Estimation device and estimation method

Abstract

To provide an estimation apparatus and an estimation method.SOLUTION: An estimation apparatus comprises: an acquisition unit for acquiring data related to strain occurring in a storage element; and an estimation unit for estimating the internal stress of the storage element based on the data acquired by the acquisition unit using a simulation model that represents the internal dynamic state of the storage element.SELECTED DRAWING: Figure 5

Description

[Technical Field]

[0001] The present invention relates to an estimation device and an estimation method. [Background technology]

[0002] In recent years, energy storage devices such as lithium-ion batteries have been used in a wide range of fields, including as power sources for laptop personal computers, smartphones, and other mobile devices, renewable energy storage systems, and IoT device power sources.

[0003] The development of lithium-ion batteries is progressing with the aim of achieving higher capacity and higher energy density, and new electrode materials are being explored. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Laid-Open No. 2016-207318 [Patent Document 2] Japanese Patent Application Publication No. 2019-091615 Summary of the Invention [Problem to be solved by the invention]

[0005] In many cases, electrode materials that can achieve high capacity and high energy density are known to expand in volume with charge / discharge and degradation (see, for example, Patent Documents 1 and 2). Electrodes are usually placed inside some kind of housing and are mechanically constrained. This volume expansion generates internal stress within the battery.

[0006] It is known that internal stress affects battery characteristics such as the internal resistance and precipitation of reaction products. However, no method has been proposed to correlate internal stress with battery characteristics for performance evaluation or status monitoring of batteries.

[0007] The present invention has been made in view of the above circumstances, and has an object to provide an estimation device and an estimation method for estimating internal stress generated inside a battery as one of the internal behaviors of the battery. [Means for solving the problem]

[0008] The estimation device includes an acquisition unit that acquires data related to distortion occurring in the storage element, and an estimation unit that estimates the internal stress of the storage element based on the data acquired by the acquisition unit using a simulation model that represents the internal mechanical state of the storage element.

[0009] The estimation method involves acquiring data related to the distortion occurring in the storage element, and using a simulation model representing the internal mechanical state of the storage element, having a computer perform a process to estimate the internal stress of the storage element based on the data acquired by the acquisition unit. [Effects of the Invention]

[0010] According to the above configuration, it is possible to estimate the internal stress generated inside the battery as one of the behaviors inside the battery. [Brief explanation of the drawings]

[0011] [Figure 1] 1 is a schematic diagram illustrating the overall configuration of an estimation system according to a first embodiment. [Figure 2] FIG. 2 is an explanatory diagram illustrating the configuration of an energy storage element. [Figure 3] FIG. 2 is an explanatory diagram illustrating the internal structure of a solid electrolyte layer. [Figure 4] FIG. 2 is a block diagram showing the internal configuration of the estimation device. [Figure 5] 4 is a flowchart illustrating a procedure for estimating an internal stress according to the first embodiment. [Figure 6] 10 is a graph showing the relationship between internal stress and ohmic resistance of an energy storage element. [Figure 7] FIG. 2 is a circuit diagram illustrating an example of an equivalent circuit model. DETAILED DESCRIPTION OF THE INVENTION

[0012] The estimation device includes an acquisition unit that acquires data related to distortion occurring in the storage element, and an estimation unit that estimates the internal stress of the storage element based on the data acquired by the acquisition unit using a simulation model that represents the internal mechanical state of the storage element. The data relating to the distortion may be measurement data obtained by a distortion sensor. According to this configuration, the internal stress of the energy storage element, which cannot be directly observed, can be estimated by simulation from data on the strain occurring in the energy storage element.

[0013] In the estimation device, the simulation model may be configured to include parameters of an inherent strain of the energy storage element and a constraint force on the energy storage element, and to output data related to the internal stress of the energy storage element in response to input of data related to the strain. With this configuration, for example, the internal stress of the energy storage element can be estimated by taking into account the balance of forces based on the constraint force on the energy storage element, the inherent strain of the energy storage element, and the internal stress of the energy storage element.

[0014] In the estimation device, the inherent strain may be a strain of the energy storage element caused by at least one of isolation of active material particles, growth of precipitates, and thermal expansion. With this configuration, the internal stress of the energy storage element can be estimated in consideration of the inherent strain of the energy storage element caused by at least one of isolation of active material particles, growth of precipitates, and thermal expansion.

[0015] In the estimation device, the estimation unit may include a state estimator using a nonlinear filter. With this configuration, a nonlinear filter such as an ensemble Kalman filter, a particle filter, an extended Kalman filter, or an unscented Kalman filter is used, so that the internal stress of the energy storage element can be accurately estimated even when linearity between the inherent strain and the internal stress is not assumed.

[0016] In the estimation device, the estimation unit may estimate the internal resistance of the energy storage element as a function of the internal stress. With this configuration, the internal resistance of the energy storage element can be estimated based on the value of the internal stress, and an electrochemical phenomenon of the energy storage element that reflects the internal stress can be estimated.

[0017] In the estimation device, the energy storage element may be an all-solid-state battery having a solid electrolyte. With this configuration, it is possible to estimate the value of internal stress, which has a decisive effect on the performance of the all-solid-state battery.

[0018] In the estimation device, the electricity storage element may be a battery using metallic lithium for the negative electrode. With this configuration, it is possible to estimate the internal stress caused by the growth of precipitates in a battery in which precipitates are likely to be generated. Here, the type of positive electrode material and electrolyte is not important. The energy storage device may be an all-solid-state battery using metallic lithium for the negative electrode, or alternatively, a lithium-sulfur battery (LiS battery) using sulfur for the positive electrode. The same logic applies to energy storage devices that do not use all-solid-state batteries or batteries using metallic lithium for the negative electrode, but in which the volume of the electrodes expands with charge / discharge and degradation.

