Battery diagnosis system, vehicle equipped with the system, and battery diagnosis method

Abstract

The present application relates to a battery diagnosis system, a vehicle provided with the system, and a battery diagnosis method. The battery diagnosis system is provided with: a sensor that measures a voltage of a secondary battery; and a processor configured to diagnose whether the secondary battery is a normal product based on a voltage curve indicating a temporal change in the voltage measured by the sensor during a diagnosis target period including both a charging / discharging period and a charging / discharging suspension period.

Description

Technical Field

[0001] This disclosure relates to a battery diagnostic system, a vehicle equipped with the system, and a battery diagnostic method, and more specifically, to a technique for diagnosing secondary batteries. Background Technology

[0002] In recent years, vehicles equipped with battery packs have become increasingly common. Counterfeit battery packs manufactured outside of legitimate manufacturers may be circulating. Furthermore, legitimate battery packs may be illegally modified. Counterfeit and illegally modified products may use substandard rechargeable batteries and / or have defects in their control circuitry. Therefore, techniques for diagnosing whether a battery pack is genuine have been developed.

[0003] For example, the battery identification device disclosed in Japanese Patent Application Publication No. 2012-174367 monitors whether the battery pack is a genuine product based on a first characteristic value and a second characteristic value. The first characteristic value is the output voltage value obtained from each of the multiple battery cells when the vehicle's operation ends or when the charging of the battery pack ends. The second characteristic value is the output voltage value obtained from each of the multiple battery cells when the vehicle's operation begins for the first time or when the charging of the battery pack begins. Summary of the Invention

[0004] The need for technology to accurately diagnose whether a secondary battery is a genuine product has always existed. This disclosure relates to technology for accurately diagnosing whether a secondary battery is a genuine product.

[0005] The battery diagnostic system of the first aspect of this disclosure includes: a sensor for measuring the voltage of a secondary battery; and a processor configured to diagnose whether the secondary battery is a genuine product based on a voltage curve representing the time change of voltage measured by the sensor during a diagnostic period including both a charge / discharge period and a charge / discharge rest period.

[0006] In a first aspect of this disclosure, the processor may be configured to diagnose whether a secondary battery is a genuine product based on a comparison of a voltage curve with a regular curve representing the time-varying voltage of a genuine product during the diagnostic period.

[0007] In a first aspect of this disclosure, the processor may be configured to diagnose the secondary battery as a normal product when the voltage on the voltage curve at a first timing during a charge / discharge period is within a first voltage range determined based on a normal curve, and the voltage on the voltage curve at a second timing during a charge / discharge pause period is within a second voltage range determined based on a normal curve.

[0008] In a first aspect of this disclosure, the processor may be configured to diagnose the secondary battery as a normal product if the proportion of the change in the voltage curve during a first timing period in the charge / discharge period is within a first reference range determined based on a normal curve, and the proportion of the change in the voltage curve during a second timing period in the charge / discharge rest period is within a second reference range determined based on a normal curve.

[0009] In a first aspect of this disclosure, the processor may be configured to diagnose whether a secondary battery is a genuine product based on a comparison of a voltage curve and a normal curve made from simulated noise interference.

[0010] In a first aspect of this disclosure, the processor may be configured to diagnose whether a secondary battery is a genuine product based on multiple comparisons of the voltage curve with the normal curve during charging and discharging, and multiple comparisons of the voltage curve with the normal curve during charging and discharging pauses.

[0011] In a first aspect of this disclosure, the battery diagnostic system may further include a warning device configured to issue a warning, wherein the processor is configured to control the warning device to issue a warning when the secondary battery is diagnosed as not being a genuine product.

[0012] The vehicle of the second aspect of this disclosure is equipped with the aforementioned battery diagnostic system.

[0013] The battery diagnostic method of the third aspect of this disclosure includes: acquiring by a computer a voltage curve representing the time change of the voltage of a secondary battery as measured by a sensor during a diagnostic period including both charging and discharging periods and charging and discharging rest periods; and diagnosing by computer whether the secondary battery is a genuine product based on the voltage curve.

[0014] According to the aspects disclosed herein, it is possible to diagnose with high precision whether a secondary battery is a genuine product. Attached Figure Description

[0015] Hereinafter, with reference to the accompanying drawings, the features, advantages, and technical and industrial significance of exemplary embodiments of the present invention will be described, in which the same reference numerals denote the same elements, and wherein:

[0016] Figure 1 This is a schematic diagram illustrating the overall configuration of a vehicle equipped with the battery diagnostic system according to Embodiment 1.

[0017] Figure 2 It is a three-dimensional diagram schematically showing the structure of a storage battery.

[0018] Figure 3 This is a perspective three-dimensional drawing showing an example of the composition of a single unit.

