Hybrid vehicle driving motor fault diagnosis method based on sliding mode observer

Abstract

The invention discloses a hybrid electric vehicle driving motor fault diagnosis method based on a sliding mode observer. The method comprises the following steps: establishing a mathematical model of a hybrid electric vehicle driving system; according to a mathematical model of a hybrid electric vehicle driving system, a self-adaptive super-spiral sliding-mode observer used for fault feature observation is constructed, stability proving is carried out, and current residual error estimation is realized; aiming at the strong coupling problem of the fault characteristics of the driving motor and the dynamic working condition disturbance of the vehicle, constructing a load torque observer for disturbance estimation, and carrying out stability proving; and designing a fault detection index according to the current residual error to realize fault detection of the permanent magnet synchronous motor for the vehicle. According to the method, the self-adaptive super-spiral sliding-mode observer used for fault feature observation and the load torque observer used for disturbance estimation are constructed, and load torque disturbance is effectively separated from observation of residual current, so that the accuracy, the anti-interference capability and the robustness under complex working conditions of the fault diagnosis method are remarkably improved.

Description

Technical Field

[0001] This invention belongs to the field of hybrid vehicles and relates to fault diagnosis technology for hybrid vehicle drive motors, specifically to a fault diagnosis method for hybrid vehicle drive motors based on a sliding mode observer. Background Technology

[0002] One of the most important functions of a hybrid drive system is fault diagnosis. Its main purpose is to ensure the normal functioning of sensors, actuators, mechanical and electrical components, and the electronic control system, thereby guaranteeing vehicle safety and reliability. Due to the long history of research and industrial application of electric motors, there is a wealth of research on fault diagnosis of hybrid drive motors. The most common methods are analytical model-based and data-driven approaches. The main core components of a hybrid drive system are the drive motor and the inverter. Currently, the motors used in hybrid vehicles are mainly induction motors and permanent magnet synchronous motors (PMSMs). The main faults of induction motors and PMSMs include stator faults such as open or short circuits in the windings, rotor faults, mechanical faults such as bearing faults, air gap eccentricity faults caused by stator and rotor assembly errors, and control system faults. For rotor faults, induction motors mainly experience broken conductor bars and end rings; PMSMs mainly experience inter-turn short circuits and permanent magnet demagnetization.

[0003] Considering that most automotive motors are three-phase permanent magnet synchronous motors, which have strong nonlinear system characteristics, and that their speed and load are constantly changing during vehicle operation, the current, voltage, and speed signals of the motor exhibit non-stationary characteristics. This means that fault characteristic frequencies are no longer fixed values ​​and may even be submerged by strong background noise, posing a significant challenge to fault feature extraction. Furthermore, the drive motor is tightly coupled with the engine, battery, inverter, and vehicle control unit (VCU). Anomalies in the motor control system, power device failures in the inverter, or errors in the battery management system can all manifest in the motor's current or torque, resembling the symptoms of a motor fault, leading to confusion and misdiagnosis. Data-driven diagnostic methods require a large amount of fault data for model training. However, in practical applications, samples of motor faults, especially early and weak faults, are very scarce, making large-scale collection in real-world environments difficult. This greatly limits the application and validation of artificial intelligence methods. Therefore, achieving accurate and efficient fault detection for motors, effectively handling the system's nonlinear characteristics, and exhibiting good robustness to measurement noise and uncertainties is a key research focus. Summary of the Invention

[0004] Purpose of the invention: In order to overcome the shortcomings of the existing technology, a fault diagnosis method for hybrid vehicle drive motor based on sliding mode observer is provided, which can improve the accuracy, robustness and stability of drive system fault diagnosis.

[0005] Technical Solution: To achieve the above objectives, this invention provides a method for fault diagnosis of hybrid vehicle drive motors based on a sliding mode observer, comprising the following steps:

[0006] S1: Based on mathematical formulas, establish a mathematical model of the hybrid vehicle drive system;

[0007] S2: Based on the mathematical model of the hybrid electric vehicle drive system, an adaptive super-spiral sliding mode observer for fault characteristic observation is constructed and its stability is proven, so as to estimate the d-q axis current residuals.