[0019] The estimation method involves acquiring data related to the distortion occurring in the storage element, and using a simulation model representing the internal mechanical state of the storage element, having a computer perform a process to estimate the internal stress of the storage element based on the data acquired by the acquisition unit. According to this configuration, the internal stress of the energy storage element, which cannot be directly observed, can be estimated from data on the strain occurring in the energy storage element.

[0020] The present invention will now be described in detail with reference to the drawings showing embodiments thereof. (Embodiment 1) FIG. 1 is a schematic diagram showing the overall configuration of an estimation system according to a first embodiment. The estimation system according to the first embodiment includes an estimation device 1 and an energy storage element 2. The estimation device 1 is, for example, a device such as a BMU (Battery Management Unit), which estimates the internal stress of the energy storage element 2 using a method described below and outputs information related to the estimated internal stress. In the example of FIG. 1, the estimation device 1 and the energy storage element 2 are illustrated as separate entities for convenience's sake. However, the estimation device 1 and the energy storage element 2 may be integrated into one unit. Furthermore, the estimation device 1 may be an information processing device such as a computer or a server device that is communicatively connected to a battery system including the energy storage element 2. The estimation device 1 does not need to be located close to the energy storage element 2. It may be installed in a server room in a different building or in a remote location in Japan or overseas. Furthermore, the energy storage element 2 may be in the atmosphere or outer space and the estimation device 1 may be located on Earth, or both the energy storage element 2 and the estimation device 1 may be in the atmosphere or outer space.

[0021] The energy storage element 2 in the embodiment is, for example, an all-solid-state battery. When discharging, the energy storage element 2 is connected to a load 7. The energy storage element 2 supplies DC power to the connected load 7. When charging, the energy storage element 2 is connected to a charging device (not shown). The energy storage element 2 stores power using power supplied from the connected charging device. Note that the energy storage element 2 is not limited to an all-solid-state battery, and may be any battery in which expansion of electrodes occurs.

[0022] The estimation system includes various sensors that measure the state of the energy storage element 2. One example of a sensor included in the estimation system is a strain sensor S1. The strain sensor S1 measures the strain occurring in the energy storage element 2 in a time series manner and outputs data indicating the measurement results to the estimation device 1.

[0023] The estimation system may include a temperature sensor S2 that measures the temperature of the storage element 2. The temperature sensor S2 measures the temperature of the storage element 2 in time series and outputs data indicating the measurement results to the estimation device 1. The estimation system may further include a temperature sensor S3 that estimates the environmental temperature of the storage element 2. The temperature sensor S3 measures the temperature of the ambient environment in which the storage element 2 is installed and outputs data indicating the measurement results to the estimation device 1.

[0024] The estimation system may include an ammeter S4 that measures the current flowing through the storage element 2. The ammeter S4 measures the current flowing through the storage element 2 in time series and outputs data indicating the measurement results to the estimation device 1. The estimation device system may further include a voltmeter S5 that measures the voltage of the storage element 2. The voltmeter S5 measures the voltage of the storage element 2 in time series and outputs data indicating the measurement results to the estimation device 1.

[0025] The estimation device 1 acquires measurement data measured by various sensors, and estimates the internal stress of the energy storage device 2 based on the acquired measurement data.

[0026] The configuration of the energy storage element 2 will be described below. 2 is an explanatory diagram illustrating the configuration of the energy storage element 2. The energy storage element 2 is, for example, an all-solid-state battery including a laminate made of a positive electrode current collector layer 21, a positive electrode active material layer 22, a solid electrolyte layer 23, a negative electrode active material layer 24, and a negative electrode current collector layer 25.

[0027] The positive electrode current collector layer 21 is made of a metal foil, a metal mesh, or the like. The metal constituting the positive electrode current collector layer 21 is a metal with good conductivity, such as aluminum, nickel, titanium, or stainless steel. A coating layer for adjusting contact resistance may be formed on the surface of the positive electrode current collector layer 21. An example of the coating layer is a carbon coating. The thickness of the positive electrode current collector layer 21 is not particularly limited and is, for example, 0.1 μm or more and 1 mm or less.

[0028] The positive electrode active material layer 22 is a layer containing at least a positive electrode active material. In addition to the positive electrode active material, the positive electrode active material layer 22 may contain a solid electrolyte, a conductive additive, a binder, etc. The positive electrode active material layer 22 has a thickness of, for example, 0.1 μm or more and 1 mm or less.

[0029] The cathode active material may be any suitable cathode active material suitable for use in solid-state batteries. Examples of suitable cathode active materials include lithium cobalt oxide, lithium nickel oxide, lithium manganese oxide, and spinel-based lithium compounds. The cathode active material may be particles having an average particle size (D50) of 0.5 μm to 20 μm. The particles constituting the cathode active material may be primary particles or secondary particles. The cathode active material may be in the form of a thin film or a particle. The solid electrolyte contained in the cathode active material layer 22 may be an inorganic solid electrolyte having relatively high ionic conductivity and excellent heat resistance. Examples of suitable inorganic solid electrolytes include oxide solid electrolytes such as lithium lanthanum zirconate and sulfide solid electrolytes such as Li2S-P2S5. Examples of suitable conductive additives include carbon materials such as acetylene black and ketjen black, and metal materials such as nickel, aluminum, and stainless steel. Materials such as butadiene rubber (BR), acrylate butadiene rubber (ABR), and polyvinylidene fluoride (PVdF) are used as binders.

