A method for predicting multi-state parameters of a battery pack of underwater equipment

Abstract

In order to solve the technical problem that the acquisition accuracy of each parameter of the battery pack is low in the prior art and the efficiency and safety of the battery pack of the underwater equipment cannot be accurately controlled, a method for predicting multi-state parameters of a battery pack of underwater equipment is provided, which is suitable for state parameter prediction of secondary batteries. The method fully considers the influence factors of the state of the battery pack, such as the full service cycle of the battery pack of the underwater equipment, the dynamic coupling effect of the deep sea environmental pressure and temperature, and the inconsistency of the capacity loss of the battery pack in the standby preparation stage, and the state parameter acquisition in the working cruise stage is carried out on the basis of the standby preparation stage, which greatly improves the accuracy of each parameter of the underwater equipment battery pack, and realizes the accurate prediction of the remaining power of the underwater equipment battery pack in the standby stage under the deep sea working condition, and the accurate prediction of the open circuit voltage, current, temperature and remaining power of the underwater equipment battery pack in the working cruise stage under the deep sea working condition.

Description

Technical Field

[0001] This invention relates to the field of underwater equipment battery pack technology, and more particularly to a method for predicting multi-state parameters of underwater equipment battery packs. The multi-state parameters of this invention include the remaining charge of each individual cell in the battery pack during the standby preparation phase, and the temperature, remaining charge, voltage, and current of each individual cell in the battery pack at each discharge moment during the operational cruise phase. The underwater equipment described in this invention includes underwater vehicles, pre-positioned underwater unmanned mobile platforms, underwater biomimetic submersibles, and underwater gliders, etc. Background Technology

[0002] The vast expanse of the deep sea is a strategic high ground in the competition among major maritime powers worldwide. With the continuous advancement of national strategies, underwater vehicles, pre-positioned unmanned mobile platforms, biomimetic underwater vehicles, and underwater gliders are gradually playing a leading role. In recent years, benefiting from the increased energy density of lithium batteries, underwater equipment is entering an all-electric era. In deep-sea applications, the entire service life of underwater equipment consists of two phases: the initial pre-positioning and standby preparation phase, and the operational cruise phase after receiving mission instructions. The standby preparation phase is typically accompanied by the self-discharge of the underwater equipment's battery pack; the operational cruise phase is typically accompanied by the high-current discharge of the underwater equipment's battery pack.

[0003] Accurate acquisition of various parameters of underwater equipment battery packs directly affects key technical indicators such as speed and range during deep-sea operations. Currently, there are three main issues that are overlooked by professionals when predicting the state parameters of underwater equipment:

[0004] First, the existing scheme does not consider the dynamic coupling effect of the deep-sea environment pressure and temperature on the state parameters of the battery pack during the standby preparation phase and the operational cruise phase of the underwater equipment.

[0005] Second, the existing solutions only focus on the operational cruise phase, ignoring the power loss caused by inconsistent self-discharge of the battery pack during the standby preparation phase.

[0006] Third, the existing solution ignores the impact of the inconsistent temperature distribution of the internal battery pack on the battery pack status during the operational cruise phase of the underwater equipment.

[0007] The deficiencies in the above three aspects have greatly reduced the accuracy of obtaining various parameters of the battery pack, which is very detrimental to the precise control of the performance and safety of the battery pack of underwater equipment. Summary of the Invention

[0008] To address the technical problem that existing solutions have low accuracy in acquiring various parameters of the battery pack, which is not conducive to the precise control of the performance and safety of underwater equipment battery packs, this invention provides a method for predicting multiple state parameters of underwater equipment battery packs, applicable to the prediction of state parameters of secondary batteries.

[0009] The technical solution of this invention is:

[0010] A method for predicting multi-state parameters of underwater equipment battery packs, characterized by the following steps:

[0011] Step 1: Predict the remaining charge of each individual cell in the battery pack at the end of the standby preparation phase;

[0012] Step 1.1 Select multiple individual cells of the same type as the battery pack of the underwater equipment to conduct storage experiments and record the experimental data;

[0013] Step 1.2 Based on the experimental data, establish the mathematical correlation Q = f(T, p, t); Q is the remaining charge of a single battery cell, T is the ambient temperature, p is the ambient pressure, and t is the storage time;

[0014] Step 1.3 Based on the mathematical correlation Q=f(T,p,t), combined with the normal distribution function of battery capacity loss, calculate the remaining charge of each individual battery in the battery pack at the end of the standby preparation stage, so as to predict the state parameters of each individual battery in the battery pack at the end of the standby preparation stage.

[0015] Step 2: Predict the temperature, remaining charge, voltage, and current of each individual cell in the battery pack at each discharge moment during the operational cruise phase;

[0016] Step 2.1 Divide the individual cells that have completed the storage experiment in Step 1.1 and have consistent storage environment parameters and storage time into one experimental group to conduct battery parameter measurement experiments. Based on the battery parameter measurement experiment data, establish mathematical correlations U=f(T′,p′,SOC) and R=f(T′,t,p′,SOC); U is the open-circuit voltage of the individual cell; R is the internal resistance of the individual cell; T′ is the experimental temperature, which is equal to the temperature of the individual cell during parameter measurement; p′ is the experimental pressure, which is equal to the environmental pressure of the individual cell during parameter measurement; t is the storage time; SOC is the state of charge of the individual cell.

