Energy state estimation model construction method and device and energy state estimation method and device of energy storage battery

Abstract

The invention relates to the technical field of batteries, and discloses an energy state estimation model construction method and device and an energy state estimation method and device for an energy storage battery, and the model construction method comprises the steps: carrying out the constant power discharge of a fresh-state battery at different temperatures for multiple times at a preset power, and obtaining the DC internal resistance and the energy state of the discharged fresh-state battery; constructing a first function according to the direct-current internal resistance, the energy state and the corresponding temperature of the fresh-state battery after each discharge; performing a cyclic aging test on the fresh-state battery at the standard temperature to obtain direct-current internal resistances corresponding to different service times of the fresh-state battery at the standard temperature; constructing a second function according to the direct-current internal resistance corresponding to different use time at the standard temperature; combining the first function and the second function to obtain a battery internal resistance model; and building an energy state estimation model based on the battery internal resistance model. According to the model constructed by the invention, coupling of various factors is realized, so that the estimation precision of the model is improved.

Description

Technical Field

[0001] The present invention relates to the field of battery technology, and in particular to energy state estimation model construction, a state estimation method and a device for energy storage batteries. Background Art

[0002] With the transformation of the global energy structure and the large-scale application of renewable energy, energy storage batteries, as an important energy storage medium, have played a key role in power system regulation, renewable energy grid connection, power grid, microgrid operation and other fields. The energy state (SOE) of energy storage batteries is one of the core parameters in the battery management system, and its accurate estimation directly affects the efficiency, safety and reliability of the energy storage system. SOE is usually defined as the ratio between the available energy in the battery and the rated energy under the current operating conditions, reflecting the remaining energy level of the battery. SOE not only describes the capacity characteristics of the battery, but also reflects the change in voltage, which can more comprehensively and accurately reflect the true energy state of the battery.

[0003] Battery SOE estimation methods can be divided into three categories: direct measurement-based methods, model-based methods, and data-driven methods. Direct measurement methods are easily affected by inaccurate initial values, and sensor errors will gradually accumulate, leading to increased estimation errors. Data-driven methods require a large amount of data training and have high requirements for data quality, making them difficult to implement in engineering applications. Model-based SOE estimation methods are widely used in engineering, but because the energy state of the battery is affected by the coupling of multiple factors, this method requires obtaining multiple operating condition parameters to achieve accurate estimation of SOE. However, since it is difficult to obtain parameters for different operating conditions and it is difficult to obtain the coupling relationship between multiple factors, it is impossible to accurately estimate the energy state of the energy storage battery through a model in the existing technology. Summary of the Invention

[0004] In view of this, the present invention provides an energy state estimation model construction, state estimation method and device for an energy storage battery to solve the problem in the prior art that the energy state of an energy storage battery cannot be accurately estimated by a model.

[0005] In a first aspect, the present invention provides a method for constructing an energy state estimation model of an energy storage battery, comprising: discharging a fresh battery at a preset constant power multiple times at different temperatures to obtain the DC internal resistance and energy state of the fresh battery after discharge; constructing a first function of the fresh battery based on the DC internal resistance, energy state, and corresponding temperature of the fresh battery after each discharge, the first function being used to characterize the relationship between the DC internal resistance, energy state, and temperature; performing a cycle aging test on the fresh battery at a standard temperature to obtain the DC internal resistance corresponding to different usage times of the fresh battery at the standard temperature; constructing a second function based on the DC internal resistance corresponding to different usage times at the standard temperature, the second function being used to characterize the relationship between usage time and DC resistance relationship; combining the first function and the second function to obtain a battery internal resistance model, which is used to characterize the relationship between DC internal resistance, energy state, temperature, and usage time; building an energy state estimation model based on the battery internal resistance model, the energy state estimation model includes a spatial state equation and a measurement equation, the spatial state equation is used to calculate the estimated value of the energy state according to the state estimation result and operating parameters of the battery to be detected at the current moment, and the measurement equation is used to calculate the terminal voltage measurement value according to the operating parameters of the battery to be detected at the current moment and the battery internal resistance model; inputting the operating parameters of the battery to be detected into the energy state estimation model, and using the Kalman filter algorithm to calculate the energy state estimation model, the battery energy state of the battery to be detected can be obtained.

[0006] In an embodiment of the present invention, the state estimation result and operating parameters of the battery to be tested at the current moment are input into the spatial state equation, and the estimated value of the energy state at the next moment can be calculated through the spatial state equation. However, the energy state obtained in this way has no closed-loop correction, and the estimation error will become larger and larger. Therefore, a measurement equation is also provided in the embodiment of the present invention. The measurement equation calculates the terminal voltage measurement value. The terminal voltage can be measured. Using the Kalman filter algorithm to calculate the spatial state equation and the measurement equation can realize the formation of a closed loop between prediction and correction, so that the predicted energy state is close to the actual value.

[0007] In an optional embodiment, the steps of discharging a fresh battery multiple times at a preset power and constant power to obtain the DC internal resistance and energy state of the fresh battery after discharge include: after the fresh battery is allowed to stand at a preset temperature for a first preset time, charging the fresh battery to a full-charged state at a preset power and constant power, and measuring the current DC internal resistance of the fresh battery after standing for a second preset time, and determining the value of the current energy state as the first state value; discharging the fresh battery at a preset power and constant power for a third preset time, measuring the current DC internal resistance of the fresh battery after standing for the second preset time and recording the current energy state value. The number of previous discharges is used to decrement the first state value with a preset step size to obtain the value of the current energy state, and the preset step size is determined according to the first preset number of discharges; when the current discharge number is less than the first preset number, the value of the current energy state is used as the first state value, and the steps of discharging the fresh battery at a preset constant power for a third preset time and measuring the current DC internal resistance of the fresh battery after standing for a second preset time are repeated until the current discharge number is greater than or equal to the first preset number, and obtaining multiple DC internal resistances of the fresh battery at the preset temperature and the energy states corresponding to each DC internal resistance.

