Lithium ion secondary battery management method and battery management system

Abstract

This method for managing a lithium ion secondary battery manages a battery that is a lithium ion secondary battery having a negative electrode containing a graphite-based negative electrode active material and a negative electrode current collector. A battery model is a multi-particle model in which a negative electrode active material is represented by a plurality of types of particles each representing the degree of susceptibility of a charge transfer reaction of a lithium ion and having different structural parameters and / or material property parameters. The management method comprises: a step for calculating the amount of lithium contained in each type of target particle on the basis of the reaction current density; and a step for calculating the average value of the amount of lithium in each of the plurality of types of particles as the SOC of the battery.

Description

Technical Field

[0001] The present disclosure relates to a management method and a battery management system for a lithium-ion secondary battery, and more particularly, to a technology for estimating a state of charge of a lithium-ion secondary battery. Background Art

[0002] Japanese Patent Application Laid-Open No. 2008-243373 discloses a secondary battery state estimating device that estimates the internal state of a secondary battery according to a battery model. Summary of the invention

[0003] There is always a need to estimate the state of charge (SOC) of a lithium-ion secondary battery with high accuracy. It is expected that the accuracy of estimating the SOC of a lithium-ion secondary battery can be improved by, for example, improving a battery model such as that disclosed in Japanese Patent Publication No. 2008-243373. The present inventors focus on the following problem: In particular, when the negative electrode of a lithium-ion secondary battery contains a graphite-based negative electrode active material, it is not easy to estimate the SOC with high accuracy using a battery model.

[0004] The present disclosure has been made to solve the above-mentioned problems, and one of the objects of the present disclosure is to estimate the SOC of a lithium ion secondary battery having a negative electrode including a graphite-based negative electrode active material with high accuracy.

[0005] (1) A management method for a lithium-ion secondary battery according to an aspect of the present disclosure manages a lithium-ion secondary battery having a negative electrode including a graphite-based negative electrode active material and a negative electrode collector. The management method includes a step of estimating the SOC of the lithium-ion secondary battery by a processor using a battery model for estimating the internal state of the lithium-ion secondary battery. The battery model is a multi-particle model that represents the negative electrode active material using a plurality of particles each representing the degree of susceptibility of a charge transfer reaction of lithium ions. Among the plurality of particles, at least one of the structural parameters and the material property parameters is different from each other. The structural parameters include at least one of the particle size of the negative electrode active material, the distance between the negative electrode active material and the negative electrode collector, the thickness of the negative electrode, the density of the particles of the negative electrode active material, the frequency distribution of the particle size of the negative electrode active material, and the distribution of the conductive aid contained in the negative electrode. The material property parameters include the resistivity of each material of the negative electrode active material and the negative electrode collector. The step of making the estimation includes the first step to the fourth step. The first step is a step of calculating a reaction overvoltage for each particle of the plurality of particles based on the previous value of the amount of lithium contained in the particle. The second step is a step of calculating a reaction current density of the particle based on the reaction overvoltage for each particle of the plurality of particles. The third step is a step of calculating a current value of the amount of lithium contained in the particle based on the reaction current density for each particle of the plurality of particles. The fourth step is a step of calculating an average value of the current value of the amount of lithium contained in each of the plurality of particles as the SOC of the lithium ion secondary battery.

[0006] As described in detail later, when the negative electrode of a lithium-ion secondary battery contains a graphite-based negative electrode active material, the slope change caused by the step structure of the graphite is presented in the SOC-OCV curve. The slope change varies depending on the charging and discharging method of the lithium-ion secondary battery (the starting SOC and current rate described later). In previous battery models, it is difficult to reproduce such an SOC-OCV curve. In contrast, according to the method of (1) above, in the SOC-OCV curve, the slope change caused by the step structure of graphite can be faithfully reproduced in the actual slope change. Therefore, the SOC of a lithium-ion secondary battery having a negative electrode containing a graphite-based negative electrode active material can be estimated with high accuracy.

[0007] (2) The step of calculating the amount of lithium includes the step of calculating the temporal change of the amount of lithium based on the solid phase diffusion of lithium ions for each target particle. (3) The solid phase diffusion is represented by a relational expression showing a first-order lag behavior having a time constant corresponding to the diffusion rate of lithium ions.

[0008] According to the methods (2) and (3) above, by taking solid phase diffusion into consideration, the shift in slope change (the shift in the SOC direction of the SOC-OCV curve) can be reproduced more faithfully. Therefore, the estimation accuracy of the SOC of the lithium ion secondary battery can be further improved.