[0017] Step 2.2: Use the remaining charge of each individual battery obtained in Step 1.3 as its initial charge value Q during the operational cruise phase. 0,s2,j Based on the initial power value Q 0,s2,j Calculate the state of charge (SOC) of each individual cell at the initial discharge time t0. 0,j j represents the cell number in the battery pack;

[0018] Step 2.3 Utilize the aforementioned correlations U=f(T′,p′,SOC), R=f(T′,t,p′,SOC), and the state of charge (SOC) of each individual cell at the initial discharge time t0. 0,j Battery temperature T′0,j and the pressure p′ of the seawater environment 0,j Calculate the open-circuit voltage U of each individual cell at the initial discharge time t0. 0,j and internal resistance R 0,j The battery temperature T′ 0,j Equal to the temperature of the seawater environment in which the underwater equipment was located at that time; the seawater environment pressure p′ 0,j The calculation is based on the depth at which the underwater equipment is located at the initial discharge moment t0. The depth at which the underwater equipment is located at the initial discharge moment t0 is the depth during the standby preparation stage, which is a known quantity.

[0019] Step 2.4 Based on the underwater equipment's speed, the battery pack's output power, the battery pack's series-parallel connection structure, and the open-circuit voltage U... 0,j and internal resistance R 0,j Calculate the current I of each individual cell at the initial discharge time t0. 0,j ;

[0020] Step 2.5 Obtain the heat generation h of each individual cell at the initial discharge time t0 using the P2D-based electrochemical-thermal coupling model. 0,j ;

[0021] Step 2.6 Using the heat generated h 0,j As the initial heat source for a single cell, the battery temperature T′ of each cell at discharge time t1 is obtained through thermal analysis of the battery pack. 1,j ;

[0022] Step 2.7 Utilize the battery temperature T′ 1,j Using a P2D-based electrochemical-thermal coupling model, the lithium-ion concentrations c at the positive and negative electrodes of each individual cell at discharge time t1 were calculated. 1,j Therefore, based on the lithium ion concentration c of the positive and negative electrodes 1,j Calculate the corresponding state of charge (SOC). 1,j Finally, based on the state of charge (SOC) 1,j The charge Q of each individual cell at the initial moment of discharge 0,s2,j The remaining charge Q of each individual cell at discharge time t1 is calculated. 1,s2,j ;

[0023] Step 2.8 uses the same method as step 2.3, utilizing the temperature T′ of each individual cell at discharge time t1. 1,j State of charge (SOC) 1,j and the pressure p′ of the surrounding seawater environment 1,j Calculate the open-circuit voltage U of each individual cell at discharge time t1. 1,j and internal resistance R 1,j Using the same method as in step 2.4, the open-circuit voltage U is utilized. 1,jand internal resistance R 1,j Calculate the current I of each individual cell at discharge time t1. 1,j The seawater environmental pressure p′ 1,j The depth of the underwater equipment at discharge time t1 is calculated based on the speed and heading parameters of the underwater equipment at that time.

[0024] Step 2.9: Using the same method as steps 2.5-2.8, calculate the discharge times t2, t3, ..., t f The system monitors the temperature, remaining charge, voltage, and current of each individual cell, enabling prediction of the state parameters of each cell in the battery pack at each discharge moment during the operational cruise phase.

[0025] Furthermore, the storage experiment method described in step 1.1 is as follows: the selected batteries are divided into s groups, and the batteries in the i-th group are placed at a temperature T. i and pressure p i In the storage environment, at least three individual cells are taken out at set intervals and placed under normal temperature and pressure for standard capacity testing. The average value of the individual cell capacity test values ​​is then taken as the remaining capacity Q of the individual cell under this storage environment. ik i represents the battery group number, which is 1, 2, ..., s, where s is the maximum number of groups; T1, ..., T i ...T s The intervals are 4-25℃ and have no overlapping regions; p1,…p i ,…p s The sub-interval is 101.325 kPa - 90 MPa with no overlapping regions; Q ik The storage capacity is the battery capacity under the storage environment parameters and storage time corresponding to the single cell taken from the i-th battery group for the k-th time; the total duration of the storage experiment is determined according to the duration of the underwater equipment standby preparation phase.

[0026] Further, the storage experiment method described in step 1.1 is as follows: After grouping the selected batteries, they are stored under various common and / or typical environmental parameters. At set intervals, at least three individual batteries are taken out and subjected to standard capacity tests at room temperature and pressure. The average value of the individual battery capacity test values ​​is calculated as the remaining power of the individual battery under the corresponding storage environment and storage time, and the experimental data is recorded. The total duration of the storage experiment is determined according to the duration of the underwater equipment standby preparation phase.

[0027] Correspondingly,

[0028] In step 1.2, a mathematical correlation Q is first established based on the experimental data under normal temperature and pressure. atm =f(T) atm ,p atmThe correction parameters are determined, and the mathematical correlations under normal temperature and pressure are corrected using the correction parameters to finally obtain the mathematical correlations under different ambient temperatures and pressures:

[0029]

[0030] Where: A and B are correction parameters, which can be determined from experimental values ​​through multivariate data fitting in Matlab software; T atm This is the normal temperature value; p atm This is the atmospheric pressure value; Q atm The remaining battery charge is the amount stored at room temperature and pressure for a time t.

[0031] Furthermore, in step 2.1: the individual cells that have completed the storage experiment in step 1.1 and have the same storage environment parameters and storage time are divided into an experimental group, and battery parameter measurement experiments are carried out in environments with different temperatures and pressures.

[0032] Furthermore, in step 2.1, the individual cells that completed the storage experiment in step 1.1 and whose storage environment parameters and storage time are consistent are divided into an experimental group. Battery parameter measurement experiments are then conducted under various common and / or typical environmental parameters. Based on the battery parameter measurement experimental data, a mathematical correlation formula U under normal temperature and pressure is established. atm =f(T) atm ,p atm (,SOC) and R atm =f(T) atm ,t,p atm The SOC) was determined and correction parameters were used to correct the mathematical correlations at room temperature and pressure, ultimately yielding mathematical correlations for different experimental temperatures and pressures:

[0033]

[0034]

[0035] Among them, α, β, γ and ω are correction parameters, which can be determined by multivariate data fitting using Matlab software based on experimental data of battery parameter measurement.

