JP2019160662A - Secondary battery deterioration estimation device - Google Patents

Abstract

To estimate the degree of deterioration of a secondary battery with high accuracy in consideration of physical factors that deteriorate the secondary battery.SOLUTION: A battery deterioration estimation device estimates the degree of deterioration of a secondary battery (for example, the amount of increase in internal resistance or the amount of decrease in full charge capacity). An electrode of the secondary battery includes an active material. The battery deterioration estimation device is configured to estimate the deterioration degree of the secondary battery using the surface stress of the active material. For example, the battery deterioration estimation device can estimate the deterioration rate using the surface stress of the active material, and can determine the degree of future deterioration from the estimated deterioration rate.SELECTED DRAWING: Figure 8

Description

  The present disclosure relates to a secondary battery deterioration estimation device, and more specifically, the present or future of a secondary battery in which hysteresis exists in the relationship between SOC (State Of Charge) and open circuit voltage (OCV). The present invention relates to a technique for estimating the degree of deterioration.

  Japanese Patent Laid-Open No. 2015-166710 (Patent Document 1) estimates a hysteresis curve indicating the relationship between SOC and OCV for the secondary battery as described above, and sets an estimated hysteresis curve (estimated OCV curve) as a predetermined value. A technique for estimating the degree of deterioration of a secondary battery by comparing with a hysteresis curve (reference OCV curve) of the above is disclosed.

  Note that “there is hysteresis in the relationship between SOC and OCV” means that the fully charged secondary battery is discharged to the SOC and the fully discharged secondary battery is charged. This means that the OCV when the SOC is changed is different. The relationship between SOC and OCV in which hysteresis exists is represented by a hysteresis curve. Hereinafter, a characteristic in which a significant hysteresis exists in the relationship between the SOC and the OCV in the secondary battery may be referred to as a “hysteresis characteristic”. In addition, a hysteresis curve indicating the relationship between SOC and OCV of the secondary battery may be referred to as “SOC-OCV curve”.

JP2015-166710A

"In Situ Measurements of Stress-Potential Coupling in Lithiated Silicon", V. A. Sethuraman et al., Journal of The Electrochemical Society, 157 (11) A1253-A1261 (2010)

  In the above technique described in Patent Document 1, the degree of deterioration of the secondary battery is estimated from the estimated shape of the SOC-OCV curve, and physical factors that deteriorate the secondary battery are not considered. There is room for further improvement in order to increase the estimation accuracy of the degree of deterioration of the secondary battery.

  The present disclosure has been made to solve the above-described problem, and an object thereof is to estimate the deterioration degree of the secondary battery with high accuracy in consideration of physical factors that deteriorate the secondary battery. .

  The secondary battery deterioration estimation device of the present disclosure (hereinafter also referred to as “battery deterioration estimation device”) estimates the degree of deterioration of a secondary battery including an electrode containing an active material. The battery deterioration estimation device of the present disclosure is configured to estimate the deterioration degree of the secondary battery using the surface stress of the active material.

  In a secondary battery having hysteresis characteristics, the volume of the active material of the electrode (positive electrode or negative electrode) tends to change greatly with charge / discharge. For example, the active material expands as the charge carrier is inserted, and the active material contracts as the charge carrier is desorbed. Due to the volume change (expansion or shrinkage) of the active material, an internal stress (surface stress) is generated in the active material, thereby applying a mechanical load to the active material. The active load is mechanically fatigued due to the accumulation of the mechanical load, and the deterioration of the secondary battery is promoted.

  In the battery deterioration estimation apparatus, the degree of deterioration of the secondary battery is estimated using the surface stress of the active material. By considering the surface stress of the active material as a physical factor that deteriorates the secondary battery, it is possible to estimate the degree of deterioration of the secondary battery with high accuracy.

  Examples of the degree of deterioration of the secondary battery include an increase amount of the internal resistance of the secondary battery or a decrease amount of the full charge capacity based on the initial state. The degree of deterioration of the secondary battery estimated using the surface stress of the active material may be the current degree of deterioration or the degree of deterioration in the future. For example, the battery deterioration estimation apparatus may estimate the deterioration rate using the surface stress of the active material, and obtain the future deterioration degree from the estimated deterioration rate. In addition, the battery deterioration estimation device may be configured to predict a time until the secondary battery reaches a predetermined degree of deterioration using the surface stress of the active material. According to such a battery deterioration estimation device, it becomes possible to predict the life of the secondary battery.

  The battery deterioration estimation device may be configured to estimate that the degree of deterioration of the secondary battery is larger as the number of yields of the active material due to surface stress is larger.

  The yielding of the active material due to the surface stress means that a large mechanical load is applied to the surface of the active material. Therefore, the number of yields of the active material due to surface stress and the degree of deterioration of the secondary battery are highly correlated. The battery deterioration estimation device can estimate the degree of deterioration of the secondary battery with high accuracy by using the number of yields of the active material.

  According to the present disclosure, it is possible to estimate the degree of deterioration of the secondary battery with high accuracy in consideration of physical factors that deteriorate the secondary battery.

1 is a diagram schematically illustrating an overall configuration of a vehicle on which a battery deterioration estimation device according to an embodiment of the present disclosure is mounted. FIG. It is a figure for demonstrating the structure of the cell which comprises the assembled battery shown in FIG. It is a figure which shows the relationship between the surface stress of the active material in a battery electrode, and SOC. It is a figure which shows the SOC-OCV curve of a secondary battery. It is the flowchart which showed the procedure of the estimation process of surface stress (sigma) s and SOC performed by the battery degradation estimation apparatus which concerns on embodiment of this indication. It is a figure which shows the SOC-OCV _ID curve of a secondary battery. It is a figure for demonstrating the estimation method of SOC of a secondary battery. It is the flowchart which showed the procedure of the battery deterioration estimation process performed by the battery deterioration estimation apparatus which concerns on embodiment of this indication. It is a figure which shows frequency distribution of surface stress (sigma) s. It is a figure which shows an example of the map which shows the relationship between the frequency of surface stress (sigma) s, and the amount of degradation. In embodiment of this indication, it is a figure for demonstrating the example which employ | adopted the yield frequency of the active material as deterioration conditions.

  Hereinafter, embodiments of the present disclosure will be described in detail with reference to the drawings. In the drawings, the same or corresponding portions are denoted by the same reference numerals and description thereof will not be repeated.

  Hereinafter, a configuration in which the battery deterioration estimation device is mounted on a hybrid vehicle (more specifically, a plug-in hybrid vehicle) will be described as an example. However, the battery deterioration estimation device is not limited to a hybrid vehicle, and can be applied to all vehicles (such as an electric vehicle) on which a secondary battery is mounted. Furthermore, the use of the battery deterioration estimation device is not limited to a vehicle, and may be a stationary device, for example.

  FIG. 1 is a diagram schematically showing an overall configuration of a vehicle 1 on which a battery deterioration estimation device according to this embodiment is mounted. Referring to FIG. 1, vehicle 1 is a plug-in hybrid vehicle, and includes battery system 2, motor generators 61 and 62, engine 63, power split device 64, drive shaft 65, and drive wheels 66. Is provided. The battery system 2 includes an assembled battery 10, a monitoring unit 20, a power control unit (PCU: Power Control Unit) 30, an inlet 40, a charging device 50, and an electronic control unit (ECU: Electronic Control Unit) 100. Prepare. The ECU 100 corresponds to an example of a “battery deterioration estimation device” according to the present disclosure.