[0030] The solid electrolyte layer 23 is a layer containing at least a solid electrolyte. In addition to the solid electrolyte, the solid electrolyte layer 23 may contain a binder or the like. The solid electrolyte layer 23 has a thickness of, for example, 0.1 μm or more and 1 mm or less. The solid electrolyte contained in the solid electrolyte layer 23 is an inorganic solid electrolyte such as the above-mentioned oxide solid electrolyte or sulfide solid electrolyte. The binder is the same as the binder used in the positive electrode active material layer 22.

[0031] The negative electrode active material layer 24 is a layer containing at least a negative electrode active material. In addition to the negative electrode active material, the negative electrode active material layer 24 may contain a solid electrolyte, a conductive additive, a binder, etc. The negative electrode active material layer 24 has a thickness of, for example, 0.1 μm or more and 1 mm or less.

[0032] The negative electrode active material may be any suitable negative electrode active material suitable for use in solid-state batteries. For example, metal active materials and carbon active materials are used. Examples of metal active materials include Li, In, Al, Si, and Sn. The metal active material may be a metal element or a metal composite oxide. Examples of carbon active materials include mesocarbon microbeads (MCMB), highly oriented graphite (HOPG), hard carbon, and soft carbon. The negative electrode active material may be particles having an average particle size (D50) of 0.5 μm to 20 μm. The particles constituting the negative electrode active material may be primary particles or secondary particles. The negative electrode active material may be in the form of a thin film or particles. The solid electrolyte, conductive additive, and binder used in the negative electrode active material layer 24 may be the same as those used in the positive electrode active material layer 22.

[0033] The negative electrode current collector layer 25 is made of a metal foil, a metal mesh, or the like. The metal constituting the negative electrode current collector layer 25 is a metal with good conductivity, such as copper, nickel, titanium, or stainless steel. A coating layer for adjusting contact resistance may be formed on the surface of the negative electrode current collector layer 25. An example of the coating layer is a carbon coating. The thickness of the negative electrode current collector layer 25 is not particularly limited and is, for example, 0.1 μm or more and 1 mm or less.

[0034] The energy storage device 2 is restrained by a restraining member 3. The restraining member 3 includes, for example, a case 31 that houses the energy storage device 2 and an elastic member 32 that is arranged in a compressed state within the case 31. The case 31 is, for example, a rectangular parallelepiped container, and includes a case body 310 that is configured with a bottom portion 311 and side portions 312, and a lid 313 that closes an opening of the case body 310. The case body 310 (bottom portion 311 and side portions 312) and the lid 313 are formed of a weldable metal such as stainless steel, aluminum, or an aluminum alloy. Alternatively, the case body 310 (bottom portion 311 and side portions 312) and the lid 313 may be formed of resin. After the energy storage device 2 is housed in the case body 310, the case body 310 is sealed by the lid 313.

[0035] The elastic member 32 is disposed in a compressed state between the bottom layer (positive electrode current collector layer 21 in the example of FIG. 2) of the energy storage element 2 and the bottom surface portion 311, and between the top layer (negative electrode current collector layer 25 in the example of FIG. 2) of the energy storage element 2 and the lid 313. The elastic member 32 is, for example, a rubber sheet. The elastic force of the elastic member 32 applies a restraining force to the energy storage element 2 in the stacking direction (the vertical direction in the figure).

[0036] In the example of FIG. 2, a configuration is adopted in which an elastic member 32 is disposed inside the case 31, thereby applying a restraining force to the energy storage element 2. Alternatively, a restraining force may be applied to the energy storage element 2 by filling the case 31 with a high-pressure fluid. In this case, the fluid is preferably one that does not cause unwanted reactions with the battery materials. For example, an inert gas such as nitrogen or dry air may be used. Alternatively, a configuration may be adopted in which the energy storage element 2 is sandwiched between plate members on both sides in the stacking direction, and the plate members are connected together while a restraining force is applied to the energy storage element 2, thereby applying a restraining force to the energy storage element 2.

[0037] The strain sensor S1 that measures the strain of the energy storage element 2 is attached to a location where it can measure the strain that occurs in response to the internal stress of the energy storage element 2. In the example of FIG. 2 , the strain that occurs in response to the internal stress of the energy storage element 2 appears on the side surface portion 312 of the case 31, so the strain sensor S1 that measures the strain is preferably attached to an appropriate location on this side surface portion 312. Alternatively, the strain sensor S1 may be attached to the bottom surface portion 311 or the lid 313 of the case 31. Furthermore, the strain sensor S1 may be attached to the energy storage element 2.

[0038] FIG. 3 is an explanatory diagram illustrating the internal structure of the solid electrolyte layer 23. In the example of FIG. 3, active material particles are shown as hatched spheres, and solid electrolytes are shown as unhatched spheres. For simplicity, the conductive additive and binder are omitted from FIG. 3. In conventional liquid electrolyte lithium-ion batteries, the active material particles are surrounded by the electrolyte, and the entire surface of the active material is in contact with the electrolyte. In contrast, in all-solid-state batteries using a solid electrolyte, the solid electrolyte and active material particles are in contact with each other over a very small contact area (point), as indicated by the black circles in the figure. The contact area between the solid electrolyte and active material particles varies depending on the restraining force and internal stress that restrain the energy storage element.

[0039] In all-solid-state batteries, the contact area between the solid electrolyte and active material particles changes depending on the restraining force and internal stress, which significantly changes the battery characteristics. Estimating the internal stress is essential to accurately estimate the battery characteristics (charge / discharge characteristics, etc.) of all-solid-state batteries. In batteries that use metallic lithium as the anode, the rate of precipitate formation changes depending on the internal stress, so estimating the internal stress is essential.

