Robust switching coordinated control method for hybrid electric vehicles based on super-helical sliding mode algorithm

Abstract

The present invention discloses a hybrid electric vehicle robust switching coordination control method based on a super spiral sliding mode algorithm, which comprises the following steps: S1: an on-board speed sensor collects the actual driving speed signal of the vehicle and transmits it to a driver model; by comparing the target speed, the required torque of the whole vehicle is calculated and transmitted to a basic mode switching controller, and the initial target torque of the power source compensated by the basic motor for the engine response lag is derived and calculated; S2: a super spiral sliding mode controller integrating adaptive compensator and uncertainty interference filter estimation is designed, and a power source input expression for ensuring the robustness of the switching system is derived, and the expression is converted into the secondary target torque of the engine and the motor according to the output U of the super spiral sliding mode controller; S3: a final target torque instruction after the initial target torque of the power source and the secondary target torque are superimposed is transmitted to the corresponding actuator; the present invention can realize smooth and efficient mode switching of hybrid electric vehicles.

Description

Technical Field

[0001] The invention relates to the technical field of automobile dynamic control, in particular to a hybrid electric vehicle robust switching coordination control method based on a super spiral sliding mode algorithm. Background Art

[0002] In recent years, my country's energy-saving and new energy vehicle industries have developed rapidly and achieved remarkable results. It is expected that by 2035, the annual sales of energy-saving vehicles and new energy vehicles will each account for 50%, and energy-saving vehicles will be fully hybrid. Hybrid vehicles can effectively balance the power, passability and fuel economy of the vehicle due to their variable working modes. However, during the mode switching process, the power source torque demand changes suddenly, and is affected by different degrees of parameter perturbations, external interference and nonlinearity of the vehicle system, which can easily cause obvious longitudinal switching shocks and vehicle speed tracking errors, which not only reduce ride comfort and power, but also easily cause serious consequences of vehicle instability. In order to reduce the torque fluctuation at the output end of the transmission shaft during the mode switching process and improve the smoothness and robustness of the switching system, it is necessary to effectively and stably coordinate the dynamic control of multiple power sources. Summary of the invention

[0003] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0004] In view of the above and / or existing problems in power switching of existing hybrid electric vehicles, the present invention is proposed.

[0005] Therefore, the purpose of the present invention is to provide a hybrid electric vehicle robust switching coordination control method based on a super-helical sliding mode algorithm, which derives the final target torque command of the power source by integrating an adaptive compensator and a super-helical sliding mode controller estimated by an uncertainty interference filter, thereby achieving smooth and efficient mode switching quality.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: a hybrid electric vehicle robust switching coordinated control method based on a super spiral sliding mode algorithm, which comprises the following steps:

[0007] The S1 on-board speed sensor collects the actual vehicle speed signal and transmits it to the driver model. By comparing it with the target speed, the required torque of the vehicle is calculated and transmitted to the basic mode switching controller. The energy management strategy based on the target mode is derived and calculated to calculate the initial target torque of the power source that is used by the basic motor to compensate for the engine response lag.

[0008] S2 designs a super-helical sliding mode controller that integrates adaptive compensator and uncertainty interference filter estimation, and derives the power source input expression that guarantees the robustness of the switching system. The output U of the super-helical sliding mode controller can be converted into the secondary target torque of the engine and the motor.

[0009] S3 transmits the final target torque command after superimposing the initial target torque of the power source and the secondary target torque to the corresponding actuator. Each actuator receives and completes the command. The vehicle speed sensor collects the current actual vehicle speed in real time, and updates the torque command at the next moment by reducing the error with the target speed.

[0010] As a preferred solution of the hybrid electric vehicle robust switching coordinated control method based on the super spiral sliding mode algorithm in the present invention, wherein: in step S1, the power source input expression is,

[0011]

[0012] Among them, B is the coefficient matrix of the state space model of the dual planetary gear dynamic coupling mechanism, and the state variable X = [ω e ,ω out ]T,ω e is the engine speed, ω out is the speed of the output shaft of the planetary coupling mechanism, X d is the expected value of X, Ks = diag(K s1 ,K s2 )∈R 2×2 is a positive definite matrix, K s1 and K s2 is the error coefficient, e=XX d ,λ s =[λ s1 ,λ s2 ]T∈R 2 ,λ s1 and λ s2 is the symbol coefficient, h s =[h s1 ,h s2 ] T ∈R 2 ,h s1 and h s2 is the integral coefficient, sgn() is the sign function, and the operator is the matrix Hadamard product, is the estimated value of Γ, Ф a is the adaptive compensation, and the nonlinearity of the vehicle system, external disturbance and model uncertainty are integrated into the lumped disturbance term Γ.