[0019] Figure 4This is a chart showing the first case during the diagnosis period.

[0020] Figure 5 This is a chart showing the second case during the diagnosis period.

[0021] Figure 6 It is a graph showing the voltage change curves of regular products and non-regular products.

[0022] Figure 7 This is a diagram used to illustrate the diagnostic method for a standard battery in Embodiment 1.

[0023] Figure 8 This is a diagram illustrating the impedance components of the internal resistance of a battery.

[0024] Figure 9 This is a flowchart illustrating the battery diagnostic process in Implementation 1.

[0025] Figure 10 This is a diagram used to illustrate the diagnostic method for a standard battery in Embodiment 2.

[0026] Figure 11 This is a flowchart illustrating the battery diagnostic process in Implementation 2.

[0027] Figure 12 It is a conceptual diagram used to illustrate noise.

[0028] Figure 13 It is a graph used to illustrate the effect of noise interference on voltage change curves.

[0029] Figure 14 It is a graph showing the voltage change curves of normal and non-normal products under the condition of noise interference.

[0030] Figure 15 This is a flowchart illustrating the battery diagnostic process in Implementation 3. Detailed Implementation

[0031] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. Furthermore, the same or equivalent parts in the drawings will be labeled with the same reference numerals, and their descriptions will not be repeated.

[0032] The following embodiments illustrate an example of a vehicle equipped with the battery diagnostic system of this disclosure. However, the application of the battery diagnostic system of this disclosure is not limited to vehicles; for example, it can also be used for stationary installations.

[0033] Implementation Method 1

[0034] System Composition

[0035] Figure 1 This diagram schematically illustrates the overall configuration of a vehicle equipped with the battery diagnostic system according to Embodiment 1. In this embodiment, vehicle 1 is an electric vehicle (BEV). However, vehicle 1 is not limited to any vehicle equipped with a battery pack. Vehicle 1 can be a hybrid electric vehicle (HEV), a plug-in hybrid electric vehicle (PHEV), or a fuel cell electric vehicle (FCEV).

[0036] Vehicle 1 includes an entrance 10, an AC / DC converter 20, a charging relay (CHR) 30, a battery pack 40, a power control unit (PCU) 51, a motor generator (MG) 52, a warning device 60, and an integrated electronic control unit (ECU) 70. The battery pack 40 includes a battery 41, a monitoring unit 42, and a battery ECU 43.

[0037] The inlet 10 is configured to allow insertion of a charging connector located at the end of the charging cable 91. The vehicle 1 is electrically connected to a charging device 92 located outside the vehicle 1 via the charging cable 91. Thus, the battery 41 is charged (plug-in charging) using power supplied from the charging device 92.

[0038] AC / DC converter 20 is electrically connected between inlet 10 and charging relay 30. AC / DC converter 20 converts AC power supplied from charging device 92 via inlet 10 into DC power and outputs the DC power to charging relay 30. Additionally, AC / DC converter 20 converts DC power supplied from battery 41 (or PCU 51) via charging relay 30 into AC power and outputs the AC power to inlet 10. Charging relay 30 is electrically connected to the power line connecting AC / DC converter 20 and battery 41. Charging relay 30 is opened / closed according to control signals from integrated ECU 70.

[0039] The battery 41 stores power for driving the motor generator 52 and supplies power to the motor generator 52 via the PCU 51. Additionally, the battery 41 is charged using power output from the AC / DC converter 20 when plugged in for charging. Furthermore, the battery 41 is also charged via the PCU 51 when the motor generator 52 generates electricity (e.g., during regenerative braking).

[0040] The monitoring unit 42 includes a voltage sensor 421, a current sensor 422, and a temperature sensor 423. The voltage sensor 421 detects the voltage V of the battery 41 (more specifically, each individual cell). The current sensor 422 detects the current I relative to the input / output of the battery 41. The temperature sensor 423 detects the temperature T of the battery 41 (more specifically, a particular individual cell). Each sensor outputs a signal representing the detection result to the battery ECU 43. The voltage sensor 421 is an example of a "sensor" as defined in this disclosure.

[0041] The battery ECU 43 includes a processor 431 such as a CPU (Central Processing Unit), a memory 432 such as ROM (Read Only Memory) and RAM (Random Access Memory), and input / output ports (not shown) for inputting and outputting various signals. The battery ECU 43 manages the battery 41 while coordinating with the integrated ECU 70, based on input signals from various sensors of the monitoring unit 42 and the mappings and programs stored in the memory 432. In this embodiment, a primary process performed by the battery ECU 43 is the "battery diagnostic process," which diagnoses whether the battery 41 is a genuine product. The battery diagnostic process based on the battery ECU 43 will be described later.