[0036] Furthermore, step 2.4 specifically involves:

[0037] Step 2.4.1 Calculate the battery pack output power using the following formula:

[0038]

[0039] Where: P is the output power of the battery pack at a given speed; ρ is the density of seawater; S is the maximum cross-sectional area of ​​the underwater equipment; v is the speed of the underwater equipment; C dη is the drag coefficient; η1 is the thruster efficiency in the underwater equipment; η2 is the efficiency of the motor and drive in the underwater equipment.

[0040] Step 2.4.2 Based on the open-circuit voltage U calculated in Step 2.3 0,j and internal resistance R 0,j Based on the series and parallel connection of the underwater equipment battery pack, the open-circuit voltage U0 and internal resistance R0 of the underwater equipment battery pack are calculated.

[0041] Step 2.4.3 Combine the battery pack output power obtained in Step 2.4.1 and the battery pack open-circuit voltage U obtained in Step 2.4.2. 0,j and internal resistance R 0,j Substitute into the formula Calculate the total current I0 of the battery pack;

[0042] Step 2.4.4 Based on the series and parallel connection method of the battery pack and the total current I0 of the battery pack, calculate the current I flowing through each individual battery cell at the initial time t0 of the underwater equipment battery pack. 0,j .

[0043] Furthermore, in step 2.6, the influence of fluid buoyancy on the battery pack temperature is considered during the thermal analysis calculation of the battery pack.

[0044] Furthermore, in step 2.7: the lithium ion concentration c of the positive and negative electrodes 1,j The mass transfer equations for lithium ions at the positive and negative electrodes in the P2D-based electrochemical-thermal coupling model were calculated; the state of charge (SOC) was calculated. 1,j According to formula c 1,j =c max ×SOC 1,j Calculate; the remaining power Q 1,s2,j According to formula Q 1,s2,j =SOC 1,j ×Q 0,s2,j calculate.

[0045] Furthermore, the underwater equipment battery pack is composed of pressure-bearing single-cell batteries.

[0046] The beneficial effects of this invention are:

[0047] 1. This invention fully considers the dynamic coupling effects of the entire service life of underwater equipment battery packs (standby preparation phase and operational cruise phase), deep-sea environmental pressure (especially high pressure and dynamic changes) and temperature (low temperature and dynamic changes), as well as the inconsistency of battery pack capacity loss during the standby preparation phase. These factors affect the state of the battery pack. Furthermore, the acquisition of state parameters during the operational cruise phase is based on the standby preparation phase, which greatly improves the accuracy of the acquired parameters of the underwater equipment battery pack. It enables accurate prediction of the remaining power of the underwater equipment battery pack under deep-sea conditions during the standby phase, and accurate prediction of state parameters such as open-circuit voltage, current, temperature, and remaining power under deep-sea conditions during the operational cruise phase. This is of great significance for improving the research and development of high-performance underwater equipment in my country.

[0048] 2. This invention uses an electrochemical-thermal coupled calculation model in the process of evaluating the temperature of individual cells, and specifically considers the inconsistency in the temperature distribution of the battery pack caused by the influence of fluid buoyancy on the temperature of each individual cell in the underwater equipment battery pack, thereby further improving the prediction accuracy of the state parameters of each individual cell in the underwater equipment battery pack.

[0049] 3. Considering that the long standby preparation period can also affect the battery's internal resistance, this invention conducts battery parameter measurement experiments on batteries that have completed storage experiments during the operational cruise phase. Then, based on the battery parameter measurement experiment data, a mathematical correlation is established between the open-circuit voltage and internal resistance of a single battery and different operating parameters, providing a basis for accurately predicting the battery's open-circuit voltage and current during the operational cruise phase.

[0050] 4. This invention has universal applicability and can be used for the accurate prediction of state parameters such as voltage, current, temperature, and remaining charge of both pressurized and non-pressurized battery packs in underwater equipment, especially suitable for the accurate prediction of pressurized battery packs. The underwater equipment battery packs to which this invention is applicable include underwater vehicle battery packs, underwater pre-installed unmanned mobile platform battery packs, underwater biomimetic submersible battery packs, and underwater glider battery packs, etc. Attached Figure Description

[0051] Figure 1 This is a flowchart of the method for evaluating multi-state parameters of an underwater vehicle battery pack according to the present invention. Figure 1 The mid-operation cruise phase only shows the state acquisition process of each individual cell in the battery pack from the initial discharge time t0 to the discharge time t1. The state parameters of the battery pack at different discharge times can be obtained by iterative calculation. Detailed Implementation

[0052] The present invention will be further described below with reference to the accompanying drawings.

[0053] This invention provides a method for predicting multiple state parameters of underwater equipment battery packs, involving two main stages: state parameter prediction during the standby preparation phase (typically more than 6 months) and state parameter prediction during the operational cruise phase. For the standby preparation phase, since the battery pack does not undergo high-current discharge during this phase, only the remaining charge Q of each individual cell in the battery pack at the end of this phase is obtained. s1 For the operational cruise phase, it refers to the battery pack's internal resistance state and remaining charge Q at the end of the standby preparation phase. s1 Based on this, obtain the remaining charge Q of each individual cell in the battery pack at different times. s2 Current, temperature, and open-circuit voltage.