[0008] In an optional embodiment, a cyclic aging test is performed on a fresh battery at a standard temperature to obtain the DC internal resistance of the fresh battery corresponding to different usage times at the standard temperature, including: after the fresh battery is allowed to stand at the standard temperature for a fourth preset time, the fresh battery is charged to a first preset voltage at a preset constant power and allowed to stand for a fifth preset time; the fresh battery is discharged to a second preset voltage at a preset constant power, and after standing for a fifth preset time, the fresh battery is charged to the first preset voltage at a preset constant power, completing one discharge and charge as one cycle, and recording the current usage time, DC internal resistance and discharge energy after the second preset number of cycles; if the current discharge energy is greater than the preset value, repeatedly performing the steps of discharging the fresh battery to the second preset voltage at a preset constant power, charging the fresh battery to the first preset voltage at a preset constant power after standing for a fifth preset time, completing one discharge and charge as one cycle, and recording the current usage time, DC internal resistance and discharge energy after the second preset number of cycles, until the current discharge energy is greater than the preset value and less than or equal to the preset value, thereby obtaining the DC internal resistance of the fresh battery corresponding to different usage times at the standard temperature.

[0009] In an optional embodiment, the spatial state equation includes a first state equation and a second state equation, the first state equation is used to calculate the estimated value of the energy state based on the state estimation result at the current moment and the current maximum charging energy in the operating parameters, when the battery to be detected is in a charging state, the battery energy state of the battery to be detected is calculated by the first state equation; the second state equation is used to calculate the estimated value of the energy state based on the state estimation result at the current moment and the current maximum discharging energy in the operating parameters, when the battery to be detected is in a discharging state, the battery energy state of the battery to be detected is calculated by the second state equation.

[0010] In an optional embodiment, the maximum charging energy and the maximum discharging energy are obtained based on the operating environment temperature and operating time of the battery to be tested in the operating parameters and the pre-established corresponding relationship between the operating environment temperature, operating time and the maximum charging and discharging energy.

[0011] In an optional embodiment, the step of establishing a correspondence between the operating environment temperature, the operating time and the maximum charge and discharge energy includes: performing constant power charge and discharge tests on the fresh battery at a preset power at different temperatures to obtain the rated charge energy and rated discharge energy of the fresh battery at different temperatures; performing a cycle aging test on the fresh battery at a standard temperature to obtain the maximum charge energy and maximum discharge energy corresponding to the fresh battery at different usage times at the standard temperature; correcting the maximum charge energy and maximum discharge energy at different usage times at the standard temperature according to the rated charge energy and rated discharge energy of the fresh battery at different temperatures to obtain the maximum charge energy and maximum discharge energy of the fresh battery at different usage times, and establishing a correspondence between the operating environment temperature, the operating time and the maximum charge and discharge energy.

[0012] In an optional embodiment, a constant power charge and discharge test is performed on a fresh battery at different temperatures with a preset power to obtain the rated charging energy and rated discharging energy of the fresh battery at different temperatures, comprising: at any temperature, after the fresh battery is allowed to stand at a preset temperature for a first preset period of time, the fresh battery is discharged to a second voltage with a preset constant power; after standing for a second preset period of time, the fresh battery is charged to a first voltage with a preset constant power, after standing for a second preset period of time, the fresh battery is discharged to a second voltage with a preset constant power, after standing for a second preset period of time, the charging energy and the discharging energy are recorded; and the rated charging energy and the rated discharging energy of the fresh battery at the current temperature are determined based on the charging energy and the discharging energy.

[0013] In a second aspect, the present invention provides an energy state estimation method for an energy storage battery, comprising: obtaining operating parameters of a battery to be detected, the operating parameters including the operating environment temperature, current, terminal voltage, and usage time of the battery to be detected; if the battery to be detected is in a static state and the time in the static state is greater than a static threshold, obtaining the DC internal resistance of the battery to be detected, and determining the energy state of the battery to be detected using a pre-established relationship between the DC internal resistance and the energy state; otherwise, inputting the operating parameters into a pre-constructed energy state estimation model, using a Kalman filter algorithm to calculate the energy state estimation model to obtain the battery energy state of the battery to be detected, the energy state estimation model being constructed according to the energy state estimation model construction method for an energy storage battery provided in any of the above embodiments.

[0014] In a third aspect, the present invention provides a device for constructing an energy state estimation model of an energy storage battery, including: a constant power discharge module, which is used to discharge a fresh battery at a preset constant power multiple times at different temperatures to obtain the DC internal resistance and energy state of the fresh battery after discharge; a first function construction module, which is used to construct a first function of the fresh battery according to the DC internal resistance, energy state and corresponding temperature of the fresh battery after each discharge, and the first function is used to characterize the relationship between the DC internal resistance, energy state and temperature; a cycle aging test module, which is used to perform a cycle aging test on the fresh battery at a standard temperature to obtain the DC internal resistance corresponding to the fresh battery at different usage times at the standard temperature; a second function construction module, which is used to construct a second function according to the DC internal resistance corresponding to different usage times at the standard temperature, and the second function is used to characterize the relationship between the DC internal resistance, energy state and temperature during use. and the relationship between the DC internal resistance; a battery internal resistance model construction module, used to combine the first function and the second function to obtain a battery internal resistance model, the battery internal resistance model is used to characterize the relationship between the DC internal resistance, energy state, temperature, and usage time; an estimation model construction module, used to build an energy state estimation model based on the battery internal resistance model, the energy state estimation model includes a spatial state equation and a measurement equation, the spatial state equation is used to calculate the estimated value of the energy state according to the state estimation result and operating parameters of the battery to be detected at the current moment, and the measurement equation is used to calculate the terminal voltage measurement value according to the operating parameters of the battery to be detected at the current moment and the battery internal resistance model; the operating parameters of the battery to be detected are input into the energy state estimation model, and the energy state estimation model is calculated using the Kalman filter algorithm to obtain the battery energy state of the battery to be detected.

[0015] In a fourth aspect, the present invention provides an energy state estimation device for an energy storage battery, comprising: a parameter acquisition module for acquiring operating parameters of a battery to be detected, the operating parameters including the operating environment temperature, current, terminal voltage, and usage time of the battery to be detected; a first estimation module, if the battery to be detected is in a static state and the time in the static state is greater than a static threshold, the first estimation module is used to obtain the DC internal resistance of the battery to be detected, and determine the energy state of the battery to be detected using a pre-established relationship between the DC internal resistance and the energy state; a second estimation module, for inputting the operating parameters into a pre-constructed energy state estimation model, using a Kalman filter algorithm to calculate the energy state estimation model to obtain the battery energy state of the battery to be detected, the energy state estimation model being constructed according to the energy state estimation model construction method for an energy storage battery provided in any of the above embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0017] Figure 1 is a flow chart of a method for constructing an energy state estimation model for an energy storage battery according to an embodiment of the present invention;

[0018] Figure 2 is a schematic diagram of a fresh-state battery DC internal resistance set with energy state and temperature as constraints according to an embodiment of the present invention;

[0019] Figure 3 is a flow chart of a method for estimating the energy state of an energy storage battery according to an embodiment of the present invention;

[0020] Figure 4 is a schematic diagram of the error between the energy state obtained by the present invention and the actual value of the energy state;

[0021] Figure 5 is a structural block diagram of a device for constructing an energy state estimation model for an energy storage battery according to an embodiment of the present invention;

[0022] Figure 6 is a structural block diagram of an energy state estimation device for an energy storage battery according to an embodiment of the present invention;

[0023] Figure 7 Schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0024] To make the purpose, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are 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 those skilled in the art without making creative efforts shall fall within the scope of protection of the present invention.