[0008] S3: To address the strong coupling between drive motor fault characteristics and vehicle dynamic operating condition disturbances, a load torque observer for disturbance estimation is constructed and its stability is proven.

[0009] S4: Based on the current residual design, fault detection indicators are used to realize fault detection of automotive permanent magnet synchronous motors.

[0010] Furthermore, the mathematical model of the hybrid vehicle drive system in step S1 includes a drive motor model, a vehicle model, and a vehicle control model, as detailed below:

[0011] During the driving process, the vehicle is driven by a permanent magnet synchronous motor. The required torque is the product of the drive pedal signal and the motor's current maximum output torque, expressed as:

[0012] (1)

[0013] in, This indicates the required torque for the entire vehicle;

[0014] The basic dynamic equations of a vehicle during its movement are:

[0015] (2)

[0016] In the formula, F d F represents the vehicle's equivalent driving force. aero For air resistance, F f For ground rolling resistance, F acc To increase resistance, F grade For slope resistance; T L The output load torque of the motor is η, the transmission efficiency is i0, the main reduction ratio is r wh C is the effective radius of the tire. dWhere ρ is the air drag coefficient, A is the air density, and v is the frontal area. wh Where is the vehicle speed, μ is the rolling resistance coefficient, m is the vehicle mass, and γ is the road slope;

[0017] When the permanent magnet synchronous motor is in normal operating condition, the voltage equation of the permanent magnet synchronous motor on the dq axis is expressed as:

[0018] (3)

[0019] In the formula, u d,q For the d- and q-axis stator voltages, i d,q L represents the d- and q-axis stator currents. d,q For d- and q-axis inductors; R is the stator resistance, ω e It is the electric angular velocity, ψ f It's a magnetic link.

[0020] Furthermore, the construction of the adaptive superspiral sliding mode observer in step S2 includes:

[0021] For ease of control, the current state-space equations of the permanent magnet synchronous motor are rearranged as follows:

[0022] (4)

[0023] in,

[0024] (5)

[0025] For the above system, we choose s as the sliding surface and design D = s; based on this, we propose an improved super-torque sliding mode observer, which is expressed as follows:

[0026] (6)

[0027] In the formula, This is the state vector of the fault detection observer. To estimate the output vector, For the improved superhelical reaching law, the following must be satisfied:

[0028] (7)

[0029] In the formula, α and β are sliding mode gain values, which are greater than 0; ε1s and ε2s are proportional and integral terms, respectively, and ε1 and ε2 > 0.

[0030] For sliding mode gains α and β, the following must be satisfied:

[0031] (8)

[0032] Furthermore, the stability proof process in step S2 includes:

[0033] Choose the following Lyapunov function:

[0034] (9)

[0035] in

[0036] (10)

[0037] By using the definition of a quadratic function, it is shown that the designed Lyapunov function V is continuous, positive definite, radially unbounded, and except for the point... It is differentiable everywhere except in other places;

[0038] (11)

[0039] in yes The Euclidean norm, ;

[0040] Differentiating the Lyapunov function V, we get:

[0041] (12)

[0042] in

[0043] (13)

[0044] The requirement for system stability is Therefore, it is sufficient that matrices M and N are positive. The coefficients α, β, ε1, and ε2 will satisfy the following condition:

[0045] (14)

[0046] Then equation (12) is expressed as

[0047] (15)

[0048] in, , These are the smallest eigenvalues ​​of M and N, respectively;

[0049] For the quadratic inequality, we derive...

[0050] (16)

[0051] By combining formulas (15) and (16), we obtain the following:

[0052] (17)

[0053] in, ;

[0054] According to Lyapunov's stability theory: within a finite time interval, when D and d converge to 0, the system's dynamic error... It will also converge to the origin.

[0055] Furthermore, the construction of the load torque observer in step S3 includes:

[0056] The electromagnetic torque equation of a permanent magnet synchronous motor is defined as follows:

[0057] (18)

[0058] in, P and P are the electromagnetic torque and the number of pole pairs, respectively;

[0059] The mechanical equations of a permanent magnet synchronous motor (PMSM) are expressed as follows:

[0060] (19)

[0061] in , , These are mechanical angular velocity, moment of inertia, and load torque, respectively.

