A multi-condition leakage fault diagnosis method based on "electrical-thermal-gas" signal fusion

Abstract

This invention integrates electrical, thermal, and gas signal parameters that characterize battery electrolyte leakage faults to establish a fault characteristic parameter set. Based on the diagnostic time of each characteristic parameter, the effective range of each fault characteristic parameter is divided and a diagnostic priority is determined. Based on the priority of each characteristic parameter, the characteristic parameters are combined under multiple operating conditions to propose a multi-parameter fusion diagnostic method. This method can improve the reliability and timeliness of battery leakage fault diagnosis.

Description

Technical Field

[0001] The present invention relates to a method for diagnosing leakage faults of lithium-ion batteries, and in particular to a multi-operating-condition leakage fault diagnosis method based on "electricity-heat-gas" signal fusion. Background Art

[0002] With the rapid development of the electric vehicle industry in recent years, the safety performance of lithium-ion batteries has become a focus of industry attention. Generally speaking, the main causes of battery safety accidents are electrical abuse, thermal abuse, mechanical abuse, and defects in the manufacturing process. Electrolyte leakage, a typical failure of lithium-ion batteries, seriously undermines battery reliability and threatens the safe and stable operation of electric vehicles. Therefore, leakage fault diagnosis of lithium-ion batteries is essential.

[0003] During lithium-ion battery electrolyte leakage, not only does the electrolyte content decrease, but the electrolyte also continuously reacts with moisture in the air, causing increased self-discharge, damage to the negative electrode active material, and lithium deposition during charging. In severe cases, this can cause the battery system to catch fire or explode. Long-term static and charge-discharge tests on the batteries revealed that leaking batteries exhibit different performance from normal batteries in terms of voltage, current, temperature, and signals based on volatile organic compound gas monitoring.

[0004] The method for diagnosing lithium-ion battery leakage based on the electrical, thermal, and gas signal characteristics is unclear. Existing technology (Application No. 202010862452.6) that uses electrical signals for fault diagnosis takes a long time and carries the risk of misdiagnosis.

[0005] Therefore, it is necessary to diagnose leakage faults based on the differential characterization parameters of the leaking battery's electrical, thermal, and gas signals. Furthermore, to improve the timeliness and reliability of fault diagnosis results, it is necessary to integrate the parameters of these three signals and propose a multi-condition leakage fault diagnosis method based on the fusion of "electrical-thermal-gas" signals. Summary of the Invention

[0006] In order to solve the above technical problems, the present invention proposes a multi-condition leakage fault diagnosis method based on "electrical-thermal-gas" signal fusion, which includes the following steps:

[0007] Perform static and charge-discharge tests on normal batteries and leaking batteries;

[0008] Obtain the voltage and current signals of the battery during the constant current charging stage, the constant voltage charging stage, and the static stage after charging;

[0009] Obtain the battery's electrical characteristic parameters and perform leakage fault diagnosis based on electrical signals;

[0010] Acquire temperature signals during the constant current charging stage, constant voltage charging stage, and the rest stage after charging;

[0011] Obtain the thermal characteristic parameters of the battery and perform leakage fault diagnosis based on thermal signals;

[0012] Acquire volatile organic compound gas signals of the battery during a constant current charging stage, a constant voltage charging stage, and a rest stage after charging;

[0013] Obtain the gas characteristic parameters of the battery and perform leakage fault diagnosis based on the gas signal;

[0014] Integrate leakage fault parameters;

[0015] Divide the effective range of each fault characterization parameter and set the diagnosis priority;

[0016] Combine electrical characteristic parameters, thermal characteristic parameters, and gas characteristic parameters under multiple working conditions;

[0017] Perform battery leakage troubleshooting.

[0018] Based on the above solution, the steps of obtaining the battery's electrical characteristic parameters and performing leakage fault diagnosis based on electrical signals are as follows:

[0019] During the constant current charging stage, the peak characteristic parameters of the IC curves of normal batteries and leaking batteries are extracted, including but not limited to: peak position, peak height, peak area and peak width, and diagnosis is performed based on the evolution law with the number of cycles;

[0020] Extract characteristic parameters related to current during the constant voltage charging phase of the leaky battery, including but not limited to: the moment when the current starts to rise, the peak current reached after the rise, and the position and peak value of the characteristic peak of the differential curve;

[0021] Plot the static voltage and normalized differential curves of normal and leaky batteries;

[0022] Extract characteristic parameters related to the static voltage of the leaking battery, including but not limited to: characteristic peak positions and valley positions of the differential voltage curve.