[0025] According to an embodiment of the present invention, an embodiment of a method for constructing an energy state estimation model of 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.

[0026] In this embodiment, a method for constructing an energy state estimation model of an energy storage battery is provided. Figure 1 FIG. 1 is a flow chart of a method for constructing an energy state estimation model of an energy storage battery according to an embodiment of the present invention. Figure 1 As shown, the process includes the following steps:

[0027] Step S101 : discharging a fresh battery at a preset constant power multiple times at different temperatures to obtain a DC internal resistance and energy state of the fresh battery after discharge.

[0028] In an optional embodiment, multiple temperature points are set within a preset temperature range. At each temperature point, the fresh batteries are discharged multiple times at a preset constant power. At each temperature point, the DC internal resistance and corresponding energy state of a group of fresh batteries are obtained. For example, the preset temperature range is [0°C, 50°C], with the temperature points selected at 0°C, 10°C, 20°C, 30°C, 40°C, and 50°C.

[0029] In an optional embodiment, the time for discharging the fresh battery each time is the same. After each discharge, the DC internal resistance of the fresh battery is collected and the current energy state is estimated. Since the energy state cannot be directly measured, the energy state after each discharge is estimated based on the energy state in the fully charged state and the number of discharges.

[0030] In an optional embodiment, since the energy storage battery does not use a constant current charge and discharge operation strategy but a constant power charge and discharge strategy, when obtaining the DC internal resistance and energy state of the fresh battery, it is obtained by discharging multiple times at a preset power constant power. This experimental method is the same as the actual operation method of the energy storage battery. Therefore, after obtaining the relevant data in this way, the constructed energy state estimation model can accurately estimate the energy state of the battery during actual operation.

[0031] In an optional embodiment, the fresh battery refers to a battery that has not undergone charge and discharge cycles, that is, has not been used.

[0032] In an optional embodiment, the constant power discharge at a preset power is specifically 1P constant power discharge. 1P charging and discharging means that the battery is charged from 0% to 100% or discharged from 100% to 0% within 1 hour based on the nominal capacity of the battery.

[0033] Step S102 : constructing a first function of the fresh battery according to the DC internal resistance, energy state, and corresponding temperature of the fresh battery after each discharge, where the first function is used to characterize the relationship between the DC internal resistance, energy state, and temperature.

[0034] In an optional embodiment, since a set of DC internal resistances and corresponding energy states of fresh batteries are obtained at each temperature when step S101 is executed, a functional relationship between the DC internal resistance and the energy state can be constructed for each temperature. Then, the functional relationships of multiple temperatures are combined using a linear interpolation method to obtain a first function. The set of DC internal resistances of fresh batteries with SOE and temperature as constraints is as follows: Figure 2 shown.

[0035] Step S103 , performing a cycle aging test on the fresh battery at a standard temperature to obtain a DC internal resistance of the fresh battery corresponding to different usage times at the standard temperature.

[0036] In an optional embodiment, the cyclic aging test refers to constant power charging and discharging of a fresh battery at a preset power, discharging the battery with a voltage value at a first voltage at a constant power until the voltage value of the battery is at a second voltage, and after standing for a fixed period of time, charging the battery with a preset power at a constant power again until the voltage value of the battery is at the first voltage, at which time one cycle is completed.

[0037] Since the time consumed in the cycle aging test can be used as the battery usage time, the DC internal resistance corresponding to different usage times can be collected during the cycle aging test.

[0038] In an optional embodiment, the standard temperature is determined based on the ambient temperature during the actual operation of the battery. For example, the standard temperature can be set to 25°C. However, if the actual operating environment of the battery to be tested is in a low temperature state for a long time, a lower standard temperature can be set accordingly. If the actual operating environment of the battery to be tested is in a high temperature state for a long time, a higher standard temperature can be set accordingly.

[0039] Step S104 : constructing a second function according to the DC internal resistance corresponding to different usage times at the standard temperature, wherein the second function is used to characterize the relationship between the usage time and the DC internal resistance.

[0040] Step S105 : combining the first function and the second function to obtain a battery internal resistance model. The battery internal resistance model is used to characterize the relationship between DC internal resistance, energy state, temperature, and usage time.

[0041] In an embodiment of the present invention, in step S102, a fresh battery DC internal resistance set is constructed with energy state and temperature as constraints, and then the DC internal resistance is corrected based on the DC internal resistance change trend at different usage times. Ultimately, a complete DC internal resistance set with energy state, temperature, and usage time as constraints is obtained. This parameter acquisition method takes less time and has lower parameter acquisition costs.

[0042] Step S106: Build an energy state estimation model based on the battery internal resistance model. The energy state estimation model includes a spatial state equation and a measurement equation. The spatial state equation is used to calculate the estimated value of the energy state based on the state estimation result and operating parameters of the battery to be tested at the current moment. The measurement equation is used to calculate the terminal voltage measurement value based on the operating parameters of the battery to be tested at the current moment and the battery internal resistance model. The operating parameters of the battery to be tested are input into the energy state estimation model, and the energy state estimation model is calculated using the Kalman filter algorithm to obtain the battery energy state of the battery to be tested.

[0043] In an embodiment of the present invention, the state estimation result and operating parameters of the battery to be tested at the current moment are input into the spatial state equation, and the estimated value of the energy state at the next moment can be calculated through the spatial state equation. However, the energy state obtained in this way has no closed-loop correction, and the estimation error will become larger and larger. Therefore, a measurement equation is also provided in the embodiment of the present invention. The measurement equation calculates the terminal voltage measurement value. The terminal voltage can be measured. Using the Kalman filter algorithm to calculate the spatial state equation and the measurement equation can realize the formation of a closed loop between prediction and correction, so that the predicted energy state is close to the actual value.

