Automobile remote fault diagnosis system and method

Abstract

The invention discloses an automobile remote fault diagnosis system and method, and relates to the technical field of automobile detection. The method comprises the following steps: acquiring operation data of a target vehicle in a preset time period, wherein the operation data is acquired in a preset period; searching an electric quantity-open-circuit voltage mapping table according to the residual electric quantity to determine a threshold value at the current moment, and determining a fault type of the battery; if it is determined that the fault type is a single-lattice short-circuit fault, calculating a pseudo open-circuit voltage signal of the target battery unit by using the sum of the target battery unit, obtaining a differential pseudo open-circuit voltage signal, determining a normal range according to the differential pseudo open-circuit voltage signal, and positioning a short-circuit fault time period; calculating to obtain a short-circuit current signal and a short-circuit resistance signal, and comparing the short-circuit resistance with a historical short-circuit resistance threshold to diagnose the short-circuit fault degree of the battery; the fault diagnosis precision and efficiency are improved by periodically collecting the vehicle operation data, diagnosing the battery fault type, positioning the single-cell short-circuit fault time and evaluating the short-circuit degree.

Description

Technical Field

[0001] The present invention belongs to the technical field of automobile detection, and in particular relates to an automobile remote fault diagnosis system and method. Background Art

[0002] With the increasing development of new energy electric vehicles, electric vehicles have gained more and more support and attention due to their green and environmentally friendly nature. As the core power of electric vehicles, the performance of batteries is directly related to the vehicle's range, safety and service life.

[0003] The prior art (publication number: CN119986409A) discloses a battery micro-short circuit fault diagnosis method and system, which includes: obtaining real-time voltage data of each battery in a battery module, denoising to obtain a voltage matrix; calculating a dynamic reference voltage sequence of each battery in the voltage matrix within the sliding window length, and extracting the eigenvalues ​​of each battery at different times; calculating the correlation coefficient between the voltage of each battery in the sliding window at different times and the reference voltage; forming a feature point matrix of the battery module based on the eigenvalues ​​and the correlation coefficients; calculating a dynamic reference feature point sequence based on the feature point matrix, and calculating the improved Fréchet distance between the feature point sequence of each battery and the dynamic reference feature point sequence, and using the distance value as the abnormality score of each battery; and then judging whether each battery has a short circuit fault.

[0004] However, in actual applications, relying solely on collecting battery voltage data to diagnose faults has obvious limitations. The single voltage data cannot fully reflect the internal state of the battery, which can easily lead to reduced diagnostic accuracy. At the same time, after the fault is diagnosed, the lack of other data support makes it difficult to determine the specific time when the short circuit fault occurred, and the fault level cannot be diagnosed, making it impossible to take preventive measures in time, which may cause personal or economic losses. Summary of the Invention

[0005] The purpose of the present invention is to solve the problem and to provide a vehicle remote fault diagnosis system and method.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] In a first aspect of the present invention, a method for remote fault diagnosis of an automobile is first proposed, the method comprising:

[0008] Acquire the operating data of the target vehicle within a preset time period at a preset cycle; the operating data includes the current open circuit voltage signal V0(t) and the remaining power of the battery pack, and the terminal voltage signal V of each battery cell in the battery pack. m (t) and current signal I(t);

[0009] The current threshold φ is determined based on the remaining power by searching the power-open-circuit voltage mapping table. The battery fault type is determined based on the relationship between V0(t) and the threshold φ. The power-open-circuit voltage mapping table records the corresponding open-circuit voltages of the battery at different power levels under normal conditions. Fault types include single-cell short-circuit faults and multi-cell short-circuit faults.

[0010] If the fault type is determined to be a single cell short circuit fault, use the V m (t) and I(t) to calculate the pseudo open circuit voltage signal OV of the target battery cell p (t), according to the OV p (t) Get the differential pseudo open circuit voltage signal ΔOV p (t), and according to ΔOV p (t) Determine the normal range and locate the V m (t) a short circuit fault time period; the target battery cell is any one of the battery cells in the battery pack;

[0011] According to ΔOV p (t) Calculate the short-circuit current signal I sc (t) and short-circuit resistance signal R sc (t), according to R sc (t) Compare with the historical short-circuit resistance threshold to diagnose the degree of battery short-circuit fault.

[0012] Optionally, the operation data of the target vehicle within a preset time period is acquired at a preset cycle; the operation data includes the current open circuit voltage signal V0(t) and the remaining power of the battery pack, and the operation data also includes the terminal voltage signal V of each battery cell in the battery pack. m (t) and current signal I(t).

[0013] Optionally, the current threshold φ is determined by searching the power-open-circuit voltage mapping table based on the remaining power, and the battery fault type is determined based on the relationship between V0(t) and the threshold φ; the power-open-circuit voltage mapping table records the corresponding open-circuit voltages of the battery at different power levels under normal circumstances; the fault types include single-cell short-circuit faults and multi-cell short-circuit faults.