[0023] Based on the above solution, the steps of obtaining the thermal characteristic parameters of the battery and performing leakage fault diagnosis based on the thermal signal are as follows:

[0024] Based on the temperature-voltage variation curve (TV) and its differential curve (DTV) of normal batteries and leaking batteries during the constant current charging stage during the cycle aging process, characteristic parameters representing battery failure are obtained, including but not limited to: the voltage corresponding to the maximum and minimum values ​​in the battery temperature variation curve, and the sign of the temperature differential value between the battery reversal point and the end of charging;

[0025] Combined with the characteristic parameters related to the static voltage of the leaking battery, leakage fault diagnosis is performed based on the mutation of the characteristic parameters.

[0026] Based on the above scheme, the gas characteristic parameters of the battery are obtained and leakage fault diagnosis based on the gas signal is performed as follows:

[0027] The volatile organic gas generated by battery electrolyte leakage is detected by a gas sensor and compared with the gas concentration changes of a normal battery to determine whether the battery electrolyte has leaked.

[0028] On the basis of the above scheme, under both sealed and temperature-controlled environmental conditions, the gas concentration change curves of normal batteries and leaking batteries over time are obtained when the batteries are in static conditions and charging and discharging conditions, and the electrolyte leakage fault is diagnosed based on the change in gas concentration.

[0029] Based on the above solution, the integrated leakage fault parameters are specifically:

[0030] Integrate the electrical signal parameter θ that characterizes battery electrolyte leakage failure E , thermal signal parameter θ T and the gas signal parameter θ G , establish the fault characteristic parameter set θ L :

[0031] θ L ={θ E ,θ T ,θ G} (1)

[0032] Based on the above solution, the effective range of each fault characterization parameter is divided and the diagnosis priority is determined as follows:

[0033] Based on the diagnosis time of each characteristic parameter, the effective action range of each fault characterization parameter is divided, and the diagnosis priority is determined. The earlier the diagnosis time, the higher the priority.

[0034] The order of priority from high to low is: characteristic peak I electrical signal of constant current charging section, characteristic peak II1 electrical signal of constant current charging section, thermal signal, characteristic peak II2 electrical signal of constant current charging section, electrical signal characteristic parameters of constant voltage charging section, and electrical signal characteristic parameters of the static stage after charging.

[0035] On the basis of the above scheme, the characteristic parameters are combined according to their priorities, and a multi-parameter fusion electrolyte leakage fault diagnosis method is proposed:

[0036] Diagnosis is performed based on changes in gas signal concentration. Once the gas concentration increases suddenly, it is determined that the battery electrolyte is leaking.

[0037] When there is no significant change in gas concentration, leakage diagnosis is performed through electrical and thermal signals. When the battery self-discharge is detected to be increasing, the corresponding fault characteristic parameters are determined to determine whether there is a sudden change based on the battery's operating conditions.

[0038] Under constant current charging conditions, if the characteristic parameters of the characteristic peak I electrical signal of the constant current charging section or the characteristic peak II1 electrical signal of the constant current charging section are present, the negative electrode active material of the battery is degraded, and it is necessary to further determine whether the characteristic parameters of the thermal signal or the characteristic peak II2 electrical signal have mutated. If so, the battery electrolyte is leaking.

[0039] During constant voltage charging or static conditions after charging, if the characteristic parameters of the electrical signal in the constant voltage charging section or the characteristic parameters of the electrical signal in the static stage after charging appear, the battery has an electrolyte leakage fault.

[0040] Beneficial effects of the present invention:

[0041] By integrating the electrical signal parameter θ that characterizes the battery electrolyte leakage fault E , thermal signal parameter θ T and the gas signal parameter θ G , establish the fault characteristic parameter set θ L .

[0042] Based on the diagnostic time of each characteristic parameter, the effective action range of each fault characterization parameter is divided and the diagnostic priority is determined. Based on the priority of each characteristic parameter, each characteristic parameter is combined under multiple working conditions and a multi-parameter fusion diagnosis method is proposed.

[0043] This method improves the reliability and timeliness of leakage fault diagnosis. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The present invention has the following accompanying drawings:

[0045] Figure 1 Flowchart of the multi-condition leakage fault diagnosis method based on the fusion of "electricity, heat and gas";

[0046] Figure 2 Electrical signal curves of normal battery and leaking battery;

[0047] Figure 3 Thermal signal curves of normal battery and leaking battery;

[0048] Figure 4 Gas signal curves of normal battery and leaking battery;

[0049] Figure 5 Multi-condition leakage fault diagnosis method based on multi-parameter fusion. DETAILED DESCRIPTION

[0050] To make the purpose, advantages and features of the present invention more apparent, the following Figure 1-5 The present invention is further described in detail with reference to the following specific embodiments.

