Energy storage battery life evaluation method and related device

Abstract

The application belongs to a battery life evaluation method, and aims at a method for evaluating battery life by simulating working conditions in a laboratory. There is a technical problem that the difference between the test conditions and the working conditions of the actual application scene of the battery is large, and the performance degradation trend of the energy storage battery under the actual application working condition cannot be truly reflected. An energy storage battery life evaluation method and related device are provided. The energy storage battery life can be quickly evaluated by means of an energy storage battery life attenuation function. First, the corresponding experimental data is obtained through cycle life experiments and calendar life experiments under different experimental simulation working conditions, and the cycle life experiment data and the calendar life experiment data under different experimental simulation working conditions are fitted and superimposed respectively to obtain a cycle life attenuation function and a calendar life attenuation function. Then, the actual historical data of the energy storage battery is divided into intervals, and the simulated experimental results are corrected.

Description

TECHNICAL FIELD

[0001] The application belongs to a battery life evaluation method, and particularly relates to a method for evaluating the life of an energy storage battery and a related device. BACKGROUND

[0002] Battery life is generally divided into cycle life and calendar life. The full life cycle of an energy storage battery faces various dynamic and complex factors that cause the battery state of health to deteriorate. Different influencing factors have different influencing mechanisms and degrees on the cycle life and calendar life of the energy storage battery.

[0003] At present, the battery life is generally evaluated by simulating the working conditions in the laboratory. However, when simulating the working conditions, the cycle life or calendar life of the energy storage battery under a single factor is usually evaluated under a single factor. The test conditions have great differences from the working conditions in the actual application scenarios of the battery. Under the background of continuous innovation of energy storage technology, accelerated product iteration rate, and continuous expansion of energy storage application scenarios, the conventional evaluation method cannot truly reflect the performance degradation trend of the energy storage battery under the actual application working conditions. SUMMARY

[0004] The application provides a method for evaluating the life of an energy storage battery and a related device to solve the technical problem that the test conditions have great differences from the working conditions in the actual application scenarios of the battery, and the performance degradation trend of the energy storage battery under the actual application working conditions cannot be truly reflected.

[0005] To achieve the above-mentioned purpose, the application adopts the following technical solutions:

[0006] In a first aspect, the application provides a method for evaluating the life of an energy storage battery, comprising:

[0007] obtaining cycle life influencing factor data and calendar life influencing factor data of the energy storage battery as real-time data of the energy storage battery;

[0008] inputting the real-time data of the energy storage battery into a life attenuation function of the energy storage battery to obtain an evaluation result of the life of the energy storage battery; the life attenuation function of the energy storage battery is obtained by:

[0009] obtaining cycle life experimental data and calendar life experimental data of the energy storage battery under different experimental simulation working conditions; the different experimental simulation working conditions include different settings of the cycle life influencing factor data and the calendar life influencing factor data;

[0010] respectively fitting and superimposing the cycle life experimental data and the calendar life experimental data under the different experimental simulation working conditions to respectively obtain a cycle life attenuation function and a calendar life attenuation function;

[0011] obtain historical cycle life influence factor data and historical calendar life influence factor data of the energy storage battery within a preset time range, and divide the energy storage battery life attenuation time into multiple intervals according to the distribution of the historical cycle life influence factor data under the charging and discharging state and the distribution of the historical calendar life influence factor data under the static state;

[0012] The sum of the cycle life attenuation function and the calendar life attenuation function in each interval is used as the energy storage battery life attenuation function.

[0013] Further, the cycle life influence factor data includes temperature, charging and discharging rate and charging and discharging depth;

[0014] The calendar life influence factor data includes temperature and energy state.

[0015] Further, the cycle life experimental data of the energy storage battery under different experimental simulation conditions is obtained, including:

[0016] On the basis of the cycle life standard test simulation condition, each cycle life influence factor data is adjusted, a preset number of cycles is performed under each cycle life influence factor data, and the energy attenuation rate under the experimental simulation condition is obtained as the cycle life experimental data under different experimental simulation conditions;

[0017] The calendar life experimental data of the energy storage battery under different experimental simulation conditions is obtained, including:

[0018] On the basis of the calendar life standard test simulation condition, each calendar life influence factor data is adjusted, the energy storage battery is left for a preset time under each calendar life influence factor data, and the energy attenuation rate under the experimental simulation condition is obtained as the calendar life experimental data under different experimental simulation conditions.