[0054] like Figure 1 As shown, taking an underwater vehicle battery pack as an example, the method for predicting the multi-state parameters of the battery pack state provided by this invention specifically includes the following steps:

[0055] Step 1: Predict the state parameters of the battery pack at the end of the standby preparation phase;

[0056] Step 1.1: Determine the type of battery used in the underwater vehicle's battery pack;

[0057] From the perspective of the positive electrode material, batteries can be classified into lithium cobalt oxide batteries, lithium iron phosphate batteries, and ternary nickel-cobalt-manganese batteries, etc.

[0058] Batteries can be classified into pressurized batteries and non-pressurized batteries based on whether they are pressure-bearing. For battery packs composed of non-pressurized batteries, the pressure of the external seawater is borne by the hull of the underwater vehicle, and the external pressure of the battery pack is assumed to be atmospheric pressure. For battery packs composed of pressurized batteries, the pressure of the battery pack is related to its operating depth, and the relationship between the operating depth and pressure needs to be established in advance. Since the operating depth is a known parameter set by the underwater vehicle, the seawater environmental pressure corresponding to the corresponding operating depth can be calculated based on the relationship between the operating depth and pressure.

[0059] Step 1.2: Obtain the mathematical relationship between the battery's remaining current and the storage environment parameters;

[0060] Step 1.2.1 The water temperature where the underwater vehicle is located is typically between 4-25℃, and the environmental pressure is typically between 101.325 kPa (normal pressure) and 90 MPa (deep water pressure). n individual batteries of the same system type as the underwater vehicle's battery pack can be selected and divided into s groups. These s groups of batteries are then placed under different storage environment parameters for storage experiments. The specific experimental procedure is as follows: the storage environment parameter is set to temperature T. i and pressure p iThe i-th group of batteries is stored in this environment. At least three individual batteries are taken out at set intervals and placed in a standard capacity test environment at room temperature and pressure. The average value of these individual battery capacity test values ​​is calculated, and this average value is used as a single battery in the corresponding storage environment parameter (temperature T). i Pressure p i Storage time d ik Battery capacity Q under ) ik And record. Where i represents the battery number of each group in the s battery packs, taking values ​​1, 2, ..., s; T1, ..., T... i ...T s The intervals are 4-25℃ and have no overlapping regions; p1,…p i ,…p s The sub-interval is 101.325 kPa - 90 MPa with no overlapping regions; Q ik This refers to the battery capacity of the individual battery taken from the i-th battery group for the k-th time under the corresponding storage environment parameters. Generally, batteries are taken out every 10 days for standard capacity testing. The specific interval can be flexibly determined according to the required experimental efficiency and accuracy. The total storage experiment duration is determined based on the duration of the underwater vehicle's standby preparation phase.

[0061] Step 1.2.2 Calculate the battery capacity Q of the single cell obtained in Step 1.2.1 under different storage environment parameters. ik By fitting a polynomial or power function, a mathematical relationship is obtained between the remaining battery capacity Q and the storage environment parameters (temperature, pressure) and storage time, denoted as Q=f(T,p,t).

[0062] Steps 1.2.1-1.2.2 above establish a more accurate mathematical correlation between the remaining battery capacity and storage environment parameters and storage time by conducting storage experiments under different pressure and temperature environments. In practice, considering time and economic costs, storage experiments can also be conducted only under various common and / or typical environmental parameters, including ambient temperature and pressure environments, to establish a mathematical correlation Q between the remaining battery capacity and ambient temperature and pressure storage environment and storage time. atm =f(T) atm ,p atm By determining the correction parameters and then using these parameters to correct the remaining battery capacity, the mathematical relationship between the remaining battery capacity and the storage environment parameters and storage time can be obtained:

[0063]

[0064] Where: A and B are correction parameters, which can be determined from experimental values ​​through multivariate data fitting in Matlab software; T atm This is the temperature value at room temperature; patm This is the pressure value under normal atmospheric pressure.

[0065] While the accuracy of establishing mathematical correlations using the modified method is not as high as that of the methods described in steps 1.2.1-1.2.2 above, it can greatly shorten the experimental time and cost.

[0066] Step 1.3 Based on the mathematical correlation obtained in Step 1.2, predict the remaining charge of any single cell j in the battery pack at the end of the standby preparation phase.

[0067] Although the battery cells are selected from the same batch during assembly, there are certain manufacturing inconsistencies between the individual cells. After a long standby preparation period, the capacity loss (power loss) of each individual cell follows a normal distribution function. Based on the mathematical correlation between the remaining battery capacity and storage environment parameters obtained in step 1.2, combined with the normal distribution function of battery capacity loss... The remaining charge Q of cell j in the battery pack can be calculated under the corresponding storage environment parameters. s1,j j takes values ​​of 1, 2, ..., z, where z is the number of individual cells in the underwater vehicle's battery pack.

[0068] Step 2: Predict the state parameters of the battery pack at each discharge moment during the operational cruise phase;

[0069] Step 2.1: Establish mathematical relationships between the open-circuit voltage of a single cell and different operating parameters (temperature, pressure, SOC), and between the internal resistance of a single cell and different operating parameters (temperature, pressure, standby preparation storage time, SOC).

[0070] Step 2.1.1 Divide the individual cells that have completed the storage experiment in Step 1.2.1 and have the same storage environment parameters and storage time into one experimental group (the internal resistance, remaining capacity, and state of charge of the individual cells in the same experimental group are approximately the same, while the internal resistance, remaining capacity, and state of charge of the individual cells in different experimental groups are different). Place each experimental group in a temperature T′ h The pressure is p′ h In an environment where the battery temperature equals the experimental temperature, battery parameters are measured and recorded along with the corresponding experimental data; where T′ h and p′ h These represent the experimental temperature and pressure during the battery parameter measurement experiment in the h-th experimental group, respectively; 0≤T′ h For temperatures ≤70℃, interpolation should be used to estimate values ​​outside this temperature range; 0.1MPa≤p h ≤90MPa.