  Each of motor generators 61 and 62 is an AC rotating electric machine, for example, a three-phase AC synchronous motor in which a permanent magnet is embedded in a rotor. The motor generator 61 is mainly used as a generator driven by the engine 63 via the power split device 64. The electric power generated by the motor generator 61 is supplied to the motor generator 62 or the assembled battery 10 via the PCU 30.

  The motor generator 62 mainly operates as an electric motor and drives the drive wheels 66. The motor generator 62 is driven by receiving at least one of the electric power from the assembled battery 10 and the electric power generated by the motor generator 61, and the driving force of the motor generator 62 is transmitted to the driving shaft 65. On the other hand, when braking the vehicle or reducing acceleration on a downward slope, the motor generator 62 operates as a generator to perform regenerative power generation. The electric power generated by the motor generator 62 is supplied to the assembled battery 10 via the PCU 30.

  The engine 63 is an internal combustion engine that outputs power by converting combustion energy generated when an air-fuel mixture is burned into kinetic energy of a moving element such as a piston or a rotor.

  Power split device 64 includes, for example, a planetary gear mechanism (not shown) having three rotation shafts of a sun gear, a carrier, and a ring gear. The power split device 64 divides the power output from the engine 63 into power for driving the motor generator 61 and power for driving the drive wheels 66.

  The assembled battery 10 includes a plurality of cells 11. Details of the cell 11 will be described later (see FIG. 2). The assembled battery 10 stores electric power for driving the motor generators 61 and 62 and supplies the electric power to the motor generators 61 and 62 through the PCU 30. The assembled battery 10 is charged by receiving the generated power through the PCU 30 when the motor generators 61 and 62 generate power.

  The monitoring unit 20 includes a voltage sensor 21, a current sensor 22, and a temperature sensor 23. The voltage sensor 21 detects the voltage (inter-terminal voltage) of the assembled battery 10. The current sensor 22 detects a current IB input / output to / from the assembled battery 10. The current IB during charging is represented by a positive number, and the current IB during discharging is represented by a negative number. The temperature sensor 23 detects the temperature of the assembled battery 10. Each sensor outputs the detection result to ECU 100.

  PCU 30 performs bidirectional power conversion between assembled battery 10 and motor generators 61 and 62 in accordance with a control signal from ECU 100. The PCU 30 is configured such that the states of the motor generators 61 and 62 can be controlled separately. For example, the motor generator 62 can be put in a power running state while the motor generator 61 is in a regenerative state (power generation state). PCU 30 includes, for example, two inverters provided corresponding to motor generators 61 and 62, and a converter (both not shown) that boosts a DC voltage supplied to each inverter to an output voltage of assembled battery 10 or higher. It is configured to include.

  The inlet 40 is configured to be able to connect a charging cable. The inlet 40 receives power supply from a power supply 90 provided outside the vehicle 1 via a charging cable. The power source 90 is, for example, a commercial power source.

  The charging device 50 converts the electric power supplied from the power supply 90 through the charging cable and the inlet 40 into electric power suitable for charging the assembled battery 10 in accordance with a control signal from the ECU 100. The charging device 50 includes, for example, an inverter and a converter (both not shown).

  The ECU 100 includes a CPU (Central Processing Unit) 101, a memory 102, and an input / output port (not shown) for inputting / outputting various signals. The memory 102 includes a ROM (Read Only Memory), a RAM (Random Access Memory), and a rewritable nonvolatile memory. The ECU 100 controls each device so that the vehicle 1 and the battery system 2 are in a desired state based on a signal received from each sensor of the monitoring unit 20 and a program and map stored in the memory 102. The various controls performed by the ECU 100 are not limited to processing by software, and can be processed by dedicated hardware (electronic circuit).

  The ECU 100 also includes a timer (not shown) that cumulatively counts the elapsed time from when the assembled battery 10 is mounted (at the start of use). Such a timer function can also be realized by software.

  In the ECU 100, the CPU 101 outputs the acquired information (calculation results and the like) to the memory 102 (for example, a rewritable nonvolatile memory) and stores it in the memory 102. The memory 102 stores in advance information (variables, maps, etc.) used for running control of the vehicle 1, charge / discharge control of the assembled battery 10, and surface stress σs estimation, SOC estimation, and battery deterioration estimation described later.

  FIG. 2 is a diagram for explaining the structure of each cell 11 in more detail. The cell 11 in FIG. 2 is shown through the inside. In this embodiment, each cell 11 is a lithium ion secondary battery.

  With reference to FIG. 2, the cell 11 has a battery case 111 having a square shape (substantially rectangular parallelepiped shape). The upper surface of the battery case 111 is sealed with a lid 112. One end of each of the positive electrode terminal 113 and the negative electrode terminal 114 protrudes from the lid 112 to the outside. The other ends of the positive electrode terminal 113 and the negative electrode terminal 114 are respectively connected to an internal positive electrode terminal and an internal negative electrode terminal (both not shown) inside the battery case 111. An electrode body 115 is accommodated in the battery case 111. The electrode body 115 is formed by laminating a positive electrode 116 and a negative electrode 117 via a separator 118 and winding the laminated body. The electrolytic solution is held by the positive electrode 116, the negative electrode 117, the separator 118, and the like.

  The positive electrode 116 includes a positive electrode current collector (for example, an aluminum foil) and a positive electrode active material layer. For example, by applying a positive electrode mixture containing a positive electrode active material, a binder, and a conductive additive to the surface of the positive electrode current collector, a positive electrode active material layer is formed on both surfaces of the positive electrode current collector. In addition, the negative electrode 117 includes a negative electrode current collector (for example, a copper foil) and a negative electrode active material layer. For example, the negative electrode active material layer is formed on both surfaces of the negative electrode current collector by applying a negative electrode mixture containing a negative electrode active material, a binder, and a conductive additive to the surface of the negative electrode current collector.

As the positive electrode 116, the separator 118, and the electrolytic solution, known structures and materials can be used for the positive electrode, the separator, and the electrolytic solution of the lithium ion secondary battery, respectively. As an example, a lithium-containing nickel-cobalt-manganese composite oxide (a ternary material in which a part of lithium cobaltate is substituted with nickel and manganese) can be used for the positive electrode active material. A polyolefin (for example, polyethylene or polypropylene) can be used for the separator. The electrolyte includes an organic solvent (for example, a mixed solvent of DMC (dimethyl carbonate), EMC (ethyl methyl carbonate) and EC (ethylene carbonate)), a lithium salt (for example, LiPF ₆ ), and an additive (LiBOB ( lithium bis (oxalate) borate), Li [PF ₂ (C ₂ O ₄ ) ₂ ] and the like) can be used.

  The configuration of the cell is not particularly limited, and the electrode body may have a laminated structure instead of a wound structure. Further, the battery case is not limited to a rectangular battery case, and a cylindrical or laminated battery case can also be employed. In place of the electrolytic solution, a polymer electrolyte may be used, or an inorganic solid electrolyte such as an oxide or sulfide may be used.