[0014] Optionally, if the fault type is determined to be a single cell short circuit fault, the V m (t) and I(t) to calculate the pseudo open circuit voltage signal OV of the target battery cell p (t), the specific formula is:

[0015] OV p (t) = V m (t)+R0(SC(t))·I(t)

[0016] Among them, Vm (t) is the battery terminal voltage measured at sampling time t, I(t) is the battery current measured at sampling time t, R0(SC(t)) is the resistance corresponding to the charge state at time t, obtained from the relationship table;

[0017] According to the OV p (t) Get the differential pseudo open circuit voltage signal ΔOV p (t), the formula is:

[0018] ΔOV p (t)=0V p (t)-OV p (t-1)

[0019] According to the differential pseudo open circuit voltage ΔOV p (t) Calculate the short-circuit fault threshold ξ and compare ξ with V m (t) is compared to perform short-circuit fault diagnosis and short-circuit fault time location. In order to enhance the robustness to large current pulses that may cause non-fault voltage transients, a scaling factor γ greater than 1 is introduced to relax the threshold ξ. The specific process is:

[0020] ξ - =γ·F(ΔOV p ,α)

[0021] ξ + =γ·F(ΔOV p ,1-α)

[0022] Among them, ξ - is the lower threshold, ξ + is the upper threshold, α represents the probability of abnormal fluctuation allowed under normal conditions, and the α interval is [0,1];

[0023] If V m (t)<ξ - , it is diagnosed as the start of a short-circuit fault; the sampling time t at this time is the fault start time;

[0024] If V m (t ′ )>ξ + , the diagnosis is that the short circuit fault ends; the sampling time at this time is t ′ This is the fault end time;

[0025] The fault time is located based on the fault start time and fault end time.

[0026] Optionally, according to ΔOV p (t) Calculate the short-circuit current signal I sc (t) and short-circuit resistance signal R sc(t), the specific process is:

[0027] Short-circuit fault causes short-circuit current I sc (t) Leakage, this current flows through the resistor R0, and the corresponding voltage drop appears as a residual value ΔOV p (t) in:

[0028] ΔOV p (t)=R0·I sc (t)

[0029] So the short-circuit current is calculated as:

[0030]

[0031] According to R sc (t) is compared with the historical short-circuit resistance threshold to diagnose the degree of battery short-circuit fault:

[0032] Considering that the short-circuit branch is connected in parallel with the battery terminal, the terminal voltage V at the time of the fault is m (t) reflects the voltage on the short-circuit path. The short-circuit resistance calculation process is:

[0033]

[0034] R sc Compared with the resistance threshold, the resistance threshold is obtained by historical experimental statistics. The resistance threshold includes two resistance thresholds, namely the first resistance threshold and the second resistance threshold. The first resistance threshold is greater than the second resistance threshold. If R sc < the second resistance threshold, the short circuit fault level is high risk; if R sc > the first resistance threshold, the short circuit fault level is low risk; if the second resistance threshold ≤ R sc < the first resistance threshold, the short-circuit fault level is medium risk.

[0035] In a second aspect of the present invention, a vehicle remote fault diagnosis system is provided, comprising:

[0036] The data acquisition module is used to collect the operating data of the target vehicle within a preset time period at a preset cycle; the operating data includes the current open circuit voltage signal V0(t) and the remaining power of the battery pack, and the terminal voltage signal V of each battery cell in the battery pack. m (t) and current signal I(t);

[0037] The first diagnostic module is configured to determine a current threshold value φ based on the remaining power by searching a power-to-open-circuit voltage mapping table, and determine a battery fault type based on a relationship between V0(t) and the threshold value φ. The power-to-open-circuit voltage mapping table records the corresponding open-circuit voltages of the battery at different power levels under normal conditions. Fault types include single-cell short-circuit faults and multi-cell short-circuit faults.

[0038] Short circuit fault diagnosis module, used to use the V m (t) and I(t) to calculate the pseudo open circuit voltage signal OV of the target battery cell p (t), according to the OV p (t) Get the differential pseudo open circuit voltage signal ΔOV p (t), and according to ΔOV p (t) Determine the normal range and locate the V m (t) a short circuit fault time period; the target battery cell is any one of the battery cells in the battery pack;

[0039] Short circuit fault degree assessment module, based on ΔOV p (t) Calculate the short-circuit current signal I sc (t) and short-circuit resistance signal R sc (t), according to R sc (t) Compare with the historical short-circuit resistance threshold to diagnose the degree of battery short-circuit fault.

[0040] Optionally, the data acquisition module has the following specific functions:

[0041] It is used to collect the operating data of the target vehicle within a preset time period at a preset cycle; the operating data includes the current open circuit voltage signal V0(t) and the remaining power of the battery pack, and the terminal voltage signal V of each battery cell in the battery pack. m (t) and current signal I(t).

[0042] Optionally, the first diagnostic module includes a fault type determination module:

[0043] The fault type determination module is configured to determine that a single-cell short-circuit fault exists in the battery if the open-circuit voltage at each moment of V0(t) is less than a threshold value φ and not less than a failure threshold value; and to determine that a multi-cell short-circuit fault exists in the battery if the open-circuit voltage at each moment of V0(t) is less than the failure threshold value; the failure threshold value is the lowest voltage at which the battery can operate normally, and the failure threshold value is less than the threshold value φ.