[0042] PCU 51 includes, for example, converters and alternators (neither shown). PCU 51 performs bidirectional power conversion between battery 41 and motor generator 52 according to control signals from integrated ECU 70.

[0043] The motor generator 52 is, for example, a three-phase AC rotating motor with permanent magnets embedded in the rotor (not shown). The motor generator 52 uses power supplied from the battery 41 to rotate the drive shaft. Additionally, the motor generator 52 can also generate electricity using regenerative braking. The AC power generated by the motor generator 52 is converted into DC power by the PCU 51 and used to charge the battery 41.

[0044] Warning device 60 may be, for example, a warning light that illuminates on the dashboard. Warning device 60 may be a display of a navigation system capable of displaying warning messages, or a speaker capable of producing warning sounds.

[0045] Like the battery ECU 43, the integrated ECU 70 includes a processor 701, a memory 702, and input / output ports (not shown). Based on input signals from various sensors located in the vehicle 1 and mappings and programs stored in the memory, the integrated ECU 70 controls devices (AC / DC converter 20, charging relay 30, and PCU 51) to bring the vehicle 1 to a desired state. For example, the integrated ECU 70 controls the charging and discharging of the battery 41 by controlling the AC / DC converter 20 and / or the PCU 51.

[0046] Battery diagnostic processing can also be performed by the integrated ECU 70 instead of the battery ECU 43. Alternatively, a portion of the battery diagnostic processing can be performed by the battery ECU 43, while the remaining processing is performed by the integrated ECU 70. One or both of the processor 431 of the battery ECU 43 and the processor 701 of the integrated ECU 70 are examples of the "processor" involved in this disclosure.

[0047] Battery composition

[0048] Figure 2 This is a perspective view schematically illustrating the structure of the storage battery 41. The storage battery 41 is a battery pack comprising multiple stacks 410 (also referred to as modules or blocks). The multiple stacks 410 can be connected in series or in parallel with each other. Figure 2 One of the multiple heaps 410 is shown in the figure.

[0049] Stack 410 includes multiple monomers 81, multiple resin frames 82, a pair of end plates 83, and a pair of restraint straps 84. In stack 410, a laminate is formed by stacking multiple monomers 81 and multiple resin frames 82. Hereinafter, the height direction of the laminate will be referred to as HG, the length direction (lamination direction) of the laminate will be referred to as LN, and the width direction of the laminate will be referred to as WD.

[0050] In this embodiment, each of the multiple cells 81 is a lithium-ion battery. However, each cell 81 can also be another type of rechargeable battery, such as a nickel-metal hydride battery. The number of cells included in the stack 410 is not particularly limited. The configuration of each cell 81 is common. Regarding the configuration of the cell 81, Figure 3 Please provide an explanation.

[0051] Multiple resin frames 82 are each disposed between two adjacent monomers 81 in the lamination direction. A pair of end plates 83 are disposed at the first and second ends of the laminate in the lamination direction. That is, the end plates 83 are configured to clamp the laminate from both sides in the lamination direction. A pair of constraint straps 84 are disposed on the upper and lower surfaces of the resin frames 82. The pair of end plates 83 are in a state where the constraint straps 84 mutually constrain and clamp the laminate.

[0052] Figure 3This is a perspective view showing an example of the structure of cell 81. As mentioned above, in this example, cell 81 is a lithium-ion battery.

[0053] Unit 81 is a square unit with a generally rectangular parallelepiped shape. The upper surface of the housing of unit 81 is closed by a cover 811. A positive terminal 812 and a negative terminal 813 are provided on the cover 811. The first end of each of the positive terminal 812 and the negative terminal 813 protrudes from the cover 811 to the outside. The second end of each of the positive terminal 812 and the negative terminal 813 is electrically connected to an internal positive terminal and an internal negative terminal (not shown) inside the housing, respectively. Although not shown, two adjacent units 81 are electrically connected to each other via a busbar.

[0054] An electrode body 814 is housed inside the housing. The electrode body 814 is formed, for example, by stacking a positive electrode 815 and a negative electrode 816 with a separator 817 in a cylindrical shape and then winding them together. The electrolyte is held in the positive electrode 815, the negative electrode 816, and the separator 817. Alternatively, a laminated body may be used instead of a wound body as the electrode body 814.

[0055] For the positive electrode 815, negative electrode 816, separator 817, and electrolyte, conventionally known compositions and materials used as positive electrodes, negative electrodes, separators, and electrolytes in lithium-ion secondary batteries can be used. As an example, the positive electrode 815 includes NCM (LiNi) as a positive electrode additive. 1 / 3 Co 1 / 3 Mn 1 / 3 The positive electrode 816 comprises graphite (C) as a negative electrode agent and copper (Cu) foil as a negative electrode foil. For the separator, polyolefins (e.g., polyethylene, polypropylene) can be used. The electrolyte comprises an organic solvent (e.g., a mixture of DMC (dimethyl carbonate), EMC (ethyl methyl carbonate), and EC (ethylene carbonate), a lithium salt (e.g., LiPF6), and additives (e.g., LiBOB (lithium bis(oxalate)borate) or Li[PF2(C2O4)2]).