[0009] (4) In the battery model, by setting a weight for each structural parameter or material property parameter represented by the target particle, the number of particles is reduced compared to a case where the weight is not set.

[0010] According to the method (4) above, by setting weights for each structural parameter or material property parameter, the number of particles can be reduced compared to the case where all particles are assumed to have different structural parameters or material property parameters. Therefore, the calculation load of the processor can be reduced.

[0011] (5) The lithium-ion secondary battery has a positive electrode containing a positive electrode active material. The positive electrode active material is a material having a large flat region. The flat region is a region where the potential change accompanying the SOC change is small. (6) The positive electrode active material contains lithium iron phosphate.

[0012] In positive electrode active materials such as lithium iron phosphate, the flat area is large, so the slope change caused by the step structure of graphite is more affected. That is, the accuracy of SOC estimation is easily reduced. Therefore, according to the methods of (5) and (6) above, the management method of lithium ion secondary batteries involved in the present disclosure is applied to such positive electrode active materials, so the effect of being able to estimate SOC with high accuracy is particularly significant.

[0013] (7) A battery management system according to another aspect of the present disclosure includes the above-mentioned processor.

[0014] According to the configuration of (7), similarly to the method of (1), the SOC of a lithium ion secondary battery having a negative electrode containing a graphite-based negative electrode active material can be estimated with high accuracy. The foregoing and other objects, features, aspects and advantages of the present invention will become more apparent from the following detailed description of the present invention when taken in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a diagram schematically showing the overall configuration of a vehicle equipped with a battery management system according to an embodiment of the present disclosure. Figure 2 It is a perspective view schematically showing an example of a single body structure. Figure 3 It is a diagram showing an example of the structure of an electrode body. Figure 4 This is a conceptual diagram used to illustrate the multi-particle model. Figure 5 This is a conceptual diagram used to explain the multiple particles included in the multi-particle model. Figure 6 This is a diagram for explaining an overview of the relationship between parameters in the multi-particle model. Figure 7 : is a flowchart showing the processing procedure of the SOC estimation process in the present embodiment. Figure 8 This is a diagram showing an example of a lithium amount-negative electrode OCP curve. Fig. 9 This is a diagram for explaining the step structure of graphite. Fig.10 It is a diagram showing an example of an SOC-OCV curve. DETAILED DESCRIPTION

[0016] Hereinafter, the embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. In addition, the same reference numerals are given to the same or corresponding parts in the drawings, and their description will not be repeated.

[0017] In the following, the battery management system (BMS: Battery Management System) involved in the present disclosure is described as an example in which it is installed in a pure electric vehicle (BEV: Battery Electric Vehicle). However, the battery management system involved in this embodiment can be applied to all vehicles equipped with lithium-ion secondary batteries for driving. That is, the battery management system involved in this embodiment can also be installed in hybrid vehicles (HEV: Hybrid Electric Vehicle), plug-in hybrid vehicles (PHEV: Plug-in Hybrid Electric Vehicle), fuel cell vehicles (FCEV: Fuel Cell Electric Vehicle), etc. The use of the battery management system involved in the present disclosure is not limited to vehicles, and can also be used for fixed placement, for example.

[0018] [Implementation Method] <Overall composition> Figure 11 is a diagram schematically showing the overall structure of a vehicle equipped with a battery management system according to an embodiment of the present disclosure. A vehicle 900 is provided with a battery management system 100. The battery management system 100 includes a battery 10 as a battery pack, a monitoring unit 20, a system main relay (SMR: System Main Relay) 30, and a battery ECU (Electronic Control Unit) 40. The vehicle 900 also includes an inlet 91, a power conversion device 92, a charging relay (CHR) 93, a power control unit (PCU: Power Control Unit) 94, an electric generator (MG: Motor Generator) 95, a power transmission gear 96, a drive wheel 97, and an integrated ECU 98.

[0019] The battery 10 stores electric power for driving the MG 95 and supplies the electric power to the MG 95 through the PCU 94. When the MG 95 generates electric power, the battery 10 receives the generated electric power through the PCU 94 and is charged. The battery 10 is a battery pack including one or more battery modules. Each of the one or more battery modules includes a plurality of cells 50. Each cell 50 is a lithium ion secondary battery in the present embodiment. Figure 2 and Figure 3 The structure of the single body 50 will be described.