[0040] The configuration of the estimation device 1 will be described below. 4 is a block diagram showing the internal configuration of the estimation device 1. The estimation device 1 includes, for example, a calculation unit (estimation unit) 11, a storage unit 12, an input unit 13, and an output unit .

[0041] The calculation unit 11 is an arithmetic circuit including a central processing unit (CPU), a read-only memory (ROM), a random access memory (RAM), and the like. The CPU included in the calculation unit 11 executes various computer programs stored in the ROM and the storage unit 12 and controls the operation of each of the above-mentioned hardware components, thereby causing the entire device to function as a state estimator (also referred to as an observer) for estimating the internal stress of the energy storage element 2. Specifically, the calculation unit 11 uses a simulation model MD1 that simulates the internal mechanical state of the energy storage element 2 to perform calculations to estimate the internal stress of the energy storage element 2 based on measurement data of strain input via the input unit 13. Alternatively, the calculation unit 11 may perform calculations to estimate the internal stress of the energy storage element 2 using virtual data of strain created by a user. The calculation unit 11 may perform calculations to estimate the internal stress of the energy storage element 2 using virtual data of strain generated by the estimation device 1 or an external computer.

[0042] Alternatively, the calculation unit 11 may be any processing circuit or calculation circuit including multiple CPUs, a multi-core CPU, a GPU (Graphics Processing Unit), a microcomputer, a volatile or non-volatile memory, etc. Furthermore, the calculation unit 11 may have functions such as a timer that measures the elapsed time from when an instruction to start measurement is given until when an instruction to end measurement is given, a counter that counts numbers, a clock that outputs date and time information, etc.

[0043] The storage unit 12 includes a storage device such as a flash memory or a hard disk. Various computer programs and data are stored in the storage unit 12. The computer programs stored in the storage unit 12 include an estimation program PG1 that causes a computer to execute a process of estimating the internal stress of the energy storage element 2 using a simulation model MD1. The simulation model MD1 may be described in the estimation program PG1. The data stored in the storage unit 12 include parameters used in the simulation model MD1, parameters used in the estimation program PG1, data generated by the calculation unit 11, and the like.

[0044] The estimation program PG1 may be written in commercially available numerical analysis software or programming languages such as MATLAB (registered trademark), Amesim (registered trademark), Twin Builder (registered trademark), MATLAB & Simulink (registered trademark), Simplorer (registered trademark), ANSYS (registered trademark), Abaqus (registered trademark), Modelica (registered trademark), VHDL-AMS (registered trademark), C language, C++, or Java (registered trademark). The numerical analysis software may be a circuit simulator known as 1D-CAE, or a simulator such as a finite element method or finite volume method that operates on 3D shapes. Alternatively, a reduced-order model (ROM) based on these may be used.

[0045] A computer program including the estimation program PG1 is provided by a non-transitory recording medium M on which the computer program is readably recorded. The recording medium M is a portable memory such as a CD-ROM, a USB memory, or an SD (Secure Digital) card. The calculation unit 11 reads the desired computer program from the recording medium M using a reading device (not shown) and stores the read computer program in the storage unit 12. Alternatively, the computer program may be provided by communication.

[0046] The input unit 13 includes an interface for connecting various sensors. A strain sensor S1 that measures strain occurring in the energy storage element 2 is connected to the input unit 13. The calculation unit 11 acquires measurement data of the strain measured by the strain sensor S1 via the input unit 13.

[0047] The input unit 13 may be connected to a temperature sensor S2 that measures the temperature of the energy storage element 2, a temperature sensor S3 that measures the environmental temperature of the energy storage element 2, etc. The temperature sensor S2 is a sensor that is provided at an appropriate location on the energy storage element 2 or on the case 31 that houses the energy storage element 2, and measures the temperature of the energy storage element 2. The temperature sensor S3 is a sensor that is provided around the energy storage element 2, and measures the temperature around the energy storage element 2 (environmental temperature). Existing sensors such as thermocouples and thermistors are used for the temperature sensors S2 and S3. The calculation unit 11 may obtain environmental temperature data from an external server such as a weather server.

[0048] An ammeter S4 that measures the current flowing through the storage element 2 and a voltmeter S5 that measures the voltage of the storage element 2 may be connected to the input unit 13.

[0049] The output unit 14 includes a connection interface for connecting an external device. The external device connected to the output unit 14 is a display device 140 including a liquid crystal display or the like. In this case, the calculation unit 11 outputs information related to the estimated internal stress of the energy storage element 2 from the output unit 14, thereby causing the information to be displayed on the display device 140. Alternatively, the estimation device 1 may include the display device 140.

[0050] Furthermore, the output unit 14 may include a communication interface for communicating with an external device. The external device communicatively connected to the output unit 14 is a monitoring server that monitors the state of the energy storage element 2. Alternatively, the external device communicatively connected to the output unit 14 may be a control device for a mobile terminal, an electric vehicle, or the like that operates using power supplied from the energy storage element 2.

[0051] The details of the calculation process executed by the estimation device 1 will be described below. The estimation device 1 estimates the internal stress of the energy storage element 2 based on measurement data of the strain sensor S1 input via the input unit 13, using a simulation model that represents the internal mechanical state of the energy storage element 2.

[0052] The simulation model representing the internal dynamic state of energy storage element 2 is expressed by a force balance equation, and for example, the following equation 1 is used.

[0053]

number

[0054] where F ext is the restraining force applied to the energy storage element 2 by the restraining member 3. S is the cross-sectional area of the energy storage element 2 perpendicular to the restraining force. E is the elastic modulus of the energy storage element 2. ε is the elastic strain of the energy storage element 2. ε iso,e is the inherent strain of the energy storage element 2 due to the isolation of the active material particles, and ε pre,e is the inherent strain of the energy storage element 2 due to the growth of precipitates.