[0056] Voltage variation curve

[0057] In a vehicle 1 configured as described above, the battery 41 deteriorates with use or over time. When the battery 41 has deteriorated considerably, it is considered to replace it with a new battery (or a used battery that has not deteriorated further). At this point, it is possible to replace it with a counterfeit or illegally modified battery. If a counterfeit battery is used, various performance characteristics of the vehicle 1 may decrease. Therefore, it is necessary to accurately diagnose whether the battery 41 is a genuine product.

[0058] Therefore, in this embodiment, the "voltage change curve" measured during a specific period, as described below, is used to diagnose whether the battery 41 is a genuine product. This period is referred to as the "diagnosis target period." Furthermore, as mentioned above, the configurations of the multiple cells 81 are identical, so it is possible to obtain a voltage change curve by measuring the voltage of any cell 81. Hereinafter, for simplicity, cell-specific variations are not distinguished, and the voltage change curve is described as that of the battery 41.

[0059] During the diagnosis

[0060] Figure 4 This is a diagram showing the first case during the diagnosis period. Figure 5 This is a diagram showing the second case during the diagnosis process. Figure 4 and Figure 5 In the diagram, the horizontal axis represents elapsed time. The vertical axis represents the voltage V measured by voltage sensor 421. (The following section discusses...) Figure 6 , Figure 7 , Figure 10 , Figure 13 , Figure 14 The same applies.

[0061] During the diagnosis of the subject, such as Figure 4 As shown, this includes the discharge period of battery 41 and the rest period of battery 41 during charging and discharging. The length of the discharge period can be from several seconds to tens of seconds. The length of the rest period can be tens of seconds. For example, if vehicle 1 stops for a certain period of time after driving uphill and waiting for a signal, the following can be measured: Figure 4 The voltage change curve L1 is shown.

[0062] Alternatively, during the diagnosis of the subject, it can also be like this Figure 5 As shown, this includes the charging period of battery 41 and the rest period of battery 41 during charging and discharging. The length of the charging period can be from several seconds to tens of seconds. The length of the rest period can be tens of seconds. For example, if vehicle 1 stops for a certain period of time while waiting for a signal after driving downhill, the following can be measured: Figure 5The voltage change curve L2 is shown. By temporarily stopping charging while the vehicle 1 is plugged in for charging, the same voltage change curve L2 can be measured.

[0063] The voltage change curves L1 or L2 measured during the diagnostic process are stored in the memory 432 of the battery ECU 43. Furthermore, the voltage change curves L1 or L2 are compared with voltage curves previously obtained from a standard sample of the battery 41. Hereinafter, the voltage change curve of the standard sample will also be abbreviated as "standard curve".

[0064] Figure 6 This is a graph showing the voltage change curves of regular and irregular products. For example... Figure 6 As shown, the shapes of the voltage change curves differ between genuine and non-genuine batteries 41. Therefore, by comparing the voltage change curve L1 (or L2) with the genuine curve, it is possible to diagnose whether battery 41 is genuine or non-genuine. That is, if the voltage change curve L1 matches the genuine curve, battery 41 can be diagnosed as genuine. On the other hand, if the voltage change curve L1 does not match the genuine curve, battery 41 can be diagnosed as non-genuine.

[0065] Ideally, the normal curves are prepared for each combination of (SOC, current I). For example, many normal curves are prepared by measuring voltage changes under initial conditions where the SOC differs by a predetermined amount and the current I differs by a predetermined value. In this example, many normal curves are stored in the memory 432 of the battery ECU 43. The battery ECU 43 selects a normal curve as a comparison object for the voltage change curve L1 based on the combination of (SOC, current I).

[0066] Figure 7 This is a diagram illustrating the diagnostic method for a standard battery 41 in Embodiment 1. Figure 7 In China, with Figure 4 The voltage change curve L1 shown is used as an example for explanation, but according to... Figure 5 The voltage change curve L2 shown can also be used for diagnosis in the same way.

[0067] Even with standard products, some degree of voltage fluctuation may occur. Therefore, a voltage range VR containing the standard curve is preset. The upper limit voltage of the voltage range VR is represented by UL, and the lower limit voltage is represented by LL. In Implementation 1, it is determined whether the voltage change curve L1 is within the voltage range VR. More specifically, at least one voltage during the discharge period and at least one voltage during the rest period are used.