[0020] The monitoring unit 20 includes various sensors for monitoring the state of the battery 10. Specifically, the monitoring unit 20 includes a voltage sensor 201, a current sensor 202, and a temperature sensor 203. The voltage sensor 201 detects the voltage V of the battery 10 (each cell). The current sensor 202 detects the current I for charging and discharging the battery 10. The temperature sensor 203 detects the temperature T of the battery 10. Each sensor outputs a signal indicating its detection result to the battery ECU 40.

[0021] The SMR 30 is electrically connected to a power line connecting the battery 10 and the PCU 94. The SMR 30 is closed (ON) / open (OFF) in accordance with a control command from the battery ECU 40. When the SMR 30 is closed, the battery 10 can be charged and discharged.

[0022] The battery ECU 40 includes a processor 401 and a memory 402. The processor 401 is a calculation processing device such as a CPU (Central Processing Unit) or an MPU (Micro Processing Unit). The memory 402 is a storage device including a ROM (Read Only Memory) and a RAM (Random Access Memory). In the memory 402, a system program including an OS (Operating System), a control program including a computer-readable code, and various parameters for managing the battery 10 are stored. The processor 401 implements various processes by reading out the system program, the control program, and the parameters, expanding them to the memory 402, and executing them. In the present embodiment, the main process performed by the processor 401 includes a process for estimating the SOC (State Of Charge) of the battery 10 (each cell in the battery pack). This process is also called "SOC estimation process" and will be described in detail later.

[0023] Inlet 91 is configured to be connected to a charging connector (not shown) provided at the front end of a charging cable with mechanical connection. When inlet 91 is connected to the charging connector, electrical connection between vehicle 900 and a charging facility (not shown) is ensured.

[0024] Power conversion device 92 is, for example, an AC / DC converter. Power conversion device 92 converts AC power supplied from a charging facility via a charging cable into DC power for charging battery 10. Vehicle 900 may be configured to be capable of DC charging.

[0025] The charging relay 93 is electrically connected to a power line connecting the SMR 30 and the PCU 94. When the charging relay 93 is closed and the SMR 30 is closed, power transmission between the inlet 91 and the battery 10 can be performed.

[0026] The PCU 94 is electrically connected between the SMR 30 and the MG 95. The PCU 94 includes a converter and an inverter (neither of which are shown), and drives the MG 95 in accordance with a command from the integrated ECU 98.

[0027] The MG 95 is an AC rotating electric machine, for example, a permanent magnet type synchronous motor having a rotor in which a permanent magnet is embedded. The output torque of the MG 95 is transmitted to the drive wheel 97 via the power transmission gear 96, so that the vehicle 900 travels. In addition, the MG 95 generates electricity using the rotational force of the drive wheel 97 when the vehicle 900 is braked. The generated power of the MG 95 is converted into charging power for the battery 10 by the PCU 94.

[0028] The integrated ECU 98 includes a processor 981 and a memory 982. The processor 981 executes various controls for controlling the vehicle 900 to a desired state based on signals received from various sensors and programs and maps stored in the memory 982. For example, the integrated ECU 98 controls the charging and discharging of the battery 10 by controlling the PCU 94 while coordinating with the battery ECU 40.

[0029] <Single body composition> Figure 2 It is a perspective view schematically showing an example of the structure of the single body 50 . Figure 2 5 shows a perspective view of the inside of the cell 50. The cell 50 is a sealed square battery in this example. The shape of the cell 50 can also be cylindrical. The cell 50 includes an electrode body 60, a battery case 70, and an electrolyte 80. The battery case 70 includes a positive terminal 71 and a negative terminal 72.

[0030] The electrode body 60 has an outer shape of, for example, a flat rectangular parallelepiped. The electrode body 60 is housed in a battery case 70. The battery case 70 may be made of, for example, a metal such as an aluminum (Al) alloy. The battery case 70 may also be a package of an Al laminate film or the like. The electrolyte 80 is injected into the battery case 70 to impregnate the electrode body 60. The liquid surface of the electrolyte 80 is shown by a single dotted line.

[0031] Figure 3 is a diagram showing an example of the structure of the electrode body 60. Figure 2 and Figure 3 The electrode body 60 is formed by alternately stacking and winding positive electrodes 1 and negative electrodes 2 with separators 3 interposed therebetween. Figure 3 An exploded view of the winding process is shown in FIG.