  In this embodiment, a silicon-based compound (such as Si or SiO) is employed as the negative electrode active material. By adopting the silicon compound as the negative electrode active material, the energy density of the assembled battery 10 is improved as compared with the case where a carbon material (graphite or the like) is employed.

  In the lithium ion secondary battery, lithium is a charge carrier. In the negative electrode 117 of the cell 11, the active material expands with the insertion of lithium during charging, and the active material contracts with the desorption of lithium during discharging. The volume change due to the insertion or desorption of lithium is larger for silicon compounds than for graphite. For example, the volume change (expansion coefficient) associated with the insertion of lithium is about 1.1 times the maximum for graphite when the volume in a state where lithium is not inserted is the standard, whereas silicon In the case of system compounds, the maximum is about 4 times. Since the cell 11 employs a silicon compound as the negative electrode active material, the volume change amount of the negative electrode active material accompanying charge / discharge increases. Hereinafter, an electrode (that is, the negative electrode 117) in which the volume change of the active material is particularly likely to occur in the cell 11 will be mainly described.

  Along with the volume change of the negative electrode active material as described above, stress is generated on the surface and inside of the negative electrode active material.

  The negative electrode 117 of the cell 11 includes an active material and another electrode material (hereinafter referred to as “peripheral material”) present on the surface of the active material. In the negative electrode 117, the active material is surrounded by peripheral materials. Examples of the peripheral material include a binder and a conductive aid. When a volume change (expansion or contraction) occurs in the active material, a stress due to the volume change is generated inside the active material. Further, due to the volume change of the active material, stress is also generated in the peripheral material existing on the surface of the active material. Furthermore, according to the law of action and reaction, the active material receives a force from the surrounding material, and stress is generated on the surface of the active material.

  When the negative electrode active material expands as the cell 11 is charged, compressive stress is generated on the surface of the negative electrode active material. On the other hand, when the negative electrode active material contracts as the cell 11 is discharged, tensile stress is generated on the surface of the negative electrode active material. In a lithium ion secondary battery, when a silicon-based compound is employed as a negative electrode active material, stress generated on the surface and inside of the negative electrode active material is greater than when a carbon material is employed.

By opening the terminals of the assembled battery 10, the voltage of the cell 11 is sufficiently relaxed, and the lithium concentration in the active material becomes uniform (equilibrium state) (hereinafter referred to as "relaxed state"). . The stress remaining in the negative electrode 117 in the relaxed state includes various stresses including the stress generated in the active material, the stress generated in the peripheral material due to the volume change of the active material, and the reaction force acting on the active material from the peripheral material. It can be considered as a stress when a large force is balanced in the entire system. OCV corresponds to the battery voltage in the relaxed state. The surface stress of the negative electrode active material (hereinafter sometimes referred to as “surface stress σs”) is also a residual stress that is determined including the internal stress of the active material and the interaction with the surrounding materials as described above in the relaxed state. Conceivable. Of the surface stress σs, the stress in the tensile direction (hereinafter sometimes referred to as “tensile stress σ _ten ”) is a positive number, and the stress in the compression direction (hereinafter sometimes referred to as “compressive stress σ _com ”). Indicates a negative number. That is, in the value of the surface stress σs, the absolute value indicates the magnitude of the stress, and the sign (positive / negative) indicates the direction of the stress.

  The surface stress σs can be measured (or estimated) through thin film evaluation. For example, by substituting the curvature κ of the negative electrode 117 (thin film) deformed by the surface stress σs and constants (Young's modulus, Poisson's ratio, thickness, etc.) according to the material and shape of the electrode into the Stony equation, The stress σs can be calculated (for details of the stress measurement, see, for example, Non-Patent Document 1).

In the cell 11, the volume change of the negative electrode active material as described above occurs with charge / discharge, and surface stress σs is generated in the negative electrode active material. The surface stress σs becomes a tensile stress σ _ten or a compressive stress σ _com depending on the state of the battery pack 10 (SOC or the like).

  FIG. 3 is a diagram showing the relationship between the surface stress σs and the SOC in the negative electrode 117. FIG. 3 shows that the battery pack 10 is charged at a constant charge rate from a fully discharged state (SOC = 0% state) to a fully charged state (SOC = 100% state), and then constant from a fully charged state to a fully discharged state. 6 shows the transition of the surface stress σs when the battery pack 10 is discharged at a discharge rate of. In addition, SOC represents the ratio of the current charged amount with respect to the charged amount in a fully charged state by 0 to 100%.

Referring to FIG. 3, immediately after the start of charging from the complete discharge state, the negative electrode active material is elastically deformed, and the surface stress σs decreases linearly. When charging is continued and the surface stress σs (compressive stress σ _com ) reaches the yield stress σy1 (negative number) of the negative electrode active material, the negative electrode active material becomes plastically deformed, and the surface stress σs (compressive stress σ _com ) Becomes constant. In FIG. 3, “Sa” indicates the SOC when the surface stress σs decreases to reach the yield stress σy1. In the region from SOC = Sa to SOC = 100%, the negative electrode active material is plastically deformed, and the surface stress σs becomes equal to the yield stress σy1.

Next, when the discharge is started from the fully charged state, immediately after the start of the discharge, the negative electrode active material is elastically deformed, and the surface stress σs increases linearly. When the discharge continues and the surface stress σs (tensile stress σ _ten ) reaches the yield stress σ y2 (positive number) of the negative electrode active material, the negative electrode active material enters a state of plastic deformation, and the surface stress σ s (tensile stress σ _ten ) Becomes constant. In FIG. 3, “Sb” indicates the SOC when the surface stress σs increases to reach the yield stress σy2. In the region from SOC = Sb to SOC = 0%, the negative electrode active material is plastically deformed, and the surface stress σs becomes equal to the yield stress σy2.

The unipolar potential (positive electrode potential or negative electrode potential) of the secondary battery varies depending on the state of the active material surface. For example, when the assembled battery 10 is charged, the negative electrode potential decreases due to the compressive stress σ _com , and the OCV of the assembled battery 10 increases. Further, when the battery pack 10 is discharged, the negative electrode potential increases due to the tensile stress σ _{ten and} the OCV of the battery pack 10 decreases. Then, when the surface stress σs in the battery pack 10 changes as described above (see FIG. 3), the battery pack 10 has a hysteresis characteristic. When a negative electrode active material that causes a large volume change, such as a silicon compound, is employed, the amount of change in surface stress σs that accompanies an increase or decrease in the amount of lithium increases. On the other hand, lithium ion secondary batteries employing a carbon material as the negative electrode active material tend to have very low hysteresis characteristics.

  The relationship between SOC and OCV in a secondary battery having hysteresis characteristics can be represented by an SOC-OCV curve. The SOC-OCV curve includes a curve acquired by charging (hereinafter sometimes referred to as “CHG curve”) and a curve acquired by discharging (hereinafter sometimes referred to as “DCH curve”). Hereinafter, the OCV on the CHG curve is also referred to as “charging OCV”, and the OCV on the DCH curve is also referred to as “discharging OCV”. Charging OCV and discharging OCV in the same SOC are different from each other. For example, in a lithium ion secondary battery employing a silicon compound as the negative electrode active material, the difference between the charged OCV and the discharged OCV is about 150 mV.