[0062] Under steady state, according to equation (19), by

[0063] (20)

[0064] It can be deduced The formula is the same as Irrelevant;

[0065] Ignoring the influence of friction, based on equation (18) and equation (2010)... Based on the torque equation and mechanical equation shown in (19), the motion state equation of the PMSM is constructed as follows:

[0066] (twenty one)

[0067] Further, step S3 involves estimating the equivalent load torque using a load torque observer, including:

[0068] The sliding mode observer, with mechanical angular velocity and load torque as the observed objects, is expressed as follows:

[0069] (twenty two)

[0070] In the formula, k is the sliding mode gain; This is an estimated value for the rotor's mechanical angular velocity; For traditional approach rate;

[0071] Define the speed estimation error Subtracting equation (21) from equation (22) yields the sliding mode observation error equation as follows:

[0072] (twenty three)

[0073] The speed estimation error is defined as the sliding surface, i.e. According to sliding mode control theory, when the system enters steady state and slides around the sliding surface, the following condition is met: At this point, the load torque is estimated to be:

[0074] (twenty four)

[0075] Furthermore, step S3 introduces a superspiral algorithm, designing a continuous control law containing an integral term to represent the average estimate of the load torque, directly outputting a smooth average estimate; specifically as follows:

[0076] The state equation of the proposed adaptive superspiral sliding mode observer is expressed as follows:

[0077] (25)

[0078] Where v is the designed adaptive superspiral sliding mode approach law;

[0079] (26)

[0080] in, , For sliding mode gain, This is the adaptive compensation term for the superspiral sliding mode reaching law;

[0081] (27)

[0082] Where M is a vector The first row of elements, Let z be the constant value of the integral term after it stabilizes. , It is a small positive number. The expression is

[0083] (28)

[0084] Subtracting equation (21) from equation (25), we obtain the speed observation error equation as follows:

[0085] (29)

[0086] According to sliding mode control theory, when the system enters steady state and slides around the sliding surface, the following condition is satisfied: At this point, the load torque is estimated to be:

[0087] (30)

[0088] Furthermore, the stability proof process in step S3 includes:

[0089] Choose the following Lyapunov function for equation (26):

[0090] (31)

[0091] in, P is a symmetric positive definite matrix, defined as ;

[0092] Taking the derivative with respect to V, we get

[0093] (32)

[0094] in, , The smallest eigenvalue of the matrix is ​​found by solving linear matrix inequalities. If the condition is negative definite, then P is a solution to the LMI.

[0095] According to Lyapunov's stability theory, in a finite time, when s converges to 0, the dynamic error of the system will also converge to the origin.

[0096] Furthermore, in step S4, the fault detection index FI is defined as the square root of the sum of squares of the d-q axis residual currents, thereby enabling the detection of inter-turn short circuits.

[0097] (33)

[0098] In this invention, a mathematical model of the hybrid electric vehicle drive motor system is established to accurately describe the fault diagnosis object. A sliding mode state observer is designed to achieve precise observation of fault characteristics in the motor residual current. To suppress the chattering problem inherent in traditional sliding mode observers, a super-spiral algorithm is used to improve the approach rate. Furthermore, to address the interference caused by sudden load torque changes during vehicle operation, such as emergency stops and starts, on the residual current observation, this is treated as a lumped disturbance, and an additional load torque observer is designed. An adaptive gain law is further designed to improve the observer's dynamic performance and robustness, enabling real-time estimation of the motor load.

[0099] Beneficial effects: Compared with the prior art, this invention constructs an adaptive super-helical sliding mode observer for fault feature observation and a load torque observer for disturbance estimation, which ultimately achieves effective separation of load torque disturbance from residual current observation, eliminates its interference with fault indicators, and thus significantly improves the accuracy, anti-interference ability and robustness of the fault diagnosis method under complex working conditions. Attached Figure Description

[0100] Figure 1 A schematic diagram illustrating the overall vehicle modeling principle of a hybrid electric vehicle;

[0101] Figure 2 This is a schematic diagram illustrating the implementation principle of the diagnostic method of the present invention;

[0102] Figure 3 The simulation results of the load torque estimate are shown in the figure.