[0032] The positive electrode 1 is in a sheet shape and includes a positive electrode current collector 11 and a positive electrode composite material layer 12. The positive electrode current collector 11 is electrically connected to a positive electrode terminal 71. The positive electrode current collector 11 may be, for example, an aluminum (Al) foil, an Al alloy foil, or the like.

[0033] In this example, the positive electrode composite material layer 12 is formed on both the front and back surfaces of the positive electrode current collector 11. However, the positive electrode composite material layer 12 may be formed only on the surface (either side) of the positive electrode current collector 11. The positive electrode composite material layer 12 includes a positive electrode active material 121 (see Figure 4), a conductive material (not shown), and a binder (not shown). The positive electrode active material 121 is, for example, LiFePO4 (lithium iron phosphate, LFP). The positive electrode active material 121 may also include at least one selected from LiCoO2, LiNiO2, LiMnO2, LiMn2O4, Li(NiCoMn)O2, and Li(NiCoAl)O2. The conductive material may include, for example, at least one selected from carbon black (acetylene black, etc.), vapor grown carbon fiber (VGCF), carbon nanotube (CNT), and graphene flakes. The binder may include, for example, at least one selected from carboxymethyl cellulose (CMC), styrene butadiene rubber (SBR), polyvinylidene fluoride (PVdF), and polytetrafluoroethylene (PTFE).

[0034] The negative electrode 2 is in a sheet shape and includes a negative electrode current collector 21 and a negative electrode composite material layer 22. The negative electrode current collector 21 is electrically connected to a negative electrode terminal 72. The negative electrode current collector 21 may be, for example, a copper (Cu) foil.

[0035] In this example, the negative electrode composite material layer 22 is formed on both the surface and the back of the negative electrode collector 21. However, the negative electrode composite material layer 22 can also be formed only on the surface (either side) of the negative electrode collector 21. The negative electrode composite material layer 22 includes a negative electrode active material 221 and a binder (not shown). The negative electrode active material 221 is, for example, a graphite-based material such as natural graphite, easily graphitized carbon (soft carbon), and difficultly graphitized carbon hard carbon. The binder may include, for example, at least one selected from styrene-butadiene rubber (SBR), acrylic butadiene rubber (ABR), sodium alginate, carboxymethyl cellulose, polyacrylic acid, polyacrylonitrile (PAN), polyvinylidene fluoride (PVdF), polytetrafluoroethylene (PTFE), acrylic resin, methacrylic resin, polyvinyl pyrrolidone (PVP) and polyvinyl alcohol (PVA).

[0036] The separator 3 is a strip-shaped film. The separator 3 is disposed between the positive electrode 1 and the negative electrode 2 to electrically insulate the positive electrode 1 from the negative electrode 2. The material of the separator 3 may be, for example, a porous film made of polyolefin resins such as polyethylene (PE) and polypropylene (PP), or a polyamide resin such as nylon and aromatic polyamide (aramid), or a woven or non-woven fabric made of polypropylene, polyethylene terephthalate (PET), methyl cellulose, or the like.

[0037] The electrolyte 80 contains at least a lithium (Li) salt and a solvent. The Li salt is a supporting electrolyte dissolved in a solvent, and may be, for example, LiPF6. The solvent is aprotic. The solvent may be, for example, a mixture of a cyclic carbonate and a chain carbonate. The electrolyte 80 may also contain, in addition to the Li salt and the solvent, a known functional additive.

[0038] <Multi-particle model> In the present embodiment, a multi-particle model (MPM: MultiParticle Model) is used to estimate the SOC of the battery 10 with high accuracy.

[0039] Figure 4 It is a conceptual diagram for illustrating the multi-particle model. In the single-particle model, the positive electrode active material is schematically represented as a single particle, and the negative electrode active material is schematically represented as a single particle. In contrast, in the multi-particle model, it is assumed that the positive electrode and / or the negative electrode is composed of multiple particles connected to each other electronically and ionically. In the multi-particle model in this embodiment, the negative electrode active material 221 is schematically represented as multiple particles. The negative electrode composite material layer 22 contains multiple negative electrode active material particles, and the ease of charge transfer reaction (lithium insertion / extraction reaction) will be different between these particles according to the parameters related to the structure of the battery 10 (hereinafter referred to as "structure parameters") and / or the parameters related to the material properties of the battery 10 (hereinafter referred to as "material property parameters"). The multi-particle model uses particles to express the difference (distribution) in the ease of such charge transfer reactions. The positive electrode active material 121 is schematically represented as a single particle in this example, but the positive electrode active material 121 can also be represented as multiple particles.