[0051] Refer to the attached Figure 1 The present invention provides a multi-condition leakage fault diagnosis method based on "electricity-heat-gas" signal fusion, which can effectively identify whether a battery has a leakage fault.

[0052] The present invention includes leakage fault diagnosis based on electrical signals, performing constant current and constant voltage charging tests on the battery, and obtaining the voltage curve of the battery in the constant current charging stage, the current curve in the constant voltage charging stage, and the voltage curve in the static stage after charging. The obtained voltage and current curves are differentiated to obtain corresponding differential curves. The constant current charging voltage curves of a normal battery and a leaking battery are shown in Figure 2. Figure 2 As shown in (a) and (b). As the number of cycles increases, the voltage curve of the normal battery does not change significantly, only the height of peak I of the IC curve decreases. However, as the number of cycles increases, the voltage curve of the leaking battery begins to produce a new platform II2. In addition, in addition to the decrease in the height of the characteristic peak I of the IC curve, peak II1 gradually disappears and characteristic peak II2 is formed. The peak characteristics of the IC curve of the normal battery and the leaking battery in the constant current charging stage are extracted, including but not limited to peak position, peak height, peak area and peak width, and diagnosis is performed based on their evolution with the number of cycles. In addition to the constant current charging stage, constant voltage charging and the static stage after charging can also be used to detect lithium deposition. The constant voltage charging current curves of the normal battery and the leaking battery are shown as follows: Figure 2 (c) and (d) are shown. The leakage battery has a current characteristic peak in the constant voltage charging stage. The current differential curve is obtained according to the constant voltage charging current curve. The characteristic parameters related to the current in the constant voltage charging stage of the leakage battery are extracted, including but not limited to the time when the current starts to rise, the peak current reached after the rise, and the position and peak value of the characteristic peak of the differential curve, as characteristic parameters to characterize the lithium deposition of the battery; the static voltage curves of the normal battery and the leakage battery are drawn, as shown in Figure 2 In Figures (e) and (f), the static voltage of the leaking battery shows a plateau. The static voltage curve is differentiated, and characteristic parameters related to the static voltage of the leaking battery are extracted, including but not limited to the positions of the characteristic peaks and valleys of the differential voltage curve. Leakage fault diagnosis is performed based on phenomena such as sudden changes in characteristic parameters. Electrical signal-based diagnostic methods, which combine voltage and current characteristic parameters from both the static and charging processes, provide accurate and reliable diagnostic results, but the diagnostic time is longer.

[0053] The present invention includes leakage fault diagnosis based on thermal signals. Leakage fault diagnosis based on thermal signals mainly depends on the change of battery temperature. The differential curve of battery temperature is similar to the IC curve, which can characterize the phase change reaction and side reaction of the battery active material. Based on the constant current charging temperature change curve of the battery with voltage and its differential curve during continuous cycle aging, leakage fault diagnosis is performed. During the cycle aging process, the temperature change curve of normal batteries and leaking batteries during the constant current charging stage with voltage (such as Figure 3 As shown in the figure, as the number of cycles increases, the position of the maximum value in the temperature change curve of the leaking battery begins to shift compared to the normal battery, and the phenomenon of temperature rising again at the end of charging disappears. Characteristic parameters that can characterize battery failures are obtained, including but not limited to the voltages corresponding to the maximum and minimum values ​​in the battery temperature change curve, and the sign of the temperature differential between the battery reverse point and the end of charging. Leakage failure diagnosis is performed based on the characteristic parameters. However, since the weak self-discharge process of the battery is difficult to reflect in thermal characteristics, it is impossible to determine electrolyte leakage by relying on thermal signals alone, and it needs to be combined with diagnostic methods based on electrical signals.

[0054] The present invention includes leakage fault diagnosis based on gas signals. Different from the fault diagnosis principle based on electrical and thermal signals, the diagnosis based on gas signals does not require long-term standing or continuous charging and discharging to monitor battery self-discharge, degradation of negative electrode active materials and the degree of lithium deposition. Under normal circumstances, the battery does not produce volatile gases, but after the electrolyte leaks, it reacts with moisture in the air and eventually generates volatile gases. The fault diagnosis method based on gas signals detects the volatile organic gas (VOC) generated by the leakage of battery electrolyte through a gas sensor, compares it with the gas concentration change of a normal battery, and determines whether the battery electrolyte has leaked. Figure 4 As shown, the gas concentration of a normal battery remains unchanged over a period of time, while the gas concentration of a leaking battery increases significantly over the same period of time. Diagnostic methods based on gas signals are not affected by the battery's operating conditions and can quickly diagnose leakage in a closed environment. However, they may become ineffective over time or due to environmental conditions. Based on the fact that some characteristic parameters of a leaking battery in electrical, thermal, and gas signals will experience a sudden drop, while the remaining characteristic parameters will show a significantly different trend from that of a normal battery, we have developed leakage fault diagnosis methods based on electrical, thermal, and gas signals, respectively.