  FIG. 4 is a diagram showing an SOC-OCV curve of the battery pack 10. In FIG. 4, the horizontal axis indicates the SOC of the assembled battery 10, and the vertical axis indicates the OCV of the assembled battery 10. A curve k1 indicates a CHG curve, and a curve k2 indicates a DCH curve.

  Referring to FIG. 4, the SOC-OCV curves (curves k1 and k2) are acquired as follows, for example.

  First, a fully discharged assembled battery 10 is prepared. When the assembled battery 10 is charged and reaches a predetermined SOC, the charging is stopped, and the assembled battery 10 is left until polarization is eliminated, and then the OCV of the assembled battery 10 is measured. Then, the combination (SOC, OCV) of the predetermined SOC and the measured OCV is plotted. By plotting data (for example, OCV for every 5% of SOC) from the fully discharged state to the fully charged state in such a procedure, the curve k1 is obtained.

  Thereafter, plotting data (for example, OCV every 5% of SOC) until the assembled battery 10 reaches the fully discharged state in the same procedure as described above while repeating the discharging and stopping of the assembled battery 10. Gives a curve k2.

  The charging OCV indicated by the curve k1 indicates the highest OCV value in each SOC, and the discharging OCV indicated by the curve k2 indicates the lowest OCV value in each SOC. The state of the assembled battery 10 represented by a combination of SOC and OCV is located in the intermediate region D surrounded by the curves k1 and k2. The outer periphery of the intermediate region D corresponds to the outer periphery of the line indicating the transition of the surface stress σs in FIG. 3 (the outer periphery of the parallelogram).

  As described above, in the secondary battery having hysteresis characteristics, the volume of the active material of the electrode tends to change greatly with charge / discharge. A mechanical stress is applied to the active material due to the generation of surface stress resulting from the volume change (expansion or contraction) of the active material. The active load is mechanically fatigued due to the accumulation of the mechanical load, and the deterioration of the secondary battery is promoted. Further, when a mechanical load is applied to the active material, the active material may be isolated from the charge / discharge system and may not contribute to the charge / discharge of the secondary battery.

  The negative electrode 117 of the cell 11 constituting the assembled battery 10 includes an active material into which charge carriers (lithium) are reversibly inserted and removed. The ECU 100 is configured to estimate the surface stress σs generated in the negative electrode active material as the charge carrier is inserted or removed. ECU 100 is configured to estimate the degree of deterioration of the secondary battery using surface stress σs. By considering the surface stress σs as a physical factor that degrades the secondary battery, it is possible to estimate the degree of deterioration of the secondary battery with high accuracy. Hereinafter, the battery deterioration estimation performed by the ECU 100 will be described in detail with reference to FIGS.

  FIG. 5 is a flowchart showing a procedure of surface stress σs and SOC estimation processing executed by the ECU 100. The processing shown in this flowchart is started, for example, when an ignition switch (not shown) of the vehicle 1 is turned on, and thereafter, is called from the main routine and executed at a predetermined calculation cycle Δt, and the ignition switch is turned off. When it is done, it stops. By repeatedly executing the process of FIG. 5, the SOC and the surface stress σs of the battery pack 10 are estimated.

  Hereinafter, “(t)” is used as a parameter obtained in the previous computation cycle (hereinafter also referred to as “previous cycle”) for parameters obtained in the current computation cycle (hereinafter also referred to as “current cycle”). May be distinguished by attaching “(t−Δt)”.

  Referring to FIG. 5, ECU 100 acquires voltage VB, current IB, and temperature TB of assembled battery 10 from each sensor (voltage sensor 21, current sensor 22, and temperature sensor 23) in monitoring unit 20 (step). S11). Then, the ECU 100 stores the acquired data in the memory 102.

The ECU 100 estimates the OCV (t) of the assembled battery 10 using the voltage VB, current IB, and temperature TB acquired in step S11 (step S12). Then, the ECU 100 stores the estimated OCV (t) in the memory 102. Hereinafter, the OCV of the battery pack 10 estimated in step S12 may be referred to as “OCV _E ”.

OCV _E (t) can be calculated according to the following formula (1).
OCV _E (t) = VB−IB × R (1)
In the formula (1), R represents the internal resistance of the battery pack 10. The ECU 100 refers to, for example, a map or the like stored in advance in the memory 102 (information indicating the relationship between the internal resistance of the assembled battery 10 and the temperature TB), so that the inside of the assembled battery 10 is obtained from the temperature TB acquired in step S11. Resistance can be determined.

In addition, the estimation method of OCV _E (t) can be arbitrarily changed. For example, equation (2) to which a correction term ΣΔV _i (i is a natural number) for correcting the influence of polarization generated in the assembled battery 10 may be used instead of the equation (1). The correction term ΣΔV _i corrects polarization caused by lithium diffusion in the positive electrode active material and the negative electrode active material, and lithium salt diffusion in the electrolytic solution. When considering lithium diffusion in the negative electrode active material, it is desirable to consider the influence of both the lithium concentration difference in the negative electrode active material and internal stress. The correction term ΣΔV _i is obtained from a preliminary experiment, for example, and stored in the memory 102. The sign of the correction term ΣΔV _i is positive on the charge side and negative on the discharge side, like the current IB.

OCV _E (t) = VB−IB × R−ΣΔV _i (2)
The ECU 100 estimates the SOC (t) of the assembled battery 10 using the OCV _E (t) acquired in step S12 (step S13). Then, ECU 100 stores the estimated SOC (t) in memory 102. Hereinafter, the SOC (t) estimation method will be described in detail.

Assuming a hypothetical state in which no stress remains in the negative electrode active material of the assembled battery 10 (hereinafter referred to as “ideal state”), a curve (hereinafter referred to as a relationship between the SOC and the OCV of the assembled battery 10 in the ideal state) , Referred to as “SOC-OCV _ID curve”), the SOC of the battery pack 10 can be estimated. Hereinafter, the OCV on the SOC-OCV _ID curve is referred to as “ideal OCV”.

FIG. 6 is a diagram showing an SOC-OCV _ID curve of the battery pack 10. In FIG. 6, the horizontal axis indicates the SOC of the battery pack 10, and the vertical axis indicates the OCV of the battery pack 10. A curve k10 indicates an SOC-OCV _ID curve, a curve k11 indicates a CHG curve, and a curve k12 indicates a DCH curve.

Referring to FIG. 6, when charging the assembled battery 10, the OCV of the assembled battery 10 rises to a charging OCV (curve k <b> 11) higher than the ideal OCV due to the surface stress σs (compressive stress σ _com ) of the negative electrode active material. Hereinafter, the increase amount (positive number) of the charging OCV with respect to the ideal OCV is referred to as “D _com ”. On the other hand, when the battery pack 10 is discharged, the OCV of the battery pack 10 is reduced to a discharge OCV (curve k12) lower than the ideal OCV due to the surface stress σs (tensile stress σ _ten ) of the negative electrode active material. Hereinafter, the reduction amount (negative number) of the discharge OCV with respect to the ideal OCV is referred to as “D _ten ”.