[0071] Step 2.1.2 Based on the data collected in the battery parameter measurement experiment in Step 2.1.1, establish a multi-parameter coupling correlation spectrum between the open-circuit voltage U of a single cell and the experimental temperature T′, experimental pressure p′, and state of charge (SOC), i.e., U=f(T′,p′,SOC);

[0072] Step 2.1.3 Based on the data collected in the battery parameter measurement experiment in Step 2.1.1, establish a multi-parameter coupling correlation spectrum between the internal resistance R of a single cell and the experimental temperature T′, environmental pressure p′, storage time t, and state of charge (SOC), i.e., R = f(T′, t, p′, SOC);

[0073] Steps 2.1.2 and 2.1.3 above can be interchanged.

[0074] Similarly, steps 2.1.2 and 2.1.3 above establish a more accurate mathematical correlation by conducting battery parameter measurement experiments for different experimental groups under different experimental pressures and temperatures. In practice, considering time and economic costs, battery parameter measurement experiments can also be conducted only under various common and / or typical environments, including room temperature and pressure, to establish the mathematical correlation U under the room temperature and pressure environment. atm =f(T) atm ,p atm (,SOC) and R atm =f(T) atm ,t,p atm By determining the correction parameter (SOC) and then correcting it, the mathematical correlations for different environmental pressures and experimental temperatures can be obtained:

[0075]

[0076]

[0077] Where α, β, γ, and ω are correction parameters, which can be determined by multivariate data fitting using Matlab software based on experimental values; U atm and R atm These are the open-circuit voltage and internal resistance of a single cell measured at room temperature and pressure.

[0078] While the accuracy of establishing mathematical correlations using the modified method is not as high as that of the methods described in steps 2.1.2-2.1.3 above, it can greatly shorten the experimental time and cost.

[0079] Step 2.2: Calculate the State of Charge (SOC) of cell j in the battery pack at the initial discharge time t0 during the operational cruise phase. 0，j The specific method is as follows:

[0080] The remaining charge Q of the individual cell numbered j in the battery pack at the end of the standby preparation phase, obtained in step 1.3. s1，j Q is the initial value of the charge of cell j in the battery pack during the operational cruise phase. 0，s2，j , denoted as Q 0，s2，j =Q s1，j The SOC of cell j in the battery pack at the initial discharge time t0 during the operational cruise phase is calculated using the following formula. 0，j :

[0081]

[0082] Where: Q0 is the charge of a freshly charged battery; j is 1, 2, ..., z, where z is the number of individual cells in the underwater vehicle's battery pack.

[0083] Step 2.3 Calculate the open-circuit voltage U of cell j in the battery pack at the initial discharge time t0 during the operational cruise phase. 0，j and internal resistance R 0，j The specific method is as follows:

[0084] The battery temperature T′ at the initial discharge time t0 0，j (Given quantities, equal to seawater ambient temperature), and the seawater ambient pressure p′. 0，j (Known quantities, obtained from the sea depth where the vehicle is located and its relationship with pressure) and the state of charge (SOC) of the single cell numbered j at the initial discharge time t0 calculated in step 2.2. 0，j Substituting these values ​​into the correlation equation U = f(T′, p′, SOC) between the open-circuit voltage and operating parameters of the individual cell obtained in step 2.1.2, the open-circuit voltage U of the individual cell numbered j in the battery pack at the initial discharge time t0 can be calculated. 0,j j takes the values ​​1, 2, ..., z respectively;

[0085] The battery temperature T′ at the initial discharge time t0 0,j Seawater environmental pressure p′ 0，j The standby preparation stage storage time and the state of charge (SOC) of the single cell numbered j at the initial discharge time t0 calculated in step 2.2. 0,j Substituting these values ​​into the relationship between the internal resistance R of the individual cell and the operating parameters obtained in step 2.1.3, R = f(T′, t, p′, SOC), the resistance R of the individual cell numbered j in the battery pack at the initial discharge time t0 can be calculated. 0,j j takes values ​​of 1, 2, ..., z.

[0086] Since the time used during the operational cruise phase of an underwater vehicle is very short and negligible compared to the pre-set standby phase, this invention uses the storage time during the standby preparation phase to calculate the battery internal resistance.

[0087] Step 2.4: Calculate the current I flowing through cell j in the battery pack at the initial discharge time t0 during the operational cruise phase. 0，j The specific method is as follows:

[0088] Step 2.4.1 Calculate the output power of the battery pack based on the speed requirements of the underwater vehicle. The specific calculation formula is as follows:

[0089]

[0090] Where: P is the output power of the battery pack at a given speed; ρ is the density of seawater; S is the maximum cross-sectional area of ​​the vehicle; v is the speed of the vehicle; C d η is the drag coefficient; η1 is the propulsion efficiency in the aircraft; η2 is the efficiency of the motor and drive in the aircraft.

[0091] Step 2.4.2 Calculate the open-circuit voltage U0 and resistance R0 of the vehicle's battery pack at the initial discharge time t0 during the operational cruise phase;

[0092] Based on the open-circuit voltage U of the individual cell numbered j in the battery pack at the initial discharge time t0 calculated in step 2.3 0，j and internal resistance R 0，j By combining the specific series and parallel connection methods of the individual batteries in the aircraft battery pack, the open-circuit voltage U0 and resistance R0 of the aircraft battery pack can be calculated.