[0055] In the first embodiment, the factors that cause distortion in energy storage element 2 are considered to be (1) isolation of active material particles and (2) growth of precipitates.

[0056] (1) Inherent distortion due to isolation Isolation refers to the phenomenon in which charge carriers (e.g., lithium atoms) repeatedly expand and contract during charge and discharge, causing the active material particles to crack due to stress. Isolation is also called pulverization or crack propagation. Cracks in the active material particles create gaps, increasing the apparent volume, and generating inherent strain inside the energy storage element 2.

[0057] The rate of progression of inherent strain due to isolation is expressed, for example, as in Equation 2.

[0058]

number

[0059] where ε iso,e represents the inherent distortion due to isolation. The subscript "iso" represents isolation, and the subscript "e" represents inherent distortion. The superscripts "k" and "k+1" represent the time step. k iso,0 ,k iso,1 is a rate coefficient, and represents the degree to which the inherent strain increases due to isolation over time, and the degree to which the inherent strain increases as isolation progresses due to current flow. Since isolation rarely progresses solely due to changes over time, k iso,0 = 0.0, there is usually no problem. I is the current flowing through the storage element 2. α iso,1 is the proportional power constant of the current. iso,e represents the disturbance term of the progression of isolation.

[0060] (2) Inherent strain due to precipitate growth For example, when lithium metal is used in the negative electrode of the energy storage element 2, repeated charge and discharge over a long period of time can cause deposits to form on the surface of the negative electrode. The growth of these deposits causes inherent strain inside the energy storage element 2.

[0061] The rate of progression of inherent strain due to the growth of precipitates is expressed, for example, as in Equation 3.

[0062]

number

[0063] where ε pre,e represents the inherent strain due to the growth of precipitates. The subscript "pre" represents a precipitate, and the subscript "e" represents an inherent strain. The superscripts "k" and "k+1" represent the time step. k pre,0 ,k pre,1 is a rate coefficient, and represents the degree to which the inherent strain increases as the precipitates grow with time, and the degree to which the inherent strain increases as the precipitates grow due to current application. inis the internal stress of the energy storage element 2. The velocity coefficient k pre,0 ,k pre,1 are both internal stresses σ in It is a function of σ. in may be a function of the location inside the storage element. I is the current flowing through storage element 2. α pre,1 is the proportional power constant of the current. pre,e represents the disturbance term of the inherent strain due to the growth of precipitates.

[0064] The precipitate may be a sparse lithium metal, a passive element (SEI) film, etc. Regardless of the type of precipitate that precipitates inside the energy storage element 2, the rate of progression of the inherent strain associated with the growth of the precipitate is expressed by a formula similar to Equation 3.

[0065] Restraint force F applied to the storage element 2 ext is equal to the tensile force of the side surface portion 312 constituting the restraint member 3, and is therefore expressed by the following equation 4.

[0066]

number

[0067] where E ref is the Young's modulus of the side portion 312, ε ref is the strain of the side surface portion 312 measured by the strain sensor S1, and S ref is the cross-sectional area of the side surface portion 312.

[0068] Internal stress σ of storage element 2 in Between the elastic strain ε and σ in = εE, so by using the relationships in Equation 1 and Equation 4, the internal stress σ in is expressed as follows:

[0069]

number

[0070] In Equation 5, the superscript k represents the time step.in is the disturbance term for the internal stress. In Equation 5, the inherent strain due to isolation and the inherent strain due to precipitate growth are considered as the causes of strain, but even if other factors exist, the same procedure can be applied by adding them to the sum of the inherent strain terms.

[0071] When the equations for the two inherent strains, the measurement value of the strain sensor S1, and the internal stress are expressed as a state equation, the following equation 6 is obtained.

[0072]

number

[0073] Equation 6 includes an equation for the strain of the side surface portion 312 in addition to an equation (Equation 2) that represents the rate of progression of inherent strain due to isolation, an equation (Equation 3) that represents the rate of progression of inherent strain due to precipitate growth, and an equation (Equation 5) related to internal stress. ref is an observation quantity. Also, the value measured by the ammeter S4 may be used for the current I. In Equation 6, the inherent strain is used as the state quantity, but the stress obtained by multiplying the inherent strain by the Young's modulus may also be used as the state quantity.

[0074] The state equation of Equation 6 can be rewritten as an expression using vectors as shown in Equation 7.

[0075]

number

[0076] where x k is a vector with state quantities as elements (state vector), v k is a vector (disturbance vector) whose elements are the disturbance amounts. f represents a nonlinear transformation of the state equation shown in Equation 6. The disturbance term may be calculated by setting some or all of the elements to 0.

[0077] In this embodiment, the strain of the storage element 2 is measured by the strain sensor S1, so ε refis the observable quantity. The observation equation is expressed as follows:

[0078]

number

[0079] where y k is the observed value, C T is the observation vector. A disturbance vector can also be added to the observation equation. The third component, ε ref When extracting the observation vector C T is expressed as in equation 9.

[0080]

number

[0081] The estimation device 1 according to the first embodiment uses a nonlinear filter to sequentially calculate time updates of a simulation model expressed by the state equation of Equation 7 and the observation equation of Equation 8, and calculates an internal stress σ in The time progression of is derived.

[0082] Below, a method of sequentially calculating time updates using an ensemble Kalman filter as an example of a nonlinear filter will be described.