Setting the ratio of the yield stress Shigumawai1 a ratio to equal as _{D com} and _{D ten} of the yield stress Shigumawai2 ( _{"D com: D ten = σy1:} σy2 " and) it by, _{SOC-OCV ID} curve ( Curve k10) is obtained. The SOC-OCV _ID curve is obtained in advance by experiments or the like and stored in the memory 102, for example.

FIG. 7 is a diagram for explaining a method of estimating the SOC (t) of the battery pack 10. In FIG. 7, the horizontal axis indicates the SOC of the battery pack 10, and the vertical axis indicates the ideal OCV (OCV _ID ) of the battery pack 10. A curve k20 shows an SOC-OCV _ID curve. ΔOCV indicates the amount of change in OCV due to the surface stress σs. Of ΔOCV, the OCV change amount caused by the compressive stress σ _com corresponds to the aforementioned D _com , and the OCV change amount caused by the tensile stress σ _ten corresponds to the aforementioned D _ten .

Referring to FIG. 7, ECU 100 can obtain ideal OCV by correcting ΔOCV (D _com ) from OCV _E (OCV _ES + ΔOCV). The ECU 100 can obtain the SOC from the ideal OCV by referring to the SOC-OCV _ID curve. That is, in step S13, the ECU 100 refers to the SOC-OCV _ID curve in the memory 102 and obtains SOC (t) from OCV _E (t) and ΔOCV (t−Δt). ΔOCV (t−Δt) is calculated in step S18 of the previous cycle. As ΔOCV (t−Δt) at the time of the first calculation, ΔOCV calculated at the end of the previous trip may be used, or a predetermined ΔOCV stored in the memory 102 in advance may be used. Note that one trip is performed from the time when the ignition switch is turned on (at the start of travel) to the time when the ignition switch is turned off (at the end of travel).

  Referring to FIG. 5 again, ECU 100 obtains a calculated value (hereinafter referred to as “F”) of the surface stress of the negative electrode active material by using the SOC (t) acquired in step S13 (step S14). F corresponds to a temporary stress value calculated without considering whether or not the negative electrode active material yields, and is used to estimate the surface stress σs in steps S15, S16, and S173 described later. . Hereinafter, the calculation method of F (t) will be described in detail.

  F (t) can be calculated according to the following formula (3). In the value of F (t), the absolute value indicates the magnitude of the stress, and the sign indicates the direction of the stress (positive: tension, negative: compression).

F (t) = − α (SOC (t) −SOC _B ) + σy (3)
In Equation (3), σy and SOC _B correspond to surface stress σs (= yield stress) and SOC at the time of charge / discharge switching, respectively. σy and SOC _B are set in steps S171 and S172 described later. At the first calculation, σy and SOC _B set at the end of the previous trip may be used, or predetermined σy and SOC _B stored in advance in the memory 102 may be used.

In Expression (3), (SOC (t) −SOC _B ) represents a difference (hereinafter referred to as “ΔSOC”) obtained by subtracting SOC _B from the current SOC. ΔSOC correlates with a difference obtained by subtracting the amount of lithium contained in the negative electrode active material of SOC _B from the amount of lithium contained in the current negative electrode active material. When not considering whether or not the negative electrode active material is yielding, the surface stress of the negative electrode active material is roughly proportional to the amount of lithium inserted or desorbed, and therefore F (t) is calculated according to equation (3). Can do.

  In Expression (3), α represents a linear constant of proportionality established between the surface stress σ and ΔSOC. α is a parameter determined according to the mechanical properties (such as Young's modulus) of the negative electrode active material and the peripheral material, and is obtained in advance by experiments or the like and stored in the memory 102, for example. α can vary depending on the temperature of the negative electrode active material (and hence the temperature of the assembled battery 10) and the lithium content of the negative electrode active material (and thus the SOC of the assembled battery 10). Therefore, α may be changed according to at least one of the temperature TB and SOC (t) of the assembled battery 10.

  In steps S15, S16, and S171 to S173, the ECU 100 estimates the surface stress σs using F (t) acquired in step S14.

In step S15, it is determined whether or not yielding of the negative electrode active material has occurred, assuming that the surface stress σs is a compressive stress σ _com (stress generated during charging). Specifically, ECU 100 determines whether F (t) is smaller than the yield stress σy1 of the negative electrode active material. Since the stress in the compression direction is expressed by a negative number, the fact that F (t) is smaller than the yield stress σy1 means that the magnitude of F (t) exceeds the yield stress σy1 (that is, the negative electrode active material yields) Means).

When it is determined that F (t) is smaller than yield stress σy1 (YES in step S15), ECU 100 estimates surface stress σs and stores it in memory 102 in step S171, σy and SOC _B are set. Specifically, the ECU 100 estimates that the surface stress σs is equal to the yield stress σy1 (σs = σy1). Further, the ECU 100 sets the yield stress σy1 corresponding to the surface stress σs when switching from charge to discharge to σy (σy = σy1). Further, ECU 100 sets the SOC (t) corresponding to the SOC of the battery 10 when switching from charge to discharge to the _{SOC B (SOC B = SOC (} t)). Thereafter, the process proceeds to step S18.

On the other hand, when it is determined that F (t) is greater than or equal to the yield stress σy1 (NO in step S15), it is assumed that the surface stress σs is the tensile stress σ _ten (stress generated during discharge) in step S16. Thus, it is determined whether or not the negative electrode active material has yielded. Specifically, ECU 100 determines whether F (t) is larger than the yield stress σy2 of the negative electrode active material. Since the stress in the tensile direction is represented by a positive number, the fact that F (t) is larger than the yield stress σy2 means that the magnitude of F (t) exceeds the yield stress σy2 (that is, the negative electrode active material yields) Means).

When it is determined that F (t) is greater than yield stress σy2 (YES in step S16), ECU 100 estimates surface stress σs and stores it in memory 102 in step S172, σy and SOC _B are set. Specifically, the ECU 100 estimates that the surface stress σs is equal to the yield stress σy2 (σs = σy2). Further, the ECU 100 sets the yield stress σy2 corresponding to the surface stress σs when switching from discharging to charging to σy (σy = σy2). Moreover, ECU 100 is, SOC corresponding to the SOC of the battery 10 when switching from discharging to charging (t) is set to _{SOC B (SOC B = SOC (} t)). Thereafter, the process proceeds to step S18.

  The yield stresses σy1 and σy2 are obtained in advance by experiments or the like and stored in the memory 102, for example. The yield stresses σy1 and σy2 can vary depending on the temperature of the negative electrode active material (and hence the temperature of the battery pack 10) and the lithium content of the negative electrode active material (and thus the SOC of the battery pack 10). For this reason, you may make it change the yield stress (sigma) y1 and (sigma) y2 according to at least one of temperature TB and SOC (t) of the assembled battery 10. FIG.

When it is determined in step S16 that F (t) is equal to or less than the yield stress σy2 (NO in step S16), it is considered that the negative electrode active material does not yield on either the compression side or the tension side. Therefore, F (t) provisionally calculated in step S14 can be adopted as the surface stress σs. In step S173, the ECU 100 estimates that the surface stress σs is F (t), and stores the estimated surface stress σs in the memory 102. Thereafter, the process proceeds to step S18. In step S173, σy and SOC _B are not set. For this reason, σy and SOC _B are maintained at the initial value or the previous value.