[0093] Step 2.4.3 Calculate the current I0 of the vehicle's battery pack at the initial discharge time t0 during the operational cruise phase;

[0094] Due to the output power of the battery pack Substitute the battery pack output power obtained in step 2.4.1, the battery pack open-circuit voltage U0 and internal resistance R0 obtained in step 2.4.2 into the formula. Calculate the total current I0 of the battery pack.

[0095] Step 2.4.4 Based on the series and parallel connection method of the battery pack and the total battery pack current I0 obtained in Step 2.4.3, calculate the current I flowing through the individual cell numbered j at the initial discharge moment t0 of the vehicle's battery pack. 0，j j takes values ​​of 1, 2, ..., z.

[0096] Step 2.5: Obtain the heat generation h of cell j in the battery pack at the initial discharge time t0 during the operational cruise phase. 0,j The specific method is as follows:

[0097] To more accurately assess the heat generation of the battery, the current I flowing through the single cell numbered j at the initial discharge time t0 calculated in step 2.4.4 is used. 0，j A P2D-based electrochemical-thermal coupling model was established. This model requires the pre-input of the inherent parameters of the battery materials. The following discussion focuses on the influence of externally input variables on the model. Battery heat generation includes reaction heat, polarization heat, and ohmic heat. In the electrochemical-thermal coupling model, the solutions for reaction heat and polarization heat are related to the lithium-ion concentration in the solution and the battery temperature. The solution for ohmic heat is related to the conductivity of the solid phase material, the lithium-ion concentration in the liquid phase material, and the battery temperature. At the initial discharge time t0, the current I of the single cell numbered j is... 0,j and temperature (equal to ambient temperature T) 0,j It is known that the lithium-ion concentration c at the positive and negative electrodes of a single cell is... 0,j It can be derived from equation c 0,j =c max ×SOC 0,j Calculations show that c max To set a reference maximum lithium-ion concentration, the reaction heat, polarization heat, and ohmic heat of a single cell at the initial discharge time t0 can be calculated. Adding these three together, the total heat generation h of cell j at the initial discharge time t0 can be calculated. 0,j j takes the values ​​1, 2, ..., z respectively;

[0098] Step 2.6: Calculate the temperature T′ of the individual cell numbered j in the battery pack at discharge time t1. 1,j ;

[0099] A 3D model of the battery pack is created using 3D modeling software such as UG or Solidworks. The model is then imported into software such as Fluent Meshing or ICEM for mesh generation. The meshed model is then imported into Fluent software for thermal analysis calculation of the battery pack to obtain the temperature of each individual cell in the battery pack.

[0100] Due to the buoyancy of the fluid medium in the gaps between battery cells, the temperature of each individual cell in the battery pack will be inconsistent, resulting in a higher temperature at the top of the battery pack. Therefore, the influence of fluid buoyancy on the battery pack temperature needs to be considered during the thermal analysis calculation of the battery pack in Fluent software. Given the thermal boundary conditions of the battery pack, input the temperature T′ of cell j at the initial discharge time. 0,j The heat generated by the individual cell numbered j in the battery pack at the initial discharge time t0 is h. 0,jAs the initial heat source of a single cell, given a calculation time step Δt, the temperature T′ of cell j in the battery pack corresponding to discharge time t1 can be calculated using Fluent software thermal analysis. 1,j j takes the values ​​1, 2, ..., z respectively;

[0101] Step 2.7: Calculate the remaining charge Q of the individual cell numbered j in the battery pack at discharge time t1 during the operational cruise phase. 1,s2,j ;

[0102] Step 2.7.1 Calculate the lithium-ion concentration c at the positive and negative electrodes of the single cell numbered j at discharge time t1. 1,j ;

[0103] Using the same time step Δt as in step 2.6, the battery temperature T′ corresponding to time j calculated in step 2.6 at time t1 is... 1,j Coupled with the P2D-based electrochemical-thermal coupling model established in step 2.5 (note that in this model, the electrolyte solution conductivity, lithium-ion diffusion coefficient, lithium-ion transfer number, etc., are all related to the temperature of the individual battery cell), the lithium-ion concentrations c of the positive and negative electrodes at discharge time t1 of the individual battery cell numbered j can be calculated according to the mass transfer equations of positive and negative electrodes in the P2D-based electrochemical-thermal coupling model. 1,j j takes the values ​​1, 2, ..., z respectively;

[0104] Step 2.7.2 Calculate the state of charge (SOC) of the single cell numbered j corresponding to discharge time t1. 1,j ;

[0105] From equation c 1,j =c max ×SOC 1,j Calculate the SOC of the single cell numbered j corresponding to discharge time t1. 1,j =c 1,j / c max j takes the values ​​1, 2, ..., z respectively;

[0106] Step 2.7.3 Calculate the remaining charge Q of the single cell numbered j corresponding to the discharge time t1 during the operational cruise phase using the following formula. 1,s2,j :

[0107] Q 1，s2，j =SOC 1，j ×Q 0,s2,j

[0108] Among them: Q 0,s2,j This is the initial value of the charge level of the single battery cell numbered j during the operational cruise phase.