[0040] also, Figure 4 FIG. 1 shows the charging state of the battery 10. When the battery 10 is charged, lithium ions (Li + (not shown) is extracted from the positive electrode active material 121, and the lithium ions are embedded in the negative electrode active material 221. At this time, a current (total current I described later) flows in the battery 10. Although not shown, when the battery 10 is discharged, the direction of the current is the same as Figure 4 The directions shown are opposite.

[0041] Figure 5 This is a conceptual diagram for explaining a plurality of particles included in a multi-particle model. The symbol for distinguishing the types of particles is set to j. j = 1 to N. The larger N is, the greater the amount of calculation, so N can be determined according to the calculation processing capacity of the battery ECU 40 (processor 401). In this example, N = 10.

[0042] Assume that all particles in the multi-particle model are spherical. N particles have different particle sizes (radii). The particle size of particle j (the jth particle) is recorded as r j The smaller j is, the smaller the particle size r is. j The smaller.

[0043] The surface area of ​​the particle S j =4πr j Equivalent to the reaction area of ​​the negative electrode active material. The volume of the particle V j =(4πr j3 ) / 3 is equivalent to the capacity of the negative electrode active material. In this way, the surface area / volume of the particle = S j / V j Corresponds to the reaction area / capacity of the negative electrode active material. Particle size r j The smaller the particle, the larger the reaction area / capacity of the particle, so the lithium content (lithium concentration) in the particle is more likely to change. In other words, the current accompanying the insertion / extraction of lithium ions is more likely to flow. Therefore, the particle size r j The smaller the particle, the lower the resistance. The conductivity (electrical conductivity) G, which is the reciprocal of the resistance, is j The higher.

[0044] For ease of understanding, the conductivity G j By particle size r j The condition determined by the (size of the particle aggregate) is explained as an example. Particle size is an example of a structural parameter. Other examples of structural parameters include, for example, the distance between the negative electrode active material 221 and the negative electrode collector 21 (hereinafter also recorded as "collector ear distance"), the thickness of the negative electrode 2, the density of the particles of the negative electrode active material, the frequency distribution of the particle size of the negative electrode active material, the distribution of the conductive aid contained in the negative electrode 2, etc. As material property parameters, for example, the inherent resistivity of each material of the negative electrode 2 (negative electrode active material 221 and negative electrode collector 21) can be cited. These structural parameters and / or material property parameters also determine the ease of charge transfer reaction. Therefore, the conductivity G j The conductivity G is determined based on at least one of the plurality of parameters included in the structural parameters and the material property parameters. It is preferred that the conductivity G is appropriately determined by assigning at least one of the parameters to various values. j The value of .

[0045] As described above, in this embodiment, the particle size r is considered. j The number of particles j (also called frequency) of structural parameters such as the distance between the collector tabs and / or material properties is set to n j Theoretically, the number of particles n j The distribution of the number of particles n can be determined according to the specifications of the electrode body 60 used (specifications of the particle size distribution of the negative electrode active material, the shapes of the negative electrode current collector 21 and the negative electrode composite material layer 22, etc.). However, the number of particles n is determined from the actual electrode body 60. j The distribution of is very troublesome, so in fact the number of particles n j The distribution of can also be appropriately determined. In this example, the smaller the particle number, the smaller the particle number n. j The more particles there are, the more they are. Set the number of particles n according to the sequence number of each particle j , which is equivalent to assigning different weights to each particle according to the structural parameters and / or material property parameters (refer to the averaging process in S107 described later).

[0046] Figure 6 η is a diagram for explaining the relationship between parameters in the multi-particle model. According to the multi-particle model, the current flowing in each particle is determined by the reaction overvoltage η of the particle. j The reaction overvoltage η j Also called activation overvoltage, it is the overvoltage associated with the charge transfer reaction. j According to the negative electrode potential Φ and the negative electrode open potential U j The negative electrode potential (closed circuit potential) of all particles is equal to each other. On the other hand, the negative electrode open potential U j Depends on the amount of lithium contained in the particle θ j , so it may be different for each particle.

[0047] The amount of lithium in particle j θ j It is defined as in formula (1). j is the lithium concentration on the surface of particle j (i.e., the surface of the negative electrode active material), and is a variable value that changes with the charge and discharge of the target cell. max is the maximum lithium concentration (limit lithium concentration) on the surface of the negative electrode active material and is a fixed value. j is a value in the range of 0 to 1. [Number 1]

[0048] The current I flowing in particle j j According to the reaction current density i of the particle j and surface area S j For all particles, the current I j The sum of the values ​​is the total current I flowing in the target cell.