[0019] Further, the calculation method of the cycle life attenuation function includes:

[0020] Eloss -1 =λ1×Eloss -1-1 (T1)+λ2×Eloss -1-2 (P)+λ3×Eloss -1-3 (DOD)

[0021] Wherein, Eloss -1 is the cycle life attenuation function, λ1 is the cycle life attenuation function weight varying with temperature, λ2 is the cycle life attenuation function weight varying with the charging and discharging rate, λ3 is the cycle life attenuation function weight varying with the charging and discharging depth, Eloss -1-1(T1) is a cycle life attenuation function with temperature variation, Eloss -1-2 (P) is a cycle life attenuation function with charge-discharge rate variation, Eloss -1-3 (DOD) is a cycle life attenuation function with depth of discharge variation, T1 is the average temperature of the battery in the charge-discharge working condition interval, P is the charge-discharge power of the battery in the charge-discharge working condition interval, and DOD is the depth of charge-discharge of the battery in the charge-discharge working condition interval.

[0022] Further, the cycle life attenuation function with temperature variation comprises:

[0023] E loss-1-1 (T1) = A1exp(-A2 / T1) x n A3

[0024] wherein A1 is a correction coefficient in the cycle life attenuation function, A2 is a coefficient related to activation energy in the cycle life attenuation function, A3 is a coefficient related to the battery material system in the cycle life attenuation function, and n is the number of charge-discharge cycles;

[0025] The cycle life attenuation function with charge-discharge rate variation comprises:

[0026] E loss-1-2 (P) = f(P)

[0027] wherein f(P) is a function of cycle life variation with charge-discharge rate obtained by fitting experimental data under different charge-discharge rates;

[0028] The cycle life attenuation function with depth of discharge variation comprises:

[0029] E loss-1-3 (DOD) = f(DOD).

[0030] Further, the calculation method of the calendar life attenuation function comprises:

[0031] E loss-2 = η1 x E loss-2-1 (T2) + η2 x E loss-2-2 (SOE)

[0032] wherein E loss-2 is the calendar life attenuation function, E loss-2-1 (T2) is a battery calendar life attenuation function with temperature variation, E loss-2-2 (SOE) is a battery calendar life attenuation function with state of energy variation, T2 is the average temperature of the battery in the storage time, SOE is the state of energy of the battery in storage, η1 is the weight of the battery calendar life attenuation function with temperature variation, and η2 is the weight of the battery calendar life attenuation function with state of energy variation.

[0033] Further, the battery calendar life decay function varying with temperature comprises:

[0034] E loss-2-1 (T2) = B1exp(-B2 / T2) x t B3

[0035] Wherein, B1 is a correction coefficient in the battery calendar life decay function, B2 is a coefficient related to activation energy in the battery calendar life decay function, and B3 is a coefficient related to the battery material system in the battery calendar life decay function.

[0036] The battery calendar life decay function varying with energy state comprises:

[0037] E loss-2 (SOE) = f(SOE).

[0038] In a second aspect, the application provides a life evaluation system for energy storage batteries, comprising:

[0039] An acquisition module is configured to acquire current cycle life influence factor data and calendar life influence factor data of the energy storage battery as real-time data of the energy storage battery.

[0040] An evaluation module is configured to input the real-time data of the energy storage battery into a life decay function of the energy storage battery to obtain an evaluation result of the life of the energy storage battery.

[0041] The life decay function of the energy storage battery is obtained by:

[0042] Cycle life experimental data and calendar life experimental data of the energy storage battery under different experimental simulation conditions are obtained, wherein the different experimental simulation conditions include different settings of the cycle life influence factor data and the calendar life influence factor data.

[0043] The cycle life experimental data and the calendar life experimental data under the different experimental simulation conditions are respectively fitted and superimposed to obtain a cycle life decay function and a calendar life decay function.

[0044] The historical cycle life influence factor data and the historical calendar life influence factor data of the energy storage battery within a preset time range are obtained, and the life decay time of the energy storage battery is divided into multiple intervals according to the distribution of the historical cycle life influence factor data under the charging and discharging state and the distribution of the historical calendar life influence factor data under the static state.

[0045] In a third aspect, the present application provides an electronic device, comprising: a memory, one or more processors; the memory is coupled with the processor; wherein the memory has computer program code stored therein, the computer program code comprises computer instructions, when the computer instructions are executed by the processor, the electronic device executes the steps of the energy storage battery life evaluation method.

[0046] In a fourth aspect, the present application provides a computer readable storage medium, the computer readable storage medium has a computer program stored therein, the computer program is executed by a processor to implement the steps of the energy storage battery life evaluation method.