[0044] The method provided by the embodiment of the present invention, when constructing an energy state estimation model, pre-constructs a battery internal resistance model that characterizes the relationship between DC internal resistance, energy state, temperature, and usage time, and then builds an energy state estimation model based on the battery internal resistance model. Therefore, the energy state estimation model can couple multiple factors such as temperature change, cycle aging, and charge and discharge rate change, thereby improving the estimation accuracy of the model. In addition, when establishing the battery internal resistance model required for constructing the energy state estimation model, the embodiment of the present invention first constructs the relationship between energy state, temperature, and DC internal resistance, and then corrects the above relationship based on the DC internal resistance change trend at different usage times, and finally A battery internal resistance model is obtained that characterizes the relationship between energy state, temperature, usage time, and DC internal resistance. The trend of DC internal resistance change at different usage times is obtained by performing a cycle aging test on a fresh battery at standard temperature. That is, the relationship between the usage time and DC internal resistance obtained through a single cycle aging test can be used to correct the relationship between the energy state, temperature, and DC internal resistance, thereby obtaining a battery internal resistance model that characterizes the relationship between the energy state, temperature, usage time, and DC internal resistance. There is no need to perform a cycle aging test on the fresh battery at different temperature points. This method of constructing a battery internal resistance model has a shorter parameter acquisition time and lower parameter acquisition cost.

[0045] In the above step S101, the fresh battery needs to be discharged multiple times at a preset constant power at different temperatures to obtain the DC internal resistance and energy state of the fresh battery after discharge. In an optional embodiment, the step of discharging the fresh battery multiple times at a preset constant power at a preset temperature point to obtain the DC internal resistance and energy state of the fresh battery after discharge includes:

[0046] Step a1: After the fresh battery is left to stand at a preset temperature for a first preset time, the fresh battery is charged to a full-charged state at a preset constant power, and after the fresh battery is left to stand for a second preset time, the current DC internal resistance of the fresh battery is measured, and the value of the current energy state is determined as the first state value.

[0047] In an optional embodiment, the first preset time length is greater than the second preset time length, and the values of the first preset time length and the second preset time length can be set according to actual conditions. For example, the first preset time length can be set to 5 hours, and the second preset time length can be set to 1 hour. That is, after the fresh battery is left to stand at a preset temperature for 5 hours, the fresh battery is charged to a full-charged state at a preset power constant power, and after standing for 1 hour, the current DC internal resistance of the fresh battery is measured, and the value of the current energy state is determined as the first state value. Since the fresh battery is currently being charged to a full-charged state for the first time, the energy state at this time is 100%.

[0048] Step a2: discharge the fresh battery at a preset constant power for a third preset time, measure the current DC internal resistance of the fresh battery after standing for a second preset time and record the current number of discharges, decrement the first state value by a preset step size to obtain the value of the current energy state, and the preset step size is determined based on the first preset number of discharges.

[0049] In an optional embodiment, the third preset time period may be determined to be 3 minutes, that is, the fresh battery is discharged at a preset constant power for 3 minutes, and the DC internal resistance is measured after standing for 1 hour.

[0050] In an optional embodiment, assuming that the energy state is 100% in a fully charged state and the energy state after the last discharge is 0%, if a total of 20 discharges are performed, the preset step size of the energy state change is (100%-0%) / 20=5%. At this time, after each discharge, the value of the current energy state can be determined as the value obtained by subtracting the preset step size of 5% from the value of the energy state after the last discharge.

[0051] In step a3, if the current number of discharges is less than the first preset number, the current energy state value is used as the first state value, and step a2 is repeated until the current number of discharges is greater than or equal to the first preset number, thereby obtaining multiple DC internal resistances of the fresh battery at the preset temperature and the energy states corresponding to each DC internal resistance.

[0052] For example, if the battery is discharged at a constant power of 1P, 1P charging and discharging means that the battery discharge process from 100% to 0% is completed within 1 hour based on the nominal capacity of the battery. Therefore, discharging for 3 minutes is 1 / 20 of 1 hour. At this time, the first preset number of times is set to 20, and the energy state change step is 5%. After completing 20 discharges, the DC internal resistance value of the full energy state range of [0% SOE, 100% SOE] can be obtained.

[0053] In an optional embodiment, the step of performing a cycle aging test on the fresh battery at a standard temperature to obtain the DC internal resistance of the fresh battery corresponding to different usage times at the standard temperature in step S103 includes:

[0054] Step b1: After the fresh battery is left at a standard temperature for a fourth preset time, the fresh battery is charged to a first preset voltage at a preset constant power and left at a fifth preset time.

[0055] In an optional embodiment, the fourth preset time length is greater than the fifth preset time length, and the values of the fourth preset time length and the fifth preset time length can be set according to actual conditions. For example, the fourth preset time length can be set to 5 hours, and the fifth preset time length can be set to 10 minutes, that is, after the fresh battery is left to stand at the standard temperature for 5 hours, the fresh battery is charged to the first preset voltage at a preset constant power and left to stand for 10 minutes.

[0056] In an optional embodiment, the first voltage may be 2.8V.

[0057] Step b2: discharge the fresh battery at a preset constant power to a second preset voltage, and after standing for a fifth preset time, charge the fresh battery at a preset constant power to the first preset voltage. Completing one discharge and charge is regarded as one cycle. After the second preset number of cycles, the current usage time, DC internal resistance and discharge energy are recorded.

[0058] In an optional embodiment, the second voltage may be 1.5V.

[0059] In a specific embodiment, a fresh battery is discharged to 1.5V at a constant power of 1P, and then charged to 2.8V at a constant power of 1P after standing for 10 minutes. After standing for 10 minutes, the fresh battery is discharged to 1.5V at a constant power of 1P again. One discharge and charge is regarded as one cycle. After 50 cycles, the current usage time, DC internal resistance and discharge energy are recorded.

[0060] Step b3: If the current discharge energy is greater than the preset value, repeat step b2 until the current discharge energy is greater than the preset value and less than or equal to the preset value, and obtain the DC internal resistance of the fresh battery corresponding to different usage times at standard temperature.

[0061] In a specific embodiment, the preset value may be set to 80% of the initial value of the battery discharge energy.