[0083] FIG. 5 is a flowchart illustrating the procedure for estimating internal stress in the first embodiment. The calculation unit 11 of the estimation device 1 provides an initial value of k=1 (step S101). The calculation unit 11 calculates the measured value of strain measured in advance using the strain sensor S1 as ε ref k is given as the initial value of , and the eigenstrain due to isolation ε iso,e k , the inherent strain due to precipitate growth ε pre,e k , internal stress σ in k A predetermined temporary value may be given as the initial value of

[0084] Next, the calculation unit 11 generates N particles for each state variable (step S102), where N is 10 2 ~10 6 It is about that number.

[0085] Next, the calculation unit 11 calculates v for i=1, 2, ..., N. k A random number corresponding to v is generated (step S103). k is assumed to follow a normal distribution and the variance is known.

[0086] The calculation unit 11 performs calculations based on Equation 10 for all N particles, and updates the states of the particles to the states of the particles in the next time step (step S104).

[0087]

number

[0088] The calculation unit 11 calculates the difference x between the state vector of each particle (i=1, 2, . . . , N) and the average value of the state vectors of all particles. k (i) _bar is calculated (step S105). k (i) _bar is represented by the number 11.

[0089]

number

[0090] The calculation unit 11 calculates the covariance matrix P of the predicted state quantities for all particles. k (Step S106) The covariance matrix P k is expressed by the formula 12.

[0091]

number

[0092] The calculation unit 11 acquires the sensor output of the strain sensor S1 through the input unit 13 (step S107). The acquired sensor output of the strain sensor S1 is the observed value y of each particle at time step k. k i Give.

[0093] The calculation unit 11 calculates the observation error r of the i-th particle at time step k. k i is calculated (step S108). k is the observed disturbance. The observation error r k i is expressed by the following equation (13):

[0094]

number

[0095] The calculation unit 11 calculates the Kalman gain K at time step k. k (Step S109). k is expressed by the following equation (14):

[0096]

number

[0097] The calculation unit 11 calculates the estimated value x of the i-th particle. k (i) The estimated value x is calculated (step S110). k (i) hat is expressed by Equation 15. That is, the calculation unit 11 calculates the initial predicted value of Equation 10 by multiplying the observation error r k i and the Kalman gain K in equation 14 k and correct it using

[0098]

number

[0099] The calculation unit 11 calculates the average value x of each particle. k _hat is calculated (step S111). The average value x of each particle is calculated. k _hat represents the state vector estimate obtained by the ensemble Kalman filter and is calculated by the following equation:

[0100]

number

[0101] The estimated value obtained by Equation 16 (average value of each particle x k _hat) contains the internal stress σ in Includes estimates of

[0102] Next, the calculation unit 11 determines whether or not to end the calculation (step S112). For example, if an end instruction is given by the user, the calculation unit 11 determines to end the calculation. If it is determined not to end the calculation (S112: NO), the calculation unit 11 returns the process to step S102 and executes the calculation in the next time step.

[0103] When it is determined that the calculation unit is to be terminated (S112: YES), the calculation unit 11 calculates the estimated internal stress σ in The information relating to the internal stress σ is output from the output unit 14 (step S113), and the processing according to this flowchart is terminated. in The information relating to the internal stress σ may be the value of the internal stress itself, or may be a physical quantity derived based on the internal stress (for example, the internal resistance of the energy storage element 2). in The information on the internal stress σ in It may be a graph showing the time progression of stress, or a two-dimensional or three-dimensional graph or contour map showing stress distribution.

[0104] As described above, the estimation device 1 uses the ensemble Kalman filter to estimate the internal stress σ inThe ensemble Kalman filter is a filter method that targets state space models that have nonlinearity or non-Gaussianity, and can also be used for more general state space models. The ensemble Kalman filter has a relatively simple algorithm and can be easily implemented in the estimation device 1.

[0105] 5, the calculation method using the ensemble Kalman filter has been described as an example. Alternatively, the estimation device 1 may use a nonlinear filter such as a particle filter, an extended Kalman filter, or an unscented Kalman filter to calculate the internal stress σ of the energy storage element 2. in may be estimated.

[0106] In the embodiment, the linearity between the inherent strain and the internal stress is taken into consideration when deriving Equation 5, but the relationship between the two may be nonlinear. Even if the relationship between the inherent strain and the internal stress is nonlinear, the internal stress σ of the energy storage element 2 can be calculated by performing a calculation using a nonlinear filter. in can be estimated.

[0107] (Embodiment 2) In the second embodiment, a method for estimating internal stress by further taking into consideration inherent strain caused by temperature will be described. The configurations of the estimation device 1 and the storage element 2 are the same as those in the first embodiment, and therefore a description thereof will be omitted.

[0108] In the first embodiment, (1) isolation of active material particles, (2) growth of precipitates, and (3) thermal expansion are considered as factors that cause distortion in the energy storage element 2. The inherent distortion caused by isolation of active material particles and the inherent distortion caused by growth of precipitates are the same as those in the first embodiment, and therefore will not be described here.

[0109] (3) Inherent strain due to thermal expansion Thermal expansion is a phenomenon in which the volume of the energy storage element 2 increases as the temperature rises. Thermal expansion is unrelated to the deterioration of the energy storage element 2 and is determined only by the temperature at a given moment. In this embodiment, it is assumed that thermal expansion is proportional to temperature, and a model is described in which an inherent strain occurs according to the difference from the reference temperature.

[0110] The inherent distortion due to temperature is expressed, for example, as in Equation 17.

[0111]

number

[0112] where ε th,e represents the inherent strain due to thermal expansion. α th is the linear thermal expansion coefficient, T is the temperature at a certain time, T ref is the reference temperature. th,e is the disturbance term for thermal expansion. The superscript k represents the time step. The subscript th represents the temperature (thermal). The temperature T is the measurement data of the temperature sensor S2, and the reference temperature T ref The measurement data of the temperature sensor S3 is used.