[0047] Compared with the prior art, the present application has the following beneficial effects:

[0048] The present application provides an energy storage battery life evaluation method, which only needs to obtain the current cycle life influence factor data and calendar life influence factor data of the energy storage battery, and can quickly complete the evaluation of the energy storage battery life by means of the energy storage battery life attenuation function. The present application comprehensively considers the overall evaluation of cycle life and calendar life. The present application first obtains the corresponding experimental data through cycle life experimental data and calendar life experimental data under different experimental simulation conditions, and then superimposes the fitted cycle life attenuation function and calendar life attenuation function under different experimental simulation conditions, respectively. Then, the actual historical data of the energy storage battery is divided into intervals, the simulated experimental results are corrected, and the energy storage battery life attenuation function used for evaluation which is more consistent with the actual working condition is obtained. The present application closely relates the battery life attenuation to the actual application scenario, can intuitively evaluate the influence of the dynamic change of the key influence factor on the battery life, and is convenient for more accurate health state evaluation of the energy storage battery.

[0049] The present application also provides an energy storage battery life evaluation system, an electronic device and a computer storage medium, which have all the advantages of the energy storage battery life evaluation method. BRIEF DESCRIPTION OF DRAWINGS

[0050] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments. It should be understood that the following drawings only show some embodiments of the present application, and therefore should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can also be obtained without creative labor.

[0051] Figure 1 The first flowchart of the energy storage battery life evaluation method of the present application;

[0052] Figure 2A second flowchart of a method for evaluating the life of an energy storage battery according to the present application;

[0053] Figure 3 A schematic diagram of a system for evaluating the life of an energy storage battery according to the present application. DETAILED DESCRIPTION

[0054] In order to make the objectives, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application but not all the embodiments. The components of the embodiments of the present application described and shown in the drawings can be arranged and designed in various different configurations.

[0055] Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the claimed present application, but only represents selected embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without creative labor fall within the scope of protection of the present application.

[0056] It should be noted that: similar reference numbers and letters represent similar items in the following drawings, therefore, once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings.

[0057] In the description of the embodiments of the present application, it should be noted that if the terms "upper", "lower", "horizontal", "inner" and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, or the orientation or positional relationship when the product of the present application is usually placed, and are only for the convenience of describing the present application and simplifying the description, and therefore cannot be understood as indicating or implying that the indicated device or element must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as limiting the present application. In addition, the terms "first", "second" and the like are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0058] In addition, if the term "horizontal" appears, it does not mean that the component must be absolutely horizontal, but can be slightly inclined. For example, "horizontal" only means that its direction is relatively more horizontal than "vertical", and does not mean that the structure must be completely horizontal, but can be slightly inclined.

[0059] In the description of the embodiments of this application, it should also be noted that, unless otherwise explicitly specified and limited, the terms "set," "install," "connect," and "link" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.

[0060] Battery life assessment is a crucial step in ensuring the reliability and economy of energy storage systems, directly impacting battery usage costs, maintenance frequency, and overall system energy efficiency. Battery life is typically divided into two dimensions: cycle life and calendar life. Cycle life refers to the process by which a battery's performance (such as capacity and internal resistance) decreases to a predetermined threshold after a certain number of charge-discharge cycles. Calendar life, on the other hand, refers to the time span from the date of manufacture, regardless of whether the battery is used, until its performance gradually declines until it no longer meets application requirements.

[0061] Throughout their lifespan, energy storage batteries are affected by a variety of dynamic and complex internal and external factors. These factors work together to influence the battery's health, leading to a gradual degradation of its performance. Internal factors may include battery material aging, electrolyte decomposition, and loss of active materials; external factors encompass temperature changes, charge / discharge rates, depth of discharge, overcharging and over-discharging, and environmental factors (such as humidity and vibration). These factors not only act individually but may also interact, forming complex coupling effects that further exacerbate the complexity of battery performance degradation.

[0062] Currently, simulating operating conditions in the laboratory is one of the mainstream methods for evaluating battery life, but this method has significant limitations. First, simulation conditions often simplify or focus on a single factor, such as only considering the effect of temperature or specific charge-discharge cycle patterns, while ignoring the combined effects and dynamic changes of these factors in actual applications. Second, the laboratory environment cannot fully replicate the complex operating conditions in real-world scenarios, such as the irregular dispatch demands in grid energy storage systems. These factors can lead to significant discrepancies between laboratory test results and actual application performance.

[0063] Based on the above, this application proposes a method for evaluating the lifespan of energy storage batteries. The following is a detailed description of this application in conjunction with embodiments and accompanying drawings.

[0064] like Figure 1 The diagram shown is a schematic representation of the first flowchart of the energy storage battery life evaluation method of this application, which may include:

[0065] S101, obtain the current cycle life influence factor data and calendar life influence factor data of the energy storage battery as the real-time data of the energy storage battery.