[0044] Optionally, the short circuit fault diagnosis module has the following specific functions:

[0045] If the fault type is determined to be a single cell short circuit fault, the V m (t) and I(t) to calculate the pseudo open circuit voltage signal OV of the target battery cell p (t) The process is:

[0046] OV p (t) = V m (t)+R0(SC(t))·I(t)

[0047] Among them, V m (t) is the battery terminal voltage measured at sampling time t, I(t) is the battery current measured at sampling time t, and R0(SC(t)) is the resistance corresponding to the charge state at time t, which is obtained from the relationship table;

[0048] According to the OV p (t) Get the differential pseudo open circuit voltage signal ΔOV p (t), the process is;

[0049] ΔOV p (t)=0V p (t)-OV p (t-1)

[0050] According to the differential pseudo open circuit voltage ΔOV p (t) Calculate the short-circuit fault threshold ξ and compare ξ with V m (t) is compared to perform short-circuit fault diagnosis and short-circuit fault time location. In order to enhance the robustness to large current pulses that may cause non-fault voltage transients, a scaling factor γ greater than 1 is introduced to relax the threshold ξ. The specific process is:

[0051] ξ - =γ·F(ΔOV p ,α)

[0052] ξ + =γ·F(ΔOV p ,1-α)

[0053] Among them, ξ - is the lower threshold, ξ + is the upper threshold, α represents the probability of abnormal fluctuation allowed under normal conditions, and the α interval is [0,1];

[0054] If V m (t)<ξ - , it is diagnosed as the start of a short-circuit fault; the sampling time t at this time is the fault start time;

[0055] If V m (t ′ )>ξ +, the diagnosis is that the short circuit fault ends; the sampling time at this time is t ′ This is the fault end time;

[0056] The fault time can be located based on the fault start time and fault end time.

[0057] Optionally, the short-circuit fault degree assessment module has the following specific functions:

[0058] According to ΔOV p (t) Calculate the short-circuit current I sc (t) and short-circuit resistance R sc (t), according to R sc (t) is compared with the historical short-circuit resistance threshold to diagnose the degree of battery short-circuit fault. The specific method is as follows:

[0059] Short-circuit fault causes short-circuit current I sc (t) Leakage, this current flows through the resistor R0, and the corresponding voltage drop appears as a residual value ΔOV p (t) in:

[0060] ΔOV p (t)=R0·I sc (t)

[0061] So the short-circuit current is calculated as:

[0062]

[0063] Considering that the short-circuit branch is connected in parallel with the battery terminal, the terminal voltage V at the time of the fault is m (t) reflects the voltage on the short-circuit path. The short-circuit resistance calculation process is:

[0064]

[0065] R sc Compared with the resistance threshold, the resistance threshold is obtained by historical experimental statistics. The resistance threshold includes two resistance thresholds, namely the first resistance threshold and the second resistance threshold. The first resistance threshold is greater than the second resistance threshold. If R sc < the second resistance threshold, the short circuit fault level is high risk; if R sc > the first resistance threshold, the short circuit fault level is low risk; if the second resistance threshold ≤ R sc < the first resistance threshold, the short-circuit fault level is medium risk.

[0066] Beneficial effects of the present invention:

[0067] The present invention proposes a method for remote fault diagnosis of automobiles, which obtains the operating data of the target vehicle within a preset time period at a preset cycle; the operating data includes the current open circuit voltage signal V0(t) and the remaining power of the battery pack, and the terminal voltage signal V of each battery cell in the battery pack. m (t) and current signal I(t); according to the remaining power, the power-open circuit voltage mapping table is searched to determine the current threshold φ, and the battery fault type is determined according to the relationship between V0(t) and the threshold φ; the power-open circuit voltage mapping table records the corresponding open circuit voltage of the battery at different power levels under normal circumstances; the fault type includes single-cell short circuit fault and multi-cell short circuit fault; if the fault type is determined to be a single-cell short circuit fault, the V m (t) and I(t) to calculate the pseudo open circuit voltage signal OV of the target battery cell p (t), according to OV p (t) Get the differential pseudo open circuit voltage signal ΔOV p (t), and according to ΔOV p (t) Determine the normal range and position V m (t) short circuit fault time period; the target battery cell is any one of the battery cells in the battery pack; according to ΔOV p (t) Calculate the short-circuit current signal I sc (t) and short-circuit resistance signal R sc (t), according to R sc (t) Compare with the historical short-circuit resistance threshold to diagnose the degree of battery short-circuit fault.

[0068] The present invention proposes a method for remote automobile fault diagnosis. By periodically collecting vehicle operation data, the method accurately determines the type of battery fault based on a battery capacity-open-circuit voltage mapping table. It can also further locate the time of a single-cell short-circuit fault and assess the degree of the short circuit, effectively improving the accuracy and efficiency of fault diagnosis. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] The present invention will be further described below with reference to the accompanying drawings.