[0103] Figure 4 The figure shows the simulation results of fault indicator detection. Detailed Implementation

[0104] The present invention will be further illustrated below with reference to the accompanying drawings and specific embodiments. It should be understood that these embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. After reading this invention, any modifications of the invention in various equivalent forms by those skilled in the art will fall within the scope defined by the appended claims.

[0105] Example 1:

[0106] This embodiment provides a method for diagnosing faults in the drive motor of a hybrid vehicle based on a sliding mode observer, including the following steps:

[0107] S1: Based on mathematical formulas, establish a mathematical model of the hybrid electric vehicle drive motor system;

[0108] The mathematical model of a hybrid electric vehicle drive motor system includes a drive motor model, a vehicle model, and a vehicle control model, as detailed below:

[0109] Reference Figure 1 During the driving process, the vehicle is mainly driven by a permanent magnet synchronous motor. The required torque is the product of the drive pedal signal and the current maximum output torque of the motor, expressed as:

[0110] (1)

[0111] in, This indicates the required torque for the entire vehicle;

[0112] During vehicle operation, the driving force of the entire vehicle needs to overcome its driving resistance in order to complete the vehicle's target movement. The basic dynamic equation is:

[0113] (2)

[0114] In the formula, F d F represents the vehicle's equivalent driving force. aero For air resistance, F f For ground rolling resistance, F acc To increase resistance, F grade For slope resistance; T L The output load torque of the motor is η, the transmission efficiency is i0, the main reduction ratio is r wh C is the effective radius of the tire. d Where ρ is the air drag coefficient, A is the air density, and v is the frontal area. wh Where is the vehicle speed, μ is the rolling resistance coefficient, m is the vehicle mass, and γ is the road slope;

[0115] When the permanent magnet synchronous motor is in normal operating condition, the voltage equation of the permanent magnet synchronous motor on the dq axis is expressed as:

[0116] (3)

[0117] In the formula, u d,q For the d- and q-axis stator voltages, i d,q L represents the d- and q-axis stator currents. d,q For d- and q-axis inductors; R is the stator resistance, ω e It is the electric angular velocity, ψ f It's a magnetic chain;

[0118] S2: Based on the mathematical model of the hybrid electric vehicle drive motor system, an adaptive super-spiral sliding mode observer for fault characteristic observation is constructed and its stability is proven, so as to estimate the d-q axis current residuals.

[0119] Reference Figure 2 The construction of the adaptive superspiral sliding mode observer includes:

[0120] For ease of control, the current state-space equations of the permanent magnet synchronous motor are rearranged as follows:

[0121] (4)

[0122] in,

[0123] (5)

[0124] For the above system, we choose s as the sliding surface and design D = s; based on this, we propose an improved superspiral sliding mode observer (ISTA-SMO), which is expressed as follows:

[0125] (6)

[0126] In the formula, This is the state vector of the fault detection observer. To estimate the output vector, For the improved superhelical reaching law, the following must be satisfied:

[0127] (7)

[0128] In the formula, α and β are sliding mode gain values, which are greater than 0; ε1s and ε2s are proportional and integral terms, respectively, and ε1 and ε2 > 0.

[0129] For sliding mode gains α and β, the following must be satisfied:

[0130] (8)

[0131] Choose the following Lyapunov function:

[0132] (9)

[0133] in

[0134] (10)

[0135] By using the definition of a quadratic function, it is shown that the designed Lyapunov function V is continuous, positive definite, radially unbounded, and except for the point... It is differentiable everywhere except in other places;

[0136] (11)

[0137] in yes The Euclidean norm, ;

[0138] Differentiating the Lyapunov function V, we get:

[0139] (12)

[0140] in

[0141] (13)

[0142] The requirement for system stability is Therefore, it is sufficient that matrices M and N are positive. The coefficients α, β, ε1, and ε2 will satisfy the following condition:

[0143] (14)

[0144] Then equation (12) is expressed as

[0145] (15)

[0146] in, , These are the smallest eigenvalues ​​of M and N, respectively;

[0147] For the quadratic inequality, we derive...