[0066] In practical applications, when evaluating the energy storage battery, the current real-time state information of the energy storage battery needs to be obtained first, so as to perform subsequent life evaluation. The cycle life influence factor data can include charging and discharging current, charging and discharging depth, charging and discharging rate, charging and discharging times, temperature change during charging and discharging, etc.; and the calendar life influence factor data can include battery storage temperature, storage time, storage energy state, etc. These data can be obtained in real time through a battery management system or other monitoring equipment. The specific selected influence factors can be adjusted according to the evaluation requirements.

[0067] S102, input the real-time data of the energy storage battery into the energy storage battery life attenuation function to obtain the energy storage battery life evaluation result.

[0068] It should be noted that the energy storage battery life evaluation result is used to represent the energy storage battery life attenuation and comprehensively considers the calendar life and cycle life.

[0069] The method for obtaining the energy storage battery life attenuation function comprises:

[0070] (1) Obtain cycle life experimental data and calendar life experimental data of the energy storage battery under different experimental simulation working conditions; the different experimental simulation working conditions include different settings of cycle life influence factor data and calendar life influence factor data.

[0071] In a laboratory environment, by simulating various possible operating conditions (such as different charging and discharging conditions, temperature conditions, etc.), cycle life tests and calendar life tests can be performed on the energy storage battery. These tests will generate a large amount of experimental data, which can include battery capacity attenuation and internal resistance change under different working conditions.

[0072] (2) respectively fitting and superimposing the cycle life experimental data and the calendar life experimental data under different experimental simulation working conditions to obtain a cycle life attenuation function and a calendar life attenuation function.

[0073] The collected cycle life experimental data and calendar life experimental data are analyzed, and mathematical or statistical methods (such as regression analysis, neural network, etc.) are used to fit the data to obtain a function describing the change of battery performance with cycle number and time. Then, the cycle life attenuation function and the calendar life attenuation function are superimposed to comprehensively consider the influence of both on the battery life

[0074] (3) Obtain historical cycle life influence factor data and historical calendar life influence factor data of the energy storage battery within a preset time range. According to the distribution of the historical cycle life influence factor data under the charging and discharging state and the distribution of the historical calendar life influence factor data under the static state, the energy storage battery life attenuation time is divided into multiple intervals according to the charging and discharging state and the static state.

[0075] Collect the operation data of the energy storage battery in the past period of time, including historical cycle life influence factor data and historical calendar life influence factor data, which will be used to further refine the life attenuation function to improve the accuracy of the evaluation. By analyzing the historical data, the performance change characteristics of the energy storage battery under different states can be understood. According to these characteristics, the life attenuation process of the battery is divided into multiple intervals, each interval corresponding to different charging and discharging states or static states, which can more accurately describe the life attenuation law of the energy storage battery under different states.

[0076] (4) Take the sum of the cycle life attenuation function and the calendar life attenuation function in each interval as the energy storage battery life attenuation function.

[0077] The present application can obtain a piecewise described battery life attenuation function that can comprehensively consider the cycle life and calendar life influence, which can be used to evaluate the remaining life of the battery at different time points and under different states.

[0078] As shown in FIG. 1, it is a first flowchart of the energy storage battery life evaluation method of the present application, which can include: Figure 2

[0079] S201, determine the main influence factors of the energy storage battery life.

[0080] In practical applications, the energy attenuation of the energy storage battery during the cycle charging and discharging, i.e. the cycle life, is mainly related to factors such as temperature, charging and discharging rate, and charging and discharging depth, which can be used as cycle life influence factors. Among them, too high or too low temperature can cause the chemical reaction inside the battery to speed up or slow down, thereby affecting the life of the battery. The charging and discharging rate refers to the speed of battery charging and discharging. If the charging and discharging speed is too fast, it will cause the battery temperature to rise or the electrode material structure to be damaged, resulting in shortened life. The charging and discharging depth refers to the percentage of the battery's charge and discharge capacity, and deep charging and discharging will accelerate the aging of the battery, because the battery is more vulnerable to damage in the full charge and full discharge state.

[0081] ​The energy attenuation of the energy storage battery during storage, i.e., the calendar life, is mainly related to temperature and energy state, which can be used as a calendar life influencing factor. For the calendar life, temperature is also an important influencing factor, and appropriate storage temperature can prolong the calendar life of the battery. As for the energy state, the battery stored in a partially charged state is better than that stored in a fully charged or fully discharged state.

[0082] In S202, an experimental simulation working condition is designed, battery life experimental data of the energy storage battery under the experimental simulation working condition is obtained, and an energy storage battery simulation working condition database is established.