[0070] Figure 1 A flowchart of a method for remote fault diagnosis of an automobile provided by an embodiment of the present invention;

[0071] Figure 2 A block diagram of an automobile remote fault diagnosis system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0072] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, but not all the embodiments of the present application. The term "and / or" in this document is only used to describe the association relationship of associated objects, and can represent three relationships, for example, A and B can represent three cases of existence of A alone, existence of A and B simultaneously, and existence of B alone. In addition, the description of "first", "second" and the like in the present application is only for the purpose of description, and cannot be understood as indicating or implying the relative importance of the indicated technical features or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first", "second" can explicitly or implicitly include at least one of the features. In addition, the technical solutions of each embodiment can be combined with each other, but it must be based on the fact that a person skilled in the art can realize it. When the combination of technical solutions appears contradictory or unachievable, it should be considered that the combination of technical solutions does not exist, and is not within the protection scope of the present application.

[0073] Based on the embodiments in the present application, all other embodiments obtained by a person skilled in the art without creative labor are within the protection scope of the present application.

[0074] The embodiment of the present application provides a vehicle remote fault diagnosis method. Referring to Figure 1 , Figure 1 The embodiment of the present application provides a flow chart of a vehicle remote fault diagnosis method. The method comprises the following steps:

[0075] S101, acquiring running data of a target vehicle in a preset time period collected in a preset period;

[0076] S102, determining a threshold value φ of the current time according to the residual capacity and searching the capacity-open circuit voltage mapping table, and determining the fault type of the battery according to the relationship between the current open circuit voltage V0(·) and the threshold value φ;

[0077] S103, if the determined fault type is a single cell short circuit fault, calculating the pseudo open circuit voltage signal OV m (t) of the target battery unit using V p (t) and I(t), obtaining the differential pseudo open circuit voltage signal ΔOV p (t) according to OV p (t), and determining the normal range according to ΔOV p (t) and positioning the V m (t) short circuit fault time period;

[0078] S104, calculating the short circuit current signal I p (t) according to ΔOV sc(t) and short-circuit resistance signal R sc (t), according to R sc (t) Compare with the historical short-circuit resistance threshold to diagnose the degree of battery short-circuit fault.

[0079] A remote automobile fault diagnosis method provided by an embodiment of the present invention periodically collects vehicle operating data, accurately determines the type of battery fault based on a battery capacity-open-circuit voltage mapping table, and can further locate the time of a single-cell short-circuit fault and assess the degree of the short circuit, effectively improving the accuracy and efficiency of fault diagnosis.

[0080] In one implementation, the threshold φ is the result obtained from experimental data statistics, and the power-open-circuit voltage mapping table records the open-circuit voltage corresponding to the battery at different power levels under normal circumstances; the fault types include single-cell short-circuit faults and multi-cell short-circuit faults; the target battery cell is any one of the battery cells in the battery pack; wherein, the power-open-circuit voltage mapping table is obtained by experimenters based on historical data statistics and input into the battery management system database in advance.

[0081] In one embodiment, the operation data of the target vehicle within a preset time period is acquired at a preset cycle; the operation data includes the current open circuit voltage signal t0(t) and the remaining power of the battery pack, and the operation data also includes the terminal voltage signal V of each battery cell in the battery pack. m (t) and current signal I(t);

[0082] In one implementation, the target vehicle's operating data within a preset time period is obtained, including the current open circuit voltage signal of the battery pack, the remaining power, and the terminal voltage signal V of each battery cell. m (t) and current signal I(t), which can reflect the real-time working status of the battery pack and each battery cell, and provide a data basis for subsequent battery performance evaluation and fault diagnosis; based on the collected detailed operating data, the status of the battery pack and each battery cell can be analyzed, for example, by monitoring the terminal voltage signal V of each battery cell m The dynamic changes of the current signal I(t) and the current signal I(t) can detect battery cell problems in a timely manner.

[0083] In one embodiment, a battery capacity-open circuit voltage mapping table is searched based on the remaining power to determine the current threshold φ, and the battery fault type is determined based on the relationship between V0(t) and the threshold φ. The battery capacity-open circuit voltage mapping table records the corresponding open circuit voltages of the battery at different power levels under normal circumstances. Fault types include single-cell short circuit faults and multi-cell short circuit faults.

[0084] In one implementation, a threshold value φ is determined by looking up a power-to-open-circuit voltage mapping table and comparing it with the current open-circuit voltage signal of the battery pack to diagnose whether the battery is in a faulty state. This method can distinguish between normal and faulty states, improving the accuracy of fault diagnosis. Based on the relationship with the threshold value φ, the specific fault type of the battery can be quickly determined, whether it is a single-cell short-circuit fault or a multi-cell short-circuit fault.