  In step S18, the ECU 100 calculates ΔOCV (t) using the surface stress σs acquired in steps S171 to S173 (step S18). ΔOCV corresponds to the amount of change in OCV with reference to the ideal OCV. ΔOCV (t) can be calculated according to the following formula (4).

ΔOCV (t) = k × σs × Ω / Faraday (4)
In Formula (4), Ω (unit: m ³ / mol) represents the volume increase of the negative electrode active material when 1 mol of lithium is inserted, and Faraday represents the Faraday constant (unit: C / mol). k is a constant obtained experimentally including the sign.

  As shown in Expression (4), a linear relationship is generally established between the surface stress σs and ΔOCV. In step S18, the ECU 100 can calculate ΔOCV (t) by substituting the value of the surface stress σs together with other constants (k, Ω, and Faraday) into the equation (4). The calculated ΔOCV (t) is used for estimating the SOC in the next step S13. Thereafter, the process is returned to the main routine.

  Next, a battery deterioration estimation process performed by ECU 100 using the parameters (surface stress σs, SOC (t), etc.) acquired by the process of FIG. 5 will be described. FIG. 8 is a flowchart showing a procedure of battery deterioration estimation processing executed by ECU 100. The processing shown in this flowchart is called from the main routine, for example, when a predetermined condition is satisfied, and is repeatedly executed at a predetermined calculation cycle. 8 is repeatedly executed in parallel with the process of FIG. 5, the usage history (and hence the deterioration history) of the assembled battery 10 is accumulated in steps S21 to S22, and the predetermined calculation timing is reached (step S23). In step S24 to S25, the deterioration rate of the battery pack 10 is estimated. The deterioration rate is a ratio of the current internal resistance R2 to the initial internal resistance R1 (= R2 / R1). The greater the deterioration rate of the assembled battery 10, the greater the degree of deterioration of the assembled battery 10. The degree of deterioration of the assembled battery 10 is not limited to the internal resistance (deterioration rate) of the assembled battery 10 and is arbitrary.

  Referring to FIG. 8, ECU 100 reads from memory 102 the current temperature TB, SOC, and surface stress σs of battery pack 10 obtained by the processing of FIG. 5 (steps S11, S13, and S171 to S173). (Step S21).

  The temperature TB, SOC, and surface stress σs of the assembled battery 10 correspond to deterioration conditions (parameters that affect the deterioration rate of the assembled battery 10). The degree of deterioration (for example, the deterioration rate) of the assembled battery 10 increases with time. Under the same conditions, the degree of deterioration of the battery pack 10 and the n-th root of the elapsed time (where n is a value greater than 1) tend to be generally proportional. However, when conditions change, the deterioration rate of the assembled battery 10 may change. For example, the deterioration rate of the assembled battery 10 changes depending on the occurrence frequency of the deterioration conditions shown below.

  The ECU 100 updates the occurrence frequency of each deterioration condition (temperature TB, SOC, and surface stress σs) acquired in step S21 (step S22). The frequency of occurrence of each deterioration condition (for example, the frequency distribution shown below) is stored in the memory 102 and is updated each time the process of step S22 is executed. While it is determined in the next step S23 that the deterioration rate calculation timing has not come, the processing of steps S21 to S22 is repeatedly executed. Thereby, the frequency distribution of each deterioration condition is created.

  FIG. 9 illustrates an example of the frequency distribution of the surface stress σs. In FIG. 9, the horizontal axis has a plurality of sections corresponding to the magnitude of the surface stress σs, and the vertical axis exemplifies the frequency of occurrence for each section (the numerical range of the surface stress σs) provided on the horizontal axis. is doing.

  Referring to FIG. 9, the data (surface stress σs) collected in step S21 is distributed to each section according to the magnitude of surface stress σs. That is, in step S22, the frequency of the section corresponding to the magnitude of the surface stress σs acquired in step S21 is incremented by 1 (counted up). By repeatedly executing the processes of steps S21 to S22, a frequency distribution indicating the occurrence frequency of each section of the surface stress σs is created.

  Although FIG. 9 shows only the frequency distribution of the surface stress σs, similar frequency distributions are created for other deterioration conditions (temperature TB and SOC). In the frequency distribution, the number of sections and the numerical range of each section can be arbitrarily set.

  Referring to FIG. 8 again, in step S23, ECU 100 determines whether or not the deterioration rate calculation timing has come, and if it is determined that the deterioration rate calculation timing has not come (NO in step S23). When it is determined that the process is returned to the main routine and the deterioration rate calculation timing has come (YES in step S23), the deterioration rate of the assembled battery 10 is calculated in steps S24 to S25.

  The calculation timing of the deterioration rate can be arbitrarily set. The deterioration rate calculation timing may be, for example, a timing at which the use time (elapsed time from the start of use) of the battery pack 10 exceeds a predetermined value, or the number of data collected in step S21 may be a predetermined value. The timing may be exceeded. The accumulated travel distance of the vehicle 1 may be adopted instead of the usage time of the assembled battery 10. In addition, the timing of the second and subsequent deterioration rate calculation may be determined based on the timing of the previous deterioration rate calculation. The timing for calculating the deterioration rate may be, for example, a timing when a predetermined time has elapsed since the previous deterioration rate calculation, or a timing when the predetermined number of data is increased since the previous deterioration rate calculation.

  In this embodiment, the first deterioration rate calculation is performed at the timing when the usage time of the assembled battery 10 exceeds one month, and thereafter, the deterioration rate is calculated every one month. That is, at the time of executing the first deterioration rate calculation, the frequency distribution of each deterioration condition based on the data for one month is stored in the memory 102 through steps S21 to S22. In this embodiment, the frequency distribution of each deterioration condition is created and stored in the memory 102 for each predetermined period. More specifically, a frequency distribution of each deterioration condition is created by being distinguished every month in step S <b> 22 and stored in the memory 102. In step S24 shown below, the ECU 100 calculates the deterioration amount using only the latest frequency distribution (frequency distribution based on the data for the most recent one month).

  In step S24, using the frequency distribution of each deterioration condition in the memory 102, a deterioration amount (an increase amount of the deterioration rate) due to each deterioration condition is calculated. The ECU 100 refers to a map or the like stored in advance in the memory 102 (information indicating the relationship between the frequency of deterioration conditions and the amount of deterioration), for example, and obtains the amount of deterioration due to each deterioration condition from the frequency distribution of each deterioration condition. be able to.

  FIG. 10 is a diagram showing an example of a map showing the relationship between the frequency of the surface stress σs and the deterioration amount. In FIG. 10, curves σs-A, σs-B, σs-C, σs-D, and σs-E are sections A, B, C, D, and E (corresponding to the magnitude of the surface stress σs, respectively. The degradation characteristics of the section corresponding to the frequency distribution are shown. The frequencies t11, t12, t13, t14, and t15 indicate the frequencies of the sections A, B, C, D, and E in the frequency distribution, respectively. If the surface stress σs is arranged in ascending order, sections A, B, C, D, and E are obtained.