[0113] The equation of state including temperature is written as follows:

[0114]

number

[0115] In the second embodiment, the rate coefficient k iso,0 ,k iso,1 is a function of temperature T. A monotonically increasing function of temperature T is used as the function form. For example, an Arrhenius function is used as the monotonically increasing function, which indicates that the higher the temperature, the faster the rate of progress of isolation. In the second embodiment, the rate coefficient k pre,0 ,k pre,1 is the temperature T and the internal stress σ inThe function is a monotonically decreasing function of the temperature T. For example, a function that expresses the characteristics that the lower the temperature, the faster the progress of the growth of precipitates is used as the monotonically decreasing function. The fifth equation of equation 18 includes the inherent strain ε due to thermal expansion. th,e k A section on this will be added.

[0116] The state equation of Equation 18 can be rewritten as an expression using vectors as shown in Equation 19.

[0117]

number

[0118] The observation equation is the same as Equation 8 described in the first embodiment. However, in the second embodiment, the strain and temperature of the storage element 2 are extracted as the observation quantities, so the observation vector C T is expressed as the number 20.

[0119]

number

[0120] The estimation device 1 according to the second embodiment uses a nonlinear filter to sequentially calculate time updates of a simulation model (time series model) expressed by the state equation of Equation 19 and the observation equation of Equation 8, and calculates an internal stress σ in The calculation method is the same as in the first embodiment, and the estimation device 1 performs calculations according to the procedure of the flowchart shown in FIG. in Estimate.

[0121] The estimation device 1 according to the first and second embodiments is configured to estimate the internal stress of the energy storage element 2 by executing an estimation program PG1. Alternatively, the estimation device 1 may also use an estimation program that estimates the deterioration of the energy storage element 2, and simultaneously simulate the deterioration of the electrochemical characteristics and the expansion of the energy storage element 2 that accompany use of the energy storage element 2. As an estimation program that estimates the deterioration of the energy storage element 2, for example, the method described in Japanese Patent Application No. 2020-48369 can be used.

[0122] In the second embodiment, the inherent strain of the energy storage device 2 due to thermal expansion is taken into consideration. Alternatively, the inherent strain due to expansion and contraction accompanying the insertion and desorption of active material particles may also be taken into consideration. Such expansion and contraction is not related to deterioration of the energy storage device 2, but occurs due to the insertion and desorption of active material particles into and from the positive and negative electrodes during normal charging and discharging. The inherent strain due to the insertion and desorption of active material particles is reversible and is expressed, for example, as a function of the SOC (State Of Charge).

[0123] In the second embodiment, a model that takes into account the influence of temperature via thermal stress has been described. Alternatively, it is possible to consider only the temperature dependence of the rate coefficients of isolation and precipitate growth without taking into account the inherent strain due to thermal expansion. In this case, the third equation in Equations 18 and 19 can be omitted.

[0124] (Embodiment 3) In the third embodiment, when estimating the electrochemical phenomenon of the energy storage element 2, the internal stress σ estimated by the estimation device 1 is in A configuration using the value of is described below. The configurations of the estimation device 1 and the storage element 2 are the same as those in the first embodiment, and therefore a description thereof will be omitted.

[0125] The electrochemical phenomenon of the energy storage element 2 is described by a physical model such as the Newman model or the Randle model. The observation equation is written as shown in Equation 21, for example.

[0126]

number

[0127] Here, V is the terminal voltage of the storage element 2, and is the value observed by the voltmeter S5. p (c p,1 ) is the equilibrium potential of the positive electrode, and the concentration of absorbed lithium ions at the interface of the positive electrode active material particles, c p,1 It is a function of OCP. n (c n,1 ) is the equilibrium potential of the negative electrode, and the concentration of absorbed lithium ions at the interface of the negative electrode active material particles, c n,1 is a function of R ohm (σ in ) represents the ohmic resistance (internal resistance) of the storage element 2. R ohm (σ in ) is the ohmic resistance of the internal stress σ in The internal stress σ is a function of in The value estimated by the estimation device 1 is used as the value of R ohm (σ in ) may be a function of temperature T. I is the current flowing through the storage element 2. That is, R ohm (σ in The term η represents the voltage drop due to ohmic resistance. act,p (c p,1 , I) is the activation overvoltage at the interface of the positive electrode active material particles, and the occluded lithium ion concentration c p,1 , current I, and temperature T. η act,n (c n,1 , I) is the activation overvoltage at the interface of the negative electrode active material particles, and the occluded lithium ion concentration c n,1 , current I, and temperature T. That is, the observed voltage V is a nonlinear function of the occluded lithium ion concentration c p,1 , the concentration of absorbed lithium ions at the interface of the negative electrode active material particles c n,1 , current I, and temperature T.

[0128] Fig. 6 shows the internal stress σ of the energy storage element 2. in and ohmic resistance R ohm The horizontal axis of the graph represents the internal stress σ of the energy storage element 2.in The vertical axis represents the ohmic resistance R of the storage element 2. ohm As shown in the graph in Figure 6, the higher the compressive stress, the lower the ohmic resistance. ohm The functional form of is ∂R ohm / ∂σ in ≧0. The storage unit 12 of the estimation device 1 stores the internal stress σ in is the ohmic resistance R ohm may be stored, and the internal stress σ in is the ohmic resistance R ohm A conversion table for converting the

[0129] The estimation device 1 estimates the internal stress σ in The value of ohmic resistance R is calculated according to a predetermined function (or table). ohm The estimator 1 converts the ohmic resistance R ohm Using the value of (21), state estimation is performed based on Equation 21 to estimate physical quantities including the equilibrium potentials and activation overvoltages of the positive and negative electrodes. For example, the method described in Japanese Patent Application No. 2020-160971 is used as the estimation method.