[0055] Since the diagnosis method based on a single signal has shortcomings in timeliness and reliability, in order to improve the timeliness and reliability of the fault diagnosis results, the electrical signal parameter θ that characterizes the battery electrolyte leakage fault is integrated. E , thermal signal parameter θ T and the gas signal parameter θ G , establish the fault characteristic parameter set θ L :

[0056] θ L ={θ E ,θ T ,θ G} (1)

[0057] As electrolyte leakage continues to develop, the effective range of each fault characterization parameter is divided according to the diagnostic time of each characteristic parameter, and a diagnostic priority is determined. Among all characteristic parameters, gas has the earliest diagnosis time and the highest priority. The second is the electrical signal during the long-term static stage, which can be diagnosed after a certain period of static state after the electrolyte leak. The remaining electrical and thermal characteristic parameters require a certain number of cycles before they can undergo mutations, and their diagnosis time is later, so they have a lower priority. In descending order of priority, they are: characteristic peak I electrical signal of the constant current charging stage, characteristic peak II1 electrical signal of the constant current charging stage, thermal signal, characteristic peak II2 electrical signal of the constant current charging stage, electrical signal characteristic parameters of the constant voltage charging stage, and electrical signal characteristic parameters of the static stage after charging.

[0058] In addition to determining the priority of each characteristic parameter, it is still necessary to determine the combination relationship of each characteristic parameter. It is often difficult to diagnose electrolyte leakage faults based on independent parameters of battery electrical and thermal signals. For example, the increase in battery self-discharge is not only caused by electrolyte leakage, but may also be caused by external short circuit or internal short circuit. Deterioration of battery negative electrode active materials or lithium deposition may be caused by low battery temperature, high rate charging, long cycle time, etc. Therefore, according to the priority of each characteristic parameter, each characteristic parameter is combined, and a multi-parameter fusion electrolyte leakage fault diagnosis method is proposed, such as Figure 5 shown.

[0059] First, diagnosis is performed based on changes in gas signal concentration. A sudden increase in gas concentration indicates a battery electrolyte leak. Second, if there is no significant change in gas concentration, leakage diagnosis is performed using electrical and thermal signals. When the battery self-discharge is detected, the corresponding fault characteristic parameters are determined to determine whether a sudden change has occurred, depending on the battery's operating conditions. Under constant current charging conditions, if the electrical signal characteristic parameters of Peak I or Peak II1 are present during the constant current charging phase, the battery's negative electrode active material is degraded. Further determination is needed to determine whether a sudden change has occurred in the thermal signal characteristic parameters or Peak II2. If so, the battery electrolyte leaks. Similarly, under constant voltage charging or post-charging static conditions, if the electrical signal characteristic parameters of the constant voltage charging phase or the post-charging static phase are present, the battery electrolyte leaks. This leakage fault diagnosis method, based on the fusion of electrical, thermal, and gas parameters, makes diagnosis based on the priority and combination of each characteristic parameter, making it more timely and accurate, while also improving the reliability of electrolyte leakage diagnosis.

[0060] The above embodiments are only used to illustrate the present invention, and are not intended to limit the present invention. Ordinary technicians in the relevant technical field may make various changes and modifications without departing from the essence and scope of the present invention. Therefore, all equivalent technical solutions also fall within the scope of the present invention. The scope of patent protection of the present invention should be defined by the claims.

[0061] The contents not described in detail in this specification belong to the prior art known to those skilled in the art.