[0062] In an optional embodiment, the DC internal resistance corresponding to different usage times of a fresh battery at a standard temperature can be fitted to obtain a functional relationship between usage time and DC internal resistance:

[0063] DCR ref (t) = a0 + a1·t

[0064] Where t is the usage time; DCR ref is the DC internal resistance of the battery at standard temperature.

[0065] In a specific embodiment, the functional relationship between the above-mentioned usage time and the DC internal resistance is compared with Figure 2Combining the corresponding relationships among DC internal resistance, energy state, and temperature, we can obtain the battery internal resistance model:

[0066] DCR=f(T,t,SOE),

[0067] Where T represents temperature, t represents usage time, and SOE represents state of energy.

[0068] In an optional embodiment, the energy state estimation model is:

[0069] Spatial state equation:

[0070]

[0071] Among them, SOE k+1 Represents the energy state at time k+1, SOE k represents the energy state at time k, η is the charge and discharge efficiency, η = discharge energy / charge energy, U L,k represents the terminal voltage at time k, i k represents the current at time k, E c / d represents the maximum charge energy or maximum discharge energy, Δt represents the time interval, w k is the noise in the process of computing the spatial state equation.

[0072] Measurement equation:

[0073] U L,k+1 =U OC,k+1 -DCR(T,t,SOE)·i k +v k

[0074] Among them, U L,k+1 represents the terminal voltage at time k+1, U OC,k+1 represents the open circuit voltage at time k+1, DCR=f(T,t,SOE) represents the DC internal resistance model, i k represents the current at time k, v k To measure the noise during the operation of the equation.

[0075] In an optional embodiment, in the above spatial state equation, E c / d Represents the maximum charging energy or the maximum discharging energy. It can be seen that the spatial state equation includes the first state equation and the second state equation:

[0076] The first state equation is used to calculate an estimated value of the energy state based on the state estimation result at the current moment and the current maximum charging energy in the operating parameters. When the battery to be tested is in a charging state, the battery energy state of the battery to be tested is calculated using the first state equation.

[0077] The second state equation is used to calculate an estimated value of the energy state based on the state estimation result at the current moment and the current maximum discharge energy in the operating parameters. When the battery to be tested is in the discharge state, the battery energy state of the battery to be tested is calculated using the second state equation.

[0078] In an embodiment of the present invention, when the battery to be tested is in a charging state, the battery energy state of the battery to be tested is calculated using a first state equation. When the battery to be tested is in a discharging state, the battery energy state of the battery to be tested is calculated using a second state equation. The model provided by the embodiment of the present invention can estimate the energy state of the charging and discharging segments, and ultimately combine them to form an energy state estimation result for the entire life of the battery, thereby avoiding the occurrence of an erroneous estimation result of SOE>100%.

[0079] In an optional embodiment, the maximum charging energy and the maximum discharging energy are obtained based on the operating environment temperature and operating time of the battery to be tested in the operating parameters and the pre-established corresponding relationship between the operating environment temperature, operating time and the maximum charging and discharging energy.

[0080] In an optional embodiment, the step of establishing a corresponding relationship between the operating environment temperature, the operating time, and the maximum charge and discharge energy includes:

[0081] In step c1, constant power charge and discharge tests are performed on the fresh battery at different temperatures with a preset power to obtain the rated charge energy and rated discharge energy of the fresh battery at different temperatures.

[0082] In an optional embodiment, when obtaining the rated charge energy and rated discharge energy of a fresh battery at any temperature, the following steps are performed:

[0083] In step c11 , after the fresh battery is left at a preset temperature for a first preset time, the fresh battery is discharged to a second voltage at a preset constant power.

[0084] In a specific embodiment, the battery cell is left at a preset temperature for 5 hours and then discharged at a constant power of 1P to 1.5V.

[0085] Step c12: After standing for a second preset time, the fresh battery is charged to a first voltage at a preset constant power; after standing for a second preset time, the fresh battery is discharged to a second voltage at a preset constant power; after standing for a second preset time, the charging energy and the discharging energy are recorded.

[0086] In one embodiment, after standing for 10 minutes, the battery is charged to 2.8V at a constant power of 1P, then allowed to stand for 10 minutes, and then discharged to 1.5V at a constant power of 1P, then allowed to stand for 10 minutes. The charging energy and the discharging energy at the preset temperature are recorded.

[0087] Step c13: determining the rated charging energy and the rated discharging energy of the fresh battery at the current temperature according to the charging energy and the discharging energy.

[0088] In a specific embodiment, for each temperature point, step c12 can be repeated multiple times, and the average of the charging energy collected multiple times is used as the rated charging energy of the fresh battery at that temperature, and the average of the discharge energy collected multiple times is used as the rated discharge energy of the fresh battery at that temperature.

[0089] Step c2: performing a cycle aging test on the fresh battery at a standard temperature to obtain the maximum charge energy and maximum discharge energy of the fresh battery corresponding to different usage times at the standard temperature.

[0090] In an optional embodiment, a cyclic aging test is performed on fresh batteries at a standard temperature to measure the charge and discharge energies corresponding to different usage times. The maximum charge and discharge energies corresponding to different usage times are then calculated based on the charge and discharge energies corresponding to the different usage times. The cyclic aging test process is detailed in step S103 of the above embodiment. The difference from step S103 is that, whereas the charge and discharge energies are measured after the cyclic charge and discharge cycles in step c2, the DC internal resistance is measured after the total charge and discharge cycles in step S103.

[0091] The formula for calculating the maximum charging energy and maximum discharging energy at different usage times based on the charging energy and discharging energy at different usage times is:

[0092]

[0093] Among them, E c (T, t) is the current maximum charging energy, E d (T, t) is the current maximum discharge energy, E N,c (T) is the rated charging energy of the fresh battery, E N,d (T) is the rated discharge energy of the fresh battery, P is the preset power, T ref (t) is the standard temperature, R is the molar gas constant, E a is the battery activation energy, t is the usage time, a 1,c 、a 2,c 、a 3,c is the parameter to be identified during the battery charging process, a 1,d 、a 2,d 、a 3,d is the parameter to be identified during the battery discharge process.

[0094] Step c3, correcting the maximum charge energy and maximum discharge energy of the fresh battery at different usage times at the standard temperature based on the rated charge energy and rated discharge energy of the fresh battery at different temperatures, obtaining the maximum charge energy and maximum discharge energy of the fresh battery at different temperatures and different usage times, and establishing a corresponding relationship between the operating environment temperature, operating time and maximum charge and discharge energy.