[0068] exist Figure 7In the example shown, it is determined whether the voltage V(t11) at time t11 during the discharge period (an example of the "first timing" involved in this disclosure) is within the first voltage range between the upper limit voltage UL and the lower limit voltage LL, and whether the voltage V(t21) at time t21 during the rest period (an example of the "second timing" involved in this disclosure) is within the second voltage range between the upper limit voltage UL and the lower limit voltage LL.

[0069] Regarding a certain voltage change curve L1A, voltage V(t11) exceeds the upper limit voltage UL of the first voltage range, and voltage V(t21) exceeds the upper limit voltage UL of the second voltage range. In this case, based on voltage change curve L1A, battery 41 is diagnosed as a non-standard product. Conversely, regarding another voltage change curve L1B, voltage V(t11) is within the first voltage range, and voltage V(t21) is within the second voltage range. In this case, based on voltage change curve L1B, battery 41 is diagnosed as a standard product.

[0070] Furthermore, for ease of understanding, an example of implementing a diagnosis based on one voltage value during the discharge period and one voltage value during the rest period is illustrated here. However, it is also possible to implement a diagnosis based on multiple voltage values ​​during the discharge period and multiple voltage values ​​during the rest period. By performing a diagnosis based on multiple voltage values, the accuracy of the diagnosis can be improved.

[0071] The impedance component of internal resistance

[0072] In this embodiment, both the voltage value during the discharge period and the voltage value during the rest period are used. The reason for this is explained.

[0073] Figure 8 This is a diagram illustrating the impedance composition of the internal resistance of battery 41. Figure 8 The diagram shows an example of an equivalent circuit diagram of the positive and negative terminals and separators of the storage battery 41 (each cell 81). The impedance components of the storage battery 41 can be classified into DC resistance R. DC , reaction resistance Rc, diffusion resistance Rd.

[0074] The so-called DC resistance R DC It is the impedance component associated with the movement of lithium ions and electrons between the positive and negative electrodes. DC resistance R DC The DC resistance R increases due to biases in the electrolyte salt concentration distribution, etc., when a high load is applied to the battery 41 (high voltage applied, large current flowing). DC In the equivalent circuit diagram, it is represented by the active material resistor Ra1 as the positive electrode, the active material resistor Ra2 as the negative electrode, and the electrolyte resistor R3 as the separator.

[0075] The reactive resistance Rc is an impedance component related to the transfer of charge (charge movement) at the interface between the electrolyte and the active materials (the surfaces of the positive and negative electrode active materials). The reactive resistance Rc increases due to factors such as the growth of the coating at the active material / electrolyte interface under high-temperature conditions in a high-SOC battery 41. In an equivalent circuit diagram, the reactive resistance Rc is represented as the resistive component Rc1 for the positive electrode and Rc2 for the negative electrode.

[0076] The diffusion resistance Rd is an impedance component related to the diffusion of salts in the electrolyte or charge-carrying substances in the active material. The diffusion resistance Rd increases due to factors such as the breakdown of active materials under high loads. The diffusion resistance Rd is determined based on the equilibrium voltage Veq1 generated at the positive electrode, the equilibrium voltage Veq2 generated at the negative electrode, and the salt concentration overvoltage Vov3 generated within the monomer (an overvoltage caused by the salt concentration distribution of the active material within the separator).

[0077] The voltage V (CCV: Closed Circuit Voltage), OCV (Open Circuit Voltage), current I, and the aforementioned impedance components R are measured by voltage sensor 421. DC The following relationship (1) holds between Rc, Rd, and the dividing voltage ΔVp: V = OCV - I × (R DC +Rc+Rd)-ΔVp···(1)

[0078] As mentioned above, for the voltage change curve L1 and the normal curve, the curves with equivalent combinations of (SOC, current I) are compared. Furthermore, there is a correspondence between SOC and OCV. Therefore, the difference between OCV and current I in equation (1) between the voltage change curve L1 and the normal curve can be approximated as sufficiently small.

[0079] The voltage V measured during discharge reflects the various impedance components R. DC Rc, Rd, and the dividing voltage ΔVp. During discharge, based on each impedance component R... DC The voltage drops of Rc and Rd are significantly greater than the polarity voltage ΔVp. Therefore, comparing the voltage V(t11) measured during discharge with the corresponding voltage on the normal curve (first voltage range) is mainly to compare the impedance components Rc, Rd, and Rd of the battery under diagnostics with those of the normal product. DC An example of comparing Rc and Rd.

[0080] On the other hand, during the rest period, I = 0, so the voltage V measured during the rest period reflects the polarity voltage ΔVp. Therefore, comparing the voltage V(t21) measured during the rest period with the corresponding voltage (second voltage range) on the normal curve is an example of comparing the polarity voltage ΔVp between the battery 41 under diagnosis and the normal product.