[0083] The rated charge-discharge power defined by the energy storage battery manufacturer is usually 1P, and the 1P 100%DOD full charge-discharge cycle condition at 25°C is the standard experimental simulation working condition for battery cycle life, and the battery calendar life under the 50%SOE storage condition at 25°C is the standard experimental simulation working condition for battery calendar life.

[0084] For the cycle life of the energy storage battery, there are three influencing factors of temperature, charge-discharge rate and charge-discharge depth. Based on the standard experimental simulation working condition of cycle life, the values of the three main influencing factors are adjusted, and orthogonal cycle charge-discharge tests of different temperatures, charge-discharge rates and charge-discharge depths are carried out. In practical applications, the temperature can be set to 25°C, 45°C and 5°C, respectively, the charge-discharge rate can be set to 1P and 0.5P, respectively, and the charge-discharge depth can be set to 100%DOD, 90%DOD (5%~95%SOE), 50%DOD (25%~75%SOE) and 20%DOD (40%~60%SOE). Every 50 cycles, i.e., once every 50 cycles, the energy calibration is carried out, the energy attenuation rate under different simulation working conditions is obtained, and the battery cycle life simulation working condition database under different experimental simulation working conditions is established.

[0085] For the calendar life of the energy storage battery, there are two main influencing factors of temperature and energy state. Based on the standard experimental simulation working condition of calendar life, the values of the two main influencing factors are adjusted, and the storage tests under different temperatures and different energy states are carried out. The temperature can be set to 25°C, 45°C and 5°C, and the energy state can be set to 50%SOE, 100%SOE and 10%SOE. Every half month, i.e., once every half month, the charge-discharge energy calibration is carried out, the energy attenuation rate at different storage time nodes is obtained, and the battery calendar life simulation working condition database under different experimental simulation working conditions is established.

[0086] The above charge-discharge energy calibration is to obtain the actual charge-discharge energy of the battery under the condition of 1P charge-discharge cycle at 25°C.

[0087] S203, the data of the energy storage battery under different cycle life experiment simulation conditions are fitted to obtain the energy storage battery energy attenuation rate functions under temperature, charge-discharge rate, and charge-discharge depth.

[0088] Battery cycle life attenuation function with temperature change:

[0089] E loss-1-1 (T1)=A1exp(-A2 / T1)xn A3

[0090] Wherein, A1 is the correction coefficient in the cycle life attenuation function, A2 is the coefficient related to the activation energy in the cycle life attenuation function, A3 is the coefficient related to the battery material system in the cycle life attenuation function, which are obtained by fitting the experimental data under different temperatures, n is the charge-discharge cycle number, T1 is the average temperature of the battery in the charge-discharge condition interval.

[0091] Cycle life attenuation function with charge-discharge rate change:

[0092] E los-1-2 (P)=f(P)

[0093] Wherein, f(P) is the function of cycle life change with charge-discharge rate obtained by fitting the experimental data under different charge-discharge rates.

[0094] Cycle life attenuation function with charge-discharge depth change, including:

[0095] E loss-1-3 (DOD)=f(DOD).

[0096] DOD is the charge-discharge depth of the battery in the charge-discharge condition interval.

[0097] Therefore, the battery cycle life attenuation function Eloss -1 is:

[0098] Eloss -1 =λ1×Eloss -1-1 (T1)+λ2×Eloss -1-2 (P)+λ3×Eloss -1-3 (DOD)

[0099] Wherein, λ1 is the cycle life attenuation function weight with temperature change, λ2 is the cycle life attenuation function weight with charge-discharge rate change, and λ3 is the cycle life attenuation function weight with charge-discharge depth change. The battery cycle life attenuation function Eloss -1 is obtained by fitting the battery cycle attenuation data under temperature, charge-discharge rate, and charge-discharge depth.

[0100] S204, fitting the data of the energy storage battery under different calendar life experiment simulation conditions to obtain the energy storage battery energy attenuation rate function under single variable conditions such as temperature and energy state.

[0101] Battery calendar life attenuation function E loss-2-1 (T2):

[0102] E loss-2-1 (T2)=B1exp(-B2 / T2)×t B3

[0103] Wherein, B1 is the correction coefficient in the battery calendar life attenuation function, B2 is the coefficient related to the activation energy in the battery calendar life attenuation function, B3 is the coefficient related to the battery material system in the battery calendar life attenuation function, which is obtained by fitting the experimental data under different temperatures.

[0104] Battery calendar life attenuation function E loss-2 (SOE):

[0105] E loss-2 (SOE)=f(SOE)

[0106] Wherein, T2 is the average temperature of the battery during the storage time, and SOE is the energy state of the battery during the storage time.