[0085] In one embodiment, if the fault type is determined to be a single cell short circuit fault, the V m (t) and I(t) to calculate the pseudo open circuit voltage signal OV of the target battery cell p (t), the specific formula is:

[0086] OV p (t) = V m (t)+R0(SC(t))·I(t) (1)

[0087] Among them, V m (t) is the battery terminal voltage measured at sampling time t, I(t) is the battery current measured at sampling time t, and R0(SC(t)) is the resistance corresponding to the charge state at time t, which is obtained from the relationship table;

[0088] According to OV p (t) Get the differential pseudo open circuit voltage signal ΔOV p (t), the formula is:

[0089] ΔOV p (t)=0V p (t)-OV p (t-1) (2)

[0090] According to the differential pseudo open circuit voltage ΔOV p (t) Calculate the short-circuit fault threshold ξ and compare ξ with V m (t) is compared to perform short-circuit fault diagnosis and short-circuit fault time location. In order to enhance the robustness to large current pulses that may cause non-fault voltage transients, a scaling factor γ greater than 1 is introduced to relax the threshold ξ. The specific process is:

[0091] ξ - =γ·F(ΔOV p ,α) (3)

[0092] ξ + =γ·F(ΔOV p ,1-α) (4)

[0093] Among them, ξ - is the lower threshold, ξ +is the upper threshold, α represents the probability of abnormal fluctuation allowed under normal conditions, and the α interval is [0,1];

[0094] If V m (t)<ξ - , it is diagnosed as the start of a short-circuit fault; the sampling time t at this time is the fault start time;

[0095] If V m (t ′ )>ξ + , the diagnosis is that the short circuit fault ends; the sampling time at this time is t ′ This is the fault end time;

[0096] The fault time is located based on the fault start time and fault end time.

[0097] In one implementation, the relationship table is obtained by the experimenter based on historical data statistics and input into the battery management system database in advance. For example, at a certain time t, when the remaining battery power is 50%, the relationship table query obtains R0 = 0.001Ω, and the terminal voltage V m (t) = 3.5V, current I(t) = 10A, α = 0.005, substitute into formula (1) to get the pseudo open circuit voltage OV p (t), and then use formula (1) to get the pseudo open circuit voltage OV at time t-1 p (t-1), the differential pseudo open circuit voltage ΔOV is obtained by formula (2) p (t), according to formula (3) and formula (4), the short-circuit fault lower threshold ξ is obtained - and upper threshold ξ + , the terminal voltage V m (t) and the lower threshold ξ - and upper threshold ξ + By comparison, the fault time can be diagnosed.

[0098] In one embodiment, according to ΔOV p (t) Calculate the short-circuit current signal I sc (t) and short-circuit resistance signal R sc (t), the specific process is:

[0099] Short-circuit fault causes short-circuit current I sc (t) Leakage, this current flows through the resistor R0, and the corresponding voltage drop appears as a residual value ΔOV p (t) in:

[0100] ΔOV p (t)=R0·I sc (t) (5)

[0101] So the short-circuit current is calculated as:

[0102]

[0103] According to R sc (t) is compared with the historical short-circuit resistance threshold to diagnose the degree of battery short-circuit fault:

[0104] Considering that the short-circuit branch is connected in parallel with the battery terminal, the terminal voltage V at the time of the fault is m (t) reflects the voltage on the short-circuit path. The short-circuit resistance calculation process is:

[0105]

[0106] R sc Compared with the resistance threshold, the resistance threshold is obtained by historical experimental statistics. The resistance threshold includes two resistance thresholds, namely the first resistance threshold and the second resistance threshold. The first resistance threshold is greater than the second resistance threshold. If R sc < the second resistance threshold, the short circuit fault level is high risk; if R sc > the first resistance threshold, the short circuit fault level is low risk; if the second resistance threshold ≤ R sc < the first resistance threshold, the short-circuit fault level is medium risk.

[0107] In one implementation, battery short-circuit faults can be detected in a timely manner by accurately calculating short-circuit current and resistance signals. When a short circuit occurs, the voltage drop caused by the leakage current appears as a residual value. The short-circuit current and resistance are calculated using a formula to provide key data support for fault diagnosis. The calculated short-circuit resistance is compared with the historical short-circuit resistance threshold to effectively diagnose the degree of battery short-circuit fault. The short-circuit path voltage reflected by the terminal voltage when the short-circuit branch is connected in parallel with the battery terminal is analyzed to achieve accurate calculation of the short-circuit resistance and improve diagnostic accuracy. In actual detection scenarios, the first resistance threshold is 20mΩ and the second resistance threshold is 5mΩ; when a battery short-circuit fault occurs, R sc will be less than 50mΩ, when R sc The temperature rise rate between 20mΩ and 50mΩ is 0.5℃-2℃ / min, which will cause thermal runaway and prompt maintenance warning. sc <5mΩ, the temperature rise rate is higher than 5℃ / min, there may be a risk of explosion, and a danger warning is issued; when 5mΩ≤R sc When the resistance is <20mΩ, the temperature rises by 2℃-5℃ / min, and a fire may occur within 10 minutes, and an early warning is issued.

[0108] Based on the same inventive concept, the present invention also provides a remote fault diagnosis system for automobiles. Figure 2 , Figure 2A block diagram of an automobile remote fault diagnosis system provided by an embodiment of the present invention includes:

[0109] The data acquisition module is used to collect the operating data of the target vehicle within a preset time period at a preset cycle; the operating data includes the current open circuit voltage signal V0(t) and the remaining power of the battery pack, and the terminal voltage signal V of each battery cell in the battery pack. m (t) and current signal I(t);

[0110] The first diagnostic module is used to determine the current threshold φ based on the remaining power by searching the power-open-circuit voltage mapping table. The fault type of the battery is determined based on the relationship between V0(t) and the threshold φ. The power-open-circuit voltage mapping table records the corresponding open-circuit voltages of the battery at different power levels under normal conditions. Fault types include single-cell short-circuit faults and multi-cell short-circuit faults.

