WO2024044889A1 - 记忆效应检测方法、装置、计算机设备及存储介质 - Google Patents

记忆效应检测方法、装置、计算机设备及存储介质 Download PDF

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WO2024044889A1
WO2024044889A1 PCT/CN2022/115516 CN2022115516W WO2024044889A1 WO 2024044889 A1 WO2024044889 A1 WO 2024044889A1 CN 2022115516 W CN2022115516 W CN 2022115516W WO 2024044889 A1 WO2024044889 A1 WO 2024044889A1
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battery
memory effect
preset
tested
voltage
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PCT/CN2022/115516
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English (en)
French (fr)
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王羽臻
陈宁
史东洋
邓亚茜
金海族
李白清
梁金鼎
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宁德时代新能源科技股份有限公司
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Priority to PCT/CN2022/115516 priority Critical patent/WO2024044889A1/zh
Publication of WO2024044889A1 publication Critical patent/WO2024044889A1/zh

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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries

Definitions

  • This application relates to the field of batteries, and specifically to a memory effect detection method, device, computer equipment, storage medium and computer program product.
  • the present application provides a memory effect detection method, device, computer equipment, storage medium and computer program product, which can detect whether a battery has a memory effect.
  • this application provides a memory effect detection method, including:
  • the above memory effect detection method after receiving the detection confirmation signal, extracts the discharge characteristic curve during the discharge process of the battery to be tested, and then analyzes the discharge characteristic curve within the preset phase transition voltage interval to determine whether the battery to be tested has a memory effect. detection. Through this solution, it can be detected in time when the memory effect occurs in the battery, so that corresponding processing can be carried out.
  • obtaining the discharge characteristic curve of the battery to be tested includes: fully charging the battery to be tested and then fully discharging it, and obtaining the discharge characteristics of the battery to be tested during the complete discharge process. curve.
  • the battery to be tested when the battery to be tested is used to detect the memory effect, the battery to be tested needs to be fully charged and then fully discharged to ensure that the obtained discharge characteristic curve can reasonably characterize the entire discharge process of the battery to be tested and improve memory effect detection. accuracy.
  • obtaining the discharge characteristic curve of the battery to be tested during the complete discharge process includes: obtaining the voltage and battery capacity during the complete discharge process of the battery to be tested; according to the voltage and the battery capacity The battery capacity is described, and the discharge characteristic curve is obtained by analysis. This solution combines the battery capacity and the voltage during the discharge process to achieve the acquisition of the discharge characteristic curve, thereby accurately representing the battery capacity change performance of the battery, correlating the memory effect detection with the battery capacity, and effectively improving the memory effect detection accuracy.
  • analyzing and obtaining the discharge characteristic curve according to the voltage and the battery capacity includes: obtaining the capacity of the battery capacity as the voltage decreases according to the voltage and the battery capacity. Amount of change; based on the amount of change in capacity and the voltage, the discharge characteristic curve is obtained.
  • the memory effect detection method includes any one of the following: the first item: the voltage includes a negative electrode discharge voltage, and the discharge characteristic curve includes a negative electrode discharge characteristic curve; the second item: the voltage includes Full battery discharge voltage, the discharge characteristic curve includes a full battery discharge characteristic curve.
  • this solution can obtain the negative electrode discharge characteristic curve or the full battery discharge characteristic curve. No matter which method is used, it can effectively detect the memory effect and improve the diversity of the memory effect detection scheme.
  • determining whether a memory effect occurs in the battery to be tested based on the discharge characteristic curve and the preset phase inversion voltage interval includes: when the discharge characteristic curve is in the preset phase inversion voltage interval. If the memory reaches a peak value, it is determined that the battery under test has a memory effect.
  • This solution specifically uses whether there is a peak value in the discharge characteristic curve within the preset phase-inversion voltage range to detect whether the memory effect occurs in the battery to be tested, and has the advantage of high detection efficiency.
  • the maximum value of the discharge characteristic curve is not at any boundary of the preset phase-turn voltage interval, and the range of the discharge characteristic curve within the preset phase-turn voltage interval. If the standard deviation of the discharge characteristic curve in the preset phase-turn voltage interval is greater than a preset multiple, it is determined that the discharge characteristic curve has a peak value in the preset phase-turn voltage interval.
  • the range and standard deviation within the range can effectively avoid the misconception that the discharge characteristic curve has peak parameters within the preset phase-turn voltage interval due to measurement errors, etc., and effectively improve the peak detection accuracy.
  • the voltage includes a negative electrode discharge voltage
  • the preset phase-inversion voltage interval includes a preset negative electrode phase-inversion voltage interval
  • the preset negative electrode phase-inversion voltage interval is based on the battery type of the battery to be tested.
  • the negative electrode phase transition potential range of the same battery is determined. This solution combines the negative electrode phase transition potential range of the battery of the same type as the battery to be tested when phase transition occurs to obtain the preset negative electrode phase transition voltage range required for the battery to be tested, ensuring the accuracy of the preset negative electrode phase transition voltage range. degree, improving the accuracy of memory effect detection.
  • the voltage includes a full-battery discharge voltage
  • the preset phase-inversion voltage interval includes a preset full-battery phase-inversion voltage interval
  • the method of determining the preset full-battery phase-inversion voltage interval includes: according to The preset negative pole phase-turn voltage interval of the battery to be tested is of the same battery type, and the reference electrode test is performed on the battery to obtain the preset full-battery phase-turn voltage interval.
  • This solution combines the preset negative phase-turn voltage range corresponding to the battery to be tested, and obtains the preset full-battery phase-turn voltage range through the reference electrode test, ensuring the accuracy of the obtained preset full-battery phase-turn voltage range, thereby Improve detection accuracy of memory effects.
  • the voltage includes a full battery discharge voltage
  • the preset phase-inversion voltage interval includes a preset full-battery phase-inversion voltage interval
  • the method of determining the preset full-battery phase inversion voltage interval includes: obtaining and The negative electrode silicon content parameter of the battery of the same battery type as the battery to be tested; according to the relationship between the negative electrode silicon content parameter and the preset silicon content parameter and the full battery phase transition voltage interval, the preset full battery phase transition voltage is obtained by matching interval.
  • This solution combines the analysis of the silicon content parameters of the negative electrode of the negative electrode of the same type of battery to be tested, and obtains the preset full-cell phase transition voltage range that matches it. Even if the reference electrode type is not set for the battery, it can be obtained Reasonable preset full battery phase transition voltage range. At the same time, this solution does not require additional testing of the battery, and the preset full-battery phase transition voltage range acquisition method is relatively simple, effectively improving the memory effect detection efficiency.
  • the memory effect detection method further includes: when it is determined that the battery to be tested has a memory effect, based on the peak value of the discharge characteristic curve within the preset phase inversion voltage interval, and the preset peak value and memory The relationship between effect strength, matching to obtain the current memory effect strength.
  • This solution can convert the memory effect into different memory effect intensities, so as to intuitively obtain the severity of the memory effect in the battery to be tested, and ensure that when a serious memory effect occurs, it can be dealt with in a timely manner to avoid further degradation of the battery capacity.
  • the method before receiving the detection start signal and obtaining the discharge characteristic curve of the battery to be tested, the method further includes: performing a trigger analysis of memory effect detection on the battery to be tested, and determining whether the battery to be tested satisfies the memory requirement. Trigger conditions for effect detection. This solution performs trigger analysis on the battery to be tested so that when a memory effect occurs in the battery to be tested, corresponding actions can be executed in a timely manner, thereby improving the operational reliability of memory effect detection.
  • the memory effect detection method further includes: when it is determined that the battery to be tested meets the triggering conditions for memory effect detection, pushing a detection plan to the user terminal.
  • This solution can also push the detection plan to the user terminal when the trigger conditions are met. The user only needs to perform corresponding operations according to the pushed detection plan, which effectively improves the convenience of memory effect detection.
  • the method before receiving the detection start signal and obtaining the discharge characteristic curve of the battery to be tested, the method further includes: obtaining the estimated detection duration of the memory effect detection; and pushing the estimated detection duration to the user terminal.
  • This solution can also feedback the estimated detection time to the user when performing memory effect detection, so that the user can decide whether to turn on the memory effect detection based on actual needs.
  • obtaining the estimated detection duration of the memory effect detection includes: obtaining the estimated detection duration of the memory effect detection based on the current state-of-charge parameters of the battery to be tested and a preset detection current. This solution analyzes the preset detection current and the current state of charge of the battery, and calculates the estimated time required to obtain the discharge characteristic curve of the battery under test through charging and discharging. It has the advantage of high calculation accuracy.
  • the battery under test meets the triggering conditions for memory effect detection, including any one of the following: first, the decline speed of the health state of the battery under test is greater than or equal to a preset speed threshold; second The first item, the increase in the decline speed of the health state of the battery to be tested is greater than or equal to the preset speed increase threshold; the third item, the health state of the battery to be tested is less than the estimated health state corresponding to the current moment; the fourth item, The running time of the battery to be tested is greater than or equal to the preset running time; the fifth item is receiving a memory effect detection instruction.
  • This solution sets a variety of different trigger conditions for memory effect detection. In actual operation, as long as any trigger condition is met, the corresponding memory effect detection operation will be performed to ensure that the memory effect of the battery can be detected in time, improving Detection reliability of memory effects.
  • this application provides a memory effect detection device, including:
  • the discharge characteristic analysis module is used to obtain the discharge characteristic curve of the battery to be tested when receiving the detection start signal; the memory effect detection module is used to determine whether the battery to be tested is based on the discharge characteristic curve and the preset phase-inversion voltage interval. A memory effect occurs.
  • the present application provides a computer device, including a memory and a processor.
  • the memory stores a computer program.
  • the processor executes the computer program, the steps of the above memory effect detection method are implemented.
  • the present application provides a computer-readable storage medium on which a computer program is stored.
  • the computer program is executed by a processor, the steps of the above memory effect detection method are implemented.
  • the present application provides a computer program product, including a computer program that implements the steps of the above memory effect detection method when executed by a processor.
  • Figure 1 is a schematic diagram of battery capacity fading in some embodiments of the present application.
  • Figure 2 is a schematic diagram of application scenarios of the memory effect detection method in some embodiments of the present application.
  • Figure 3 is a schematic flow chart of a memory effect detection method according to some embodiments of the present application.
  • Figure 4 is a schematic flow chart of discharge characteristic curve analysis according to some embodiments of the present application.
  • Figure 5 is a schematic diagram of the discharge characteristic curve analysis process of other embodiments of the present application.
  • Figure 6 is a schematic flow chart of a memory effect detection method according to other embodiments of the present application.
  • Figure 7 is a schematic flow chart of a memory effect detection method according to some embodiments of the present application.
  • Figure 8 is a schematic flow chart of a memory effect detection method in some embodiments of the present application.
  • Figure 9 is a schematic flow chart of a memory effect detection method according to other embodiments of the present application.
  • Figure 10 is a schematic flow chart of a memory effect detection method in some embodiments of the present application.
  • Figure 11 is a schematic flow chart of a memory effect detection method in some embodiments of the present application.
  • Figure 12 is a schematic diagram comparing battery capacity fading in some embodiments of the present application.
  • Figure 13 is a schematic diagram comparing negative electrode discharge characteristic curves of some embodiments of the present application.
  • Figure 14 is a schematic diagram comparing battery capacity fading in other embodiments of the present application.
  • Figure 15 is a schematic diagram comparing full battery discharge characteristic curves in some embodiments of the present application.
  • Figure 16 is a schematic structural diagram of a memory effect detection device according to some embodiments of the present application.
  • Figure 17 is a schematic structural diagram of a memory effect detection device according to other embodiments of the present application.
  • Figure 18 is a schematic structural diagram of a memory effect detection device according to some further embodiments of the present application.
  • Figure 19 is a schematic diagram of the internal structure of a computer device according to some embodiments of the present application.
  • an embodiment means that a particular feature, structure or characteristic described in connection with the embodiment can be included in at least one embodiment of the present application.
  • the appearances of this phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those skilled in the art understand, both explicitly and implicitly, that the embodiments described herein may be combined with other embodiments.
  • Power batteries are not only used in energy storage power systems such as hydropower, thermal power, wind power and solar power stations, but are also widely used in electric vehicles such as electric bicycles, electric motorcycles and electric cars, as well as in many fields such as military equipment and aerospace. . As the application fields of power batteries continue to expand, their market demand is also constantly expanding.
  • the memory effect of the battery refers to the reversible capacity failure problem caused by the battery not being fully charged or discharged for a long time. It is generally believed that the memory effect mostly occurs in nickel-cadmium batteries, less in nickel-metal hydride batteries, and does not occur in lithium batteries.
  • the inventor of this application noticed that with people's pursuit of higher battery energy density, the pure graphite negative electrode with a specific capacity of 372mAh/g can no longer fully meet the demand, and has a high specific capacity of 3579mAh/g (milliamp hours per gram). Silicon with specific capacity has become a new generation of lithium battery anode material. Unlike pure graphite anodes, lithium batteries containing silicon anodes also have a memory effect.
  • Figure 1 shows a lithium-ion battery with 15% silicon and 85% graphite as the negative electrode.
  • the battery cycles between 10% SOC (State of Charge, state of charge) and 97% SOC. Capacity decay curve. It can be seen from the figure that when the lithium-ion battery has not been fully charged and discharged for 50 cycles, the battery capacity has attenuated by nearly 8%, and an obvious memory effect has occurred. This is a lithium-ion battery that has an attenuation of 20% as the end of its battery life. The capacity decays very quickly.
  • SOC State of Charge, state of charge
  • the existence of the memory effect will cause the battery capacity to decay rapidly, thereby affecting the use of the battery. This phenomenon is related to the silicon content in the negative electrode, the cycle SOC range or the voltage range.
  • the inventor found that when a memory effect occurs in a silicon anode lithium-ion battery, its discharge characteristic curve is within the phase transition potential interval between the crystalline phase and the amorphous phase, which is obviously different from the discharge characteristic curve of conventional batteries without memory effect. . Therefore, in the technical solution of this application, further analysis can be performed based on the discharge characteristic curve of the battery to be tested and the preset phase transition potential interval between the crystalline phase and the amorphous phase to determine whether the memory effect occurs in the battery to be tested.
  • the inventor of the present application found that the discharge characteristic curve of the silicon anode lithium-ion battery in which the memory effect occurs has an obvious peak in the phase transition potential interval between the crystalline phase and the amorphous phase, but no memory occurs.
  • the discharge characteristic curve of the silicon negative electrode lithium-ion battery does not have this phenomenon. Therefore, in a more detailed embodiment, whether a peak occurs in the phase transition potential interval between the crystalline phase and the amorphous phase can also be determined by detecting the discharge characteristic curve of the silicon negative electrode lithium ion battery during the discharge process. Detection of memory effects.
  • the memory effect detection method provided by the embodiments of the present application can be applied to, but is not limited to, silicon anode lithium-ion batteries, and can also be used in other batteries containing silicon anodes.
  • the batteries provided by the embodiments of the present application can be used in, but are not limited to, mobile phones, tablets, laptops, electric toys, electric tools, battery cars, electric vehicles, ships, spacecraft and other electrical devices.