[0081] Between genuine and non-genuine products, the specifications differ, including the resistor R. DC The resistances Rc, Rd, and the voltage ΔVp also differ. Therefore, by comparing the voltage V on the voltage change curve measured during discharge with the corresponding voltage on the normal curve, it is possible to diagnose whether the battery 41 is a normal product. In particular, by comparing the resistance Rc, Rd, and the voltage ΔVp, it is possible to diagnose whether the battery 41 is a normal product. DC By comparing Rc, Rd, and the polarity voltage ΔVp, it is possible to diagnose with high precision whether the battery 41 of the test object is a genuine product.

[0082] Battery diagnostic process

[0083] Figure 9 This is a flowchart illustrating the battery diagnostic process in Embodiment 1. The flowchart is invoked from the main routine (not shown) and executed when predetermined conditions are met (e.g., when the battery pack 40 is replaced). Each step is implemented through software processing based on the battery ECU 43, but can also be implemented through hardware (electrical circuits) incorporated into the battery ECU 43. Hereinafter, the steps will be abbreviated as S.

[0084] In the flowchart, any one of the multiple stacks 410 contained in the battery 41 is designated as the diagnostic object. By performing the same process on the stacks 410 other than the one specified, it is possible to diagnose more than two stacks 410.

[0085] In S101, the battery ECU 43 determines whether it has passed through... Figure 4 or Figure 5 The diagnostic period is described (e.g., a discharge period of several seconds to tens of seconds and a subsequent rest period of tens of seconds). The battery ECU 43 sequentially acquires voltage V from the voltage sensor 421 and temporarily stores the acquired voltage V (i.e., the timing data of voltage V) in the memory 432. If the diagnostic period has not passed (no in S101), the battery ECU 43 continues the temporary storage of voltage V. In this case, the portion of the old timing data of voltage V that exceeds the predetermined storage capacity is erased from the memory 432. On the other hand, if the diagnostic period has passed (yes in S101), the battery ECU 43 does not erase the timing data of voltage V during the diagnostic period but retains it (S102).

[0086] In S103, the battery ECU 43 obtains the voltage V1 at a predetermined timing during the charging or discharging period (during charging or discharging) from the timing data (voltage change curve) of the maintained voltage V. Figure 7 In the example, the voltage V(t11) at time t11 is an example of voltage V1. Furthermore, the predetermined timing here can be determined based on prior experimental results, such as the time after X seconds have elapsed since the start of charging / discharging at time t10.

[0087] In S104, the battery ECU 43 obtains the voltage V2 at a predetermined timing during the rest period from the timing data of the held voltage V. Figure 7 In the example, the voltage V(t21) at time t21 is an example of voltage V2. The predetermined timing here can be determined, for example, as the time after Y seconds have elapsed from the rest start time (charge / discharge stop time) t20. Y can be the same as or different from X.

[0088] In S105, the battery ECU 43 determines whether voltage V1 is within the first voltage range. Furthermore, in S106, the battery ECU 43 determines whether voltage V2 is within the second voltage range. As mentioned above, the normal curve is selected based on the combination of (SOC, current I). Therefore, both the first and second voltage ranges can vary according to the combination of (SOC, current I).

[0089] If voltage V1 is within the first voltage range and voltage V2 is within the second voltage range (yes in S105 and S106), the battery ECU 43 diagnoses the battery 41 as a genuine product (S107).

[0090] Conversely, if voltage V1 is not within the first voltage range (no in S105) or voltage V2 is not within the second voltage range (no in S106), the battery ECU 43 diagnoses the battery 41 as non-standard (S108). In this case, the battery ECU 43, in coordination with the integrated ECU 70, controls the warning device 60 to illuminate the warning light, and / or display a warning message, and / or generate a warning sound (S109). The battery ECU 43 can also, in coordination with the integrated ECU 70, prevent the vehicle 1 from moving.

[0091] As described above, in Embodiment 1, the battery 41 is diagnosed as a genuine product based on the voltage value of the voltage change curve (time data of voltage V) measured by the voltage sensor 421 during the diagnostic period, which includes both the charging / discharging period and the rest period. Since the diagnosis is performed based on the voltage during both the charging / discharging period and the rest period, differences in the specifications of the battery 41 can be accurately detected. Therefore, according to Embodiment 1, it is possible to diagnose whether the battery 41 is a genuine product with high accuracy.