[0095] In an optional embodiment, after obtaining the maximum charging energy and maximum discharging energy at different usage times at a standard temperature, the maximum charging and discharging energy is temperature-corrected based on the Arrhenius formula to obtain the maximum charging energy and maximum discharging energy of a fresh battery at different temperatures and different usage times. This process does not require cycle testing of the battery at different temperatures, so the parameter acquisition time is shorter and the parameter collection cost is lower.

[0096] In an optional embodiment, the maximum charge and discharge energy is corrected for temperature using the following formula:

[0097]

[0098] Among them, E c (T, t) is the current maximum charging energy, E d (T, t) is the current maximum discharge energy, E ref,c (t) is the current maximum charging energy at standard temperature, E ref,d (t) is the current maximum discharge energy at standard temperature, R is the molar gas constant, E a is the battery activation energy, T ref (t) is the standard temperature, and T(t) is the temperature at the current cycle time.

[0099] In an optional embodiment, when solving the energy state model using the extended Kalman filter algorithm, the following steps are performed:

[0100] The first step is to initialize the state variables and covariance matrix P:

[0101]

[0102] Step 2: Update the state variable forecast estimate:

[0103]

[0104] Step 3: Update the state covariance prediction estimate:

[0105]

[0106] Step 4: Calculate the Kalman gain:

[0107]

[0108] Step 5: Optimal estimation of state variables:

[0109]

[0110] Step 6: Optimal estimation of error covariance:

[0111] P k =(IK k C k )P kk-1

[0112] Step 7: Determine whether k meets the stopping condition. If not, add 1 to k and return to step 2 to loop until the stopping condition is met.

[0113] In the above calculation process, A k 、B k 、C k 、D k They are:

[0114]

[0115] The embodiment of the present invention also provides a method for estimating the energy state of an energy storage battery, such as Figure 3 Shown, including:

[0116] Step S201 , obtaining operating parameters of the battery to be tested, the operating parameters including operating environment temperature, current, terminal voltage, and usage time of the battery to be tested.

[0117] If the battery to be tested is in a static state and has been in the static state for a period greater than the static threshold, step S202 is executed to obtain the DC internal resistance of the battery to be tested. The energy state of the battery to be tested is determined using the pre-established relationship between the DC internal resistance and the energy state. The process of establishing the relationship between the DC internal resistance and the energy state is described in detail in step S102 in the above embodiment and is not repeated here.

[0118] The method provided in an embodiment of the present invention uses the OCV-SOE curve to perform online correction of the estimated result if the battery's rest time exceeds a rest threshold, preventing the accumulation and expansion of estimation errors. The OCV-SOE curve is used to characterize the relationship between open circuit voltage (OCV) and state of energy (SOE).

[0119] Otherwise, execute step S203, input the operating parameters into a pre-constructed energy state estimation model, use the Kalman filter algorithm to calculate the energy state estimation model, and obtain the battery energy state of the battery to be tested. The energy state estimation model is constructed according to the energy state estimation model construction method of the energy storage battery provided in any of the above embodiments.

[0120] In an optional embodiment, the OCV-SOE curve is obtained by the following steps:

[0121] Step d1: After the battery cell is left at a preset temperature for 5 hours, it is charged to a full charge state at a constant power of 1P and left at rest for 1 hour. The battery voltage at this time is measured to obtain the open circuit voltage (OCV) at 100% SOE;

[0122] Step d2: Discharge at a constant power of 1P for 3 minutes, and measure the battery voltage after standing for 1 hour;

[0123] Step d3: Repeat step d2 twenty times to obtain the OCV value of the full SOE range [0% SOE, 100% SOE], where the SOE step is 5%.

[0124] Step d4: Fit the test data using a quintic polynomial to obtain the OCV-SOE function relationship:

[0125] OCV(T)=p0+p1·SOE(T)+p2·SOE(T) 2 +p3·SOE(T) 3 +p4·SOE(T) 4 +p5·SOE(T) 5 .

[0126] In an optional embodiment, if the life expiration condition is not reached, steps S201 to S203 are repeatedly performed until the life expiration condition is reached, and monitoring is stopped.

[0127] In an optional embodiment, the battery life end condition is that the battery state of health (SOH) is less than 80%.

[0128] In the specific implementation process, the energy state estimated by the energy state estimation method provided by the embodiment of the present invention is compared with the actual value of the energy state, and the comparison result is as follows: Figure 4 As shown, according to Figure 4 It can be seen that the maximum error between the energy state obtained by the present invention and the actual value of the energy state is only about 1.6%. It can be seen that the present invention can achieve accurate estimation of the energy state.

[0129] In this embodiment, a device for constructing an energy state estimation model for an energy storage battery is also provided. The device is used to implement the above-mentioned embodiments and preferred implementation methods. The details that have been described will not be repeated here. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware, is also possible and contemplated.

[0130] This embodiment provides a device for constructing an energy state estimation model of an energy storage battery. Figure 5 Shown, including:

[0131] The constant power discharge module 301 is used to discharge the fresh battery at a preset constant power multiple times at different temperatures to obtain the DC internal resistance and energy state of the fresh battery after discharge;

[0132] A first function construction module 302 is configured to construct a first function of the fresh battery according to the DC internal resistance, energy state, and corresponding temperature of the fresh battery after each discharge, wherein the first function is configured to characterize the relationship between the DC internal resistance, energy state, and temperature;

[0133] The cycle aging test module 303 is used to perform a cycle aging test on a fresh battery at a standard temperature to obtain the DC internal resistance of the fresh battery corresponding to different usage times at the standard temperature;

[0134] A second function construction module 304 is configured to construct a second function based on the DC internal resistance corresponding to different usage times at a standard temperature, wherein the second function is configured to characterize the relationship between usage time and DC internal resistance;

[0135] A battery internal resistance model building module 305 is used to combine the first function and the second function to obtain a battery internal resistance model, which is used to characterize the relationship between DC internal resistance, energy state, temperature, and usage time;

[0136] The estimation model construction module 306 is used to build an energy state estimation model based on the battery internal resistance model. The energy state estimation model includes a spatial state equation and a measurement equation. The spatial state equation is used to calculate the estimated value of the energy state based on the state estimation result and operating parameters of the battery to be tested at the current moment. The measurement equation is used to calculate the terminal voltage measurement value based on the operating parameters of the battery to be tested at the current moment and the battery internal resistance model. The operating parameters of the battery to be tested are input into the energy state estimation model, and the energy state estimation model is calculated using the Kalman filter algorithm to obtain the battery energy state of the battery to be tested.