  • the batteries pointed out in the following embodiments can be understood as silicon anode lithium batteries used in electric vehicles.
  • the memory effect detection method provided by this application can be applied to the application environment shown in Figure 2, in which the BMS (Battery Management System) of the battery is connected to the user terminal for communication, and the BMS detects that the battery to be tested satisfies the memory requirement. After the triggering condition of the effect detection, relevant prompt information will be returned to the user through the user terminal. The user can select the corresponding detection plan through the user terminal, and finally move the battery to be tested to the corresponding position of the detection device to determine whether the battery to be tested has occurred. Detection of memory effects.
  • BMS Battery Management System
  • the specific type of user terminal is not unique. It can be a host computer that communicates with the BMS, or it can be a terminal device such as a mobile phone or wearable device that is convenient for the user to carry.
  • the specific type is not limited.
  • the specific type of detection device used for battery memory effect detection is not unique.
  • the detection device will also differ depending on the detection scheme selected by the user. For example, in one embodiment, when the user selects the charging self-test solution, that is, during the process of charging the battery to be tested, the battery to be tested is automatically tested for the memory effect.
  • a charging pile specifically, a charging pile with a charge and discharge function
  • the multifunctional charging pile is used as a detection device to charge and discharge the battery to be tested.
  • the BMS of the battery to be tested realizes memory effect detection during the process of the detection device charging and discharging the battery to be tested.
  • a dedicated charging and discharging device can be used.
  • the device serves as a detection device to charge and discharge the battery to be tested, and the BMS of the battery to be tested realizes memory effect detection during the process of the detection device charging and discharging the battery to be tested.
  • the memory effect detection method is applied to the BMS for explanation.
  • the memory effect detection method includes step 302 and step 304 .
  • Step 302 When receiving the detection start signal, obtain the discharge characteristic curve of the battery to be tested.
  • the detection start signal is a control signal instructing the BMS to perform memory effect detection
  • the battery to be tested is the battery that needs to perform memory effect detection.
  • the discharge characteristic curve can be a curve drawn based on the battery's discharge voltage and discharge time, or a curve drawn based on the battery's discharge voltage and battery capacity. The curve formed represents the state change of the battery during discharge.
  • the detection start signal may be sent by the user to the BMS of the battery to be tested through the user terminal.
  • the detection start signal may also be sent to the BMS of the battery to be tested through the detection device after the battery to be tested is connected to a detection device for memory effect detection. For example, in one embodiment, after the battery under test is connected to a charging pile with charging and discharging functions, a detection start signal is sent to the BMS of the battery under test through the charging pile.
  • Step 304 Determine whether the battery to be tested has a memory effect based on the discharge characteristic curve and the preset phase-inversion voltage interval.
  • the preset phase transition voltage interval is the preset discharge voltage interval range corresponding to when the battery material undergoes a transition from a crystalline phase to an amorphous phase during the discharge process of the battery to be tested. It can be understood that the preset phase-reversal voltage interval can be obtained when the battery to be tested leaves the factory by analyzing the same type of battery as the battery to be tested, and then the phase-reversal voltage interval is directly stored in the BMS of the battery to be tested. , as the preset phase-inversion voltage range of the battery to be tested, it can be called directly when there is subsequent use demand.
  • the way to obtain the phase-inversion voltage range by analyzing batteries of the same type as the battery to be tested is not unique. In one embodiment, it can be directly based on experience, combining the types of active materials and their contents of the same type of batteries. , the corresponding phase-inversion voltage interval is obtained, and stored in the BMS of the battery under test. In another embodiment, the actual discharge test can also be performed on the same type of battery to obtain the final phase-inversion voltage range, and then pre-stored in the BMS of the battery to be tested.
  • the above memory effect detection method after receiving the detection confirmation signal, extracts the discharge characteristic curve during the discharge process of the battery to be tested, and then analyzes the discharge characteristic curve within the preset phase transition voltage interval to determine whether the battery to be tested has a memory effect. detection.
  • it can be detected in time when the memory effect occurs in the battery, so that corresponding processing can be carried out to avoid the battery operating in the memory effect state for a long time, causing rapid attenuation of the battery capacity.
  • obtaining the discharge characteristic curve of the battery to be tested includes: fully charging the battery to be tested and then fully discharging it, and obtaining the discharge characteristic curve of the battery to be tested during the complete discharge process.
  • full charge is when the battery reaches a state where all available active materials do not significantly increase capacity when charged under selected conditions.
  • Full discharge is the corresponding state when the battery is discharged until the battery capacity is lower than the preset capacity threshold, or until the battery voltage is lower than the preset voltage threshold.
  • the battery to be tested may be charged with a set charging current until the battery to be tested is charged to the predetermined level. Set an upper voltage limit to achieve full charging. Then, the battery to be tested is discharged with the set discharge current until the battery to be tested is discharged to the preset lower limit voltage to achieve complete discharge.
  • full charging can be done by charging at 0.33C (that is, 0.33 times the rated current) until the voltage of the battery to be tested is greater than or equal to 4.25V, and then Maintain constant voltage charging until the current drops to 0.05C (that is, 0.05 times the rated current).
  • Full discharge is to discharge at 0.33C until the voltage of the battery under test is less than or equal to 2.8V. It can be understood that in other embodiments, depending on the actual capacity of the battery to be tested, the preset voltage upper limit and the preset voltage lower limit will be different. The specific settings should be based on the actual battery to be tested, and will not be described in detail here.
  • the battery to be tested when the battery to be tested is used to detect the memory effect, the battery to be tested needs to be fully charged and then fully discharged to ensure that the obtained discharge characteristic curve can reasonably characterize the entire discharge process of the battery to be tested and improve memory effect detection. accuracy.
  • the discharge characteristic curve of the battery to be tested is obtained, including step 402 and step 404 .
  • Step 402 Obtain the voltage and battery capacity of the battery under test during complete discharge;
  • Step 404 Analyze and obtain the discharge characteristic curve based on the voltage and battery capacity.
  • the voltage during the complete discharge process of the battery is also the discharge voltage of the battery; the battery capacity is the remaining capacity of the battery during the complete discharge process, which is the SOC of the battery.
  • the BMS of the battery under test obtains the voltage and battery capacity of the battery under test in real time. Based on the voltage and battery capacity of the battery under test, , to realize the extraction of discharge characteristic curve.
  • This solution combines the battery capacity and the voltage during the discharge process to achieve the acquisition of the discharge characteristic curve, thereby accurately representing the battery capacity change performance of the battery, correlating the memory effect detection with the battery capacity, and effectively improving the memory effect detection accuracy.
  • step 404 includes step 502 and step 504 .
  • Step 502 According to the voltage and battery capacity, obtain the capacity change amount of the battery capacity as the voltage decreases; Step 504: Obtain the discharge characteristic curve based on the capacity change amount and voltage.
  • the voltage of the battery to be tested can be directly collected from the battery to be tested through the BMS during the actual test process.
  • the capacity change of the battery capacity as the voltage decreases is not unique. It can be analyzed based on the ampere-hour integration method, open circuit voltage method, internal resistance method, neural network and Kalman filtering method.
  • the ampere-hour integration method is used as an example for explanation.
  • the memory effect detection method includes any one of the following: the first item: the voltage includes the negative electrode discharge voltage, and the discharge characteristic curve includes the negative electrode discharge characteristic curve; the second item: the voltage includes the full battery discharge voltage, and the discharge characteristic curve includes Full battery discharge characteristic curve.
  • the negative electrode discharge voltage is the voltage obtained by the BMS collecting the voltage of the negative electrode of the battery to be tested during the complete discharge process of the battery to be tested.
  • the negative electrode discharge characteristic curve is the discharge characteristic curve obtained by analyzing the negative electrode discharge voltage of the battery to be tested as the voltage for discharge characteristic curve analysis.
  • the full battery discharge voltage is the relative voltage between the positive and negative electrodes collected by the BMS during the complete discharge process of the battery to be tested.
  • the full battery discharge characteristic curve is based on the full battery discharge voltage of the battery to be tested. When analyzing the voltage of the characteristic curve, analyze the discharge characteristic curve obtained.
  • this solution can obtain the negative electrode discharge characteristic curve or the full battery discharge characteristic curve. No matter which method is used, it can effectively detect the memory effect and improve the diversity of the memory effect detection scheme.
  • step 304 includes step 602.
  • Step 602 When the discharge characteristic curve has a peak value within the preset phase-inversion voltage range, it is determined that the battery to be tested has a memory effect.
  • the BMS After the BMS obtains the discharge characteristic curve, it will analyze it in conjunction with its internal pre-stored preset phase-turn voltage interval to determine whether there is a peak in the discharge characteristic curve within the preset phase-turn voltage interval. If the discharge characteristic curve has a peak value within the preset phase-turn voltage range, it means that the battery under test has a memory effect; if the discharge characteristic curve does not have a peak value within the preset phase-turn voltage range, it means that the battery under test has not A memory effect occurs.
  • This solution specifically uses whether there is a peak value in the discharge characteristic curve within the preset phase-inversion voltage range to detect whether the memory effect occurs in the battery to be tested, and has the advantage of high detection efficiency.
  • the maximum value of the discharge characteristic curve is not at any boundary of the preset phase-turn voltage interval, and the range of the discharge characteristic curve within the preset phase-turn voltage interval is greater than the preset multiple of the preset phase
  • the standard deviation of the discharge characteristic curve within the phase rotation voltage interval determines that the discharge characteristic curve has a peak value within the preset phase rotation voltage interval.
  • the discharge characteristic curve when testing the discharge characteristic curve to see whether there is a peak value in the preset phase-turn voltage interval, in addition to satisfying the maximum value of the discharge characteristic curve within the preset phase-turn voltage interval, it is not outside the boundary of the preset phase-turn voltage interval. , it is also necessary to perform range calculation and standard deviation calculation based on the discharge characteristic curve within the preset phase-turn voltage interval. Only the various parameters corresponding to the discharge characteristic curve within the preset phase-turn voltage interval have a range greater than the preset multiple of the standard deviation, the discharge characteristic curve will be considered to have a peak value within the preset phase-inversion voltage range.
  • the value detected at this time is The maximum value is caused by detection error or voltage fluctuation during the detection process. In fact, there is no peak value in the discharge characteristic curve.
  • the size of the preset multiple is not unique.
  • the preset multiple can be set to 3, which is the maximum value of the discharge characteristic curve and is not within the preset phase-inversion voltage range.
  • the range of each parameter of the discharge characteristic curve in the preset phase-inversion voltage interval is greater than 3 times its standard deviation, it is considered that there is a peak at this time.
  • the voltage includes a negative electrode discharge voltage
  • the preset phase-turn voltage interval includes a preset negative electrode phase-turn voltage interval
  • the preset negative electrode phase-turn voltage interval is based on the negative electrode phase-change potential of a battery of the same battery type as the battery to be tested. Scope determined.
  • batteries of the same type refer to batteries whose materials and contents are consistent in all parts of the battery.
  • the corresponding voltage is also Basically the same.
  • the negative electrode phase transition potential range can be directly used as the preset negative electrode phase transition voltage interval.
  • the upper limit value and/or the lower limit value can also be changed based on the actual situation, based on the negative electrode phase transition potential range, to obtain the final preset negative electrode phase transition voltage range.
  • the negative electrode phase transition potential range from the crystalline phase to the amorphous phase is about 430mV to 470mV.
  • the preset negative phase-inversion voltage interval can be set to 430mV to 470mV.
  • the negative electrode phase transition potential range can be further expanded on the basis of 430mV to 470mV to obtain a preset negative electrode phase transition voltage range.
  • the negative electrode phase transition potential range is expanded to 400mV to 500mV.
  • this embodiment can set the preset negative electrode phase transition voltage range to 400mV to 500mV.
  • the negative electrode phase transition potential range can be further reduced on the basis of 430 mV to 470 mV to obtain a preset negative electrode phase transition voltage interval.
  • the negative electrode phase transition potential range is reduced to 440 mV to 460 mV.
  • this embodiment can set the preset negative electrode phase transition voltage range to 440 mV to 460 mV.
  • This solution combines the negative electrode phase transition potential range of the battery of the same type as the battery to be tested when phase transition occurs to obtain the preset negative electrode phase transition voltage range required for the battery to be tested, ensuring the accuracy of the preset negative electrode phase transition voltage range. degree and improve the accuracy of memory effect detection.
  • the voltage includes a full-battery discharge voltage
  • the preset phase-inversion voltage interval includes a preset full-battery phase-inversion voltage interval
  • the method of determining the preset full-battery phase-inversion voltage interval includes: according to the battery type of the battery to be tested. For the same preset negative pole phase-turn voltage range of the battery, conduct a reference electrode test on the battery to obtain the preset full-battery phase-turn voltage range.
  • the solution of this embodiment in order to detect the memory effect of the battery to be tested in the preset full-battery phase-turn voltage interval, it is necessary to obtain the preset full-battery phase-turn voltage interval and Stored in BMS.
  • the solution of this embodiment is suitable for batteries to be tested with a reference electrode type. When detecting the memory effect of this type of battery, it can be tested at the reference electrode in combination with the preset negative electrode phase-turn voltage range to obtain the actual The full battery phase-turn voltage range is stored as the preset full-battery phase-turn voltage range.
  • the reference electrode test operation is not performed every time the memory effect is detected, but is obtained by testing one or more batteries of the same type when the battery leaves the factory, and is thus stored in BMS for other batteries of the same type.
  • the reference electrode test method is not the only one.
  • the negative electrode discharge voltage and the discharge voltage at the reference electrode can be collected simultaneously during the complete discharge process of the battery.
  • the negative electrode discharge voltage reaches the predetermined level
  • the When setting the negative electrode phase-turn voltage interval record the discharge voltage at the corresponding reference electrode, and store the recorded voltage interval as the full-battery phase-turn voltage interval to obtain the preset full-battery phase-turn voltage interval.
  • ⁇ f(T1, T2, I, SOC0), where ⁇ is the discharge voltage at the reference electrode, T1 is the test environment temperature, T2 is the test battery temperature, I is the test current, SOC0
  • the initial SOC of the battery test that is, the discharge voltage at the reference electrode is related to the test environment temperature, test battery temperature, test current and the initial SOC of the test.
  • This solution combines the preset negative phase-turn voltage range corresponding to the battery to be tested, and obtains the preset full-battery phase-turn voltage range through the reference electrode test, ensuring the accuracy of the obtained preset full-battery phase-turn voltage range, thereby Improve detection accuracy of memory effects.
  • the voltage includes a full-battery discharge voltage
  • the preset phase-inversion voltage interval includes a preset full-battery phase-inversion voltage interval
  • the method of determining the preset full-battery phase-inversion voltage interval includes: obtaining the battery type of the battery to be tested.
  • the negative electrode silicon content parameter of the same battery according to the relationship between the negative electrode silicon content parameter and the preset silicon content parameter and the full battery phase transition voltage interval, the preset full battery phase transition voltage interval is obtained by matching.