[0049] <Processing Flow> Figure 7 4 is a flowchart showing the processing sequence of the SOC estimation process in the present embodiment. The process shown in the flowchart is executed when a predetermined condition is satisfied (for example, every predetermined period). Each step is implemented by software processing performed by the battery ECU 40 (processor 401), but can also be implemented by hardware (circuitry) configured in the battery ECU 40.

[0050] In this example, the SOC estimation process is performed for each monomer. The monomer that is the object of the SOC estimation process is recorded as the "object monomer". The SOC estimation process is repeatedly performed. The value in the current SOC estimation process (this value) is indicated by the reference numeral (t), and the value in the previous SOC estimation process (previous value) is indicated by the reference numeral (t-1). The step is abbreviated as S.

[0051] In S101 , the battery ECU 40 obtains various variables (parameters) used in the current SOC estimation process from sensors or reads them from the memory 402 .

[0052] In S102, the battery ECU 40 subtracts the negative electrode open potential (negative electrode OCP) U from the negative electrode potential Φ for each particle. j The reaction overvoltage η is calculated j (Refer to formula (2)). [Number 2] η j (t)=Φ-U j (t) = Φ-U(θ j (t-1))…(2)

[0053] The negative electrode potential Φ is obtained by, for example, adding the overvoltage η of all particles in the previous SOC estimation process to the predetermined equilibrium potential (fixed value) of the negative electrode. j When a reference electrode (not shown) is provided on the cell 50, the negative electrode potential Φ may be a negative electrode potential (measured value) based on the potential of the reference electrode.

[0054] The U as the negative electrode OCP was calculated using a curve showing the relationship between the lithium amount and the negative electrode OCP (lithium amount-negative electrode OCP curve). j The lithium amount-negative electrode OCP curve is prepared in advance and stored in the memory 402 .

[0055] Figure 8 : is a diagram showing an example of a lithium amount-negative electrode OCP curve. The horizontal axis represents the lithium amount θ, and the vertical axis represents the negative electrode OCP (U). Such a curve is prepared by experiment and stored in the memory 402 of the battery ECU 40 in the form of a map or table. The battery ECU 40 calculates U according to the lithium amount θ by referring to the curve. j More specifically, the battery ECU 40 converts the curve into j (t)+Δθ j (t)) The corresponding negative electrode OCP is U j This lithium amount is a value calculated by the previous SOC estimation process.

[0056] Refer again Figure 7 In S103, the battery ECU 40 calculates the reaction overvoltage η for each particle based on the reaction overvoltage η calculated in S102. j The reaction current density i is calculated j . Reaction current density i j For example, it can be calculated according to the following Butler-Volmer formula (3). j yes Figure 5 The conductivity described above is a fixed value determined in advance by simulation or experiment. F is the Faraday constant. R is the gas constant. T is the temperature measured by the temperature sensor. [Number 3]

[0057] In S104, the battery ECU 40 calculates the current I flowing in the particle j for each particle. j (t). The reaction current I flowing in particle j j (t) is calculated by multiplying the reaction current density ij by the surface area S j And we get (refer to formula (4)). [Number 4] I j (t) = S j ·i j (t)…(4)

[0058] In S105, the battery ECU 40 obtains the current I flowing in each particle. j The total current I flowing in the target cell is calculated by summing the total current I of the target cell (see equation (5)). The process of S105 may be omitted. [Number 5]

[0059] In S106, the battery ECU 40 calculates the lithium amount for each particle taking into account the solid phase diffusion of lithium ions (diffusion between the surface and the inside of the particle). Generally, the negative electrode OCP is determined based on the lithium concentration on the particle surface of the negative electrode active material and the lithium concentration of the electrolyte. Therefore, in this embodiment, by taking into account the time change of the lithium amount on the particle surface based on the solid phase diffusion of lithium ions, the negative electrode OCP can be faithfully reproduced.