Claims

1. A multi-condition leakage fault diagnosis method based on "electrical-thermal-gas" signal fusion, characterized in that: The steps include: Perform static and charge-discharge tests on normal batteries and leaking batteries; Obtain the voltage and current signals of the battery during the constant current charging stage, the constant voltage charging stage, and the static stage after charging; Obtain the battery's electrical characteristic parameters and perform leakage fault diagnosis based on electrical signals; Acquire temperature signals during the constant current charging stage, constant voltage charging stage, and the rest stage after charging; Obtain the thermal characteristic parameters of the battery and perform leakage fault diagnosis based on thermal signals; Acquire volatile organic compound gas signals of the battery during a constant current charging stage, a constant voltage charging stage, and a rest stage after charging; Obtain the gas characteristic parameters of the battery and perform leakage fault diagnosis based on the gas signal; Integrate electrical signal parameters to characterize battery electrolyte leakage failure , thermal signal parameters and gas signal parameters , establish fault characteristic parameter set : ； Based on the diagnosis time of each characteristic parameter, the effective action range of each fault characterization parameter is divided, and the diagnosis priority is determined. The earlier the diagnosis time, the higher the priority. The order of priority from high to low is: Characteristic peak of constant current charging section Characteristic peaks of electrical signals and constant current charging segments Characteristic peaks of electrical signals, thermal signals, and constant current charging segments Electrical signal, characteristic parameters of the electrical signal during the constant voltage charging phase, and characteristic parameters of the electrical signal during the rest phase after charging; A multi-parameter fusion method for electrolyte leakage fault diagnosis is proposed: diagnosis is based on the concentration change of gas signals. Once the gas concentration increases suddenly, it is determined that the battery electrolyte has leaked. When there is no significant change in gas concentration, leakage diagnosis is performed through electrical and thermal signals. When the battery self-discharge is detected to be increasing, the corresponding fault characteristic parameters are determined to determine whether there is a sudden change based on the battery's operating conditions. Under constant current charging conditions, if the characteristic peak I electrical signal characteristic parameters of the constant current charging segment or the characteristic peak II1 electrical signal characteristic parameters of the constant current charging segment, that is, the characteristic peak I of the constant current charging segment in the IC curve decreases or the characteristic peak II1 of the constant current charging segment disappears, then the battery negative active material is degraded, and it is necessary to further determine whether the thermal signal characteristic parameters or the characteristic peak II2 electrical signal characteristic parameters mutate. If mutated, that is, the characteristic peak II2 is formed in the IC curve, the characteristic peak II2 is in the battery electrolyte leakage; the characteristic peak I of the constant current charging segment is between 3.6 and 3.8 volts, the characteristic peak II1 of the constant current charging segment is between 3.5 and 3.6 volts, and the characteristic peak II2 is between 3.9 and 4.1 volts after 100 cycles; During constant voltage charging or static conditions after charging, if the characteristic parameters of the electrical signal in the constant voltage charging section or the characteristic parameters of the electrical signal in the static stage after charging appear, the battery has an electrolyte leakage fault.

2. The multi-condition leakage fault diagnosis method based on "electricity-heat-gas" signal fusion according to claim 1 is characterized in that: The steps of obtaining the battery's electrical characteristic parameters and performing leakage fault diagnosis based on electrical signals are as follows: During the constant current charging stage, the peak characteristic parameters of the IC curves of normal batteries and leaking batteries are extracted and diagnosed based on the evolution law with the number of cycles; Extract the characteristic parameters related to current during the constant voltage charging phase of leaky batteries; Plot the static voltage and normalized differential curves of normal and leaky batteries; Extract characteristic parameters related to the static voltage of leaky batteries.

3. The multi-condition leakage fault diagnosis method based on "electricity-heat-gas" signal fusion according to claim 2 is characterized in that: The method of obtaining the thermal characteristic parameters of the battery and performing leakage fault diagnosis based on the thermal signal is specifically as follows: According to the temperature-voltage variation curve (TV) and its differential curve (DTV) of normal batteries and leaking batteries during the constant current charging stage during the cycle aging process, characteristic parameters representing battery failure are obtained; Combined with the characteristic parameters related to the static voltage of the leaking battery, leakage fault diagnosis is performed based on the mutation of the characteristic parameters.

4. The multi-condition leakage fault diagnosis method based on "electricity-heat-gas" signal fusion according to claim 1 is characterized in that: The gas characteristic parameters of the battery are obtained and leakage fault diagnosis based on the gas signal is performed as follows: The volatile organic gas generated by battery electrolyte leakage is detected by a gas sensor and compared with the gas concentration changes of a normal battery to determine whether the battery electrolyte has leaked.

5. The multi-condition leakage fault diagnosis method based on "electricity-heat-gas" signal fusion according to claim 4 is characterized in that: Under both sealed and temperature-controlled environmental conditions, obtain the gas concentration change curves of normal batteries and leaking batteries over time when the battery is in static conditions and charging and discharging conditions, and diagnose electrolyte leakage faults based on the changes in gas concentration.

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