  • the negative electrode silicon content parameter is the mass ratio of the doped silicon mass to the total active material of the negative electrode in the negative electrode of the battery to be tested. Since the voltage range of the crystalline phase-amorphous phase transition is directly related to the negative electrode silicon content parameter, the solution of this embodiment directly uses the negative electrode silicon content parameter of the same type of battery, combined with the preset silicon content parameter and the full battery phase transition voltage. The relationship between the intervals is matched to obtain the preset full-battery phase-inversion voltage interval corresponding to the battery under test, and is stored in the BMS of the battery under test.
  • the method for determining the preset full-battery phase-turn voltage range corresponding to the solution of this embodiment can be to directly perform matching analysis on the same type of battery when the battery to be tested leaves the factory, and then store the preset full-battery phase-turn voltage range after obtaining it. in the battery under test. It is also possible to store the relationship between the preset silicon content parameters and the full battery phase transition voltage interval in the BMS of the battery to be tested. When the battery to be tested is tested for the memory effect, the negative electrode silicon content parameters of the battery to be tested are obtained. Then perform matching analysis to get it.
  • the method of obtaining the negative electrode silicon content parameters of the battery under test is not unique.
  • the negative electrode silicon content parameters of the battery under test can be directly pre-stored in the BMS of the battery under test, and can be directly adjusted when there is a need for use. Pick. In other embodiments, it may also be sent to the BMS of the battery to be tested through a user terminal or the like when there is a demand for use.
  • the relationship between the preset silicon content parameters and the full battery phase transition voltage range can be stored in the form of a database or a chart. There is no specific limit and the selection can be based on actual usage scenarios. For example, in a more detailed embodiment, the relationship between the silicon content parameter and the full cell phase transition voltage range can be shown in the following table:
  • the negative electrode material is used as an example.
  • the full-cell phase transition voltage range of the battery is related to the negative electrode silicon content parameter. As shown in the following table:
  • This solution combines the analysis of the silicon content parameters of the negative electrode of the negative electrode of the same type of battery to be tested, and obtains the preset full-cell phase transition voltage range that matches it. Even if the reference electrode type is not set for the battery, it can be obtained Reasonable preset full battery phase transition voltage range. At the same time, this solution does not require additional testing of the battery, and the preset full-battery phase transition voltage range acquisition method is relatively simple, effectively improving the memory effect detection efficiency.
  • the memory effect detection method further includes step 702.
  • Step 702 When it is determined that a memory effect occurs in the battery under test, the current memory effect intensity is obtained by matching based on the peak value of the discharge characteristic curve within the preset phase inversion voltage interval and the relationship between the preset peak value and the memory effect intensity.
  • the memory effect intensity parameter is a parameter that represents the severity of the memory effect in the battery to be tested.
  • the BMS of the battery further stores the relationship between the preset peak value and the strength of the memory effect. Whenever the BMS analyzes the discharge characteristic curve and the preset phase-turn voltage interval and obtains that the discharge characteristic curve has a peak value within the preset phase-turn voltage interval. , and will further combine the obtained peak value with the preset relationship between the peak value and the memory effect intensity to match the current memory effect intensity of the memory effect intensity of the battery under test.
  • the relationship between the preset peak value and the intensity of the memory effect can be stored in the form of a database or a chart. There is no specific limit and the selection can be based on actual usage scenarios. For example, in a more detailed embodiment, the relationship between the peak value and the strength of the memory effect can be shown in the following table:
  • This solution can convert the memory effect into different memory effect intensities, so as to intuitively obtain the severity of the memory effect in the battery to be tested, and ensure that when a serious memory effect occurs, it can be dealt with in a timely manner to avoid further degradation of the battery capacity.
  • step 302 the method further includes step 802.
  • Step 802 Perform memory effect detection trigger analysis on the battery to be tested to determine whether the battery to be tested meets the trigger conditions for memory effect detection.
  • the detection start signal is received when the battery under test meets the triggering conditions for memory effect detection.
  • the specific reception method is not unique. It can be sent to the BMS of the battery under test through the user terminal, or through the BMS of the battery under test.
  • the detection device for charging and discharging is sent to the BMS of the battery to be tested, or the BMS of the battery to be tested can be automatically generated when the trigger condition is met to realize automatic detection of the memory effect.
  • Trigger analysis is the analysis of detecting whether the battery under test triggers memory effect detection, which is specifically achieved by detecting whether the battery under test meets the trigger conditions. Depending on the set trigger conditions, the corresponding trigger analysis operations will also be different.
  • the trigger analysis operation needs to be performed in real time; if the trigger condition only needs to be combined with the operating status parameters of the battery under test under a specific condition, there is no need to perform trigger analysis in real time. , just perform the trigger analysis operation when the specific condition is met.
  • This solution performs trigger analysis on the battery to be tested so that when a memory effect occurs in the battery to be tested, corresponding actions can be executed in a timely manner, thereby improving the operational reliability of memory effect detection.
  • the memory effect detection method further includes step 902.
  • Step 902 When it is determined that the battery to be tested meets the triggering conditions for memory effect detection, push the detection plan to the user terminal.
  • the BMS After the BMS detects that the battery under test meets the triggering conditions for memory effect detection, it will push the corresponding detection solution to the user terminal based on the actual usage scenario of the battery under test. For example, it can be recommended that users independently perform memory effect detection when charging the battery at a charging pile (with charging and discharging functions); or it can be recommended that users take the battery to an after-sales service point and have professionals assist in performing memory effect detection.
  • the BMS may recommend a memory effect detection solution to the user based on the actual usage scenario of the battery.
  • all optional memory effect detection schemes can be pushed to the user terminal, and the user can decide which detection scheme to use for the final memory effect detection.
  • the battery will be connected to the corresponding detection device, and the detection start signal will be fed back to the BMS through the user terminal or detection device, or the BMS will detect that the trigger conditions are met. Afterwards, a detection start signal is automatically generated to enable the BMS to start performing the corresponding memory effect detection operation. That is to say, the BMS extracts the discharge characteristic curve when the detection device discharges the battery, and finally uses the extracted discharge characteristic curve and the preset phase-inversion voltage interval to realize memory effect detection.
  • This solution can also push the detection plan to the user terminal when the trigger conditions are met.
  • the user only needs to perform corresponding operations according to the pushed detection plan, which effectively improves the convenience of memory effect detection.
  • the method before step 302, the method further includes step 102 and step 104.
  • Step 102 Obtain the estimated detection time for memory effect detection
  • Step 104 Push the estimated detection time to the user terminal.
  • this solution will also push the estimated detection time required for memory effect detection to the user, so that the user can finally make a decision on whether to turn on memory effect detection based on the estimated detection time and detection plan.
  • step 102 and step 104 may be executed after step 902, may be executed before step 902, or may be executed at the same time as step 902.
  • This embodiment takes the detection scheme as an example of recommending users to use charging piles for charging self-test.
  • the BMS will push the charging self-test scheme to the user terminal.
  • the specific push form is not unique.
  • the BMS can push the inquiry information "whether to perform memory effect detection before charging at the charging pile next time" to the user terminal, which means that the BMS sends a message to the user terminal. Pushed the memory effect detection plan. If the user feedbacks "yes" through the user terminal, it means that the BMS receives the detection confirmation signal fed back by the user terminal according to the charging self-test plan, and the user agrees to perform memory effect detection at the charging pile.
  • the BMS starts to detect whether the battery to be tested is connected to the charging pile.
  • the memory effect detection time of the battery to be tested is estimated, and the estimated detection time is obtained and pushed to the user terminal. If the user believes that the expected detection time is within an acceptable range, the user terminal returns a detection start signal to the BMS to implement the discharge characteristic curve extraction operation of the battery to be tested.
  • the memory effect detection does not stop completely.
  • the estimated detection duration will be pushed to the user terminal again. The user can Based on actual needs, restart the memory effect detection before the next charge.
  • This solution can also feedback the estimated detection time to the user when performing memory effect detection, so that the user can decide whether to turn on the memory effect detection based on actual needs.
  • step 102 includes step 112.
  • Step 112 Obtain the estimated detection time of the memory effect detection based on the current state-of-charge parameters of the battery to be tested and the preset detection current.
  • the preset detection current is the corresponding charging and discharging current required when the battery to be tested performs memory effect detection. If the battery to be tested is in a low or depleted state when performing memory effect detection, the battery to be tested needs to be charged before the discharge characteristic curve can be extracted. If the battery capacity of the battery under test can meet the detection requirements during memory effect detection (for example, the battery is already fully charged), there is no need to charge the battery under test and it can be directly discharged to extract the discharge characteristic curve.
  • the method of obtaining the preset detection current is not unique.
  • the preset detection current may be set in the BMS and directly called when there is a need for detection.
  • the preset detection current since the charging and discharging operations are implemented through the detection device when performing memory effect detection on the battery, the preset detection current may be pre-stored in the detection device. After the battery is connected to the detection device, the BMS sends a signal to the detection device. Obtained by device request.
  • This solution analyzes the preset detection current and the current state of charge of the battery, and calculates the estimated time required to obtain the discharge characteristic curve of the battery under test through charging and discharging. It has the advantage of high calculation accuracy.
  • the battery to be tested meets the triggering conditions for memory effect detection, including any one of the following: the first item, the declining speed of the health state of the battery to be tested is greater than or equal to the preset speed threshold; the second item, the battery to be tested is The increase in the decline rate of the battery's health state is greater than or equal to the preset speed increase threshold; the third item, the health state of the battery to be tested is less than the estimated health state corresponding to the current moment; the fourth item, the running time of the battery to be tested is greater than or It is equal to the preset running time; the fifth item is to receive the memory effect detection command.
  • the state of health is the remaining available capacity of the battery.
  • the rate of decline of the health status of the battery under test can be calculated based on the charge and discharge cycle, or based on the running time of the battery under test.
  • the rate of decline of the health state is the amount of decline in the health state after one or more charge and discharge cycles.
  • the rate of decline in health status is the amount of decline in health status after one or more running time periods.
  • the increase in the decline speed of the health state is the increase in the decline speed of the two adjacent health states. Specifically, it is the difference between the current detected decline speed and the last detected decline speed, and the difference between the last detected decline speed and the current detected decline speed. The ratio of the falling speed to .
  • a charge and discharge cycle can be used as an example for explanation.
  • the decline rate of the battery health state is (S1-S0)/1cycle, where S1 is the detected value of the current charge and discharge cycle.
  • S1 is the detected value of the current charge and discharge cycle.
  • the health status of the battery under test S0 is the health status of the battery under test detected in the previous charge and discharge cycle
  • 1 cycle is one charge and discharge cycle.
  • the preset speed threshold can be set to 0.2%/1cycle at this time.
  • a running time cycle is 30 days.
  • the decline rate of battery health status is (S3-S2)/30 days, where S3 is the current health status of the battery to be tested, and S2 is the health status detected 30 days ago.
  • the preset speed threshold may be set to 2%/30 days at this time.
  • the decline rate increase of the battery's health state can also be counted based on the charge and discharge cycle, or based on the battery's operating time.
  • the decrease speed increase is expressed as: (health state of the previous charge and discharge cycle - health state of the current charge and discharge cycle) / (health state of the previous two charge and discharge cycles - previous Health status during charge and discharge cycles) -100%.
  • the increase in the decline rate can be expressed as: health status corresponding to 30 days ago - health status corresponding to the current situation)/(health status corresponding to 60 days ago - health status corresponding to 30 days ago) - 100%.
  • the method of obtaining the estimated health status is not the only one.
  • the BMS is set with a battery health status decay curve. During normal use of the battery to be tested, as the usage time increases, the battery health status is calculated through the battery health status decay curve. The decay curve can estimate the current health status of the battery to be tested, that is, the estimated health status can be obtained. If the health status actually detected by the BMS at the current moment is lower than the estimated health status, it means that the health status of the battery to be tested has dropped abnormally at this time, and the memory effect detection operation will also be triggered.
  • the BMS can also time the running time of the battery to be tested. After the BMS detects that the battery running time is greater than or equal to the preset running time, the memory effect detection operation will be triggered. It can be understood that the size of the preset running time is not unique. In a more detailed embodiment, the preset running time can be set to be greater than or equal to 6 months. In more detail, in one embodiment, the preset running time can be set to be greater than or equal to 12 months.
  • the memory effect detection instruction is specifically sent by the user through the user terminal. If the user has memory effect detection requirements for the battery, the corresponding detection operation can be actively triggered. At this time, the user sends a memory effect detection instruction to the BMS through the user terminal. After the BMS receives the memory effect detection instruction from the user terminal, the memory effect detection operation will be triggered.
  • the battery health status acquisition cycle is different, and the preset speed threshold will also be different. It can be set according to the actual battery usage and acquisition cycle.
  • This solution sets a variety of different trigger conditions for memory effect detection. In actual operation, as long as any trigger condition is met, the corresponding memory effect detection operation will be performed to ensure that the memory effect of the battery can be detected in time, improving Detection reliability of memory effects.
  • the BMS collects the health status of the battery under test in real time and performs the timing operation of the battery operation time under test. Combined with the battery health status collected each time, the corresponding decline rate of the battery health status is obtained. As well as the decline rate increase of the battery health state, the comparison and analysis are performed with the preset speed threshold and the preset speed increase threshold respectively. At the same time, the collected battery health state is also compared with the estimated health state obtained through the battery health state decay curve analysis. analyze.
  • the BMS will consider that the triggering conditions for memory effect detection are met and trigger subsequent detection operations.
  • the BMS then pushes the memory effect detection solution to the user terminal, for example, recommending to the user terminal a solution for testing at an after-sales service point or autonomous charging detection.
  • the test plan is pushed to the after-sales service point, after the user learns the test plan through the user terminal, he will take the battery to the after-sales service point and connect to the corresponding test device, and return the test start signal to the BMS through the user terminal or the test device. Under the action of this signal, the detection device fully charges the battery. In this process, the detection device specifically charges at a preset rate to the upper limit voltage, and then discharges at the preset rate to the lower discharge limit voltage, that is, full discharge is completed.
  • the charging and discharging process can be charging to the maximum voltage at 0.33C.
  • the battery voltage is greater than or equal to 4.25V, and then charging at a constant voltage to 0.05C.
  • discharge at 0.33C to the lower discharge limit voltage which is lower than 2.8V.
  • the BMS obtains the negative electrode discharge voltage or the full battery discharge voltage of the battery in real time (for ease of understanding, the negative electrode discharge voltage is used as an example for explanation below), and the battery capacity is calculated based on the ampere-hour integration method. Capacity change of negative electrode discharge voltage.
  • the negative electrode discharge characteristic curve is constructed by taking the ratio of the change in battery capacity to the preset voltage drop amount when the negative electrode discharge voltage decreases by the preset voltage drop amount during the discharge process as the ordinate, and the negative electrode discharge voltage as the abscissa. After the battery is fully discharged, analyze whether the obtained negative electrode discharge characteristic curve has a peak value in the preset negative electrode phase transition voltage range.
  • the maximum value of the negative electrode discharge characteristic curve is within the preset negative electrode phase-turn voltage interval, it is not at the boundary of the preset negative electrode phase-turn voltage interval, and the range of each parameter of the negative electrode discharge characteristic curve within this interval is greater than 3 times the standard deviation. , then it is considered that there is a peak value, and at this time, the battery under test has a memory effect. BMS will combine the relationship between the preset peak value and the memory effect intensity to match the current memory effect intensity and push it to the user terminal to inform the user. When there is no peak in the analysis, it is considered that no memory effect has occurred.