[0111] Short circuit fault diagnosis module, used to use the V m (t) and I(t) to calculate the pseudo open circuit voltage signal OV of the target battery cell p (t), according to OV p (t) Get the differential pseudo open circuit voltage signal ΔOV p (t), and according to ΔOV p (t) Determine the normal range and position V m (t) short circuit fault time period; the target battery cell is any one of the battery cells in the battery pack;

[0112] Short circuit fault degree assessment module, based on ΔOV p (t) Calculate the short-circuit current signal I sc (t) and short-circuit resistance signal R sc (t), according to R sc (t) Compare with the historical short-circuit resistance threshold to diagnose the degree of battery short-circuit fault.

[0113] A system provided by an embodiment of the present invention periodically collects vehicle operation data, accurately determines the type of battery fault based on a power-open-circuit voltage mapping table, and can further locate the time of a single-cell short-circuit fault and evaluate the degree of the short circuit, effectively improving the accuracy and efficiency of fault diagnosis.

[0114] In one embodiment, the data acquisition module has the following specific functions:

[0115] It is used to collect the operating data of the target vehicle within a preset time period at a preset cycle; the operating data includes the current open circuit voltage signal V0(t) and the remaining power of the battery pack, and the terminal voltage signal V of each battery cell in the battery pack. m(t) and current signal I(t).

[0116] In one embodiment, the first diagnostic module has the following specific functions:

[0117] Used to look up the battery capacity-open circuit voltage mapping table based on the remaining power to determine the current threshold φ, and determine the battery fault type based on the relationship between V0(t) and the threshold φ. The battery capacity-open circuit voltage mapping table records the corresponding open circuit voltages of the battery at different power levels under normal conditions. Fault types include single-cell short circuit faults and multi-cell short circuit faults.

[0118] In one embodiment, the short circuit fault diagnosis module has the following specific functions:

[0119] If the fault type is determined to be a single cell short circuit fault, the V m (t) and I(t) to calculate the pseudo open circuit voltage signal OV of the target battery cell p (t) The process is:

[0120] OV p (t) = V m (t)+R0(SC(t))·I(t)

[0121] Among them, V m (t) is the battery terminal voltage measured at sampling time t, I(t) is the battery current measured at sampling time t, and R0(SC(t)) is the resistance corresponding to the charge state at time t, which is obtained from the relationship table;

[0122] According to OV p (t) Get the differential pseudo open circuit voltage signal ΔOV p (t), the process is;

[0123] ΔOV p (t)=0V p (t)-OV p (t-1)

[0124] According to the differential pseudo open circuit voltage ΔOV p (t) Calculate the short-circuit fault threshold ξ and compare ξ with V m (t) is compared to perform short-circuit fault diagnosis and short-circuit fault time location. In order to enhance the robustness to large current pulses that may cause non-fault voltage transients, a scaling factor γ greater than 1 is introduced to relax the threshold ξ. The specific process is:

[0125] ξ - =γ·F(ΔOV p ,α)

[0126] ξ + =γ·F(ΔOV p ,1-α)

[0127] Among them, ξ - is the lower threshold, ξ + is the upper threshold, and α represents the probability of abnormal fluctuation allowed under normal conditions.

[0128] If V m (t)<ξ - , it is diagnosed as the start of a short-circuit fault; the sampling time t at this time is the fault start time;

[0129] If V m (t ′ )>ξ + , the diagnosis is that the short circuit fault ends; the sampling time at this time is t ′ This is the fault end time;

[0130] The fault time can be located based on the fault start time and fault end time.

[0131] In one embodiment, the short circuit fault degree assessment module has the following specific functions:

[0132] According to ΔOV p (t) Calculate the short-circuit current I sc (t) and short-circuit resistance R sc (t), according to R sc (t) is compared with the historical short-circuit resistance threshold to diagnose the degree of battery short-circuit fault. The specific method is as follows:

[0133] Short-circuit fault causes short-circuit current I sc (t) Leakage, this current flows through the resistor R0, and the corresponding voltage drop appears as a residual value ΔOV p (t) in:

[0134] ΔOV p (t)=R0·I sc (t)

[0135] So the short-circuit current is calculated as:

[0136]

[0137] Considering that the short-circuit branch is connected in parallel with the battery terminal, the terminal voltage V at the time of the fault is m (t) reflects the voltage on the short-circuit path. The short-circuit resistance calculation process is:

[0138]

[0139] R scCompared with the resistance threshold, the resistance threshold is obtained by historical experimental statistics. The resistance threshold includes two resistance thresholds, namely the first resistance threshold and the second resistance threshold. The first resistance threshold is greater than the second resistance threshold. If R sc < the second resistance threshold, the short circuit fault level is high risk; if R sc > the first resistance threshold, the short circuit fault level is low risk; if the second resistance threshold ≤ R sc < the first resistance threshold, the short-circuit fault level is medium risk.