[0107] Therefore, the calendar life attenuation function E loss-2 :

[0108] E loss-2 =η1×E loss-2-1 (T2)+η2×E loss-2-2 (SOE)

[0109] Wherein, η1 is the weight of the battery calendar life attenuation function changing with temperature, and η2 is the weight of the battery calendar life attenuation function changing with energy state. The calendar life attenuation function is obtained by fitting the battery calendar life attenuation data under the conditions of temperature, energy state and other variables.

[0110] S205, obtaining the historical operation parameters of the energy storage battery in the energy storage system within a certain time range, determining the main distribution of the battery parameters under the conditions of charging and discharging and static state, and dividing the whole life attenuation time of the battery into multiple intervals according to the charging and discharging cycle conditions and storage conditions.

[0111] The historical operation parameters can include temperature, charging and discharging power, charging and discharging depth, energy state and other data.

[0112] S206, determining the energy storage battery life attenuation function.

[0113] The energy storage battery life attenuation function is:

[0114] E loss =

[0115] Wherein, m is the number of working condition intervals. That is, the energy storage battery life attenuation function is equal to the sum of the cycle life attenuation function and the calendar life attenuation function under multiple working condition intervals.

[0116] The application determines the main influencing factors of the energy storage battery life, establishes an energy storage battery simulation working condition database, fits the simulation working condition data, establishes cycle life and calendar life attenuation model functions, and obtains a battery total life attenuation function in combination with actual operation working condition data of the energy storage battery. The energy storage battery life can be accurately evaluated.

[0117] As shown in FIG. 1, it is a schematic diagram of an energy storage battery life evaluation system according to an embodiment of the application, which can include: Figure 3 An acquisition module is configured to acquire cycle life influencing factor data and calendar life influencing factor data of the energy storage battery at present as real-time data of the energy storage battery.

[0118] An evaluation module is configured to input the real-time data of the energy storage battery into an energy storage battery life attenuation function to obtain an energy storage battery life evaluation result. The acquisition method of the energy storage battery life attenuation function includes:

[0119] Acquiring cycle life experimental data and calendar life experimental data of the energy storage battery under different experimental simulation working conditions; the different experimental simulation working conditions include different settings of the cycle life influencing factor data and the calendar life influencing factor data;

[0120] Fitting and superimposing the cycle life experimental data and the calendar life experimental data under different experimental simulation working conditions respectively to obtain a cycle life attenuation function and a calendar life attenuation function respectively;

[0121] Acquiring historical cycle life influencing factor data and historical calendar life influencing factor data of the energy storage battery within a preset time range, and dividing the energy storage battery life attenuation time into multiple intervals according to the distribution of the historical cycle life influencing factor data under the charging and discharging state and the distribution of the historical calendar life influencing factor data under the static state;

[0122] Taking the sum of the cycle life attenuation function and the calendar life attenuation function in each interval as the energy storage battery life attenuation function.

[0123]

[0124] ​It should be noted that in the several embodiments provided in the present application, it should be understood that the disclosed device and method can be implemented in other ways. For example, the system embodiments described above are merely illustrative. For example, the division of the modules is only a logical function division. In actual implementation, there can be another division manner. For example, a plurality of modules can be combined or integrated into another device, or some features can be ignored or not executed. The modules described as separate components can be or can not be physically separated. The components displayed as modules can be a physical unit or multiple physical units, that is, can be located in one place, or can be distributed to multiple different places. According to actual needs, part or all of the modules can be selected to achieve the purpose of the embodiment scheme.

[0125] In addition, each module in each embodiment of the present application can be integrated in a processing unit, or each module can exist physically, or two or more modules can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0126] The embodiments of the present application also provide an electronic device, which can include one or more processors, memories and communication interfaces.

[0127] The memory, the communication interface and the processor are coupled together. For example, the memory, the communication interface and the processor can be coupled together through a bus.

[0128] The communication interface is configured to perform data transmission with other devices. The memory stores computer program codes. The computer program codes include computer instructions, and when the computer instructions are executed by the processor, the electronic device performs the steps of the energy storage battery life evaluation method.

[0129] The processor can be a processor or a controller, for example, can be a central processing unit (CPU), a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, transistor logic device, hardware component or any combination thereof. It can implement or execute various exemplary logical blocks, modules and circuits described in combination with the present disclosure. The processor can also be a combination that implements computing functions, such as one or more microprocessor combinations, combinations of DSP and microprocessor, etc. The processor can be used to support the electronic device to perform the method steps provided in the above embodiments.

[0130] The bus can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc.