  • a detection confirmation signal will be returned to confirm that the memory effect test will be performed on the battery to be tested during the next charge.
  • the user connects the battery to be tested to the charging pile (using the charging pile as the detection device), and the BMS obtains the preset detection current from the charging pile, or combines the preset detection current stored inside the BMS with the current state of charge of the battery to be tested. , calculate the estimated detection time, and push it to the user terminal to inform the user. If the user terminal returns a detection start signal, the battery to be tested will be tested for memory effect; if the user terminal does not return a detection start signal, there is no need to perform memory effect detection and the battery to be tested will be charged normally.
  • the charging pile Under the action of detecting the start signal, the charging pile fully charges the battery to be tested. During this process, the charging pile charges at a preset rate to the upper limit voltage, and then discharges at the preset rate to the lower discharge limit voltage. That is to say, full discharge is completed.
  • the charging and discharging process can be charging to the maximum voltage at 0.33C. At this time, the battery voltage is greater than or equal to 4.25V, and then charging at a constant voltage to 0.05C. After charging, discharge at 0.33C to the lower discharge limit voltage, which is lower than 2.8V.
  • the BMS obtains the negative electrode discharge voltage of the battery in real time, and uses the ampere-hour integration method to calculate the corresponding capacity change of the battery capacitance following the negative electrode voltage. Finally, the preset value is calculated for every reduction in the negative electrode discharge voltage during the discharge process. When the voltage drops, the ratio of the change in battery capacity to the preset voltage drop is used as the ordinate, and the negative electrode discharge voltage is used as the abscissa to build the negative electrode discharge characteristic curve. After the battery is fully discharged, analyze whether the obtained negative electrode discharge characteristic curve has a peak value in the preset negative electrode phase transition voltage range.
  • the maximum value of the negative electrode discharge characteristic curve is within the preset negative electrode phase-turn voltage interval, it is not at the boundary of the preset negative electrode phase-turn voltage interval, and the range of each parameter of the negative electrode discharge characteristic curve within this interval is greater than 3 times the standard deviation. , it is considered that there is a peak value, and the battery has a memory effect at this time. BMS will combine the relationship between the preset peak value and the memory effect intensity to match the current memory effect intensity and push it to the user terminal to inform the user. When there is no peak in the analysis, it is considered that no memory effect has occurred.
  • the memory effect detection method of this application does not require detection current or temperature during the detection process, which reduces the requirements for charging and discharging equipment and environmental sensitivity, and has strong detection reliability.
  • a lithium-ion battery with a negative electrode composed of 25% silicon and 75% graphite is used as an example.
  • the upper curve in the figure is when the battery is cyclically charged and discharged in a range of 20 to 97% SOC.
  • Capacity fading curve the curve below is the capacity fading curve when the battery is charged and discharged cyclically within 30-97% SOC.
  • the upper curve is the capacity decay curve of a lithium-ion battery with a negative electrode composed of 25% silicon and 75% graphite, which is charged and discharged in a 15-97% SOC range.
  • the curve below is the capacity decay curve when the battery is charged and discharged in cycles between 30% and 97% SOC.
  • steps in the flowcharts involved in the above embodiments are shown in sequence as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this article, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least some of the steps in the flowcharts involved in the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same time, but may be executed at different times. The execution order of these steps or stages is not necessarily sequential, but may be performed in turn or alternately with other steps or at least part of the steps or stages in other steps.
  • embodiments of the present application also provide a memory effect detection device for implementing the above-mentioned memory effect detection method.
  • the solution to the problem provided by this device is similar to the solution described in the above method. Therefore, for the specific limitations in the embodiments of one or more memory effect detection devices provided below, please refer to the above description of the memory effect detection method. Limitations will not be repeated here.
  • the present application provides a memory effect detection device, including a discharge characteristic analysis module 162 and a memory effect detection module 164 .
  • the discharge characteristic analysis module 162 is used to obtain the discharge characteristic curve of the battery to be tested when receiving the detection start signal; the memory effect detection module 164 is used to determine whether the battery to be tested has a memory effect based on the discharge characteristic curve and the preset phase inversion voltage interval.
  • the discharge characteristic analysis module 162 is also used to fully discharge the battery to be tested after fully charging it, and obtain the discharge characteristic curve of the battery to be tested during the complete discharge process.
  • the discharge characteristic analysis module 162 is also used to obtain the voltage and battery capacity during complete discharge of the battery to be tested; analyze and obtain the discharge characteristic curve according to the voltage and battery capacity.
  • the discharge characteristic analysis module 162 is also used to obtain the capacity change of the battery capacity as the voltage decreases based on the voltage and battery capacity; and obtain the discharge characteristic curve based on the capacity change and the voltage.
  • the memory effect detection module 164 is also used to determine that the battery under test has a memory effect when the discharge characteristic curve has a peak value within the preset phase-inversion voltage interval.
  • the memory effect detection module 164 is also used when the maximum value of the discharge characteristic curve is not at any boundary of the preset phase-turn voltage interval, and the discharge characteristic curve within the preset phase-turn voltage interval is extremely poor, The standard deviation of the discharge characteristic curve within the preset phase-turn voltage interval that is greater than the preset multiple determines that the discharge characteristic curve has a peak value within the preset phase-turn voltage interval.
  • the memory effect detection device further includes an intensity matching module 172 .
  • the intensity matching module 172 is used to obtain the current memory effect intensity by matching based on the peak value of the discharge characteristic curve within the preset phase inversion voltage interval and the relationship between the preset peak value and the memory effect intensity when it is determined that the battery to be tested has a memory effect.
  • the memory effect detection device further includes a trigger activation module 182 .
  • the trigger start module 182 is used to perform trigger analysis on the memory effect detection of the battery to be tested, and determine whether the battery to be tested meets the trigger conditions for memory effect detection.