[0140] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and should not be considered to limit the scope of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A remote fault diagnosis system for automobiles, characterized in that: The system comprises: The data acquisition module is used to collect the operating data of the target vehicle within a preset time period at a preset cycle; the operating data includes the current open circuit voltage signal V0(t) and the remaining power of the battery pack, and the terminal voltage signal V of each battery cell in the battery pack. m (t) and current signal I(t); The first diagnostic module is configured to determine a current threshold value φ based on the remaining power by searching a power-to-open-circuit voltage mapping table, and determine a battery fault type based on a relationship between V0(t) and the threshold value φ. The power-to-open-circuit voltage mapping table records the corresponding open-circuit voltages of the battery at different power levels under normal conditions. Fault types include single-cell short-circuit faults and multi-cell short-circuit faults. Short circuit fault diagnosis module, used to use the V m (t) and I(t) to calculate the pseudo open circuit voltage signal OV of the target battery cell p (t), according to the OV p (t) Get the differential pseudo open circuit voltage signal ΔOV p (t), and according to ΔOV p (t) Determine the normal range and locate the V m (t) a short circuit fault time period; the target battery cell is any one of the battery cells in the battery pack; Short circuit fault degree assessment module, according to ΔOV p (t) Calculate the short-circuit current signal I sc (t) and short-circuit resistance signal R sc (t), according to R sc (t) Compare with the historical short-circuit resistance threshold to diagnose the degree of battery short-circuit fault.

2. The automobile remote fault diagnosis system according to claim 1, characterized in that: The data acquisition module has the following specific functions: It is used to collect the operating data of the target vehicle within a preset time period at a preset cycle; the operating data includes the current open circuit voltage signal V0(t) and the remaining power of the battery pack, and the terminal voltage signal V of each battery cell in the battery pack. m (t) and current signal I(t).

3. The automobile remote fault diagnosis system according to claim 1, characterized in that: The first diagnostic module includes a fault type determination module: The fault type determination module is configured to determine that a single-cell short-circuit fault exists in the battery if the open-circuit voltage at each moment of V0(t) is less than a threshold value φ and not less than a failure threshold value; and to determine that a multi-cell short-circuit fault exists in the battery if the open-circuit voltage at each moment of V0(t) is less than the failure threshold value; the failure threshold value is the lowest voltage at which the battery can operate normally, and the failure threshold value is less than the threshold value φ.

4. The automobile remote fault diagnosis system according to claim 1, characterized in that: The specific functions of the short circuit fault diagnosis module are: If the fault type is determined to be a single cell short circuit fault, the V m (t) and I(t) to calculate the pseudo open circuit voltage signal OV of the target battery cell p (t) The process is: OV p (t)=V m (t)+R0(SC(t))·I(t) Among them, V m (t) is the battery terminal voltage measured at sampling time t, I(t) is the battery current measured at sampling time t, and R0(SC(t)) is the resistance corresponding to the charge state at time t, which is obtained from the relationship table; According to the OV p (t) Get the differential pseudo open circuit voltage signal ΔOV p (t), the process is; ΔOV p (t)=OV p (t)-OV p (t-1) According to the differential pseudo open circuit voltage ΔOV p (t) Calculate the short-circuit fault threshold ξ and compare ξ with V m (t) is compared to perform short-circuit fault diagnosis and short-circuit fault time location. In order to enhance the robustness to large current pulses that may cause non-fault voltage transients, a scaling factor γ greater than 1 is introduced to relax the threshold ξ. The specific process is: x - =γ·F(ΔOV p ,a) x + =γ·F(ΔOV p ,1-a) Among them, ξ - is the lower threshold, ξ + is the upper threshold, α represents the probability of abnormal fluctuation allowed under normal conditions, and the α interval is [0,1]; If V m (t)<ξ - , it is diagnosed as the start of a short-circuit fault; the sampling time t at this time is the fault start time; If V m (t ′ )>ξ + , the diagnosis is that the short circuit fault ends; the sampling time at this time is t ′ This is the fault end time; The fault time can be located based on the fault start time and fault end time.

5. The automobile remote fault diagnosis system according to claim 4, characterized in that: The specific functions of the short-circuit fault degree assessment module are: According to ΔOV p (t) Calculate the short-circuit current I sc (t) and short-circuit resistance R sc (t), according to R sc (t) is compared with the historical short-circuit resistance threshold to diagnose the degree of battery short-circuit fault. The specific method is as follows: Short-circuit fault causes short-circuit current I sc (t) Leakage, this current flows through the resistor R0, and the corresponding voltage drop appears as a residual value ΔOV p (t) in: ΔOV p (t)=R0·I sc (t) So the short-circuit current is calculated as: Considering that the short-circuit branch is connected in parallel with the battery terminal, the terminal voltage V at the time of the fault is m (t) reflects the voltage on the short-circuit path. The short-circuit resistance calculation process is: R sc Compared with the resistance threshold, the resistance threshold is obtained by historical experimental statistics. The resistance threshold includes two resistance thresholds, namely the first resistance threshold and the second resistance threshold. The first resistance threshold is greater than the second resistance threshold. If R sc < the second resistance threshold, the short circuit fault level is high risk; if R sc > the first resistance threshold, the short circuit fault level is low risk; if the second resistance threshold ≤ R sc < the first resistance threshold, the short-circuit fault level is medium risk.