  Referring to FIG. 10, from the curve σs-A indicating the deterioration characteristic when the surface stress σs is in section A, the deterioration rate of the battery pack 10 is increased by the deterioration amount Δd1 at the frequency t11 in which the surface stress σs is in section A. I understand that Similarly, the deterioration rate of the battery pack 10 is increased by the deterioration amounts Δd2, Δd3, Δd4, Δd5 depending on the frequencies t12, t13, t14, t15 in which the surface stress σs is in the sections B, C, D, E. It can be seen from the curves σs-B, σs-C, σs-D, and σs-E, respectively. As shown by the curves σs-A to σs-E, the deterioration of the assembled battery 10 tends to be faster as the surface stress σs is larger.

  The ECU 100 can acquire the deterioration amounts Δd1 to Δd5 from the curves σs-A to σs-E. Further, the ECU 100 can obtain the total deterioration amount (= Δd1 + Δd2 + Δd3 + Δd4 + Δd5) by all frequencies by accumulating the obtained deterioration amounts Δd1 to Δd5. FIG. 10 shows an example in which the deterioration amounts are added in the order of the curves σs-A to σs-E. However, even if the order of adding the deterioration amounts is changed, the total deterioration amount finally obtained is Will be equal. For convenience of explanation, only five curves are shown in FIG. 10, but the map used for calculating the deterioration amount includes a number of curves corresponding to the frequency distribution section. By using such a map, the total deterioration amount due to all frequencies included in the frequency distribution can be obtained.

  FIG. 10 shows only the map used for calculating the deterioration amount due to the surface stress σs, but similar maps are stored in the memory 102 for other deterioration conditions (temperature TB and SOC). Using the map for each deterioration condition, the deterioration amount due to each deterioration condition can be obtained by the same method as described above.

  Referring to FIG. 8 again, in step S25, ECU 100 estimates the deterioration rate of the assembled battery 10 in the future by using the deterioration amounts obtained by the respective deterioration conditions acquired in step S24. Thereafter, the process is returned to the main routine. Hereinafter, the estimation method of the deterioration rate of the assembled battery 10 in step S25 will be described.

  The sum of the deterioration amounts obtained by the respective deterioration conditions acquired in step S24 corresponds to the deterioration amount of the assembled battery 10 in the latest one month (that is, the increase amount of the deterioration rate of the assembled battery 10 per month). The deterioration rate of the assembled battery 10 after one month can be predicted by assuming that the assembled battery 10 deteriorates at the same deterioration rate as the latest one month during the next month. Further, assuming that the assembled battery 10 deteriorates at the same deterioration rate as the latest one month for the next three months, the deterioration rate of the assembled battery 10 after three months can be predicted. Thus, the future deterioration rate (for example, deterioration rate) of the assembled battery 10 can be predicted by predicting the future deterioration rate from the deterioration rate of the most recent one month. Further, based on the predicted future deterioration rate, the time until the assembled battery 10 reaches a predetermined degree of deterioration can also be predicted. That is, it is possible to predict the life of the assembled battery 10.

  Note that the deterioration rate for the most recent one month may not be used as it is as the future deterioration rate. For example, the ECU 100 may estimate the future deterioration rate using the usage time of the assembled battery 10 in addition to the amount of deterioration of the assembled battery 10 in the most recent month.

  As described above, ECU 100 according to the present embodiment is configured to perform the processes shown in FIGS. In the process of FIG. 8, the future deterioration rate of the assembled battery 10 is estimated based on the surface stress σs of the negative electrode active material obtained in the process of FIG. According to such a method, the deterioration rate of the assembled battery 10 can be estimated with high accuracy.

  In the above embodiment, the occurrence frequency for each section (numerical range) is counted for each of the three types of deterioration conditions including the surface stress σs (temperature of the battery pack 10, SOC, and surface stress σs), and the occurrence frequency is calculated. The amount of deterioration is calculated using the amount of deterioration, the future deterioration rate is predicted using the amount of deterioration, and the future deterioration rate is predicted using the future deterioration rate. However, deterioration conditions other than the surface stress σs can be arbitrarily changed. For example, the current of the assembled battery 10 or the like may be adopted as the deterioration condition in addition to or in place of the temperature and SOC of the assembled battery 10.

  FIG. 11 is a diagram for explaining an example (modified example) in which the number of yields of the negative electrode active material is adopted as the deterioration condition. Hereinafter, an example in which the ECU 100 performs the process of FIG. 11 instead of the process of FIG. 8 will be described.

  The process shown in FIG. 11 is called from the main routine, for example, when a predetermined condition is satisfied, and is repeatedly executed at a predetermined calculation cycle. The process of FIG. 11 is repeatedly executed in parallel with the process of FIG. 5 to estimate the deterioration rate of the assembled battery 10.

Referring to FIG. 11, ECU 100 executes steps S31 to S32 according to steps S21 to S22 of FIG. Next, in step S33, assuming that the surface stress σs is the compressive stress σ _com (stress generated during charging), the ECU 100 determines whether or not the negative electrode active material has yielded to the compression side. Specifically, ECU 100 determines whether or not a predetermined compression-side yield condition is satisfied. In this example, when the surface stress σs (t−Δt) of the previous cycle is larger than the yield stress σy1 of the negative electrode active material, and the surface stress σs (t) of the current cycle is equal to the yield stress σy1 of the negative electrode active material. The compression side yield condition is satisfied. Since the stress in the compression direction is represented by a negative number, the fact that the compression stress σ _com is larger than the yield stress σy1 means that the negative electrode active material has not yielded on the compression side. In this state, the compression stress σ _com being equal to the yield stress σy1 means that the negative electrode active material has yielded to the compression side.

If it is determined that the compression-side yield condition is satisfied (YES in step S33), ECU 100 adds 1 to count value C _A indicating the number of times the negative electrode active material has yielded to the compression side in step S351. After (counting up), the process proceeds to step S36. The count value C _A is stored in the memory 102, for example.

On the other hand, when it is determined that the compression-side yield condition is not satisfied (NO in step S33), ECU 100 determines that surface stress σs is tensile stress σ _ten (stress generated during discharge) in step S34. Assuming the case, it is determined whether or not the negative electrode active material has yielded to the tensile side. Specifically, ECU 100 determines whether or not a predetermined pulling-side yield condition is satisfied. In this example, when the surface stress σs (t−Δt) of the previous cycle is smaller than the yield stress σy2 of the negative electrode active material, and the surface stress σs (t) of the current cycle is equal to the yield stress σy2 of the negative electrode active material. The above pull-side yield conditions are met. Since the stress in the tensile direction is represented by a positive number, the tensile stress σ _ten being smaller than the yield stress σ y2 means that the negative electrode active material has not yielded on the tensile side. Then, from this state, the tensile stress σ _ten is equal to the yield stress σ y2 means that the negative electrode active material has yielded to the tensile side.

If the above tensile side yield condition is determined to be satisfied (YES in step S34) is, ECU 100 is summed at step S352, 1 to the count value C _B indicating the number of times the negative electrode active material has surrendered the pulling side After (counting up), the process proceeds to step S36. The count value _{C B} is, for example, stored in the memory 102.