  • the trigger startup module 182 is also used to push a detection plan to the user terminal when it is determined that the battery to be tested meets the trigger conditions for memory effect detection.
  • the trigger startup module 182 is also used to obtain the estimated detection duration of the memory effect detection; and push the estimated detection duration to the user terminal.
  • the trigger start module 182 is also used to obtain the expected detection duration of the memory effect detection based on the current state-of-charge parameters of the battery to be tested and the preset detection current.
  • Each module in the above memory effect detection device can be implemented in whole or in part by software, hardware and combinations thereof.
  • Each of the above modules may be embedded in or independent of the processor of the computer device in the form of hardware, or may be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.
  • the above-mentioned memory effect detection device after receiving the detection confirmation signal, extracts the discharge characteristic curve during the discharge process of the battery to be tested, and then analyzes the discharge characteristic curve within the preset phase-inversion voltage interval to determine whether the battery to be tested has a memory effect. detection. Through this solution, it can be detected in time when the memory effect occurs in the battery, so that corresponding processing can be carried out.
  • a computer device is provided.
  • the computer device may be a terminal, and its internal structure diagram may be as shown in Figure 19.
  • the computer equipment includes a processor, memory, communication interface, display screen and input device connected by a system bus.
  • the processor of the computer device is used to provide computing and control capabilities.
  • the memory of the computer device includes non-volatile storage media and internal memory.
  • the non-volatile storage medium stores operating systems and computer programs. This internal memory provides an environment for the execution of operating systems and computer programs in non-volatile storage media.
  • the communication interface of the computer device is used for wired or wireless communication with external terminals.
  • the wireless mode can be implemented through WIFI, mobile cellular network, NFC (Near Field Communication) or other technologies.
  • the computer program when executed by the processor, implements a memory effect detection method.
  • Figure 19 is only a block diagram of a partial structure related to the solution of the present application, and does not constitute a limitation on the computer equipment to which the solution of the present application is applied.
  • the specific computer equipment may May include more or fewer parts than shown, or combine certain parts, or have a different arrangement of parts.
  • a computer device including a memory and a processor.
  • a computer program is stored in the memory.
  • the processor executes the computer program, it implements the steps of any of the above memory effect detection methods.
  • a computer-readable storage medium on which a computer program is stored.
  • the computer program is executed by a processor, the steps of any of the above memory effect detection methods are implemented.
  • a computer program product including a computer program that implements the steps of any one of the above memory effect detection methods when executed by a processor.
  • the computer program can be stored in a non-volatile computer-readable storage.
  • the computer program when executed, may include the processes of the above method embodiments.
  • Any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory.
  • Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive memory (ReRAM), magnetic variable memory (Magnetoresistive Random Access Memory (MRAM), ferroelectric memory (Ferroelectric Random Access Memory, FRAM), phase change memory (Phase Change Memory, PCM), graphene memory, etc.
  • Volatile memory may include random access memory (Random Access Memory, RAM) or external cache memory, etc.
  • RAM Random Access Memory
  • RAM random access memory
  • RAM Random Access Memory
  • the databases involved in the various embodiments provided in this application may include at least one of a relational database and a non-relational database.
  • Non-relational databases may include blockchain-based distributed databases, etc., but are not limited thereto.
  • the processors involved in the various embodiments provided in this application may be general-purpose processors, central processing units, graphics processors, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to this.
  • the above-mentioned computer equipment, storage media and computer program products after receiving the detection confirmation signal, extract the discharge characteristic curve during the discharge process of the battery to be tested, and then analyze the discharge characteristic curve within the preset phase transition voltage interval to realize the test Detection of battery memory effect. Through this solution, it can be detected in time when the memory effect occurs in the battery, so that corresponding processing can be carried out.

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Abstract

本申请公开了一种记忆效应检测方法、装置、计算机设备、存储介质及计算机程序产品，在接收到检测确认信号之后，提取待测电池放电过程中的放电特性曲线，之后对预设相转电压区间内的放电特性曲线进行分析，实现待测电池是否发生记忆效应的检测。避免电池长时间处于记忆效应状态下运行，造成电池容量的快速衰减。

Description

记忆效应检测方法、装置、计算机设备及存储介质 技术领域
本申请涉及电池领域，具体涉及一种记忆效应检测方法、装置、计算机设备、存储介质及计算机程序产品。
背景技术
随着科学技术的发展和节能减排的提出，电动交通工具由于其节能环保的优势，越来越广泛应用在人们日常生活中。对于电动交通工具而言，电池技术又是关乎其发展的一项重要因素。
然而，电池在使用过程中，会由于长时间未进行满充满放，带来可逆容量失效的问题，也即发生记忆效应，记忆效应的存在会导致电池容量的快速衰减。因此，亟需对电池进行记忆效应检测。
发明内容
鉴于上述问题，本申请提供一种记忆效应检测方法、装置、计算机设备、存储介质及计算机程序产品，能够对电池是否发生记忆效应进行检测。
第一方面，本申请提供了一种记忆效应检测方法，包括：
当接收检测启动信号，获取待测电池的放电特性曲线；
根据所述放电特性曲线以及预设相转电压区间，确定所述待测电池是否发生记忆效应。
上述记忆效应检测方法，在接收到检测确认信号之后，提取待测电池放电过程中的放电特性曲线，之后对预设相转电压区间内的放电特性曲线进行分析，实现待测电池是否发生记忆效应的检测。通过该方案，能够在电池发生记忆效应时及时检测得到，从而进行对应的处理。
在一些实施例中，所述获取待测电池的放电特性曲线，包括：对所述待测电池进行完全充电后进行完全放电，并在完全放电的过程中，获得所述待测电池的放电特性曲线。该方案在待测电池进行记忆效应检测时，需要对待测电池进行完全充电后，再进行完全放电，以保证所获取的放电特性曲线，能够合理表征待测电池的整个放电过程，提高记忆效应检测准确性。
在一些实施例中，所述在完全放电的过程中，获得所述待测电池的放电特性曲线，包括：获取所述待测电池完全放电过程中的电压和电池容量；根据所述电压和所述电池容量，分析得到所述放电特性曲线。该方案结合电池容量和放电过程中的电压，实现放电特性曲线的获取，从而精确表示电池的电池容量变化性能，将记忆效应检测与电池容量关联起来，有效提高记忆效应检测检测精度。
在一些实施例中，所述根据所述电压和所述电池容量，分析得到所述放电特性曲线，包括：根据所述电压和电池容量，获取所述电池容量随所述电压的下降量的容量变化量；基于所述容量变化量和所述电压，获得所述放电特性曲线。该方案，在基于电池容量和电压进行放电特性曲线的获取时，结合电池容量追随电压的下降量来实现，进一步提高记忆效应检测检测精度。
在一些实施例中，所述记忆效应检测方法，包括以下任意一项：第一项：所述电压包括负极放电电压，所述放电特性曲线包括负极放电特性曲线；第二项：所述电压包括全电池放电电压，所述放电特性曲线包括全电池放电特性曲线。该方案在进行电池放电特性曲线获取时，可获取负极放电特性曲线或者全电池放电特性曲线，无论何 种方式均能有效实现记忆效应的检测，提高记忆效应检测方案的多样性。
在一些实施例中，所述根据所述放电特性曲线以及预设相转电压区间，确定所述待测电池是否发生记忆效应，包括：当所述放电特性曲线在所述预设相转电压区间内存在峰值，确定所述待测电池发生记忆效应。该方案，具体以放电特性曲线在预设相转电压区间内是否存在峰值，实现待测电池是否发生记忆效应的检测，具有检测效率高的优点。
在一些实施例中，当所述放电特性曲线的最大值未处于所述预设相转电压区间的任意一个边界，且所述预设相转电压区间内的所述放电特性曲线的极差，大于预设倍数的所述预设相转电压区间内的所述放电特性曲线的标准差，确定所述放电特性曲线在所述预设相转电压区间内存在峰值。该方案，在对放电特性曲线进行是否存在峰值的分析时，除了需要放电特性曲线的最大值未处于预设相转电压区间的边界之外，还需结合放电特性曲线在预设相转电压区间内的极差和标准差进行进一步分析，可有效避免由于测量误差等，误认为放电特性曲线在预设相转电压区间内存在峰值参数的情况发生，有效提高峰值检测准确度。
在一些实施例中，所述电压包括负极放电电压，所述预设相转电压区间包括预设负极相转电压区间，所述预设负极相转电压区间根据与所述待测电池的电池类型相同的电池的负极相转变电位范围确定。该方案，结合与待测电池类型相同的电池，在发生相转变时的负极相转变电位范围，得到待测电池所需的预设负极相转电压区间，保证预设负极相转电压区间的准确度，提高记忆效应检测精度。
在一些实施例中，所述电压包括全电池放电电压，所述预设相转电压区间包括预设全电池相转电压区间，所述预设全电池相转电压区间的确定方式包括：根据与所述待测电池的电池类型相同的电池的预设负极相转电压区间，对所述电池进行参比电极测试，得到预设全电池相转电压区间。该方案，结合待测电池所对应的预设负极相转电压区间，通过参比电极测试得到预设全电池相转电压区间，保证所得到的预设全电池相转电压区间的准确性，从而提高记忆效应的检测精度。
在一些实施例中，所述电压包括全电池放电电压，所述预设相转电压区间包括预设全电池相转电压区间，所述预设全电池相转电压区间的确定方式包括：获取与所述待测电池的电池类型相同的电池的负极硅含量参数；根据所述负极硅含量参数和预设的硅含量参数与全电池相转电压区间的关系，匹配得到预设全电池相转电压区间。
该方案，结合待测电池相同类型的电池的负极中，负极硅含量参数进行分析，得到与之相匹配的预设全电池相转电压区间，即使没有设置参比电极类型的电池，也能得到合理的预设全电池相转电压区间。同时该方案不需要对电池进行额外的测试，预设全电池相转电压区间获取方式较为简单，有效提高记忆效应检测效率。
在一些实施例中，记忆效应检测方法还包括：当确定所述待测电池发生记忆效应，根据所述放电特性曲线在所述预设相转电压区间内的峰值，以及预设的峰值与记忆效应强度的关系，匹配得到当前记忆效应强度。该方案可将记忆效应转换为不同的记忆效应强度，以便直观得到待测电池发生记忆效应的严重程度，保障在发生较严重记忆效应时，能够及时处理，避免电池容量进一步衰减。