6. A method for remote fault diagnosis of an automobile, characterized in that: The method comprises: Acquire the operating data of the target vehicle within a preset time period at a preset cycle; the operating data includes the current open circuit voltage signal V0(t) and the remaining power of the battery pack, and the terminal voltage signal V of each battery cell in the battery pack. m (t) and current signal I(t); The current threshold φ is determined based on the remaining power by searching the power-open-circuit voltage mapping table. The battery fault type is determined based on the relationship between V0(t) and the threshold φ. The power-open-circuit voltage mapping table records the corresponding open-circuit voltages of the battery at different power levels under normal conditions. Fault types include single-cell short-circuit faults and multi-cell short-circuit faults. If the fault type is determined to be a single cell short circuit fault, use the V m (t) and I(t) to calculate the pseudo open circuit voltage signal OV of the target battery cell p (t), according to the OV p (t) Get the differential pseudo open circuit voltage signal ΔOV p (t), and according to ΔOV p (t) Determine the normal range and locate the V m (t) a short circuit fault time period; the target battery cell is any one of the battery cells in the battery pack; According to ΔOV p (t) Calculate the short-circuit current signal I sc (t) and short-circuit resistance signal R sc (t), according to R sc (t) Compare with the historical short-circuit resistance threshold to diagnose the degree of battery short-circuit fault.

7. A method for remote fault diagnosis of an automobile according to claim 6, characterized in that: Acquire the operating data of the target vehicle within a preset time period at a preset cycle; the operating data includes the current open circuit voltage signal V0(t) and the remaining power of the battery pack, and the terminal voltage signal V of each battery cell in the battery pack. m (t) and current signal I(t).

8. The method for remote fault diagnosis of an automobile according to claim 6, characterized in that: The battery capacity-open-circuit voltage mapping table is searched based on the remaining power to determine the current threshold φ. The battery fault type is determined based on the relationship between V0(t) and the threshold φ. The battery capacity-open-circuit voltage mapping table records the corresponding open-circuit voltages of the battery at different power levels under normal circumstances. Fault types include single-cell short-circuit faults and multi-cell short-circuit faults.

9. The method for remote automobile fault diagnosis according to claim 6, characterized in that: If the fault type is determined to be a single cell short circuit fault, use the V m (t) and I(t) to calculate the pseudo open circuit voltage signal OV of the target battery cell p (t), the specific formula is: OV p (t)=V m (t)+R0(SC(t))·I(t) Among them, V m (t) is the battery terminal voltage measured at sampling time t, I(t) is the battery current measured at sampling time t, and R0(SC(t)) is the resistance corresponding to the charge state at time t, which is obtained from the relationship table; According to the OV p (t) Get the differential pseudo open circuit voltage signal ΔOV p (t), the formula is: ΔOV p (t)=OV p (t)-OV p (t-1) According to the differential pseudo open circuit voltage ΔOV p (t) Calculate the short-circuit fault threshold ξ and compare ξ with V m (t) is compared to perform short-circuit fault diagnosis and short-circuit fault time location. In order to enhance the robustness to large current pulses that may cause non-fault voltage transients, a scaling factor γ greater than 1 is introduced to relax the threshold ξ. The specific process is: x - =γ·F(ΔOV p ,a) x + =γ·F(ΔOV p ,1-a) Among them, ξ - is the lower threshold, ξ + is the upper threshold, α represents the probability of abnormal fluctuation allowed under normal conditions, and the α interval is [0,1]; If V m (t)<ξ - , it is diagnosed as the start of a short-circuit fault; the sampling time t at this time is the fault start time; If V m (t ′ )>ξ + , the diagnosis is that the short circuit fault ends; the sampling time at this time is t ′ This is the fault end time; The fault time is located based on the fault start time and fault end time.

10. The method for remote automobile fault diagnosis according to claim 9, characterized in that: According to ΔOV p (t) Calculate the short-circuit current signal I sc (t) and short-circuit resistance signal R sc (t), the specific process is: Short-circuit fault causes short-circuit current I sc (t) Leakage, this current flows through the resistor R0, and the corresponding voltage drop appears as a residual value ΔOV p (t) in: ΔOV p (t)=R0·I sc (t) So the short-circuit current is calculated as: According to R sc (t) is compared with the historical short-circuit resistance threshold to diagnose the degree of battery short-circuit fault: Considering that the short-circuit branch is connected in parallel with the battery terminal, the terminal voltage V at the time of the fault is m (t) reflects the voltage on the short-circuit path. The short-circuit resistance calculation process is: R sc Compared with the resistance threshold, the resistance threshold is obtained by historical experimental statistics. The resistance threshold includes two resistance thresholds, namely the first resistance threshold and the second resistance threshold. The first resistance threshold is greater than the second resistance threshold. If R sc < the second resistance threshold, the short circuit fault level is high risk; if R sc > the first resistance threshold, the short circuit fault level is low risk; if the second resistance threshold ≤ R sc < the first resistance threshold, the short-circuit fault level is medium risk.

Images