On the other hand, when the tensile side yield condition is not judged to be satisfied (NO in step S34), the process proceeds to step S36 without passing through step S352, counting up of the count value C _B is performed Absent.

Next, after the ECU 100 executes steps S36 to S38 according to steps S23 to S25 in FIG. 8, the process is returned to the main routine. However, in step S37, in addition to the deterioration amount due to each of the above-described three types of deterioration conditions (temperature of the assembled battery 10, SOC, and surface stress σs), the deterioration amount due to each of the count values C _A and C _B is also calculated. The The ECU 100 uses, for example, a map indicating the relationship between the count value C _A and the deterioration amount and a map indicating the relationship between the count value C _B and the deterioration amount, and causes the deterioration amount due to each of the count values C _A and C _B. Ask for. In step S38, the ECU 100 estimates the deterioration rate of the assembled battery 10 in the future using the total amount of deterioration due to each deterioration condition acquired in step S37.

  In the above example, the ECU 100 is configured to estimate the deterioration rate of the assembled battery 10 using the number of breakdowns of the negative electrode active material. More specifically, it is estimated that the deterioration rate of the assembled battery 10 is larger as the number of yields of the negative electrode active material is larger. The yielding of the active material due to the surface stress means that a large mechanical load is applied to the surface of the active material. Therefore, the number of yields of the active material due to surface stress and the degree of deterioration of the secondary battery are highly correlated. For this reason, it becomes possible to estimate the deterioration rate of the assembled battery 10 with high accuracy by using the number of yields of the active material.

In the above example, the number of yields on the compression side (count value C _A ) and the number of yields on the tension side (count value C _B ) are counted separately. By doing so, it is possible to weight the deterioration amount with respect to the yield that is more likely to deteriorate. However, the present invention is not limited to this, and the number of yields (the total number of yields on the compression side and the yield on the tension side) may be counted without distinguishing between the yield on the compression side and the yield on the tension side.

A method of reflecting the deterioration amount due to each deterioration condition (surface stress σs and the like) on the deterioration degree (for example, deterioration rate) of the assembled battery 10 is arbitrary. For example, the future deterioration rate estimated using deterioration conditions other than the surface stress σs is corrected using the surface stress σs (for example, the frequency distribution of the surface stress σs or the count values C _A and C _B ). It may be.

  In the embodiment and the modification, the deterioration rate of the assembled battery 10 in the future is estimated. However, the present invention is not limited to this, and the ECU 100 may estimate the current deterioration rate of the assembled battery 10 using the surface stress σs. The ECU 100 calculates, for example, the amount of deterioration due to each deterioration condition (surface stress σs, etc.) using not only the latest data but also all data from the start of use of the assembled battery 10 to the present, The current deterioration rate of the assembled battery 10 can be obtained by adding the total amount of deterioration due to each deterioration condition to the initial value (= 1) or the previous value of the deterioration rate. It should be noted that each period acquired by the process of FIG. 5 at the end (immediately before the process of FIG. 5 is stopped) in a period in which the process of FIG. 5 is not executed (for example, a period in which the ignition switch is off). The frequency of occurrence in step S22 may be updated using the value of the deterioration condition (surface stress σs or the like).

  The degree of deterioration of the assembled battery 10 estimated by the ECU 100 (battery deterioration estimating device) is not limited to the internal resistance (the aforementioned deterioration rate) of the assembled battery 10 and is arbitrary. For example, the full charge capacity of the assembled battery 10 may be adopted as the degree of deterioration.

  The configuration of the vehicle to which the battery deterioration estimation device is applied is not limited to the configuration shown in FIG. 1 and can be changed as appropriate. In addition, the configuration of the secondary battery that is the target of deterioration estimation in the battery deterioration estimation device can be arbitrarily changed. For example, predetermined cells (one or a plurality of cells 11) included in the assembled battery 10 may be targeted for deterioration estimation. Further, the estimation of the surface stress σs and the SOC may be performed for a predetermined cell included in the assembled battery 10. The monitoring target of the monitoring unit 20 (and thus the battery voltage and battery temperature detected by the voltage sensor 21 and the temperature sensor 23) can be changed according to the target of deterioration estimation. For example, you may provide the voltage sensor 21 so that the voltage for every cell 11 or the voltage for every several cell 11 connected in series may be detected. Moreover, you may provide the temperature sensor 23 so that the temperature for every cell 11 or the temperature for every several adjacent cells 11 may be detected.

  The secondary battery to be subjected to deterioration estimation is not limited to the assembled battery, and may be a single battery. Also, the type of the battery is not limited to the lithium ion secondary battery, and another secondary battery (for example, a nickel metal hydride battery) may be targeted for deterioration estimation. The secondary battery may be an all-solid battery. However, the battery deterioration estimation device according to the present disclosure is suitable for estimating deterioration of a secondary battery having hysteresis characteristics. For example, when an active material whose volume is largely changed by charging / discharging of the secondary battery is used, the secondary battery comes to have hysteresis characteristics. Examples of such active materials include, in addition to silicon-based compounds, tin-based compounds (such as Sn or SnO), germanium (Ge) -based compounds, and lead (Pb) -based compounds. When these active materials are employed as the negative electrode active material of the lithium ion secondary battery, the volume change amount of the negative electrode active material associated with charge / discharge is larger than when the carbon-based material is employed.

  Secondary batteries having hysteresis characteristics include secondary batteries in which significant hysteresis exists only in some SOC regions. Such a secondary battery can be obtained, for example, by adopting a composite material containing a silicon-based material and graphite or a composite material containing a silicon-based material and lithium titanate as a negative electrode active material of a lithium ion secondary battery.

  Surface stress can also occur on the positive electrode side of the secondary battery. In addition to or instead of the surface stress of the negative electrode active material of the secondary battery, the degree of deterioration of the secondary battery may be estimated using the surface stress of the positive electrode active material. Moreover, it is good also as an parameter | index which calculates the stress of a peripheral material and estimates the deterioration degree of a secondary battery.

  The embodiment disclosed this time should be considered as illustrative in all points and not restrictive. The scope of the present invention is shown not by the above description of the embodiment but by the scope of claims, and is intended to include all modifications within the meaning and scope equivalent to the scope of claims.

  1 vehicle, 2 battery system, 10 assembled battery, 11 cell, 20 monitoring unit, 21 voltage sensor, 22 current sensor, 23 temperature sensor, 30 PCU, 40 inlet, 50 charging device, 61, 62 motor generator, 63 engine, 64 Power split device, 65 drive shaft, 66 drive wheel, 90 power supply, 100 ECU, 101 CPU, 102 memory, 111 battery case, 112 lid, 113 positive terminal, 114 negative terminal, 115 electrode body, 116 positive electrode, 117 negative electrode, 118 Separator.

Claims (2)

A battery deterioration estimation device for a secondary battery comprising an electrode containing an active material,
A secondary battery deterioration estimation device that estimates the degree of deterioration of the secondary battery using the surface stress of the active material.   The secondary battery deterioration estimation apparatus according to claim 1, wherein the secondary battery deterioration estimation apparatus estimates that the degree of deterioration of the secondary battery is greater as the number of yields of the active material due to the surface stress increases.

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