when the program is run, any one of the above-mentioned energy storage battery fault warning methods is executed.

[0095] According to another aspect of an embodiment of the present invention, a computer program product is provided, including computer instructions, and when the computer instructions are executed by a processor, any one of the above-mentioned energy storage battery failure warning methods is executed.

[0096] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0097] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0098] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0099] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0100] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0101] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0102] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A fault warning method for an energy storage battery, characterized in that: include: Acquire battery operating parameters and actual temperature of the energy storage battery in real time, wherein the battery operating parameters are used to reflect the operating status of the energy storage battery; The battery operating parameters are input into a temperature prediction model, so as to process the battery operating parameters using the temperature prediction model to obtain a predicted temperature of the energy storage battery, wherein the temperature prediction model is obtained by fitting multiple groups of historical battery operating parameters through mathematical analysis modeling, the historical battery operating parameters are the battery operating parameters of the energy storage battery in a historical time period, and the predicted temperature is the temperature obtained by predicting the temperature at a second moment according to the battery operating parameters at a first moment by the temperature prediction model, the first moment being any moment in the process of monitoring the energy storage battery, and the second moment being a moment after the first moment; Determining the actual temperature of the energy storage battery at the second moment as a target actual temperature; Determining a temperature deviation ratio of the energy storage battery at the second moment according to the target actual temperature and the predicted temperature; When the temperature deviation ratio is not less than the deviation ratio threshold, a fault warning signal is issued.

2. The energy storage battery failure warning method according to claim 1, characterized in that: Before inputting the battery operating parameters into the temperature prediction model to process the battery operating parameters using the temperature prediction model to obtain the predicted temperature of the energy storage battery, the method further includes: Acquire multiple groups of historical battery operating parameters of the energy storage battery at multiple historical moments in a historical time period; Determine the historical battery operating parameters during the charging and discharging period as the target historical battery operating parameters, wherein the charging and discharging period refers to a time period when the energy storage battery is in a charging state or a discharging state; Determine the historical battery operating parameters of the energy storage battery at the first historical target moment as the target historical battery operating parameters, and obtain the historical battery box internal ambient temperature of the energy storage battery at the first target historical moment and the historical battery surface temperature at the second target historical moment, wherein the first historical target moment is any moment among the multiple target historical moments, and the second target historical moment is a moment after the first target historical moment; Determine that the difference between the historical battery surface temperature and the historical battery box internal ambient temperature is the heat generated by the energy storage battery during the historical temperature rise period, wherein the historical temperature rise period is the period between the second target historical moment and the first target historical moment; Solving a binomial parabola model according to the target historical battery operating parameters and the heat to obtain model coefficients of the binomial parabola model, wherein the binomial parabola model is a model established by performing nonlinear polynomial regression using the least squares method based on the thermal effect process of the energy storage battery during the historical temperature rise period; The temperature prediction model is constructed according to the model coefficients.

3. The energy storage battery failure warning method according to claim 2, characterized in that: Solving the binomial parabola model according to the target historical battery operating parameters and the heat to obtain model coefficients of the binomial parabola model includes: Substituting the target historical battery operating parameters and the heat into the first expression of the binomial parabola model for solving, the model coefficients of the binomial parabola model are obtained, wherein the first expression is: t m-1 represents the first target historical moment, represents the historical current in the target historical battery operating parameter, represents the heat generated by the energy storage battery during the historical temperature rise period, and a, b, and c are the model coefficients of different terms in the binomial parabola model.

4. The energy storage battery failure warning method according to claim 2, characterized in that: Constructing the temperature prediction model according to the model coefficients includes: The temperature prediction model is constructed according to the model coefficients, wherein the second standard expression of the temperature prediction model is: t n represents the second moment, t n-1 represents the first moment, represents the predicted temperature, represents the current in the battery operating parameters, Indicates the ambient temperature inside the battery box of the energy storage battery at the first moment.

5. The energy storage battery failure warning method according to claim 1, characterized in that: Determining the temperature deviation ratio of the energy storage battery at the second moment according to the target actual temperature and the predicted temperature includes: The temperature deviation ratio of the energy storage battery at the second moment is determined using a first formula according to the target actual temperature and the predicted temperature, wherein the first formula is: σ represents the temperature deviation ratio, t n represents the second moment, represents the target actual temperature at the second moment, represents the predicted temperature at the second moment.

6. The energy storage battery failure warning method according to claim 1, characterized in that: When the temperature deviation ratio is not less than the deviation ratio threshold, a fault warning signal is issued, including at least one of the following: When the temperature deviation ratio is not less than the deviation ratio threshold, controlling the voice component to issue a voice warning signal; When the temperature deviation ratio is not less than the deviation ratio threshold, controlling the sound and light component to emit a sound and light warning signal; When the temperature deviation ratio is not less than the deviation ratio threshold, the communication component is controlled to issue a pop-up warning signal.

7. The energy storage battery failure warning method according to any one of claims 1 to 6, characterized in that: Also includes: In the process of using the temperature prediction model to process the battery operating parameters to obtain the predicted temperature of the energy storage battery, the temperature prediction model is adaptively optimized using the battery operating parameters and the predicted temperature corresponding to the battery operating parameters, and the temperature prediction model is updated to the optimized temperature prediction model.

8. A fault warning device for an energy storage battery, characterized in that: include: A first acquisition unit, used for acquiring a battery operating parameter and an actual temperature of the energy storage battery in real time, wherein the battery operating parameter is used to reflect the operating state of the energy storage battery; a second acquisition unit, configured to input the battery operating parameters into a temperature prediction model, so as to process the battery operating parameters using the temperature prediction model to obtain a predicted temperature of the energy storage battery, wherein the temperature prediction model is obtained by fitting multiple groups of historical battery operating parameters through mathematical analysis modeling, the historical battery operating parameters are the battery operating parameters of the energy storage battery in a historical time period, the predicted temperature is the temperature obtained by predicting the temperature at a second moment according to the battery operating parameters at a first moment by the temperature prediction model, the first moment being any moment in the process of monitoring the energy storage battery, and the second moment being a moment after the first moment; A first determining unit, configured to determine the actual temperature of the energy storage battery at the second moment as a target actual temperature; A second determining unit, configured to determine a temperature deviation ratio of the energy storage battery at the second moment according to the target actual temperature and the predicted temperature; The early warning unit is used to send out a fault early warning signal when the temperature deviation ratio is not less than a deviation ratio threshold.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein the program executes the fault warning method for the energy storage battery according to any one of claims 1 to 7.

10. A computer program product comprising computer instructions, characterized in that: When the computer instructions are executed by the processor, the fault warning method for the energy storage battery described in any one of claims 1 to 7 is executed.

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