[0131] The computer readable storage medium provided by the embodiments of the present application stores a computer program, and the computer program is executed by the processor to realize the steps of the energy storage battery life evaluation method.

[0132] The computer readable storage medium involved in the present application includes random access memory (RAM), memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, register, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the technical field.

[0133] The related parts of the energy storage battery life evaluation system, electronic device and computer readable storage medium provided by the embodiments of the present application are described in detail in the corresponding part of the energy storage battery life evaluation method provided by the embodiments of the present application. Here, it is not described again. In addition, the part of the above technical solution provided by the embodiments of the present application which is consistent with the implementation principle of the corresponding technical solution in the prior art is not described in detail, so as not to be too much described.

[0134] The above merely provides preferred embodiments of the present application, and is not used to limit the present application. For those skilled in the art, the present application can have various modifications and changes. Any modifications, equivalent replacements, improvements, etc. made within the principles and technical scope of the present application shall fall into the scope of the present application.

Claims

1. A method for evaluating the life of an energy storage battery, characterized by, include: Obtain the current cycle life impact factor data and calendar life impact factor data of the energy storage battery as real-time data of the energy storage battery; Real-time data from the energy storage battery is input into the energy storage battery life decay function to obtain the energy storage battery life evaluation results. The method for obtaining the energy storage battery life decay function includes: Acquire cycle life and calendar life experimental data of energy storage batteries under different experimental simulation conditions; the different experimental simulation conditions include different settings of cycle life influencing factor data and calendar life influencing factor data. After fitting and superimposing the cycle life experimental data and calendar life experimental data under different experimental simulation conditions, the cycle life decay function and calendar life decay function are obtained respectively. The method for calculating the cycle lifetime decay function includes: Eloss -1 = λ1 x Eloss -1-1 (T1) + λ2 x Eloss -1-2 (P) + λ3 x Eloss -1-3 (DOD) wherein, Eloss -1 is a cycle life attenuation function, λ1 is a cycle life attenuation function weight varying with temperature, λ2 is a cycle life attenuation function weight varying with charge-discharge rate, λ3 is a cycle life attenuation function weight varying with charge-discharge depth, Eloss -1-1 (T1) is a cycle life attenuation function varying with temperature, Eloss -1-2 (P) is a cycle life attenuation function varying with charge-discharge rate, Eloss -1-3 (DOD) is a cycle life attenuation function varying with charge-discharge depth, T1 is the average temperature of the battery in the charge-discharge working condition interval, P is the charge-discharge power of the battery in the charge-discharge working condition interval, and DOD is the charge-discharge depth of the battery in the charge-discharge working condition interval. The temperature-dependent cycle life decay function includes: E loss-1-1 (T1) = A1exp(-A2 / T1) x n A3 Where A1 is the correction coefficient in the cycle life decay function, A2 is the coefficient related to activation energy in the cycle life decay function, A3 is the coefficient related to the battery material system in the cycle life decay function, and n is the number of charge-discharge cycles; The cycle life decay function that varies with charge / discharge rate includes: E loss-1-2 (P) = f(P) Where f(P) is a function of cycle life as a function of charge / discharge rate, obtained by fitting experimental data at different charge / discharge rates. The cycle lifetime decay function that varies with charge / discharge depth includes: E loss-1-3 (DOD) = f(DOD); Acquire historical cycle life influencing factor data and historical calendar life influencing factor data of energy storage batteries within a preset time range. Based on the distribution of historical cycle life influencing factor data under charge and discharge conditions and the distribution of historical calendar life influencing factor data under static conditions, divide the life decay time of energy storage batteries into multiple intervals according to charge and discharge conditions and static conditions. The sum of the cycle life decay function and the calendar life decay function within each interval is used as the life decay function of the energy storage battery.

2. The method of claim 1, wherein The cycle life influencing factors include temperature, charge / discharge rate, and depth of charge / discharge. The calendar lifespan influencing factor data includes temperature and energy status.

3. The method of claim 2, wherein the method is used to evaluate the life of an energy storage battery. Obtain cycle life experimental data of energy storage batteries under different experimental simulation conditions, including: Based on the standard test simulation conditions of cycle life, the data of each cycle life influencing factor were adjusted, and a preset number of cycles were performed under each cycle life influencing factor data to obtain the energy decay rate under different experimental simulation conditions, which served as the cycle life test data under different experimental simulation conditions. Obtain calendar life experimental data of energy storage batteries under different experimental simulation conditions, including: Based on the calendar life standard test simulation conditions, the data of each calendar life influencing factor were adjusted. Under each calendar life influencing factor data, the energy storage battery was placed for a preset time to obtain the energy decay rate under different experimental simulation conditions, which served as the calendar life test data under different experimental simulation conditions.