[0137] This embodiment provides an energy state estimation device for an energy storage battery, such as Figure 6 Shown, including:

[0138] The parameter acquisition module 401 is used to obtain the operating parameters of the battery to be tested, including the operating environment temperature, current, terminal voltage, and usage time of the battery to be tested;

[0139] A first estimation module 402 is configured to obtain a DC internal resistance of the battery to be tested, and determine an energy state of the battery to be tested by using a pre-established relationship between the DC internal resistance and the energy state, if the battery to be tested is in a static state and the time in the static state is greater than a static threshold.

[0140] The second estimation module 403 is used to input the operating parameters into a pre-constructed energy state estimation model, use the Kalman filter algorithm to calculate the energy state estimation model, and obtain the battery energy state of the battery to be tested. The energy state estimation model is constructed according to the energy state estimation model construction method of the energy storage battery provided in any of the above embodiments.

[0141] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0142] The energy state estimation model construction device and the energy state estimation device of the energy storage battery in this embodiment are presented in the form of functional units, where the units refer to ASIC (Application Specific Integrated Circuit) circuits, processors and memories that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0143] The embodiment of the present invention also provides a computer device having the above Figure 5 or Figure 6 The energy state estimation model building device of the energy storage battery or the energy state estimation device of the energy storage battery shown.

[0144] See also Figure 7 , Figure 7 is a structural diagram of a computer device provided by an optional embodiment of the present invention, such as Figure 7As shown, the computer device includes: one or more processors 10, memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components utilize different buses to communicate with each other and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in the memory or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Equally, multiple computer devices can be connected, and each device provides part of the necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 7 A processor 10 is taken as an example.

[0145] The processor 10 may be a central processing unit, a network processor, or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit, a programmable logic device, or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic, or any combination thereof.

[0146] The memory 20 stores instructions that can be executed by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiment.

[0147] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and application programs required for at least one function; the data storage area may store data created based on the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0148] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0149] The computer device also includes an input device 30 and an output device 40. The processor 10, the memory 20, the input device 30 and the output device 40 can be connected via a bus or other means. Figure 7 The bus connection is taken as an example.

[0150] The input device 30 can receive input digital or character information and generate key signal input related to user settings and function control of the computer device, such as a touch screen, a keypad, a mouse, a trackpad, a touch pad, an indicator stick, one or more mouse buttons, a trackball, a joystick, etc. The output device 40 can include a display device, an auxiliary lighting device (e.g., an LED), and a tactile feedback device (e.g., a vibration motor). The above-mentioned display device includes but is not limited to a liquid crystal display, a light emitting diode, a display, and a plasma display. In some optional embodiments, the display device can be a touch screen.

[0151] The embodiment of the present invention also provides a computer-readable storage medium. The above-mentioned method according to the embodiment of the present invention can be implemented in hardware, firmware, or implemented as a computer code that can be recorded in a storage medium, or implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and downloaded through a network and will be stored in a local storage medium, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state drive, etc.; further, the storage medium can also include a combination of the above-mentioned types of memory. It can be understood that a computer, a processor, a microprocessor controller or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor or hardware, the method shown in the above embodiment is implemented.

[0152] Although the embodiments of the present invention have been described with reference to the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention. Such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A method for constructing an energy state estimation model of an energy storage battery, characterized in that: include: Discharging the fresh battery at a preset constant power multiple times at different temperatures to obtain the DC internal resistance and energy state of the fresh battery after discharge; constructing a first function of the fresh-state battery according to the DC internal resistance, energy state, and corresponding temperature of the fresh-state battery after each discharge, wherein the first function is used to characterize the relationship between the DC internal resistance, energy state, and temperature; Perform a cycle aging test on fresh batteries at a standard temperature to obtain the DC internal resistance of the fresh batteries corresponding to different usage times at the standard temperature; constructing a second function based on the DC internal resistance corresponding to different usage times at a standard temperature, wherein the second function is used to characterize the relationship between the usage time and the DC internal resistance; Combining the first function and the second function to obtain a battery internal resistance model, wherein the battery internal resistance model is used to characterize the relationship between DC internal resistance, energy state, temperature, and usage time; An energy state estimation model is constructed based on the battery internal resistance model. The energy state estimation model includes a spatial state equation and a measurement equation. The spatial state equation is used to calculate an estimated value of the energy state based on the current state estimation result and operating parameters of the battery to be tested. The measurement equation is used to calculate a terminal voltage measurement value based on the current operating parameters of the battery to be tested and the battery internal resistance model. The operating parameters of the battery to be detected are input into the energy state estimation model, and the energy state estimation model is calculated using a Kalman filter algorithm to obtain the battery energy state of the battery to be detected.

2. The method according to claim 1, characterized in that The step of discharging a fresh battery at a preset constant power multiple times to obtain the DC internal resistance and energy state of the fresh battery after discharge includes: After the fresh battery is allowed to stand at a preset temperature for a first preset time, the fresh battery is charged to a full-charge state at the preset constant power, and after the fresh battery is allowed to stand for a second preset time, the current DC internal resistance of the fresh battery is measured, and the value of the current energy state is determined as a first state value; discharging the fresh battery at the preset constant power for a third preset time, measuring the current DC internal resistance of the fresh battery after standing for a second preset time and recording the current number of discharges, and decrementing the first state value by a preset step size to obtain a current energy state value, wherein the preset step size is determined based on the first preset number of discharges; If the current number of discharges is less than the first preset number, the value of the current energy state is used as the first state value, and the steps of discharging the fresh battery at the preset constant power for a third preset time and measuring the current DC internal resistance of the fresh battery after standing for a second preset time are repeatedly performed until the current number of discharges is greater than or equal to the first preset number, thereby obtaining multiple DC internal resistances of the fresh battery at the preset temperature and the energy states corresponding to each DC internal resistance.