[0013] As a preferred solution of the hybrid electric vehicle robust switching coordinated control method based on the super spiral sliding mode algorithm in the present invention, wherein: in step S2, the following uncertainty interference filter is designed:

[0014]

[0015] T is the time constant of the first-order Butterworth filter, that is

[0016]

[0017] An adaptive compensator based on a multi-objective optimization algorithm is added, and the comprehensive performance index functional including the lumped disturbance estimation error, variable state error, and impulse error is:

[0018]

[0019] q 1 ,q 2 ,q 3 is the weight coefficient of the comprehensive performance functional, j is the longitudinal impact degree of vehicle mode switching, and j d is the impact target value. On this basis, combined with the executable domain of the power source, the genetic algorithm is used to iteratively search for the optimal solution of the adaptive compensation amount. a .

[0020] As a preferred solution of the hybrid vehicle robust switching coordinated control method based on the super spiral sliding mode algorithm in the present invention, the restriction conditions in the process of solving the comprehensive performance index functional by using the genetic algorithm are:

[0021]

[0022] Where ω is the corresponding component speed, T E is the engine output torque, T E-max is the maximum engine torque, T MG1 and T MG2 are the output torques of motors MG1 and MG2, T MG1-min and T MG1-max are the minimum and maximum output torques of motor MG1, T MG2-min and T MG2-max are the minimum torque and maximum torque of motor MG2.

[0023] As a preferred solution of the hybrid vehicle robust switching coordinated control method based on the super spiral sliding mode algorithm in the present invention, wherein: in step S2, the control parameter λ in the super spiral sliding mode controller s and h s Derived from Lyapunov stability theory, the Lyapunov function V is:

[0024]

[0025] Take the derivative of V and make it satisfy the following conditions,

[0026]

[0027] That is, P and Q are both about the control parameter λ s and h s A positive definite matrix can be used.

[0028] As a preferred solution of the hybrid vehicle robust switching coordinated control method based on the super spiral sliding mode algorithm in the present invention, the following state space model including the nonlinearity of the whole vehicle system, external interference and model uncertainty can be obtained according to the speed torque balance equation of the dual planetary gear power coupling mechanism:

[0029]

[0030] Among them, k 1 and k 2 are the characteristic parameters of the front and rear planetary gears in the power coupling mechanism, T E , T MG1 , T MG2 are the actual output torques of the engine, motor MG1, and motor MG2, respectively, T out It is the load at the output end of the power coupling mechanism.

[0031] As a preferred solution of the hybrid vehicle robust switching coordinated control method based on the super spiral sliding mode algorithm in the present invention, wherein: in step S3, the final target torque instruction is,

[0032]

[0033] Among them, T E-d is the final target torque of the engine, T MG1-d is the final target torque of motor MG1, T MG2-d is the final target torque of motor MG2, T E-0 The initial target torque of the engine, T MG1-0 is the initial target torque of the motor MG1, T MG2-0 is the initial target torque of motor MG2, T E-2 The secondary target torque of the engine, T MG1-0 is the secondary target torque of motor MG1, T MG2-0 is the secondary target torque of the motor MG2.

[0034] Compared with the prior art, the present invention has the following technical effects: the present invention can realize real-time estimation of lumped interference terms during the switching process of the whole vehicle system by completing the UDE filter design based on the Butterworth transfer function. In order to further reduce the estimation error of the filter, a multi-objective optimization algorithm is used to obtain the adaptive torque compensation amount, and the super-helical sliding mode control algorithm and the basic motor torque compensation control are combined to derive the final target torque command of the power source, thereby achieving smooth and efficient mode switching quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0036] Figure 1 It is a layout diagram of the hybrid vehicle power system in the present invention.

[0037] Figure 2 It is a multi-mode switching mechanism for hybrid electric vehicles, in which Rules for switching between different modes.

[0038] Figure 3 It is the overall control scheme diagram of the present invention.

[0039] Figure 4 The figure is a comparison effect diagram of the switching impact degree between the conventional motor torque compensation control and the robust switching control proposed in the present invention in a typical mode switching process (switching from pure electric mode to hybrid drive mode).

[0040] Figure 5 The figure is a comparison effect diagram of the target vehicle speed tracking error between the conventional motor torque compensation control and the robust switching control proposed in the present invention in a typical mode switching process (switching from pure electric mode to hybrid drive mode). DETAILED DESCRIPTION

[0041] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0042] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0043] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0044] Example 1

[0045] Reference Figure 1 and Figure 2 , which is the first embodiment of the present invention. This embodiment provides a hybrid vehicle robust switching coordination control method based on a super spiral sliding mode algorithm. The use of the present invention can improve the smoothness of hybrid vehicle mode switching.

[0046] like Figure 1 The double planetary gear hybrid system studied in this application is shown, which is a prior art and is briefly described in this application. It includes a front ring gear R1, a front planet carrier C1, a front sun gear S1, a rear ring gear R2, a rear planet carrier C2, and a rear sun gear S2. The engine 2 is connected to the front planet carrier C1 through a buffer locking mechanism 1, the rotor shaft of the motor MG1 is connected to the front sun gear S1, the rotor shaft of the motor MG2 is connected to the rear sun gear S2, and the front ring gear R1, the rear planet carrier C2, and the output shaft are connected.