[0130] In an all-solid-state battery, the contact area between the solid electrolyte and the active material particles changes depending on the restraining force and internal stress, and the battery characteristics change accordingly. in Since the electrochemical phenomenon is estimated using the estimation results of (1), it is possible to accurately estimate the electrochemical phenomenon of an all-solid-state battery, whose battery characteristics can change significantly depending on the internal stress.

[0131] It is known that in batteries using metallic lithium in the negative electrode, the internal resistance, such as ohmic resistance, and the growth rate of deposits change depending on the internal stress.

[0132] (Fourth embodiment) In the fourth embodiment, a configuration for estimating an electrochemical phenomenon using an equivalent circuit model of energy storage element 2 will be described. The configurations of the estimation device 1 and the storage element 2 are the same as those in the first embodiment, and therefore a description thereof will be omitted.

[0133] 7 is a circuit diagram showing an example of an equivalent circuit model. The equivalent circuit model of the energy storage element 2 is often expressed by a combination of resistors, capacitance components, and voltage sources, for example, as shown in FIG.

[0134] In Figure 7, R0 is the ohmic resistance component, R1 is the positive electrode reaction resistance component, C1 is the positive electrode capacitance component, R2 is the negative electrode reaction resistance component, C2 is the negative electrode capacitance component, E eq is the open circuit voltage (OCV). However, Fig. 7 is an example, and there is no limitation on the combination of series and parallel connections or the number and types of electric circuit elements.

[0135] It is known that the charge / discharge characteristics of the storage element 2 are affected by temperature and SOC. The open circuit voltage (OCV) is assumed to be a function of SOC, and R0 to R2 and C1 to C2 are assumed to be functions of temperature. In this case, the observation equation is expressed by Equation 22.

[0136]

number

[0137] where y U is an observed value, and in the fourth embodiment, represents the terminal voltage V of the storage element 2. The superscript k represents the time step. OCV(SOC) is the open circuit voltage, and is expressed as a nonlinear function of SOC. C T is the observation vector, x U represents the state vector. R0(σ in ) is the ohmic resistance and the internal stress σ in The function form of R0 is ∂R0 / ∂σ in ≧0. The storage unit 12 of the estimation device 1 stores the internal stress σ in A function for converting the internal stress σ inA conversion table for converting R into the ohmic resistance R0 may be stored. u is the current flowing through the storage element 2.

[0138] The estimation device 1 estimates the internal stress σ in The value of R is converted into the value of ohmic resistance R0 according to a predetermined function (or table). The estimation device 1 uses the value of ohmic resistance R0 obtained after conversion to perform state estimation based on Equation 22, thereby estimating physical quantities including the open circuit voltage OCV. For example, the estimation method used is the method described in Japanese Patent Application No. 2020-160971.

[0139] In an all-solid-state battery, the contact area between the solid electrolyte and the active material particles changes depending on the restraining force and internal stress, and the battery characteristics change accordingly. in Since the electrochemical phenomenon is estimated using the estimation results of (1), it is possible to accurately estimate the electrochemical phenomenon of an all-solid-state battery, whose battery characteristics can change significantly depending on the internal stress.

[0140] The embodiments disclosed herein should be considered in all respects as illustrative and not restrictive. The scope of the present invention is defined by the claims, not by the above meaning, and is intended to include all modifications within the meaning and scope of the claims.

[0141] For example, the energy storage device 2 may be a module in which a plurality of cells are connected in series, a bank in which a plurality of modules are connected in series, or a domain in which a plurality of banks are connected in parallel. [Explanation of symbols]

[0142] 1 Estimation device 2. Energy storage element 3 Restraining member 11 Arithmetic section 12 Storage section 13 Input section 14 Output section 21 Positive electrode current collector layer 22 Cathode active material layer 23 Solid electrolyte layer 24 Negative electrode active material layer 25 Negative electrode current collector layer 31 cases 32 Elastic member 310 Case body 311 Bottom part 312 Side part 313 Lid MD1 Simulation Model PG1 Estimation Program S1 Strain Sensor S2, S3 temperature sensors S4 ammeter S5 Voltmeter

Claims

1. an acquisition unit that acquires data related to distortion occurring in the storage element; an estimation unit that estimates an internal stress of the energy storage element based on the data acquired by the acquisition unit using a simulation model that represents an internal mechanical state of the energy storage element; Equipped with The simulation model includes parameters of an inherent strain of the electric storage element and a restraining force on the electric storage element, and is configured to output data related to an internal stress of the electric storage element in response to input of data related to the strain. Estimation device.

2. The inherent strain is a strain of the energy storage element caused by at least one of isolation of active material particles, growth of precipitates, and thermal expansion. The estimation device according to claim 1 .

3. The estimation unit includes a state estimator using a nonlinear filter. The estimation device according to claim 1 or 2.

4. The estimation unit estimates the internal resistance of the energy storage element as a function of the internal stress. The estimation device according to any one of claims 1 to 3.

5. The energy storage element is an all-solid-state battery in which the electrolyte is solid The estimation device according to any one of claims 1 to 4.

6. The storage element is a battery using metallic lithium as the negative electrode. The estimation device according to any one of claims 1 to 5.

7. Acquire data relating to distortion occurring in the storage element; Using a simulation model that represents the internal mechanical state of the energy storage element, the internal stress of the energy storage element is estimated based on the acquired data. The processing is carried out by a computer, The simulation model includes parameters of an inherent strain of the electric storage element and a restraining force on the electric storage element, and is configured to output data related to an internal stress of the electric storage element in response to input of data related to the strain. Estimation method.

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