3. The method according to claim 1, characterized in that The cyclic aging test is performed on the fresh battery at the standard temperature to obtain the DC internal resistance of the fresh battery corresponding to different usage times at the standard temperature, including: After the fresh battery is left at a standard temperature for a fourth preset time, the fresh battery is charged to a first preset voltage at the preset constant power and left at a fifth preset time; Discharging the fresh battery at a preset constant power to a second preset voltage, and charging the fresh battery at the preset constant power to the first preset voltage after standing for a fifth preset time, wherein completing one discharge and charge cycle is considered a cycle, and after a second preset number of cycles, the current usage time, DC internal resistance, and discharge energy are recorded; If the current discharge energy is greater than the preset value, repeatedly discharge the fresh battery at a preset constant power to a second preset voltage, charge the fresh battery at the preset constant power to the first preset voltage after standing for a fifth preset time, completing one discharge and charge as one cycle, and recording the current usage time, DC internal resistance, and discharge energy after the second preset number of cycles, until the current discharge energy is greater than the preset value and less than or equal to the preset value, thereby obtaining the DC internal resistance of the fresh battery corresponding to different usage times at the standard temperature.

4. The method according to claim 1, wherein The spatial state equation includes a first state equation and a second state equation, The first state equation is used to calculate an estimated value of the energy state based on a state estimation result at a current moment and a current maximum charging energy in the operating parameters. When the battery to be tested is in a charging state, the battery energy state of the battery to be tested is calculated using the first state equation; The second state equation is used to calculate an estimated value of the energy state based on the state estimation result at the current moment and the current maximum discharge energy in the operating parameters. When the battery to be tested is in a discharging state, the battery energy state of the battery to be tested is calculated using the second state equation.

5. The method according to claim 4, characterized in that The maximum charging energy and the maximum discharging energy are obtained according to the operating environment temperature and operating time of the battery to be detected in the operating parameters and the pre-established corresponding relationship between the operating environment temperature, operating time and the maximum charging and discharging energy.

6. The method according to claim 5, characterized in that The steps of establishing the corresponding relationship between the operating environment temperature, the operating time and the maximum charge and discharge energy include: Perform constant power charge and discharge tests on fresh batteries at different temperatures at a preset power to obtain the rated charge energy and rated discharge energy of the fresh batteries at different temperatures; Perform a cycle aging test on fresh batteries at a standard temperature to obtain the maximum charge energy and maximum discharge energy of the fresh batteries corresponding to different usage times at the standard temperature; The maximum charging energy and maximum discharging energy of the fresh battery at different temperatures are corrected for different usage times at the standard temperature according to the rated charging energy and rated discharging energy of the fresh battery at different temperatures, so as to obtain the maximum charging energy and maximum discharging energy of the fresh battery at different temperatures and different usage times, and establish a corresponding relationship between the operating environment temperature, operating time and maximum charging and discharging energy.

7. The method according to claim 6, characterized in that The steps of performing constant power charge and discharge tests on fresh batteries at different temperatures at a preset power to obtain rated charge energy and rated discharge energy of the fresh batteries at different temperatures include: At any temperature, after the fresh battery is left at a preset temperature for a first preset time, the fresh battery is discharged to a second voltage at the preset constant power; After standing for a second preset time, charging the fresh battery to a first voltage at the preset constant power; after standing for a second preset time, discharging the fresh battery to a second voltage at the preset constant power; standing for a second preset time, and recording the charging energy and the discharging energy; The rated charging energy and the rated discharging energy of the fresh battery at the current temperature are determined according to the charging energy and the discharging energy.

8. A method for estimating the energy state of an energy storage battery, characterized in that: include: Acquire operating parameters of the battery to be tested, wherein the operating parameters include operating environment temperature, current, terminal voltage, and usage time of the battery to be tested; If the battery to be tested is in a static state and the time in the static state is greater than a static threshold, obtaining the DC internal resistance of the battery to be tested, and determining the energy state of the battery to be tested by using a pre-established relationship between the DC internal resistance and the energy state; Otherwise, the operating parameters are input into a pre-constructed energy state estimation model, and the energy state estimation model is calculated using a Kalman filter algorithm to obtain the battery energy state of the battery to be tested. The energy state estimation model is constructed according to the energy state estimation model construction method of the energy storage battery according to any one of claims 1-7.

9. A device for constructing an energy state estimation model for an energy storage battery, characterized in that: The device comprises: A constant power discharge module is used to discharge the fresh battery at a preset constant power multiple times at different temperatures to obtain the DC internal resistance and energy state of the fresh battery after discharge; a first function construction module, configured to construct a first function of the fresh battery according to the DC internal resistance, energy state, and corresponding temperature of the fresh battery after each discharge, wherein the first function is used to characterize the relationship between the DC internal resistance, energy state, and temperature; Cyclic aging test module, used to perform cyclic aging test on fresh batteries at standard temperature to obtain the DC internal resistance of fresh batteries corresponding to different usage time at standard temperature; A second function building module is used to build a second function according to the DC internal resistance corresponding to different usage times at a standard temperature, wherein the second function is used to characterize the relationship between the usage time and the DC internal resistance; a battery internal resistance model construction module, configured to combine the first function and the second function to obtain a battery internal resistance model, wherein the battery internal resistance model is used to characterize the relationship between DC internal resistance, energy state, temperature, and usage time; An estimation model construction module is used to build an energy state estimation model based on the battery internal resistance model. The energy state estimation model includes a spatial state equation and a measurement equation. The spatial state equation is used to calculate the estimated value of the energy state based on the state estimation result and operating parameters of the battery to be detected at the current moment. The measurement equation is used to calculate the terminal voltage measurement value based on the operating parameters of the battery to be detected at the current moment and the battery internal resistance model. The operating parameters of the battery to be detected are input into the energy state estimation model, and the energy state estimation model is calculated using a Kalman filter algorithm to obtain the battery energy state of the battery to be detected.

10. An energy state estimation device for an energy storage battery, characterized in that: include: A parameter acquisition module, configured to acquire operating parameters of the battery to be tested, wherein the operating parameters include operating environment temperature, current, terminal voltage, and usage time of the battery to be tested; a first estimation module, configured to obtain a DC internal resistance of the battery to be detected and determine an energy state of the battery to be detected by using a pre-established relationship between the DC internal resistance and the energy state, if the battery to be detected is in a static state and the time in the static state is greater than a static threshold; A second estimation module is used to input the operating parameters into a pre-constructed energy state estimation model, and use a Kalman filter algorithm to calculate the energy state estimation model to obtain the battery energy state of the battery to be tested. The energy state estimation model is constructed according to the energy state estimation model construction method of the energy storage battery according to any one of claims 1-7.

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