在一些实施例中，所述当接收检测启动信号，获取待测电池的放电特性曲线之前，还包括：对所述待测电池进行记忆效应检测的触发分析，确定所述待测电池是否满足记忆效应检测的触发条件。该方案，通过对待测电池进行触发分析，以便在待测电池发生记忆效应时，能够及时执行对应的动作，提高记忆效应检测的运行可靠性。
在一些实施例中，记忆效应检测方法还包括：当确定所述待测电池满足记忆效应检测的触发条件，向用户终端推送检测方案。该方案，在满足触发条件时，还能向用户终端推送检测方案，用户只需根据推送的检测方案进行相应的操作即可，有效提高记忆效应检测的检测便利性。
在一些实施例中，所述当接收检测启动信号，获取待测电池的放电特性曲线之前，所述方法还包括：获取记忆效应检测的预计检测时长；将所述预计检测时长向用户终端推送。该方案，在进行记忆效应检测时，还能向用户反馈预计检测时长，便于用户可结合实际需求，决定记忆效应检测是否开启。
在一些实施例中，所述获取记忆效应检测的预计检测时长，包括：根据所述待测电池的当前荷电状态参数和预设检测电流，获得所述记忆效应检测的预计检测时长。该方案，通过预设检测电流和电池当前的荷电状态进行分析，计算得到待测电池通过充放电实现放电特性曲线获取时，预计所需的时长，具有计算准确度高的优点。
在一些实施例中，所述待测电池满足记忆效应检测的触发条件，包括以下任意一项：第一项，所述待测电池的健康状态的下降速度大于或等于预设速度阈值；第二项，所述待测电池的健康状态的下降速度的增幅大于或等于预设速度增幅阈值；第三项，所述待测电池的健康状态小于当前时刻对应的预估健康状态；第四项，所述待测电池的运行时长大于或等于预设运行时长；第五项，接收到记忆效应检测指令。
该方案，设置多种不同的记忆效应检测的触发条件，在实际运行中，只要满足任意一种触发条件，均会执行相应的记忆效应检测操作，保证电池发生记忆效应时能够及时检测得到，提高记忆效应检测可靠性。
第二方面，本申请提供一种记忆效应检测装置，包括：
放电特性分析模块，用于当接收检测启动信号，获取待测电池的放电特性曲线；记忆效应检测模块，用于根据所述放电特性曲线以及预设相转电压区间，确定所述待测电池是否发生记忆效应。
第三方面，本申请提供一种计算机设备，包括存储器和处理器，所述存储器存储有计算机程序，所述处理器执行所述计算机程序时实现上述记忆效应检测方法的步骤。
第四方面，本申请提供一种计算机可读存储介质，其上存储有计算机程序，所述计算机程序被处理器执行时实现上述记忆效应检测方法的步骤。
第五方面，本申请提供一种计算机程序产品，包括计算机程序，该计算机程序被处理器执行时实现上述记忆效应检测方法的步骤。
上述说明仅是本申请技术方案的概述，为了能够更清楚了解本申请的技术手段，而可依照说明书的内容予以实施，并且为了让本申请的上述和其它目的、特征和优点能够更明显易懂，以下特举本申请的具体实施方式。
附图说明
通过阅读对下文优选实施方式的详细描述，各种其他的优点和益处对于本领域普通技术人员将变得清楚明了。附图仅用于示出优选实施方式的目的，而并不认为是对本申请的限制。而且在全部附图中，用相同的附图标号表示相同的部件。在附图中：
图1为本申请一些实施例的电池容量衰减示意图；
图2为本申请一些实施例的记忆效应检测方法应用场景示意图；
图3为本申请一些实施例的记忆效应检测方法流程示意图；
图4为本申请一些实施例的放电特性曲线分析流程示意图；
图5为本申请另一些实施例的放电特性曲线分析流程示意图；
图6为本申请另一些实施例的记忆效应检测方法流程示意图；
图7为本申请又一些实施例的记忆效应检测方法流程示意图；
图8为本申请再一些实施例的记忆效应检测方法流程示意图；
图9为本申请另一些实施例的记忆效应检测方法流程示意图；
图10为本申请又一些实施例的记忆效应检测方法流程示意图；
图11为本申请再一些实施例的记忆效应检测方法流程示意图；
图12为本申请一些实施例的电池容量衰减对比示意图；
图13为本申请一些实施例的负极放电特性曲线对比示意图；
图14为本申请另一些实施例的电池容量衰减对比示意图；
图15为本申请一些实施例的全电池放电特性曲线对比示意图；
图16为本申请一些实施例的记忆效应检测装置结构示意图；
图17为本申请另一些实施例的记忆效应检测装置结构示意图；
图18为本申请又一些实施例的记忆效应检测装置结构示意图；
图19为本申请一些实施例的计算机设备内部结构示意图。
具体实施方式
下面将结合附图对本申请技术方案的实施例进行详细的描述。以下实施例仅用于更加清楚地说明本申请的技术方案，因此只作为示例，而不能以此来限制本申请的保护范围。
除非另有定义，本文所使用的所有的技术和科学术语与属于本申请的技术领域的技术人员通常理解的含义相同；本文中所使用的术语只是为了描述具体的实施例的目的，不是旨在于限制本申请；本申请的说明书和权利要求书及上述附图说明中的术语“包括”和“具有”以及它们的任何变形，意图在于覆盖不排他的包含。
在本申请实施例的描述中，技术术语“第一”“第二”等仅用于区别不同对象，而不能理解为指示或暗示相对重要性或者隐含指明所指示的技术特征的数量、特定顺序或主次关系。在本申请实施例的描述中，“多个”的含义是两个以上，除非另有明确具体的限定。
在本文中提及“实施例”意味着，结合实施例描述的特定特征、结构或特性可以包含在本申请的至少一个实施例中。在说明书中的各个位置出现该短语并不一定均是指相同的实施例，也不是与其它实施例互斥的独立的或备选的实施例。本领域技术人员显式地和隐式地理解的是，本文所描述的实施例可以与其它实施例相结合。
在本申请实施例的描述中，术语“和/或”仅仅是一种描述关联对象的关联关系，表示可以存在三种关系，例如A和/或B，可以表示：单独存在A，同时存在A和B，单独存在B这三种情况。另外，本文中字符“/”，一般表示前后关联对象是一种“或”的关系。
目前，从市场形势的发展来看，动力电池的应用越加广泛。动力电池不仅被应用于水力、火力、风力和太阳能电站等储能电源系统，而且还被广泛应用于电动自行车、电动摩托车、电动汽车等电动交通工具，以及军事装备和航空航天等多个领域。随着动力电池应用领域的不断扩大，其市场的需求量也在不断地扩增。
普遍认为，电池的记忆效应是指电池长时间未进行满充、满放，带来的可逆容量失效问题。一般认为记忆效应大多发生在镍镉电池中，镍氢电池较少，锂电池则无此现象。然而，本申请的发明人注意到，随着人们对电池能量密度的更高追求，比容量为372mAh/g的纯石墨负极已无法完全满足需求，具有3579mAh/g(毫安时每克)高比容量的硅成为新一代的锂电池负极材料，与纯石墨负极不同，含硅负极的锂电池同样存在记忆效应。
例如，可结合参阅图1，该图为15％硅与85％石墨共同组成负极的锂离子电池，在10％SOC(State of Charge，荷电状态)到97％SOC区间循环充放电时的电池容量衰减曲线。从图中可看出，该锂离子电池未进行满充满放循环50圈时，电池容量衰减了近8％，其发生了明显的记忆效应，这对于衰减20％作为电池寿命终止的锂离子电池而言，容量衰减极快。
记忆效应的存在会导致电池容量衰减飞快，从而对电池使用造成影响，这种现象与硅在负极中的含量、循环的SOC区间或者电压区间相关。发明人通过深入研究发现，硅负极锂离子电池在发生记忆效应时，其放电特性曲线在晶体相-非晶体相的相转变电 位区间内，明显与常规未发生记忆效应的电池的放电特性曲线不同。故在本申请的技术方案中，具体可结合待测电池的放电特性曲线与预设的晶体相-非晶体相的相转变电位区间，进行进一步分析，以判断待测电池是否发生记忆效应。
更为具体地，本申请的发明人发现，发生记忆效应的硅负极锂离子电池的放电特性曲线，在晶体相-非晶体相的相转变电位区间内，存在一个明显的峰值，而未发生记忆效应的硅负极锂离子电池的放电特性曲线，则无此现象。因此，在一个更为详细的实施例中，还可通过检测硅负极锂离子电池放电过程中的放电特性曲线，在晶体相-非晶体相的相转变电位区间内是否存在峰值，来实现是否发生记忆效应的检测。
本申请实施例所提供的记忆效应检测方法，可应用但不限于硅负极锂离子电池，还可以是其它含硅负极的电池中。并且本申请实施例所提供的电池，可以但不限用于手机、平板、笔记本电脑、电动玩具、电动工具、电瓶车、电动汽车、轮船、航天器等用电装置中。为了便于理解本申请的技术方案，在一个较为详细的实施例中，下面各个实施例中所指出的电池，均可理解为应用于电动汽车的硅负极锂电池。
本申请提供的记忆效应检测方法，可以应用到图2所示的应用环境中，其中，电池的BMS(Battery Management System，电池管理系统)与用户终端通信连接，在BMS检测到待测电池满足记忆效应检测的触发条件之后，会通过用户终端向用户返回相关的提示信息，用户通过用户终端可选择对应的检测方案，最终将待测电池移动到检测装置对应的位置处，实现对待测电池是否发生记忆效应的检测。
用户终端的具体类型并不是唯一的，其可以是与BMS通信连接的上位机，也可以是方便用户携带的手机、可穿戴设备等终端设备，具体不做限定。用于电池的记忆效应检测的检测装置，其具体类型也并不是唯一的，根据用户所选择的检测方案不同，检测装置也会有所区别。例如，在一个实施例中，当用户选择充电自检方案，也即在对待测电池进行充电的过程中，自主对待测电池进行记忆效应检测，例如可采用充电桩(具体可采用具备充放电功能的多功能充电桩)作为检测装置对待测电池进行充放电，待测电池的BMS在检测装置对待测电池进行充放电的过程中，实现记忆效应检测。在另一个实施例中，当用户选择到特定检测场所(例如电池产商指定的售后服务点，或者使用该电池的用电装置对应的售后服务点等)进行检测，例如可采用专用的充放电装置作为检测装置对待测电池进行充放电，待测电池的BMS在检测装置对待测电池进行充放电的过程中，实现记忆效应检测。
请参阅图3，在一个实施例中，以记忆效应检测方法应用在BMS进行解释说明，记忆效应检测方法包括步骤302和步骤304。
步骤302，当接收检测启动信号，获取待测电池的放电特性曲线。
具体地，检测启动信号即为指示BMS执行记忆效应检测的控制信号；待测电池即为需要进行记忆效应检测的电池。电池放电过程中，放电电压会随着时间的变化而持续发生变化，因此，放电特性曲线可以是根据电池的放电电压和放电时间，绘制而成的曲线，或者根据电池的放电电压和电池容量绘制而成的曲线，以此表示电池放电过程中的状态变化。
检测启动信号的发送方式均不是唯一的，在一个实施例中，检测启动信号可以是用户通过用户终端向待测电池的BMS发送。在另外的实施例中，检测启动信号还可以是待测电池接入用于记忆效应检测的检测装置之后，通过检测装置向待测电池的BMS发送。例如，在一个实施例中，可以是待测电池接入具备充放电功能的充电桩后，通过充电桩向待测电池的BMS发送检测启动信号。
步骤304，根据放电特性曲线以及预设相转电压区间，确定待测电池是否发生记忆效应。
具体地，预设相转电压区间即为预设的在待测电池放电过程中，电池材料发生晶体相到非晶体相转变时，所对应的放电电压区间范围。可以理解，该预设相转电压区 间可以是在待测电池出厂时，根据与待测电池相同类型的电池进行分析得到相转电压区间后，直接将相转电压区间存储在待测电池的BMS中，作为待测电池的预设相转电压区间，后续有使用需求时直接调用即可。
可以理解，对与待测电池相同类型的电池进行分析得到相转电压区间的方式并不是唯一的，在一个实施例中，可以是直接根据经验，结合相同类型的电池的活性材料种类及其含量，得到对应的相转电压区间，并存储于待测电池的BMS中。在另外的实施例中，还可以是对相同类型的电池进行实际放电测试，得到最终的相转电压区间，然后预存于待测电池的BMS中。
上述记忆效应检测方法，在接收到检测确认信号之后，提取待测电池放电过程中的放电特性曲线，之后对预设相转电压区间内的放电特性曲线进行分析，实现待测电池是否发生记忆效应的检测。通过该方案能够在电池发生记忆效应时及时检测得到，从而进行对应的处理，避免电池长时间处于记忆效应状态下运行，造成电池容量的快速衰减。
在一些实施例中，获取待测电池的放电特性曲线，包括：对待测电池进行完全充电后进行完全放电，并在完全放电的过程中，获得待测电池的放电特性曲线。
具体地，完全充电即为电池在选定的条件下充电时，达到所有可利用的活性物质不会显著增加容量的状态。完全放电即为电池放电至电池容量低于预设容量阈值，或放电至电池电压低于预设电压阈值时所对应的状态。
应当指出的是，完全充电和完全放电的具体实现方式并不是唯一的，在一个较为详细的实施例中，可以是以设定的充电电流大小对待测电池进行充电，直至待测电池充电至预设上限电压，实现完全充电。之后再以设定的放电电流大小对待测电池进行放电，直至待测电池放电至预设下限电压，实现完全放电。
在一个较为详细的实施例中，以待测电池为一个电芯为例，完全充电可以是以0.33C(也即0.33倍的额定电流)充电至待测电池的电压大于或等于4.25V，再维持恒压充电至电流下降至0.05C(也即0.05倍的额定电流)。而完全放电则是以0.33C放电至待测电池的电压小于或等于2.8V。可以理解，在其他实施例中，根据待测电池实际容量大小不同，预设电压上限和预设电压下限均会有所区别，具体应当结合实际待测电池进行设置，在此不做赘述。
该方案在待测电池进行记忆效应检测时，需要对待测电池进行完全充电后，再进行完全放电，以保证所获取的放电特性曲线，能够合理表征待测电池的整个放电过程，提高记忆效应检测准确性。
请参阅图4，在一些实施例中，在完全放电的过程中，获得待测电池的放电特性曲线，包括步骤402和步骤404。
步骤402，获取待测电池完全放电过程中的电压和电池容量；步骤404，根据电压和电池容量，分析得到放电特性曲线。
具体地，在进行放电特性曲线的分析时，有两种不同的分析方式，其一为结合放电电压和放电时间绘制得到，其二为结合放电电压和电池容量绘制得到，为了便于理解本申请的技术方案，本实施例以结合放电电压和电池容量绘制放电特性曲线为例进行解释说明。
电池完全放电过程中的电压也即电池的放电电压；电池容量为电池在完全放电过程中所剩余的容量，也即电池的SOC。待测电池在完成完全充电最后，将会进行完全放电，并在完全放电的过程中，待测电池的BMS实时进行待测电池的电压以及电池容量的获取，基于待测电池的电压和电池容量，实现放电特性曲线的提取。
该方案结合电池容量和放电过程中的电压，实现放电特性曲线的获取，从而精确表示电池的电池容量变化性能，将记忆效应检测与电池容量关联起来，有效提高记忆效应检测检测精度。
请参阅图5，在一些实施例中，步骤404包括步骤502和步骤504。
步骤502，根据电压和电池容量，获取电池容量随电压的下降量的容量变化量；步骤504，基于容量变化量和电压，获得放电特性曲线。
具体地，待测电池的电压在实际测试过程中，可直接通过BMS对待测电池进行采集得到。而电池容量随电压的下降量的容量变化量，获取方式则并不是唯一的，可以是基于安时积分法、开路电压法、内阻法、神经网络和卡尔曼滤波法等进行分析得到。
在该实施例的方案中，以安时积分法为例进行解释说明。首先，待测电池在放电过程中，BMS对待测电池的放电电压进行电压采集，得到不同放电时刻所对应的电压。然后利用安时积分法，计算电压每降低预设电压下降量Δx mV(毫伏特)时，所对应的待测电池容量的变化量Δy mAh(毫安-小时)，定义该时刻的DQ/DV＝Δy/Δx，以DQ/DV作为纵轴，所采集的电压作为横轴，即可绘制得到对应的放电特性曲线。
该方案，在基于电池容量和电压进行放电特性曲线的获取时，结合电池容量追随电压的下降量来实现，进一步提高记忆效应检测检测精度。
在一些实施例中，记忆效应检测方法包括以下任意一项：第一项：电压包括负极放电电压，放电特性曲线包括负极放电特性曲线；第二项：电压包括全电池放电电压，放电特性曲线包括全电池放电特性曲线。
具体地，负极放电电压即为待测电池在完全放电的过程中，BMS对待测电池的负极进行电压采集所得到的电压。负极放电特性曲线即为以待测电池的负极放电电压作为放电特性曲线分析的电压时，分析得到的放电特性曲线。相应的，全电池放电电压即为待测电池在完全放电的过程中，BMS对所采集到的正极与负极的相对电压，全电池放电特性曲线即为以待测电池的全电池放电电压作为放电特性曲线分析的电压时，分析得到的放电特性曲线。
对应的，在一个较为详细的实施例中，在进行负极放电特性曲线分析时，BMS对负极的放电电压进行电压采集，得到不同放电时刻所对应的负极放电电压。然后利用安时积分法，计算负极放电电压每降低第一预设电压下降量Δx1mV时，所对应的待测电池容量的变化量Δy1mAh，定义该时刻的DQ/DV＝Δy1/Δx1，以DQ/DV作为纵轴，所采集的负极放电电压作为横轴，即可绘制得到对应的负极放电特性曲线。
在进行全电池放电特性曲线分析时，BMS对全电池放电电压进行电压采集，得到不同放电时刻所对应的全电池放电电压。然后利用安时积分法，计算全电池放电电压每降低第二预设电压下降量Δx2mV时，所对应的待测电池容量的变化量Δy2mAh，定义该时刻的DQ/DV＝Δy2/Δx2，以DQ/DV作为纵轴，所采集的全电池放电电压作为横轴，即可绘制得到对应的全电池放电特性曲线。
该方案在进行电池放电特性曲线获取时，可获取负极放电特性曲线或者全电池放电特性曲线，无论何种方式均能有效实现记忆效应的检测，提高记忆效应检测方案的多样性。
请参阅图6，在一些实施例中，步骤304包括步骤602。
步骤602，当放电特性曲线在预设相转电压区间内存在峰值，确定待测电池发生记忆效应。
具体地，BMS在得到放电特性曲线之后，将会结合其内部预存的预设相转电压区间进行分析，判断在预设相转电压区间内，放电特性曲线是否存在峰值。若放电特性曲线在预设相转电压区间内存在峰值，则说明当前待测电池发生了记忆效应；而若放电特性曲线在预设相转电压区间内不存在峰值，则说明当前待测电池未发生记忆效应。
该方案，具体以放电特性曲线在预设相转电压区间内是否存在峰值，实现待测电池是否发生记忆效应的检测，具有检测效率高的优点。
在一些实施例中，当放电特性曲线的最大值未处于预设相转电压区间的任意一个边界，且预设相转电压区间内的放电特性曲线的极差，大于预设倍数的预设相转电压区间内的放电特性曲线的标准差，确定放电特性曲线在预设相转电压区间内存在峰值。
具体地，在对放电特性曲线进行是否在预设相转电压区间存在峰值时，除了满足放电特性曲线在预设相转电压区间内的最大值，未处于预设相转电压区间的边界之外，还需结合位于预设相转电压区间内的放电特性曲线进行极差计算和标准差计算，只有在预设相转电压区间内的放电特性曲线所对应的各个参数，其极差大于预设倍数的标准差，才会认为放电特性曲线在预设相转电压区间内存在峰值。若仅是检测到放电特性曲线在预设相转电压区间内的最大值，未处于预设相转电压区间的边界，而极差与标准差不满足相应的关系，则认为此时检测到的最大值是由于检测误差或者检测过程中的电压波动所引起的，实质上放电特性曲线并不存在峰值。
可以理解，预设倍数的大小并不是唯一的，例如，在一个较为详细的实施例中，可将预设倍数设置为3，也即放电特性曲线的最大值，未处于预设相转电压区间的边界，同时，放电特性曲线在预设相转电压区间的各个参数的极差，大于其3倍的标准差，则认为此时存在峰值。