[0060] The lithium amount θ of particle j in this SOC treatment j (t) is expressed using the recursive formula shown in, for example, formula (6). The second term Δθ on the right side is j (t) is a component showing the change in the amount of lithium due to solid phase diffusion. [Number 6] θ j (t) = θj (t-1)+Δθ j (t)…(6)

[0061] More specifically, the solid phase diffusion component Δθ j (t) represents the behavior of the first-order hysteresis with a certain time constant. The time constant depends on the diffusion rate of lithium ions in the electrochemical reaction, etc. Specifically, the solid phase diffusion component Δθ j (t) is calculated according to, for example, formula (7). [Number 7] Δθ j (t) = (1-α)·Δθ j (t-1)+β·I j (t)…(7)

[0062] α is a forgetfulness coefficient set to a value between 0 and 1. The larger α is, the smaller (1-α) is, indicating that the previous SOC estimation process has a significant impact on the solid-phase diffusion component Δθ. j The smaller the first term on the right side of the influence of (t-1). β is a coefficient for reflecting the influence of solid phase diffusion of lithium ions in the amount of lithium. The larger β is, the greater the influence of solid phase diffusion of lithium ions is. α and β are appropriately determined by prior experiments or simulations. The amount of lithium calculated in S106 is used in the subsequent S107 and in the next SOC estimation process (processing of S102).

[0063] In addition, as described in Japanese Patent Application Publication No. 2008-243373, each particle can be divided into multiple layers, and the diffusion equation can be solved to express the diffusion of lithium ions between adjacent layers. On the other hand, according to equations (6) and (7), a more simplified method is used to express the solid phase diffusion of lithium ions, so the calculation load of the battery ECU 40 can be reduced.

[0064] In S107, the battery ECU 40 calculates the lithium amount θ of all particles. j The average value is taken as the SOC of the target monomer (refer to formula (8)). When calculating the average value, the number of particles n of each particle number is also taken into account. j . [Number 8] SOC(t)=Ave(θ j (t))…(8)

[0065] In the multi-particle model of this embodiment, as described above, for example, the larger the particle size r j The particles that are small or close to the negative electrode current collector 21 have a conductivity G j The higher the lithium content θ j The easier it is to change. Lithium amount θ jThe distribution of θ reflects the extent to which lithium ions migrate in each particle, depending on the ease with which the charge transfer reaction occurs. j The average value of is a parameter indicating the movement status of lithium ions in the entire target cell, and can be said to be equivalent to the SOC of the target cell.

[0066] In S108, the battery ECU 40 determines whether the SOC estimation of all cells has been completed. If there are cells whose SOC has not been estimated ("No" in S108), the battery ECU 40 returns the process to S101 and executes the processes of S101 to S107 for the next cell. When the SOC estimation of all cells has been completed ("Yes" in S108), the battery ECU 40 ends the process.

[0067] <Step structure of graphite> As described above, a graphite-based negative electrode active material is used in the battery 10. The graphite crystals in the negative electrode active material are formed by stacking graphene sheets (GS). Lithium ions are embedded (occluded) in the gaps between the graphene sheets. Regarding the embedding of lithium ions, it is known that graphite has a step structure.

[0068] Fig. 9 This is a diagram for illustrating the step structure of graphite. The horizontal axis represents SOC and the vertical axis represents potential. Graphite adopts four step structures according to the amount of lithium ions embedded (i.e., SOC). The nth step structure (n=1~4) refers to the structure when there is (n-1) layer without lithium ions embedded between two layers embedded with lithium ions. As the amount of lithium ions embedded increases, the step structure changes in the order of the 4th step structure (St4), the 3rd step structure (St3), the 2nd step structure (St2), and the 1st step structure (St1). Such a step structure of graphite can be reflected in the shape of the "OCV curve" that represents the correspondence between the SOC and OCV of the battery 10.

[0069] Fig.10 : is a diagram showing an example of an OCV curve. The horizontal axis represents SOC, and the vertical axis represents voltage (potential). Fig.10 In FIG. 1 , the OCV curve is shown by a solid line, and for comparison, the negative electrode OCP curve is shown by a single-dot chain line in which the negative electrode OCP curve is reversed and offset in the vertical direction.

[0070] In actual batteries, the slope (ratio of change) of the negative electrode OCP curve is different between successive steps. This change in slope appears as a "step" on the OCV curve. Also, the position and size of the step may differ depending on the SOC at the start of charging or discharging of the battery (starting SOC) and the size of the current flowing in the battery (current rate). More specifically, the lower the starting SOC, the greater the misalignment of the step ( Fig.10 In the example of , the larger the shift to the low SOC side. In addition, the smaller the current rate, the larger the size of the step (the steepness of the slope change).