[0148] (16)

[0149] By combining formulas (15) and (16), we obtain the following:

[0150] (17)

[0151] in, ;

[0152] According to Lyapunov's stability theory: within a finite time interval, when D and d converge to 0, the system's dynamic error... It will also converge to the origin.

[0153] S3: To address the strong coupling between drive motor fault characteristics and vehicle dynamic operating condition disturbances, a load torque observer for estimating disturbances is constructed and its stability is proven.

[0154] The effects of the vehicle's dynamic operating conditions on the motor are aggregated into an equivalent load torque, and the equivalent load torque is estimated in real time using a load torque observer.

[0155] The construction of the load torque observer includes:

[0156] The electromagnetic torque equation of a permanent magnet synchronous motor is defined as follows:

[0157] (18)

[0158] in, P and P are the electromagnetic torque and the number of pole pairs, respectively;

[0159] The mechanical equations of a permanent magnet synchronous motor (PMSM) are expressed as follows:

[0160] (19)

[0161] in , , These are mechanical angular velocity, moment of inertia, and load torque, respectively.

[0162] Under steady state, according to equation (19), by

[0163] (20)

[0164] It can be deduced The formula is the same as Irrelevant;

[0165] Ignoring the influence of friction, based on equation (18) and equation (2010)... Based on the torque equation and mechanical equation shown in (19), the motion state equation of the PMSM is constructed as follows:

[0166] (twenty one)

[0167] The sliding mode observer, with mechanical angular velocity and load torque as the observed objects, is expressed as follows:

[0168] (twenty two)

[0169] In the formula, k is the sliding mode gain; This is an estimated value for the rotor's mechanical angular velocity; For traditional approach rate;

[0170] Define the speed estimation error Subtracting equation (21) from equation (22) yields the sliding mode observation error equation as follows:

[0171] (twenty three)

[0172] The speed estimation error is defined as the sliding surface, i.e. According to sliding mode control theory, when the system enters steady state and slides around the sliding surface, the following condition is met: At this point, the load torque is estimated to be:

[0173] (twenty four)

[0174] Due to the discontinuity of the sign function in the sliding mode observer, the load torque observation obtained by equation (24) contains high-frequency noise, which can cause system chattering. Therefore, this invention introduces a super-spiral algorithm to design a continuous control law containing an integral term to represent the average estimate of the load torque, directly outputting a smooth average estimate and avoiding the phase delay problem caused by adding a filter; specifically as follows:

[0175] Define the speed estimation error The state equation of the proposed adaptive superspiral sliding mode observer is expressed as follows:

[0176] (25)

[0177] Where v is the designed adaptive superspiral sliding mode approach law;

[0178] (26)

[0179] in, , For sliding mode gain, This is the adaptive compensation term for the superspiral sliding mode reaching law;

[0180] (27)

[0181] Where M is a vector The first row of elements, Let z be the constant value of the integral term after it stabilizes. , It is a small positive number. The expression is

[0182] (28)

[0183] Subtracting equation (21) from equation (25), we obtain the speed observation error equation as follows:

[0184] (29)

[0185] According to sliding mode control theory, when the system enters steady state and slides around the sliding surface, the following condition is satisfied: At this point, the load torque is estimated to be:

[0186] (30)

[0187] The process of proving stability includes:

[0188] Choose the following Lyapunov function for equation (26):

[0189] (31)

[0190] in, P is a symmetric positive definite matrix, defined as ;

[0191] Taking the derivative with respect to V, we get

[0192] (32)

[0193] in, , The smallest eigenvalue of the matrix is ​​obtained by solving the linear matrix inequality (LMI). If the condition is negative definite, then P is a solution to the LMI.

[0194] According to Lyapunov's stability theory, in a finite time, when s converges to 0, the dynamic error of the system will also converge to the origin.

[0195] S4: Based on the current residual design, fault detection indicators are used to realize fault detection of automotive permanent magnet synchronous motors.

[0196] The fault detection index FI is defined as the square root of the sum of squares of the residual currents on the d-q axes, which enables the detection of inter-turn short circuits.

[0197] (33).

[0198] It can be seen that the fault characteristic quantity can be obtained by estimating the phase shift of the back EMF in the stationary coordinate system and taking the difference.