[0092] In addition, it can also be with Figure 7 Similarly, in the explanation, Figure 9 The flowchart shown also demonstrates a diagnosis of whether a battery is a genuine or non-genuine product based on multiple voltages V1 measured during charging and discharging and multiple voltages V2 measured during rest periods. In this case, if the ratio of voltages V1 and V2 within a voltage range (either the first or second voltage range) is above a predetermined value, the battery 41 is diagnosed as a genuine product; if the ratio is below the predetermined value, the battery 41 is diagnosed as a non-genuine product. By performing the diagnosis based on multiple voltages V1 and V2, the diagnostic accuracy can be further improved.

[0093] Implementation Method 2

[0094] In Embodiment 1, an example of diagnosis based on voltage values ​​on a voltage change curve was described. In Embodiment 2, an example of diagnosis based on the proportion of change in the voltage change curve (in other words, the slope of the tangent line drawn on the voltage change curve) was described. Furthermore, the vehicle configuration in Embodiment 2 is similar to the vehicle 1 configuration in Embodiment 1 (see...). Figures 1-3 They are equivalent, so I will not repeat the explanation.

[0095] Figure 10 This diagram illustrates the diagnostic method for a standard battery 41 in Embodiment 2. In Embodiment 2, it is determined whether the percentage change (slope of the tangent) of the voltage change curve L1 at a predetermined time is within a reference range predetermined based on the standard curve. More specifically, the slopes of at least one tangent during the discharge period and at least one tangent during the rest period are used. Figure 10 In the example shown, it is determined whether the slope SL1 of the tangent T1 at time t31 during the discharge period is within the first reference range (not shown), and whether the slope SL2 of the tangent T2 at time t41 during the rest period is within the second reference range (not shown).

[0096] Figure 11This is a flowchart illustrating the battery diagnostic process in Embodiment 2. The flowchart is identical to the flowchart in Embodiment 1 (see reference 1) except that it uses the slopes SL1 and SL2 of the tangents instead of voltages V1 and V2. Figure 9 They are equivalent. Therefore, a detailed explanation will not be repeated.

[0097] As described above, in Embodiment 2, the battery 41 is diagnosed as a genuine product based on the proportion (slope of the tangent) of the voltage change curve (time-series data of voltage V) measured by the voltage sensor 421 during the diagnostic period, which includes both the charging / discharging period and the rest period. Since the diagnosis is based on the proportion of change during both the charging / discharging period and the rest period, similar to Embodiment 1, differences in the specifications of the battery 41 can be accurately detected. Therefore, according to Embodiment 2, it is possible to diagnose whether the battery 41 is a genuine product with high accuracy.

[0098] Implementation Method 3

[0099] In Embodiment 3, an example of performing a diagnosis considering noise generated by electronic devices surrounding the battery pack 40 will be described. Furthermore, the vehicle configuration in Embodiment 3 is similar to the vehicle 1 configuration in Embodiments 1 and 2 (see [reference]). Figures 1-3 They are equivalent, so I will not repeat the explanation.

[0100] Figure 12 This is a conceptual diagram used to illustrate noise. The horizontal axis represents time. The vertical axis represents current. Various electronic devices may be present around the battery pack 40. Figure 1 In the vehicle configuration, the PCU 51 (converter and / or converter), charging device 92, etc., are located around the battery pack 40. Noise generated by the aforementioned electronic equipment (or its power supply) may be as follows: Figure 12 The noise is intermittently superimposed on the current (detection result of current sensor 422). Noise may also be superimposed on the voltage (detection result of voltage sensor 421). Hereinafter, this phenomenon will be referred to as "noise interference".

[0101] Figure 13 This is a graph used to illustrate the effect of noise interference on voltage variation curves. Figure 13 The voltage change curves under noise interference are shown in the figure, compared with those under no noise interference. Figure 13 As can be seen, the shape of the voltage change curve may change when noise interference occurs. Although not illustrated, the shape of the normal curve may also change. As a result, the diagnostic accuracy of whether battery 41 is a normal product may be reduced.

[0102] Therefore, in this embodiment, normalization curves are prepared in advance to anticipate noise interference that may occur in vehicle 1. That is, a ripple current generation circuit (not shown) is used in advance to generate various ripple currents simulating noise interference and apply them to the normalization curve. By simulating noise interference under various conditions, including charging and discharging and resting, many normalization curves are generated. The generated normalization curves are stored in the memory 432 of the battery ECU 43.

[0103] Figure 14 This is a graph showing the voltage change curves of normal and non-normal products under noise interference. (Example) Figure 14 As shown, under noise interference, the voltage change curves of the battery 41 differ between genuine and non-genuine products. Therefore, by comparing the voltage change curve with the genuine curve, it is possible to diagnose whether the battery 41 is a genuine or non-genuine product.

[0104] Figure 15 This is a flowchart illustrating the battery diagnostic process in Embodiment 3. The processes S301 to S304 are the same as the processes S101 to S104 in Embodiment 1 (see...). Figure 9 ) are the same.