[0071] When the positive electrode active material is lithium iron phosphate (LFP), the OCV curve has a large flat area (an area where the OCV changes slightly with the SOC change). Therefore, the characteristics of the shape of the negative electrode OCP curve generated by the graphite of the negative electrode active material are strongly reflected in the OCV curve, and the above-mentioned step problem may become particularly significant.

[0072] In the conventional battery model, i.e., the single particle model, it is difficult to reproduce the OCV curve when using a graphite-based negative electrode active material, more specifically, the above-mentioned "step". In particular, it is impossible to reproduce the differences in the position and size of the step caused by the differences in the starting SOC and the charging rate. Therefore, there is room for improvement in the accuracy of SOC estimation.

[0073] In this regard, in the present embodiment, a multi-particle model including a plurality of particles having at least one of different structural parameters and material property parameters is applied to the negative electrode active material. Thus, the difference in reaction area and capacity between the step structures of graphite can be reflected in the OCV curve, and the size of the step can be reproduced. As a result, the estimation accuracy of the SOC of the battery 10 can be improved. On this basis, by considering solid phase diffusion (refer to equations (6) and (7)), the misalignment of the step (offset in the SOC direction) can also be reproduced. Therefore, the estimation accuracy of the SOC of the battery 10 can be further improved.

[0074] In addition, in this embodiment, it is assumed that there are a plurality of particles having the same structural parameters and / or the same material property parameters. Figure 5 As explained in j , which is equivalent to setting a weight for each structural parameter and / or material property parameter. As a result, the types of particles can be reduced compared to the case where all particles are assumed to have different structural parameters and different material property parameters. As a result, the calculation load of the battery ECU 40 can be reduced. Although the embodiments of the present invention have been described, it should be understood that the embodiments disclosed this time are only illustrative in all aspects and are not restrictive. The scope of the present invention is indicated by the claims, and it is intended to include all modifications within the meaning and scope equivalent to the claims.

Claims

1. A method for managing a lithium-ion secondary battery, wherein the lithium-ion secondary battery has a negative electrode including a graphite-based negative electrode active material and a negative electrode collector, wherein: The management method includes the steps of estimating the SOC of the lithium ion secondary battery by a processor using a battery model for estimating the internal state of the lithium ion secondary battery, The battery model is a multi-particle model that represents the negative electrode active material using a plurality of types of particles each representing the susceptibility of a charge transfer reaction of lithium ions to occur. Among the plurality of particles, at least one of a structural parameter and a material property parameter is different from each other. The structural parameters include at least one of the particle size of the negative electrode active material, the distance between the negative electrode active material and the negative electrode current collector, the thickness of the negative electrode, the density of particles of the negative electrode active material, the frequency distribution of the particle size of the negative electrode active material, and the distribution of a conductive aid contained in the negative electrode, The material property parameters include the resistivity of each material of the negative electrode active material and the negative electrode current collector. The steps of the presumption include: A step of calculating a reaction overvoltage for each of the plurality of particles as a target based on a previous value of the amount of lithium contained in the particle; A step of calculating, for each of the particles being the object, a reaction current density of the particle based on the reaction overvoltage; a step of calculating, for each of the target particles, a current value of the amount of lithium contained in the particle based on the reaction current density; and and calculating an average value of the current values ​​of the lithium amounts in the respective plurality of particles as the SOC of the lithium ion secondary battery.

2. The lithium ion secondary battery management method according to claim 1, wherein: The step of calculating the amount of lithium includes the step of calculating a temporal change in the amount of lithium based on solid phase diffusion of the lithium ions for each of the target particles.

3. The method for managing a lithium-ion secondary battery according to claim 2, wherein: The solid phase diffusion is represented by a relational expression showing a behavior of first order lag having a time constant corresponding to the diffusion rate of the lithium ions.

4. The lithium ion secondary battery management method according to any one of claims 1 to 3, wherein: In the battery model, by setting a weight for each of the structural parameters or the material property parameters represented by the target particles, the number of particles is reduced compared to a case where the weight is not set.

5. The lithium ion secondary battery management method according to any one of claims 1 to 3, wherein: The lithium ion secondary battery has a positive electrode including a positive electrode active material. The positive electrode active material is a material with a large flat area. The flat region is a region where a potential change accompanying a SOC change is small.

6. The method for managing a lithium-ion secondary battery according to claim 5, wherein: The positive electrode active material includes lithium iron phosphate. 7 . A battery management system comprising the processor according to claim 1 .

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