[0109] Step 2.8: Based on the speed and heading parameters of the underwater vehicle, calculate the depth of the battery pack at discharge time t1 during the operational cruise phase, and then determine the seawater environmental pressure p′ of the individual battery cell numbered j at discharge time t1. 1,j ;

[0110] Step 2.9: Combine the temperature T′ of the single cell numbered j corresponding to the discharge time t1 calculated in Step 2.6. 1,j The state of charge (SOC) of the single cell numbered j corresponding to the discharge time t1 calculated in step 2.7. 1,j And the seawater environmental pressure p′ at discharge time t1 for the single cell numbered j, calculated in step 2.8. 1,j Using the same method as in step 2.3, the open-circuit voltage U of the individual cell numbered j in the aircraft battery pack at discharge time t1 can be calculated. 1,j and internal resistance R 1,j Combined with the open-circuit voltage U of the single cell numbered j at discharge time t1 1,j and internal resistance R 1,j Using the same method as in step 2.4, the current I flowing through the individual cell numbered j at discharge time t1 of the aircraft battery pack can be calculated. 1,j ;

[0111] Step 2.10: Using the same method as steps 2.5-2.9, calculate the discharge times t2, t3, ..., t f The temperature, remaining charge, voltage, and current of the single cell numbered j are listed below.

[0112] The above description uses the evaluation of pressurized battery packs for underwater vehicles as an example. When this invention is used to evaluate non-pressurized battery packs, simply replace all the pressures involved in the above steps with atmospheric pressure.

[0113] Furthermore, the method described above is also applicable to acquiring multi-state parameters of battery packs for other underwater equipment such as underwater pre-positioned unmanned mobile platforms, underwater biomimetic submersibles, and underwater gliders.

Claims

1. A method for predicting multi-state parameters of underwater equipment battery packs, characterized in that, Includes the following steps: Step 1: Predict the remaining charge of each individual cell in the battery pack at the end of the standby preparation phase; Step 1.1 Select multiple individual cells of the same type as the battery pack of the underwater equipment to conduct storage experiments and record the experimental data; Step 1.2 Based on the experimental data, establish the mathematical correlation Q = f(T, p, t); Q is the remaining charge of a single battery cell, T is the ambient temperature, p is the ambient pressure, and t is the storage time; Step 1.3 Based on the mathematical correlation Q=f(T,p,t), combined with the normal distribution function of battery capacity loss, calculate the remaining charge of each individual battery in the battery pack at the end of the standby preparation stage, so as to predict the state parameters of each individual battery in the battery pack at the end of the standby preparation stage. Step 2: Predict the temperature, remaining charge, voltage, and current of each individual cell in the battery pack at each discharge moment during the operational cruise phase; Step 2.1 Divide the individual cells that have completed the storage experiment in Step 1.1 and have consistent storage environment parameters and storage time into one experimental group to conduct battery parameter measurement experiments. Based on the battery parameter measurement experiment data, establish mathematical correlations U = f(T′, p′, SOC) and R = f(T′, t, p′, SOC); U is the open-circuit voltage of the individual cell; R is the internal resistance of the individual cell; T′ is the experimental temperature, which is equal to the temperature of the individual cell during parameter measurement; p′ is the experimental pressure, which is equal to the environmental pressure of the individual cell during parameter measurement; t is the storage time; SOC is the state of charge of the individual cell. Step 2.2: Use the remaining charge of each individual battery obtained in Step 1.3 as its initial charge value Q during the operational cruise phase. 0，s2，j Based on the initial power value Q 0，s2,j Calculate the state of charge (SOC) of each individual cell at the initial discharge time t0. 0,j j represents the cell number in the battery pack; Step 2.3 Utilize the aforementioned correlations U=f(T′,p′,SOC), R=f(T′,t,p′,SOC), and the state of charge (SOC) of each individual cell at the initial discharge time t0. 0,j Battery temperature T′ 0,j and the pressure p′ of the seawater environment 0,j Calculate the open-circuit voltage U of each individual cell at the initial discharge time t0. 0,j and internal resistance R 0，j The battery temperature T′ 0,j Equal to the temperature of the seawater environment in which the underwater equipment was located at that time; the seawater environment pressure p′ 0，j The depth at which the underwater equipment is located at the initial discharge moment t0 is calculated based on the depth of the underwater equipment at the initial discharge moment t0, which is the depth of the standby preparation stage. Step 2.4 Based on the underwater equipment's speed, the battery pack's output power, the battery pack's series-parallel connection structure, and the open-circuit voltage U... 0，j and internal resistance R 0,j Calculate the current I of each individual cell at the initial discharge time t0. 0,j ; Step 2.5 Obtain the heat generation h of each individual cell at the initial discharge time t0 using the P2D-based electrochemical-thermal coupling model. 0,j ; Step 2.6 Using the heat generated h 0,j As the initial heat source for a single cell, the battery temperature T′ of each cell at discharge time t1 is obtained through thermal analysis of the battery pack. 1,j ; Step 2.7 Utilize the battery temperature T′ 1,j Using a P2D-based electrochemical-thermal coupling model, the lithium-ion concentrations c at the positive and negative electrodes of each individual cell at discharge time t1 were calculated. 1,j Therefore, based on the lithium ion concentration c of the positive and negative electrodes 1，j Calculate the corresponding state of charge (SOC). 1，j Finally, based on the state of charge (SOC) 1，j The charge Q of each individual cell at the initial moment of discharge 0，s2，j The remaining charge Q of each individual cell at discharge time t1 is calculated. 1，s2，j ; Step 2.8 uses the same method as step 2.3, utilizing the temperature T′ of each individual cell at discharge time t1. 1，j State of charge (SOC) 1，j and the pressure p′ of the surrounding seawater environment 1，j Calculate the open-circuit voltage U of each individual cell at discharge time t1. 1,j and internal resistance R 1,j Using the same method as in step 2.4, the open-circuit voltage U is utilized. 1,j and internal resistance R 1,j Calculate the current I of each individual cell at discharge time t1. 1,j The seawater environmental pressure p′ 1,j The depth of the underwater equipment at discharge time t1 is calculated based on the speed and heading parameters of the underwater equipment at that time. Step 2.9: Using the same method as steps 2.5-2.8, calculate the discharge times t2, t3, ..., t f The system monitors the temperature, remaining charge, voltage, and current of each individual cell, enabling prediction of the state parameters of each cell in the battery pack at each discharge moment during the operational cruise phase.