[0199] Example 2:

[0200] To verify the effectiveness and efficacy of this invention, simulation experiments were conducted in this embodiment, and the results were obtained respectively. Figure 3 and Figure 4 The simulation data shown.

[0201] Figure 3 and Figure 4 Simulations were performed on two observers in Matlab to verify the algorithm performance. The motor operating conditions were set to a speed of 3000 rpm and a load of 195 N. m. For example Figure 3 As shown, the adaptive sliding mode load torque observer of this invention can accurately and quickly track the actual load torque compared with traditional observers. It is set to superimpose an inter-turn short-circuit fault and a load torque of 80 N at t=1 s. m, the result is as follows Figure 4 As shown, traditional observers incorrectly interpret load disturbances as changes in residual current, causing fault detection indicators to increase with rising load torque, which leads to a large number of false alarms in practice. The method of this invention, due to the real-time estimation by the load torque observer, ensures that its residual current observation value is almost unaffected by sudden load changes, thus maintaining the effectiveness of the fault indicators.

Claims

1. A method for fault diagnosis of drive motor in hybrid vehicles based on a sliding mode observer, characterized in that, Includes the following steps: S1: Based on mathematical formulas, establish a mathematical model of the hybrid vehicle drive system; S2: Based on the mathematical model of the hybrid electric vehicle drive system, an adaptive super-spiral sliding mode observer for fault characteristic observation is constructed and its stability is proven, so as to estimate the d-q axis current residuals. S3: To address the strong coupling between drive motor fault characteristics and vehicle dynamic operating condition disturbances, a load torque observer for disturbance estimation is constructed and its stability is proven. S4: Based on the current residual design, fault detection indicators are used to realize fault detection of automotive permanent magnet synchronous motors.

2. The method for fault diagnosis of hybrid vehicle drive motor based on sliding mode observer according to claim 1, characterized in that, The mathematical model of the hybrid electric vehicle drive system in step S1 includes a drive motor model, a vehicle model, and a vehicle control model, as detailed below: During the driving process, the vehicle is driven by a permanent magnet synchronous motor. The required torque is the product of the drive pedal signal and the motor's current maximum output torque, expressed as: （1）； in, This indicates the required torque for the entire vehicle; The basic dynamic equations of a vehicle during its movement are: （2）； In the formula, F d F represents the vehicle's equivalent driving force. aero For air resistance, F f For ground rolling resistance, F acc To increase resistance, F grade For slope resistance; T L The output load torque of the motor is η, the transmission efficiency is i0, the main reduction ratio is r wh C is the effective radius of the tire. d Where ρ is the air drag coefficient, A is the air density, and v is the frontal area. wh Where is the vehicle speed, μ is the rolling resistance coefficient, m is the vehicle mass, and γ is the road slope; When the permanent magnet synchronous motor is in normal operating condition, the voltage equation of the permanent magnet synchronous motor on the dq axis is expressed as: （3）； In the formula, u d,q For the d- and q-axis stator voltages, i d,q L represents the d- and q-axis stator currents. d,q For d- and q-axis inductors; R is the stator resistance, ω e It is the electric angular velocity, ψ f It's a magnetic link.

3. The method for fault diagnosis of hybrid vehicle drive motor based on sliding mode observer according to claim 2, characterized in that, The construction of the adaptive superspiral sliding mode observer in step S2 includes: For ease of control, the current state-space equations of the permanent magnet synchronous motor are rearranged as follows: （4）； in, （5）； For the above system, we choose s as the sliding surface and design D = s; based on this, we propose an improved super-torque sliding mode observer, which is expressed as follows: （6）； In the formula, This is the state vector of the fault detection observer. To estimate the output vector, For the improved superhelical reaching law, the following must be satisfied: （7）； In the formula, α and β are sliding mode gain values, which are greater than 0; ε1s and ε2s are proportional and integral terms, respectively, and ε1 and ε2 > 0. For sliding mode gains α and β, the following must be satisfied: （8）。 4. The method for fault diagnosis of hybrid vehicle drive motor based on sliding mode observer according to claim 3, characterized in that, The stability proof process in step S2 includes: Choose the following Lyapunov function: （9）； in （10）； By using the definition of a quadratic function, it is shown that the designed Lyapunov function V is continuous, positive definite, radially unbounded, and except for the point... It is differentiable everywhere except in other places; （11）； in yes The Euclidean norm, ; Differentiating the Lyapunov function V, we get: （12）； in （13）； The requirement for system stability is Therefore, it is sufficient that matrices M and N are positive. The coefficients α, β, ε1, and ε2 will satisfy the following condition: （14）； Then equation (12) is expressed as （15）； in, , These are the smallest eigenvalues ​​of M and N, respectively; For the quadratic inequality, we derive... （16）； By combining formulas (15) and (16), we obtain the following: （17）； in, ; According to Lyapunov's stability theory: within a finite time interval, when D and d converge to 0, the system's dynamic error... It will also converge to the origin.