该方案，在对放电特性曲线进行是否存在峰值的分析时，除了需要放电特性曲线的最大值未处于预设相转电压区间的边界之外，还需结合放电特性曲线在预设相转电压区间内的极差和标准差进行进一步分析，可有效避免由于测量误差等，误认为放电特性曲线在预设相转电压区间内存在峰值参数的情况发生，有效提高峰值检测准确度。
在一些实施例中，电压包括负极放电电压，预设相转电压区间包括预设负极相转电压区间，预设负极相转电压区间根据与待测电池的电池类型相同的电池的负极相转变电位范围确定。
具体地，相同类型的电池指的是电池各个部位材料及其含量均一致类型的电池，在电池实际运行过程中，对于相同类型的电池，对应的在发生相转变时，所对应的电压大小也基本一致。在实际场景中，根据与待测电池的电池类型相同的电池的负极相转变电位范围，确定预设负极相转电压区间时，可直接将负极相转变电位范围作为预设负极相转电压区间。还可以是结合实际情况，在负极相转变电位范围的基础上进行上限值和/或下限值的更改，得到最终的预设负极相转电压区间。
例如，在一个较为详细的实施例中，以晶体相-非晶体相转换为例，含有硅、氧化硅以及硅合金的负极中，晶体相-非晶体相的负极相转变电位范围约430mV至470mV，因此，可将预设负极相转电压区间设置为430mV至470mV。
进一步地，在另一个实施例中，考虑不同硅材料之间的差异性，可在430mV至470mV的基础上，进一步扩大负极相转变电位范围，得到预设负极相转电压区间。例如，将负极相转变电位范围扩大到400mV至500mV，对应的，该实施例可将预设负极相转电压区间设置为400mV至500mV。
可以理解，在其它实施例中，还可在430mV至470mV的基础上，进一步缩减负极相转变电位范围，得到预设负极相转电压区间。例如，将负极相转变电位范围缩减到440mV至460mV，对应的，该实施例可将预设负极相转电压区间设置为440mV至460mV。
该方案，结合与待测电池类型相同的电池，在发生相转变时的负极相转变电位范围，得到待测电池所需的预设负极相转电压区间，保证预设负极相转电压区间的准确度，提高记忆效应检测精度。
在一些实施例中，电压包括全电池放电电压，预设相转电压区间包括预设全电池相转电压区间，预设全电池相转电压区间的确定方式包括：根据与待测电池的电池类型相同的电池的预设负极相转电压区间，对电池进行参比电极测试，得到预设全电池相转电压区间。
具体地，在该实施例的方案中，为了实现以预设全电池相转电压区间进行待测电池的记忆效应检测，需要在进行具体地检测分析之前，得到预设全电池相转电压区间并存储于BMS中。本实施例的方案适用于设置有参比电极类型的待测电池，这一类电池在进行记忆效应检测时，可在参比电极处，结合预设负极相转电压区间进行测试，得到实际的全电池相转电压区间并存储，作为预设全电池相转电压区间。
可以理解，在一个实施例中，参比电极测试操作并不是每次进行记忆效应检测时都进行的，而是在电池出厂时，以相同类型的一个或多个电池进行测试得到，从而存储在同类型的其他电池的BMS中。
参比电极测试方式并不是唯一的，在一个较为详细的实施例中，可在电池完全放电过程中，同时进行负极放电电压和参比电极处放电电压的采集，在检测到负极放电电压达到预设负极相转电压区间时，记录下对应的参比电极处的放电电压，将所记录的电压区间范围作为全电池相转电压区间并存储，即可得到预设全电池相转电压区间。对应的，此时有：φ＝f(T1,T2,I,SOC0)，其中，φ为参比电极处的放电电压，T1为测试环境温度，T2为测试电池温度，I为测试电流，SOC0为电池测试初始的SOC，也即参比电极处的放电电压与测试环境温度、测试电池温度、测试电流和测试初始的SOC相关。
该方案，结合待测电池所对应的预设负极相转电压区间，通过参比电极测试得到预设全电池相转电压区间，保证所得到的预设全电池相转电压区间的准确性，从而提高记忆效应的检测精度。
在一些实施例中，电压包括全电池放电电压，预设相转电压区间包括预设全电池相转电压区间，预设全电池相转电压区间的确定方式包括：获取与待测电池的电池类型相同的电池的负极硅含量参数；根据负极硅含量参数和预设的硅含量参数与全电池相转电压区间的关系，匹配得到预设全电池相转电压区间。
具体地，负极硅含量参数即为待测电池的负极中，掺杂的硅质量占负极总活性物质的质量比。由于晶体相-非晶体相转变的电压区间与负极硅含量参数直接相关，该实施例的方案，直接以相同类型的电池的负极硅含量参数，结合预设的硅含量参数与全电池相转电压区间的关系，匹配得到待测电池对应的预设全电池相转电压区间，并存储于待测电池的BMS中。
本实施例的方案所对应的预设全电池相转电压区间的确定方式，可以是在待测电池出厂时，直接以相同类型的电池进行匹配分析，得到预设全电池相转电压区间后存储于待测电池中。还可以是将预设的硅含量参数与全电池相转电压区间的关系存储在待测电池的BMS中，在对待测电池进行记忆效应检测时，通过获取待测电池的负极硅含量参数之后，再进行匹配分析得到。
而获取待测电池的负极硅含量参数的方式并不是唯一的，在一个实施例中，可直接将待测电池的负极硅含量参数预存于待测电池的BMS中，在有使用需求时直接调取。在另外的实施例中，还可以是在有使用需求时，通过用户终端等发送至待测电池的BMS。
应当指出的是，预设的硅含量参数与全电池相转电压区间的关系，可以是以数据库的形式，或者是以图表等形式存储，具体不做限定，可结合实际使用场景进行选择。例如，在一个较为详细的实施例中，硅含量参数与全电池相转电压区间的关系可如下表所示：
Figure PCTCN2022115516-appb-000001
进一步地，在一个较为详细的实施例中，以镍钴锰三元正极材料，负极材料为石 墨掺杂x％含量硅的电池为例，该电池的全电池相转电压区间与负极硅含量参数如下表所示：
硅含量参数/％ 全电池相转电压区间/V
5％ 2.8～3.1
15％ 3.1～3.2
25％ 3.1～3.3
该方案，结合待测电池相同类型的电池的负极中，负极硅含量参数进行分析，得到与之相匹配的预设全电池相转电压区间，即使没有设置参比电极类型的电池，也能得到合理的预设全电池相转电压区间。同时该方案不需要对电池进行额外的测试，预设全电池相转电压区间获取方式较为简单，有效提高记忆效应检测效率。
请参阅图7，在一些实施例中，记忆效应检测方法还包括步骤702。
步骤702，当确定待测电池发生记忆效应，根据放电特性曲线在预设相转电压区间内的峰值，以及预设的峰值与记忆效应强度的关系，匹配得到当前记忆效应强度。
具体地，记忆效应强度参数即为表征待测电池发生记忆效应的严重程度的参数。电池的BMS中还进一步存储有预设的峰值与记忆效应强度的关系，每当BMS根据放电特性曲线与预设相转电压区间，分析得到放电特性曲线在预设相转电压区间内存在峰值时，还会进一步结合所得到的峰值，与预设的峰值与记忆效应强度的关系，匹配得到当前待测电池发生记忆效应强度的当前记忆效应强度。
应当指出的是，预设的峰值与记忆效应强度的关系，可以是以数据库的形式，还可以是以图表等形式存储，具体不做限定，可结合实际使用场景进行选择。例如，在一个较为详细的实施例中，峰值与记忆效应强度的关系可如下表所示：
峰值 记忆效应强度
A1 Y1’
A2 Y2’
…… ……
An Yn’
该方案可将记忆效应转换为不同的记忆效应强度，以便直观得到待测电池发生记忆效应的严重程度，保障在发生较严重记忆效应时，能够及时处理，避免电池容量进一步衰减。
请参阅图8，在一些实施例中，步骤302之前，该方法还包括步骤802。
步骤802，对待测电池进行记忆效应检测的触发分析，确定待测电池是否满足记忆效应检测的触发条件。
具体地，检测启动信号在待测电池满足记忆效应检测的触发条件时接收，而具体的接收方式则并不是唯一的，可以是通过用户终端发送至待测电池的BMS，可以是通过对待测电池进行充放电的检测装置发送至待测电池的BMS，还可以是待测电池的BMS在检测满足触发条件时，自动生成，以实现记忆效应的自动检测。触发分析即为检测待测电池是否触发记忆效应检测的分析，具体通过检测待测电池是否满足触发条件实现。所设置的触发条件不同，对应的触发分析操作也会有所区别。例如，若触发条件需要结合实时的待测电池的运行状态参数，则触发分析操作需要实时进行；若触发条件只需结合某一特定条件下的待测电池运行状态参数，则无需实时进行触发分析，只需在满足该特定条件时，执行触发分析操作即可。
该方案，通过对待测电池进行触发分析，以便在待测电池发生记忆效应时，能够及时执行对应的动作，提高记忆效应检测的运行可靠性。
请参阅图9，在一些实施例中，记忆效应检测方法还包括步骤902。
步骤902，当确定待测电池满足记忆效应检测的触发条件，向用户终端推送检测方案。
具体地，在BMS检测待测电池满足记忆效应检测的触发条件之后，会结合待测 电池的实际使用场景等，向用户终端推送对应的检测方案。例如，可以是推荐用户在充电桩(具备充放电功能)处对电池进行充电时，自主进行记忆效应检测；或者是推荐用户将电池带到售后服务点，由专业人员协助进行记忆效应检测。
可以理解，BMS向用户终端推送记忆效应检测方案时，在一个实施例中，可以是结合电池的实际使用场景，向用户推荐一种记忆效应检测方案。在另外的实施例中，还可以是将可选的记忆效应检测方案均推送给用户终端，由用户决定具体采用哪一种检测方案进行最终的记忆效应检测。
无论是何种推送方式，在用户确定合适的检测方案之后，均会在将电池接入对应的检测装置后，通过用户终端或者检测装置向BMS反馈检测启动信号，或者是BMS在检测满足触发条件之后，自动生成检测启动信号，以使BMS启动执行相应的记忆效应检测操作。也即BMS在检测装置对电池进行放电的过程中，进行放电特性曲线的提取，最终以提取到的放电特性曲线和预设相转电压区间，实现记忆效应检测。
该方案，在满足触发条件时，还能向用户终端推送检测方案，用户只需根据推送的检测方案进行相应的操作即可，有效提高记忆效应检测的检测便利性。
请参阅图10，在一些实施例中，步骤302之前，该方法还包括步骤102和步骤104。
步骤102，获取记忆效应检测的预计检测时长；步骤104，将预计检测时长向用户终端推送。
具体地，该方案在进行记忆效应检测时，还会将进行记忆效应检测所需的预计检测时长推送给用户，以便用户结合预计检测时长和检测方案，最终做出是否开启记忆效应检测的决定。
应当指出的是，步骤102和步骤104，可以是在步骤902之后执行，也可以是与在步骤902之前执行，或者是与步骤902同时执行，具体结合实际需求进行选择，为了便于理解本申请的技术方案，下面以步骤102和步骤104，在步骤902之后执行为例进行解释说明。
该实施例以检测方案为推荐用户使用充电桩充电自检为例，首先，在BMS检测到待测电池满足记忆效应检测的触发条件，BMS将会向用户终端推送充电自检方案。具体推送形式并不是唯一的，例如，在一个较为详细的实施例中，BMS可向用户终端推送“是否下次在充电桩充电前进行记忆效应检测”的问询信息，即表示BMS向用户终端推送了记忆效应检测方案。若用户通过用户终端反馈“是”，则表示BMS接收用户终端根据充电自检方案反馈的检测确认信号，用户同意在充电桩处进行记忆效应检测。
之后BMS开始检测待测电池是否接入充电桩，在检测到充电桩接入时，对待测电池的记忆效应检测时间进行预估，得到预计检测时长并推送至用户终端。若用户认为该预计检测时长在可接收范围内，则通过用户终端向BMS返回检测启动信号，以实现待测电池的放电特性曲线提取操作。
可以理解，若用户认为该预计检测时长不合理，或者出现其它突发状况，不需要进行记忆效应检测，用户将会通过用户终端返回记忆效应检测关闭的信号，以终止当前次记忆效应检测操作。
对应的，在一个实施例中，在用户记忆效应检测关闭的信号之后，记忆效应检测并未完全停止，在用户下一次通过充电桩进行充电之前，会再次向用户终端推送预计检测时长，用户可结合实际需求在下一次充电之前，重新开启进行记忆效应检测。
该方案，在进行记忆效应检测时，还能向用户反馈预计检测时长，便于用户可结合实际需求，决定记忆效应检测是否开启。
请参阅图11，在一些实施例中，步骤102包括步骤112。
步骤112，根据待测电池的当前荷电状态参数和预设检测电流，获得记忆效应检 测的预计检测时长。
具体地，预设检测电流即为待测电池进行记忆效应检测时，对应所需的充放电电流。若待测电池在进行记忆效应检测时，处于低电量或者电量已经耗尽的状态，则需要对待测电池进行充电后，才能实现放电特性曲线的提取。若待测电池在进行记忆效应检测时，其电量能够满足检测需求(例如电池已经处于完全充电完成状态)时，则无需对待测电池进行充电，直接放电进行放电特性曲线的提取即可。
应当指出的是，预设检测电流的获取方式并不是唯一的，在一个实施例中，可以是将预设检测电流设置于BMS中，在有检测需求时直接调用即可。在另外的实施例中，由于对电池进行记忆效应检测时，充放电操作是通过检测装置实现的，故预设检测电流可以是预存于检测装置中，在电池与检测装置连接之后，BMS向检测装置请求获取得到。
该方案，通过预设检测电流和电池当前的荷电状态进行分析，计算得到待测电池通过充放电实现放电特性曲线获取时，预计所需的时长，具有计算准确度高的优点。
在一些实施例中，待测电池满足记忆效应检测的触发条件，包括以下任意一项：第一项，待测电池的健康状态的下降速度大于或等于预设速度阈值；第二项，待测电池的健康状态的下降速度的增幅大于或等于预设速度增幅阈值；第三项，待测电池的健康状态小于当前时刻对应的预估健康状态；第四项，待测电池的运行时长大于或等于预设运行时长；第五项，接收到记忆效应检测指令。
具体地，健康状态(State of Health，SOH)也即电池的剩余可用容量。待测电池的健康状态的下降速度，可以以充放电周期进行统计，还可以是以待测电池的运行时间进行统计。以充放电周期进行统计时，健康状态的下降速度即为每经过一个或多个充放电周期时，健康状态下降的大小。而当以电池的运行时间进行统计时，健康状态的下降速度即为每经过一个或多个运行时间周期时，健康状态下降的大小。
健康状态的下降速度的增幅，即为相邻两次健康状态的下降速度所增加的幅度，其具体为当前次检测到的下降速度与上一次检测到的下降速度的差值，和上一次检测到的下降速度的比值。
为了便于理解，在一个较为详细的实施例中，可以一个充放电周期为例进行解释说明，电池健康状态的下降速度即为(S1-S0)/1cycle，其中，S1为当前充放电周期检测到的待测电池的健康状态，S0为上一个充放电周期检测到的待测电池的健康状态，1cycle即为一个充放电周期。相应的，在一个较为详细的实施例中，此时可将预设速度阈值设置为0.2％/1cycle。
以一个运行时间周期为30天进行解释说明，对应的，电池健康状态的下降速度即为(S3-S2)/30天，其中，S3为当前待测电池的健康状态，S2为30天以前检测到的待测电池的健康状态。相应的，在一个较为详细的实施例中，此时可将预设速度阈值设置为2％/30天。
相应的，在一个实施例中，电池健康状态的下降速度增幅，也可以以充放电周期进行统计，或者是以电池的运行时间进行统计。例如，在一个较为详细的实施例中，将下降速度增幅表示为：(上一充放电周期的健康状态-当前充放电周期的健康状态)/(上两个充放电周期的健康状态-上一个充放电周期的健康状态)-100％。或者是将下降速度增幅表示为：30天之前对应的健康状态-当前对应的健康状态)/(60天之前对应的健康状态-30天之前对应的健康状态)-100％。
预估健康状态的获取方式并不是唯一的，在一个较为详细的实施例中，BMS设置有电池健康状态衰减曲线，在待测电池正常使用过程中，随着使用时间的增加，通过电池健康状态衰减曲线可以预估待测电池的当前时刻的健康状态，也即得到预估健康状态。若BMS实际检测到的当前时刻的健康状态，低于预估健康状态，则说明此时待测电池的健康状态下降异常，同样会触发记忆效应检测操作。
在另外的实施例中，还可以是BMS可对待测电池的运行时长进行计时，在BMS检测到电池运行时长大于或等于预设运行时长之后，将会触发执行记忆效应检测操作。可以理解，预设运行时长的大小并不是唯一的，在一个较为详细的实施例中，可以将预设运行时长设置大于或等于6个月。更为详细的，在一个实施例中，可将预设运行时长设置大于或等于12个月。
在一个实施例中，记忆效应检测指令，具体由用户通过用户终端发送，若用户对电池有记忆效应检测需求时，还能主动触发相应的检测操作。此时用户通过用户终端向BMS发送记忆效应检测指令，在BMS接收来自用户终端的记忆效应检测指令之后，将会触发执行记忆效应检测操作。
应当指出的是，根据记忆效应触发检测中，电池健康状态的获取周期不同，预设速度阈值也会有所区别，具体根据电池实际使用情况以及获取周期进行设定即可。
该方案，设置多种不同的记忆效应检测的触发条件，在实际运行中，只要满足任意一种触发条件，均会执行相应的记忆效应检测操作，保证电池发生记忆效应时能够及时检测得到，提高记忆效应检测可靠性。
为了便于理解本申请的技术方案，下面结合较为详细的实施例对本申请进行解释说明。
首先，在待测电池运行过程中，BMS实时进行待测电池的健康状态采集，以及待测电池运行时间的计时操作，结合各次所采集的电池健康状态，得到相应的电池健康状态的下降速度以及电池健康状态的下降速度增幅，分别与预设速度阈值和预设速度增幅阈值进行比较分析，同时还将采集到的电池健康状态与通过电池健康状态衰减曲线分析得到的预估健康状态进行比较分析。无论是检测到下降速度大于或等于预设速度阈值、下降速度增幅大于或等于预设速度增幅阈值、健康状态小于当前时刻对应的预估健康状态，还是检测到待测电池运行时长大于或等于预设运行时长，或者接收到用户终端发送的记忆效应检测指令，BMS均会认为满足记忆效应检测的触发条件，触发执行后续的检测操作。
之后BMS向用户终端推送记忆效应检测方案，例如，向用户终端推送推荐到售后服务点进行检测或者充电自主检测的方案。若推送到售后服务点进行检测的方案，用户通过用户终端得知检测方案之后，将电池携带至售后服务点，并接入对应的检测装置，通过用户终端或者检测装置向BMS返回检测启动信号。在该信号的作用下，检测装置为电池进行完全充电。在该过程中，检测装置具体以预设倍率充电，充电至上限电压，再通过预设倍率放电，放电到放电下限电压，也即完成满充满放。例如，在一个较为详细的实施例中，以待测电池为一个电芯为例，充放电过程可以是以0.33C充电至最大电压，此时电池电压大于等于4.25V，之后以恒压充电至0.05C，充电完成后以0.33C放电至放电下限电压，该放电下限电压低于2.8V。
在以0.33C进行放电的过程中，BMS实时获取电池的负极放电电压或者全电池放电电压(为了便于理解，下面均以负极放电电压为例进行说明)，结合安时积分法计算得到电池容量跟随负极放电电压的容量变化量。最终以放电过程中负极放电电压每降低预设电压下降量时，电池容量的变化量与预设电压下降量的比值，作为纵坐标，负极放电电压作为横坐标搭建负极放电特性曲线。在电池完全放电完成之后，分析所获取的负极放电特性曲线在预设负极相转电压区间是否存在峰值。若负极放电特性曲线在预设负极相转电压区间的最大值，未处于预设负极相转电压区间的边界，且该区间内负极放电特性曲线的各个参数的极差，大于3倍的标准差，则认为存在峰值，此时待测电池发生记忆效应。BMS将会结合预设的峰值与记忆效应强度的关系，匹配得到当前记忆效应强度并推送至用户终端，以告知用户。而当分析未存在峰值时，则认为未发生记忆效应。