2. The method for predicting multi-state parameters of underwater equipment battery packs according to claim 1, characterized in that: The storage experiment method described in step 1.1 is as follows: Divide the selected batteries into s groups, and place the i-th group of batteries at a temperature T. i and pressure p i In the storage environment, at least three individual cells are taken out at set intervals and placed under normal temperature and pressure for standard capacity testing. The average value of the individual cell capacity test values ​​is then taken as the remaining capacity Q of the individual cell under this storage environment. ik i represents the battery group number, which is 1, 2, ..., s, where s is the maximum number of groups; T1, ..., T i ...T s The intervals are 4-25℃ and have no overlapping regions; p1,…p i ,…p s The sub-interval is 101.325 kPa - 90 MPa with no overlapping regions; Q ik The storage capacity is the battery capacity under the storage environment parameters and storage time corresponding to the single cell taken from the i-th battery group for the k-th time; the total duration of the storage experiment is determined according to the duration of the underwater equipment standby preparation phase.

3. The method for predicting multi-state parameters of underwater equipment battery packs according to claim 1, characterized in that: The storage experiment method described in step 1.1 is as follows: After grouping the selected batteries, they are stored under various common and / or typical environmental parameters. At set intervals, at least three individual batteries are taken out and subjected to standard capacity tests at room temperature and pressure. The average value of the individual battery capacity test values ​​is calculated as the remaining power of the individual battery under the corresponding storage environment and storage time, and the experimental data is recorded. The total duration of the storage experiment is determined based on the duration of the underwater equipment standby preparation phase. Correspondingly, In step 1.2, a mathematical correlation Q is first established based on the experimental data under normal temperature and pressure. atm =f(T) atm ,p atm The correction parameters are determined, and the mathematical correlations under normal temperature and pressure are corrected using the correction parameters to finally obtain the mathematical correlations under different ambient temperatures and pressures: Where: A and B are correction parameters, which can be determined from experimental values ​​through multivariate data fitting in Matlab software; T atm This is the normal temperature value; p atm This is the atmospheric pressure value; Q atm The remaining battery charge is the amount stored at room temperature and pressure for a time t.

4. The method for predicting multi-state parameters of underwater equipment battery packs according to claim 1, characterized in that: In step 2.1: The individual cells that have completed the storage experiment in step 1.1 and have the same storage environment parameters and storage time are divided into an experimental group, and battery parameter measurement experiments are carried out in environments with different temperatures and pressures.

5. The method for predicting multi-state parameters of underwater equipment battery packs according to claim 1, characterized in that: In step 2.1, the individual cells that completed the storage experiment in step 1.1 and whose storage environment parameters and storage time are consistent are divided into an experimental group. Battery parameter measurement experiments are conducted under various common and / or typical environmental parameters. Based on the battery parameter measurement experimental data, a mathematical correlation U under normal temperature and pressure is established. atm =f(T) atm ,p atm (SOC) and R atm =f(T) atm ,t,p atm The SOC) was determined and correction parameters were used to correct the mathematical correlations at room temperature and pressure, ultimately yielding mathematical correlations for different experimental temperatures and pressures: Among them, α, β, γ and ω are correction parameters, which can be determined by multivariate data fitting using Matlab software based on experimental data of battery parameter measurement.

6. The method for predicting multi-state parameters of underwater equipment battery packs according to any one of claims 1-5, characterized in that: Step 2.4 specifically involves: Step 2.4.1 Calculate the battery pack output power using the following formula: Where: P is the output power of the battery pack at a given speed; ρ is the density of seawater; S is the maximum cross-sectional area of ​​the underwater equipment; v is the speed of the underwater equipment; C d η is the drag coefficient; η1 is the thruster efficiency in the underwater equipment; η2 is the efficiency of the motor and drive in the underwater equipment. Step 2.4.2 Based on the open-circuit voltage U calculated in Step 2.3 0,j and internal resistance R 0,j Based on the series and parallel connection of the underwater equipment battery pack, the open-circuit voltage U0 and internal resistance R0 of the underwater equipment battery pack are calculated. Step 2.4.3 Combine the battery pack output power obtained in Step 2.4.1 and the battery pack open-circuit voltage U obtained in Step 2.4.

2. 0,j and internal resistance R 0,j Substitute into the formula Calculate the total current I0 of the battery pack; Step 2.4.4 Based on the series and parallel connection method of the battery pack and the total current I0 of the battery pack, calculate the current I flowing through each individual battery cell at the initial time t0 of the underwater equipment battery pack. 0,j .

7. The method for predicting multi-state parameters of underwater equipment battery packs according to claim 6, characterized in that: In step 2.6, the influence of fluid buoyancy on the battery pack temperature is considered during the thermal analysis calculation of the battery pack.

8. The method for predicting multi-state parameters of underwater equipment battery packs according to claim 7, characterized in that: In step 2.7: the lithium ion concentration c of the positive and negative electrodes 1,j The mass transfer equations for lithium ions at the positive and negative electrodes in the P2D-based electrochemical-thermal coupling model were calculated; the state of charge (SOC) was calculated. 1,j According to formula c 1,j =c max ×SOC 1,j Calculate; the remaining power Q 1,s2,j According to formula Q 1,s2,j =SOC 1,j ×Q 0,s2,j calculate.

9. The method for predicting multi-state parameters of underwater equipment battery packs according to any one of claims 1-5, characterized in that: The underwater equipment battery pack consists of pressurized single-cell batteries.

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