[0105] In S305, the battery ECU 43 determines whether noise interference has occurred. For example, the battery ECU 43 can determine whether noise interference has occurred based on whether the noise is superimposed on the voltage V detected by the voltage sensor 421 or the current I detected by the current sensor 422 (whether the amplitude of the noise is greater than a predetermined amount, whether the superposition time of the noise is longer than a predetermined time, etc.).

[0106] In the event of noise interference (yes in S305), the battery ECU 43 reads from the memory 432 the first voltage range and the second voltage range set according to the normal curve under simulated noise interference conditions (S306). On the other hand, in the event of no noise interference (no in S305), the battery ECU 43 reads from the memory 432 the first voltage range and the second voltage range set according to the normal curve under normal conditions (the same normal curve as in Embodiment 1 when no noise interference occurs) (S307). The processing after S308 is the same as the processing after S105 in Embodiment 1, so it will not be described again.

[0107] Furthermore, an example of diagnosis based on the voltage value on the voltage change curve, as in Embodiment 1, has been described here. However, diagnosis can also be performed based on the proportion of change in the voltage change curve (the slope of the tangent), as in Embodiment 2.

[0108] As described above, according to Embodiment 3, since the diagnosis is performed based on the voltage during both the charging and discharging periods and the rest period, similar to Embodiments 1 and 2, the difference in the specifications of the battery 41 can be accurately detected. Therefore, it is possible to diagnose whether the battery 41 is a genuine product with high precision. Furthermore, in Embodiment 3, the voltage range used in diagnosing whether the battery 41 is a genuine product (the first voltage range and the second voltage range in the processing of S308 and S309) is switched between when noise interference occurs and when it does not occur (normally). By using a normalized curve generated under a simulated noise interference environment when noise interference occurs, it is also possible to diagnose whether the battery 41 is a genuine product with high precision even when noise interference occurs.

[0109] The embodiments disclosed herein should be considered illustrative rather than restrictive in all respects. The scope of this disclosure is defined by the claims rather than the description of the embodiments, and is intended to include all modifications within the meaning and scope equivalent to the claims.

Claims

1. A battery diagnostic system, characterized in that, have: Sensors that measure the voltage of secondary batteries; and The processor is configured to diagnose whether the secondary battery is genuine based on a voltage curve representing the time-varying voltage measured by the sensor during a diagnostic period that includes both charging / discharging and charging / discharging pauses. The processor is configured to diagnose whether the secondary battery is a genuine product based on a comparison between the voltage curve and a regular curve representing the time-varying voltage of the genuine product during the diagnostic period. The processor is configured to diagnose the secondary battery as a normal product if the proportion of the change in the voltage curve during a first timing period in the charge / discharge period is within a first reference range determined based on the normal curve, and the proportion of the change in the voltage curve during a second timing period in the charge / discharge pause period is within a second reference range determined based on the normal curve.

2. The battery diagnostic system according to claim 1, characterized in that, The processor is configured to diagnose the secondary battery as a normal product if the voltage on the voltage curve at a first timing during the charge / discharge period is within a first voltage range determined based on the normal curve, and the voltage on the voltage curve at a second timing during the charge / discharge pause period is within a second voltage range determined based on the normal curve.

3. The battery diagnostic system according to claim 1 or 2, characterized in that, The processor is configured to diagnose whether the secondary battery is a genuine product based on a comparison between the voltage curve and a normal curve generated by simulated noise interference.

4. The battery diagnostic system according to claim 1 or 2, characterized in that, The processor is configured to diagnose whether the secondary battery is a genuine product based on multiple comparisons of the voltage curve with the normal curve during the charging and discharging period and multiple comparisons of the voltage curve with the normal curve during the charging and discharging rest period.

5. The battery diagnostic system according to claim 1, characterized in that, It also has a warning device configured to issue a warning. The processor is configured to control the warning device to issue a warning when it is diagnosed that the secondary battery is not a genuine product.

6. A vehicle, characterized in that, The battery diagnostic system is provided according to any one of claims 1 to 5.

7. A battery diagnostic method, characterized in that, include: The computer obtains a voltage curve representing the time-varying voltage of the secondary battery, measured by sensors, during the diagnostic period that includes both charging and discharging periods and charging / discharging pauses. and Based on the voltage curve, the computer diagnoses whether the secondary battery is a genuine product. The diagnosis includes diagnosing the secondary battery as a genuine product if the proportion of the change in the voltage curve at a first timing during the charge / discharge period is within a first reference range determined based on a regular curve representing the time-varying voltage of the genuine product during the diagnostic period, and the proportion of the change in the voltage curve at a second timing during the charge / discharge pause period is within a second reference range determined based on the regular curve.

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