若BMS推送充电自检的检测方案，用户通过用户终端得知检测方案之后，将会 返回检测确认信号，以确认在下一次充电时对待测电池进行记忆效应检测。之后用户将待测电池接入充电桩(以充电桩作为检测装置)，BMS通过向充电桩获取预设检测电流，或者结合BMS内部存储的预设检测电流，以及当前待测电池的荷电状态，计算得到预计检测时长，并推送至用户终端告知用户。若用户终端返回检测启动信号，则对待测电池进行记忆效应检测；若用户终端未返回检测启动信号，则无需进行记忆效应检测，对待测电池进行正常充电即可。
在检测启动信号的作用下，充电桩为待测电池进行完全充电，在该过程中，充电桩具体以预设倍率充电，充电至上限电压，再通过预设倍率放电，放电到放电下限电压，也即完成满充满放。例如，在一个较为详细的实施例中，以待测电池为一个电芯为例，充放电过程可以是以0.33C充电至最大电压，此时电池电压大于等于4.25V，之后以恒压充电至0.05C，充电完成后以0.33C放电至放电下限电压，该放电下限电压低于2.8V。
在以0.33C进行放电的过程中，BMS实时获取电池的负极放电电压，结合安时积分法计算得到对应的电池电容跟随负极电压的容量变化量，最终以放电过程中负极放电电压每降低预设电压下降量时，电池容量的变化量与预设电压下降量的比值，作为纵坐标，负极放电电压作为横坐标搭建负极放电特性曲线。在电池完全放电完成之后，分析所获取的负极放电特性曲线在预设负极相转电压区间是否存在峰值。若负极放电特性曲线在预设负极相转电压区间的最大值，未处于预设负极相转电压区间的边界，且该区间内负极放电特性曲线的各个参数的极差，大于3倍的标准差，则认为存在峰值，此时电池发生记忆效应。BMS将会结合预设的峰值与记忆效应强度的关系，匹配得到当前记忆效应强度并推送至用户终端，以告知用户。而当分析未存在峰值时，则认为未发生记忆效应。
本申请的记忆效应检测方法，检测过程对检测电流、检测温度均不做要求，降低了对充放电设备的要求以及环境的敏感性，具有较强的检测可靠性。
进一步地，下面以上述记忆效应检测方法对实际电池进行检测验证。
本实施例中，采用25％硅与75％石墨共同组成负极类型的锂离子电池为例，可参阅图12，图中位于上方的曲线为电池在20～97％SOC内进行循环充放电时的容量衰减曲线，下方的曲线为电池在30～97％SOC内进行循环充放电时的容量衰减曲线。在两组电池进行20圈的循环充放电之后，以1/10C的电流进行充电，充电至截止充电电压，以实现完全充电后，再以1/3C的电流进行放电，放电至截止放电电压，以实现完全放电，在完全放电过程中，进行负极放电特性曲线提取，具体如图13所示。在预设负极相转电压区间400mV～500mV之内，可看出20～97％SOC区间循环充放电20圈的电池，未发生记忆效应，而30～97％SOC区间循环充放电20圈的电池发生记忆效应。
在另外的实施例中，可参阅图14，位于上方的曲线为25％硅与75％石墨共同组成负极类型的锂离子电池，在15～97％SOC内进行循环充放电时的容量衰减曲线，下方的曲线为电池在30～97％SOC内进行循环充放电时的容量衰减曲线。在两组电池进行20圈的循环充放电之后，以1/10C的电流进行充电，充电至截止充电电压，以实现完全充电后，再以1/3C的电流进行放电，放电至截止放电电压，以实现完全放电，在完全放电过程中，进行全电池放电特性曲线提取，具体如图15所示。在预设全电池相转电压区间3.1V～3.3V之内，可看出15～97％SOC区间循环充放电20圈的电池，未发生记忆效应，而30～97％SOC区间循环充放电20圈的电池发生记忆效应。
应该理解的是，虽然如上的各实施例所涉及的流程图中的各个步骤按照箭头的指示依次显示，但是这些步骤并不是必然按照箭头指示的顺序依次执行。除非本文中有明确的说明，这些步骤的执行并没有严格的顺序限制，这些步骤可以以其它的顺序执行。而且，如上的各实施例所涉及的流程图中的至少一部分步骤可以包括多个步骤或者多个阶段，这些步骤或者阶段并不必然是在同一时刻执行完成，而是可以在不同的 时刻执行，这些步骤或者阶段的执行顺序也不必然是依次进行，而是可以与其它步骤或者其它步骤中的步骤或者阶段的至少一部分轮流或者交替地执行。
基于同样的发明构思，本申请实施例还提供了一种用于实现上述所涉及的记忆效应检测方法的记忆效应检测装置。该装置所提供的解决问题的实现方案与上述方法中所记载的实现方案相似，故下面所提供的一个或多个记忆效应检测装置实施例中的具体限定可以参见上文中对于记忆效应检测方法的限定，在此不再赘述。
在一些实施例中，如图16所示，本申请提供一种记忆效应检测装置，包括放电特性分析模块162和记忆效应检测模块164。
放电特性分析模块162用于当接收检测启动信号，获取待测电池的放电特性曲线；记忆效应检测模块164用于根据放电特性曲线以及预设相转电压区间，确定待测电池是否发生记忆效应。
在一些实施例中，放电特性分析模块162还用于对待测电池进行完全充电后进行完全放电，并在完全放电的过程中，获得待测电池的放电特性曲线。
在一些实施例中，放电特性分析模块162还用于获取待测电池完全放电过程中的电压和电池容量；根据电压和电池容量，分析得到放电特性曲线。
在一些实施例中，放电特性分析模块162还用于根据电压和电池容量，获取电池容量随电压的下降量的容量变化量；基于容量变化量和电压，获得放电特性曲线。
在一些实施例中，记忆效应检测模块164还用于当放电特性曲线在预设相转电压区间内存在峰值，确定待测电池发生记忆效应。
在一些实施例中，记忆效应检测模块164还用于当放电特性曲线的最大值未处于预设相转电压区间的任意一个边界，且预设相转电压区间内的放电特性曲线的极差，大于预设倍数的预设相转电压区间内的放电特性曲线的标准差，确定放电特性曲线在预设相转电压区间内存在峰值。
请参阅图17，在一些实施例中，记忆效应检测装置还包括强度匹配模块172。强度匹配模块172用于当确定待测电池发生记忆效应，根据放电特性曲线在预设相转电压区间内的峰值，以及预设的峰值与记忆效应强度的关系，匹配得到当前记忆效应强度。
请参阅图18，在一些实施例中，记忆效应检测装置还包括触发启动模块182。触发启动模块182用于对待测电池进行记忆效应检测的触发分析，确定待测电池是否满足记忆效应检测的触发条件。
在一些实施例中，触发启动模块182还用于当确定待测电池满足记忆效应检测的触发条件，向用户终端推送检测方案。
在一些实施例中，触发启动模块182还用于获取记忆效应检测的预计检测时长；将预计检测时长向用户终端推送。
在一些实施例中，触发启动模块182还用于根据待测电池的当前荷电状态参数和预设检测电流，获得记忆效应检测的预计检测时长。
上述记忆效应检测装置中的各个模块可全部或部分通过软件、硬件及其组合来实现。上述各模块可以硬件形式内嵌于或独立于计算机设备中的处理器中，也可以以软件形式存储于计算机设备中的存储器中，以便于处理器调用执行以上各个模块对应的操作。
上述记忆效应检测装置，在接收到检测确认信号之后，提取待测电池放电过程中的放电特性曲线，之后对预设相转电压区间内的放电特性曲线进行分析，实现待测电池是否发生记忆效应的检测。通过该方案，能够在电池发生记忆效应时及时检测得到，从而进行对应的处理。
在一个实施例中，提供了一种计算机设备，该计算机设备可以是终端，其内部结构图可以如图19所示。该计算机设备包括通过系统总线连接的处理器、存储器、通信 接口、显示屏和输入装置。其中，该计算机设备的处理器用于提供计算和控制能力。该计算机设备的存储器包括非易失性存储介质、内存储器。该非易失性存储介质存储有操作系统和计算机程序。该内存储器为非易失性存储介质中的操作系统和计算机程序的运行提供环境。该计算机设备的通信接口用于与外部的终端进行有线或无线方式的通信，无线方式可通过WIFI、移动蜂窝网络、NFC(近场通信)或其他技术实现。该计算机程序被处理器执行时以实现一种记忆效应检测方法。
本领域技术人员可以理解，图19中示出的结构，仅仅是与本申请方案相关的部分结构的框图，并不构成对本申请方案所应用于其上的计算机设备的限定，具体的计算机设备可以包括比图中所示更多或更少的部件，或者组合某些部件，或者具有不同的部件布置。
在一些实施例中，提供了一种计算机设备，包括存储器和处理器，存储器中存储有计算机程序，该处理器执行计算机程序时实现上述任意一项记忆效应检测方法的步骤。
在一些实施例中，提供了一种计算机可读存储介质，其上存储有计算机程序，计算机程序被处理器执行时实现上述任意一项记忆效应检测方法的步骤。
在一些实施例中，提供了一种计算机程序产品，包括计算机程序，该计算机程序被处理器执行时实现上述任意一项记忆效应检测方法的步骤。
本领域普通技术人员可以理解实现上述实施例方法中的全部或部分流程，是可以通过计算机程序来指令相关的硬件来完成，所述的计算机程序可存储于一非易失性计算机可读取存储介质中，该计算机程序在执行时，可包括如上述各方法的实施例的流程。其中，本申请所提供的各实施例中所使用的对存储器、数据库或其它介质的任何引用，均可包括非易失性和易失性存储器中的至少一种。非易失性存储器可包括只读存储器(Read-Only Memory，ROM)、磁带、软盘、闪存、光存储器、高密度嵌入式非易失性存储器、阻变存储器(ReRAM)、磁变存储器(Magnetoresistive Random Access Memory，MRAM)、铁电存储器(Ferroelectric Random Access Memory，FRAM)、相变存储器(Phase Change Memory，PCM)、石墨烯存储器等。易失性存储器可包括随机存取存储器(Random Access Memory，RAM)或外部高速缓冲存储器等。作为说明而非局限，RAM可以是多种形式，比如静态随机存取存储器(Static Random Access Memory，SRAM)或动态随机存取存储器(Dynamic Random Access Memory，DRAM)等。本申请所提供的各实施例中所涉及的数据库可包括关系型数据库和非关系型数据库中至少一种。非关系型数据库可包括基于区块链的分布式数据库等，不限于此。本申请所提供的各实施例中所涉及的处理器可为通用处理器、中央处理器、图形处理器、数字信号处理器、可编程逻辑器、基于量子计算的数据处理逻辑器等，不限于此。
上述计算机设备、存储介质和计算机程序产品，在接收到检测确认信号之后，提取待测电池放电过程中的放电特性曲线，之后对预设相转电压区间内的放电特性曲线进行分析，实现待测电池是否发生记忆效应的检测。通过该方案，能够在电池发生记忆效应时及时检测得到，从而进行对应的处理。
最后应说明的是：以上各实施例仅用以说明本申请的技术方案，而非对其限制；尽管参照前述各实施例对本申请进行了详细的说明，本领域的普通技术人员应当理解：其依然可以对前述各实施例所记载的技术方案进行修改，或者对其中部分或者全部技术特征进行等同替换；而这些修改或者替换，并不使相应技术方案的本质脱离本申请各实施例技术方案的范围，其均应涵盖在本申请的权利要求和说明书的范围当中。尤其是，只要不存在结构冲突，各个实施例中所提到的各项技术特征均可以任意方式组合起来。本申请并不局限于文中公开的特定实施例，而是包括落入权利要求的范围内的所有技术方案。

Claims (20)

  1. 一种记忆效应检测方法，包括：
    当接收检测启动信号，获取待测电池的放电特性曲线；
    根据所述放电特性曲线以及预设相转电压区间，确定所述待测电池是否发生记忆效应。
  2. 根据权利要求1所述的记忆效应检测方法，其中，所述获取待测电池的放电特性曲线，包括：
    对所述待测电池进行完全充电后进行完全放电，并在完全放电的过程中，获得所述待测电池的放电特性曲线。
  3. 根据权利要求2所述的记忆效应检测方法，其中，所述在完全放电的过程中，获得所述待测电池的放电特性曲线，包括：
    获取所述待测电池完全放电过程中的电压和电池容量；
    根据所述电压和所述电池容量，分析得到所述放电特性曲线。
  4. 根据权利要求3所述的记忆效应检测方法，其中，所述根据所述电压和所述电池容量，分析得到所述放电特性曲线，包括：
    根据所述电压和电池容量，获取所述电池容量随所述电压的下降量的容量变化量；
    基于所述容量变化量和所述电压，获得所述放电特性曲线。
  5. 根据权利要求3所述的记忆效应检测方法，其中，包括以下任意一项：
    第一项：所述电压包括负极放电电压，所述放电特性曲线包括负极放电特性曲线；
    第二项：所述电压包括全电池放电电压，所述放电特性曲线包括全电池放电特性曲线。
  6. 根据权利要求1所述的记忆效应检测方法，其中，所述根据所述放电特性曲线以及预设相转电压区间，确定所述待测电池是否发生记忆效应，包括：
    当所述放电特性曲线在所述预设相转电压区间内存在峰值，确定所述待测电池发生记忆效应。
  7. 根据权利要求6所述的记忆效应检测方法，其中，当所述放电特性曲线的最大值未处于所述预设相转电压区间的任意一个边界，且所述预设相转电压区间内的所述放电特性曲线的极差，大于预设倍数的所述预设相转电压区间内的所述放电特性曲线的标准差，确定所述放电特性曲线在所述预设相转电压区间内存在峰值。
  8. 根据权利要求1所述的记忆效应检测方法，其中，所述电压包括负极放电电压，所述预设相转电压区间包括预设负极相转电压区间，所述预设负极相转电压区间根据与所述待测电池的电池类型相同的电池的负极相转变电位范围确定。
  9. 根据权利要求1所述的记忆效应检测方法，其中，所述电压包括全电池放电电压，所述预设相转电压区间包括预设全电池相转电压区间，
    所述预设全电池相转电压区间的确定方式包括：
    根据与所述待测电池的电池类型相同的电池的预设负极相转电压区间，对所述电池进行参比电极测试，得到预设全电池相转电压区间。
  10. 根据权利要求1所述的记忆效应检测方法，其中，所述电压包括全电池放电电压，所述预设相转电压区间包括预设全电池相转电压区间，
    所述预设全电池相转电压区间的确定方式包括：
    获取与所述待测电池的电池类型相同的电池的负极硅含量参数；
    根据所述负极硅含量参数和预设的硅含量参数与全电池相转电压区间的关系，匹配得到预设全电池相转电压区间。
  11. 根据权利要求1-10任意一项所述的记忆效应检测方法，其中，所述方法还包括：
    当确定所述待测电池发生记忆效应，根据所述放电特性曲线在所述预设相转电压区间内的峰值，以及预设的峰值与记忆效应强度的关系，匹配得到当前记忆效应强度。
  12. 根据权利要求1-10任意一项所述的记忆效应检测方法，其中，所述当接收检测启动信号，获取待测电池的放电特性曲线之前，还包括：
    对所述待测电池进行记忆效应检测的触发分析，确定所述待测电池是否满足记忆效应检测的触发条件。
  13. 根据权利要求12所述的记忆效应检测方法，其中，所述方法还包括：
    当确定所述待测电池满足记忆效应检测的触发条件，向用户终端推送检测方案。
  14. 根据权利要求13所述的记忆效应检测方法，其中，所述当接收检测启动信号，获取待测电池的放电特性曲线之前，所述方法还包括：
    获取记忆效应检测的预计检测时长；
    将所述预计检测时长向用户终端推送。
  15. 根据权利要求14所述的记忆效应检测方法，其中，所述获取记忆效应检测的预计检测时长，包括：
    根据所述待测电池的当前荷电状态参数和预设检测电流，获得所述记忆效应检测的预计检测时长。
  16. 根据权利要求12所述的记忆效应检测方法，其中，所述待测电池满足记忆效应检测的触发条件，包括以下任意一项：
    第一项，所述待测电池的健康状态的下降速度大于或等于预设速度阈值；
    第二项，所述待测电池的健康状态的下降速度的增幅大于或等于预设速度增幅阈值；
    第三项，所述待测电池的健康状态小于当前时刻对应的预估健康状态；
    第四项，所述待测电池的运行时长大于或等于预设运行时长；
    第五项，接收到记忆效应检测指令。
  17. 一种记忆效应检测装置，包括：
    放电特性分析模块，用于当接收检测启动信号，获取待测电池的放电特性曲线；
    记忆效应检测模块，用于根据所述放电特性曲线以及预设相转电压区间，确定所述待测电池是否发生记忆效应。
  18. 一种计算机设备，包括存储器和处理器，所述存储器存储有计算机程序，所述处理器执行所述计算机程序时实现权利要求1至16中任一项所述记忆效应检测方法的步骤。
  19. 一种计算机可读存储介质，其上存储有计算机程序，所述计算机程序被处理器执行时实现权利要求1至16中任一项所述记忆效应检测方法的步骤。
  20. 一种计算机程序产品，包括计算机程序，该计算机程序被处理器执行时实现权利要求1至16中任一项所述记忆效应检测方法的步骤。
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Publication number Priority date Publication date Assignee Title
CN1332846A (zh) * 1998-09-15 2002-01-23 联合讯号公司 检测镍镉电池中记忆效应的装置和方法
US20020195999A1 (en) * 2001-06-20 2002-12-26 Matsushita Electric Industrial Co., Ltd. Method of detecting and resolving memory effect
CN105388422A (zh) * 2014-09-01 2016-03-09 横河电机株式会社 二次电池容量测量系统和二次电池容量测量方法

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1332846A (zh) * 1998-09-15 2002-01-23 联合讯号公司 检测镍镉电池中记忆效应的装置和方法
US20020195999A1 (en) * 2001-06-20 2002-12-26 Matsushita Electric Industrial Co., Ltd. Method of detecting and resolving memory effect
CN105388422A (zh) * 2014-09-01 2016-03-09 横河电机株式会社 二次电池容量测量系统和二次电池容量测量方法

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