4. The method of claim 3, wherein the method is used to evaluate the life of an energy storage battery. The method for calculating the calendar lifetime decay function includes: E loss-2 =η1×E loss-2-1 (T2)+η2×E loss-2-2 (SOE) wherein E loss-2 is a calendar life decay function, E loss-2-1 (T2) is a temperature-dependent battery calendar life decay function, E loss-2-2 (SOE) is an energy state-dependent battery calendar life decay function, T2 is the average temperature of the battery during storage, SOE is the state of energy of the battery during storage, η1 is a temperature-dependent battery calendar life decay function weight, and η2 is an energy state-dependent battery calendar life decay function weight.

5. The method of claim 4, wherein the method further comprises: The temperature-dependent battery calendar life decay function includes: E loss-2-1 (T2) = B1exp(-B2 / T2) x t B3 Wherein, B1 is a correction coefficient in the battery calendar life attenuation function, B2 is a coefficient related to activation energy in the battery calendar life attenuation function, B3 is a coefficient related to the battery material system in the battery calendar life attenuation function; The battery calendar life attenuation function changing with the energy state comprises: E loss-2 (SOE) = f(SOE).

6. An energy storage battery life evaluation system, characterized by, It comprises: The acquisition module is used for acquiring the current cycle life influence factor data and the calendar life influence factor data of the energy storage battery as the energy storage battery real-time data. The evaluation module is used for inputting the energy storage battery real-time data into the energy storage battery life attenuation function to obtain an energy storage battery life evaluation result. The acquisition method of the energy storage battery life attenuation function comprises: The cycle life experimental data and the calendar life experimental data of the energy storage battery under different experimental simulation conditions are acquired; the different experimental simulation conditions include different settings of the cycle life influence factor data and the calendar life influence factor data. The cycle life experimental data and the calendar life experimental data under different experimental simulation conditions are respectively fitted and superimposed to obtain a cycle life attenuation function and a calendar life attenuation function respectively. The calculation method of the cycle life attenuation function comprises: Eloss -1 = λ1 x Eloss -1-1 (T1) + λ2 x Eloss -1-2 (P) + λ3 x Eloss -1-3 (DOD) wherein, Eloss -1 is a cycle life attenuation function, λ1 is a cycle life attenuation function weight varying with temperature, λ2 is a cycle life attenuation function weight varying with charge-discharge rate, λ3 is a cycle life attenuation function weight varying with charge-discharge depth, Eloss -1-1 (T1) is a cycle life attenuation function varying with temperature, Eloss -1-2 (P) is a cycle life attenuation function varying with charge-discharge rate, Eloss -1-3 (DOD) is a cycle life attenuation function varying with charge-discharge depth, T1 is the average temperature of the battery in the charge-discharge working condition interval, P is the charge-discharge power of the battery in the charge-discharge working condition interval, and DOD is the charge-discharge depth of the battery in the charge-discharge working condition interval. The cycle life attenuation function changing with the temperature comprises: E loss-1-1 (T1) = A1exp(-A2 / T1) x n A3 Wherein, A1 is a correction coefficient in the cycle life attenuation function, A2 is a coefficient related to activation energy in the cycle life attenuation function, A3 is a coefficient related to the battery material system in the cycle life attenuation function, and n is the number of charge and discharge cycles. The cycle life attenuation function changing with the charge and discharge rate comprises: E loss-1-2 (P) = f(P) Wherein, f(P) is a function of the cycle life changing with the charge and discharge rate obtained by fitting experimental data under different charge and discharge rates. The cycle life attenuation function changing with the charge and discharge depth comprises: E loss-1-3 (DOD) = f(DOD); The historical cycle life influence factor data and the historical calendar life influence factor data of the energy storage battery within a preset time range are acquired, and the energy storage battery life attenuation time is divided into multiple intervals according to the distribution of the historical cycle life influence factor data under the charge and discharge state and the distribution of the historical calendar life influence factor data under the static state. The sum of the cycle life attenuation function and the calendar life attenuation function in each interval is used as the energy storage battery life attenuation function.

7. An electronic device, comprising: It comprises: The memory and one or more processors; the memory is coupled with the processor; wherein the memory has computer program code stored therein, the computer program code comprises computer instructions, when the computer instructions are executed by the processor, the electronic device executes the steps of the energy storage battery life evaluation method in any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer readable storage medium has a computer program stored therein, and the computer program is executed by the processor to realize the steps of the energy storage battery life evaluation method in any one of claims 1 to 5.

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