5. The method for fault diagnosis of hybrid vehicle drive motor based on sliding mode observer according to claim 4, characterized in that, The construction of the load torque observer in step S3 includes: The electromagnetic torque equation of a permanent magnet synchronous motor is defined as follows: （18）； in, P and P are the electromagnetic torque and the number of pole pairs, respectively; The mechanical equations of a permanent magnet synchronous motor (PMSM) are expressed as follows: （19）； in , , These are mechanical angular velocity, moment of inertia, and load torque, respectively. Under steady state, according to equation (19), by （20）； It can be deduced The formula is the same as Irrelevant; Ignoring the influence of friction, based on equation (18) and equation (2010)... Based on the torque equation and mechanical equation shown in (19), the motion state equation of the PMSM is constructed as follows: （21）。 6. The method for fault diagnosis of hybrid vehicle drive motor based on sliding mode observer according to claim 5, characterized in that, Step S3 uses a load torque observer to estimate the equivalent load torque, including: The sliding mode observer, with mechanical angular velocity and load torque as the observed objects, is expressed as follows: （22）； In the formula, k is the sliding mode gain; This is an estimated value for the rotor's mechanical angular velocity; For traditional approach rate; Define the speed estimation error Subtracting equation (21) from equation (22) yields the sliding mode observation error equation as follows: （23）； The speed estimation error is defined as the sliding surface, i.e. According to sliding mode control theory, when the system enters steady state and slides around the sliding surface, the following condition is met: At this point, the load torque is estimated to be: （24）。 7. The method for fault diagnosis of hybrid vehicle drive motor based on sliding mode observer according to claim 6, characterized in that, In step S3, a super-spiral algorithm is introduced to design a continuous control law containing an integral term to represent the average estimate of the load torque, directly outputting a smooth average estimate; specifically as follows: The state equation of the proposed adaptive superspiral sliding mode observer is expressed as follows: （25）； Where v is the designed adaptive superspiral sliding mode approach law; （26）； in, , For sliding mode gain, This is the adaptive compensation term for the superspiral sliding mode reaching law; （27）； Where M is a vector The first row of elements, Let z be the constant value of the integral term after it stabilizes. , It is a small positive number. The expression is （28）； Subtracting equation (21) from equation (25), we obtain the speed observation error equation as follows: （29）； According to sliding mode control theory, when the system enters steady state and slides around the sliding surface, the following condition is satisfied: At this point, the load torque is estimated to be: （30）。 8. The method for fault diagnosis of hybrid vehicle drive motor based on sliding mode observer according to claim 7, characterized in that, The stability proof process in step S3 includes: Choose the following Lyapunov function for equation (26): （31）； in, P is a symmetric positive definite matrix, defined as ; Taking the derivative with respect to V, we get （32）； in, , The smallest eigenvalue of the matrix is ​​found by solving linear matrix inequalities. If the condition is negative definite, then P is a solution to the LMI. According to Lyapunov's stability theory, in a finite time, when s converges to 0, the dynamic error of the system will also converge to the origin.

9. A method for fault diagnosis of a hybrid vehicle drive motor based on a sliding mode observer according to claim 8, characterized in that, In step S4, the fault detection index FI is defined as the square root of the sum of squares of the residual currents of the d-q axes, thereby realizing the detection of inter-turn short circuits. （33）。

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