[0047] According to the working state of the buffer locking mechanism and the power source, the working modes of the dual planetary gear hybrid vehicle can be divided into the following: Figure 2 The driving, braking and parking modes shown in the figure include pure electric driving, engine driving and hybrid driving, braking modes include mechanical braking and regenerative braking, and parking modes include parking charging and parking, totaling 8 common modes. In order to achieve smooth switching between various working modes, scientific and reasonable mode switching conditions are essential. From the rule-based energy management strategy, it can be seen that the mode switching rules are mainly based on the battery SOC, the required torque T req , accelerator pedal opening and vehicle speed v. Figure 2 The setting of the 20 state transition conditions shown in the figure ensures that the corresponding transition can be executed only when the corresponding event occurs and the conditions are met, so that the hybrid vehicle can switch freely among 8 modes. The controller then allocates the target torque of each power source according to different modes, and finally drives the vehicle while taking into account the vehicle's power and fuel economy.

[0048] A hybrid electric vehicle robust switching coordinated control method based on a super spiral sliding mode algorithm comprises the following steps:

[0049] During the actual driving of the S1 hybrid vehicle, the on-board speed sensor collects the actual vehicle speed signal and transmits it to the driver model. By comparing the target speed, the required torque of the vehicle is calculated and transmitted to the basic mode switching controller. The controller combines the current vehicle power, fuel economy requirements and battery SOC status to determine the hybrid vehicle mode switching requirements and issue the switching command to the key power source components of the vehicle. At the same time, the energy management strategy based on the target mode derives and calculates the initial target torque (T) of the power source compensated by the basic motor for the engine response lag. E-0 ,T MG1-0 ,T MG2-0 );

[0050] S2 designs a super-helical sliding mode controller that integrates adaptive compensator and uncertainty interference filter estimation, and derives the power source input expression that guarantees the robustness of the switching system. The output U of the super-helical sliding mode controller can be converted into the secondary target torque of the engine and the motor.

[0051] S3 transmits the final target torque command after superimposing the initial target torque of the power source and the secondary target torque to the corresponding actuator. Each actuator receives and completes the command. The vehicle speed sensor collects the current actual vehicle speed in real time, and updates the torque command at the next moment by reducing the error with the target speed.

[0052] Example 2

[0053] Reference Figure 3 , which is the second embodiment of the present invention. This embodiment is based on the previous embodiment, and can further achieve smooth and efficient mode switching of hybrid vehicles.

[0054] In step S2, considering that the mode switching process is transient and relatively sensitive to external disturbances, different degrees of parameter perturbations (vehicle mass, moment of inertia, etc.), external interference (actuator noise, road slope, resistance coefficient, etc.), and dynamic nonlinear characteristics of components such as complex nonlinear slip characteristics of tires, clutch friction element motion characteristics, and gear clearance will affect the switching smoothness and stability. The present invention integrates the nonlinearity of the whole vehicle system, external interference, and model uncertainty into a lumped interference term Γ. According to the speed torque balance equation of the dual planetary gear power coupling mechanism, the following state space model including Γ can be obtained:

[0055]

[0056] Among them, B is the coefficient matrix of the state space model of the dual planetary gear dynamic coupling mechanism, △B is the uncertainty term of the model parameters, and f i is a nonlinear term, d is external interference, U is system input, ω e is the engine speed, ωout is the speed of the output shaft of the planetary coupling mechanism, k 1 and k 2 are the characteristic parameters of the front and rear planetary gears in the power coupling mechanism, T E , T MG1 , T MG2 are the actual output torques of the engine, motor MG1, and motor MG2, respectively, T out It is the output load of the power coupling mechanism.

[0057] The present invention designs the following super-helical sliding mode controller, assuming that the target value of the state variable X is X d , then the tracking error is:

[0058]

[0059] Design the following sliding surface:

[0060] s=K s e 1 +e;

[0061] The sliding surface derivative is:

[0062]

[0063] According to the super-helical sliding mode control theory, the sliding surface derivative must satisfy the following equation:

[0064]

[0065] Among them, e 1 is the integral of the tracking error, X d is the expected value of X, K s =diag(K s1 ,K s2 )∈R 2×2 is a positive definite matrix, λ s =[λ s1 ,λ s2 ]T∈R 2 and h s =[h s1 ,h s2 ] T ∈R 2 , sign() is the symbol function, operator is the matrix Hadamard product.

[0066] From this, the power source input expression that ensures the stability of the switching system is derived:

[0067]

[0068] Since the aggregate interference term Γ is difficult to measure in real time through sensors and other devices, in order to accurately estimate the aggregate interference term Γ, the following uncertainty interference filter is designed:

[0069]

[0070] is the estimated value of Γ, T is the time constant of the first-order Butterworth filter, that is,

[0071]

[0072] Due to factors such as filter parameters, signal transmission delay, and environmental interference, there is inevitably an error between the estimated value and the true value of the UDE filter, which will further affect the calculation output of the super-helical sliding mode controller, thereby reducing the robustness of the vehicle mode switching. To solve this problem, the present invention adds an adaptive compensator based on a multi-objective optimization algorithm on the basis of the super-helical sliding mode control, and the final power source input of the switching system is:

[0073]

[0074] Among them, K s =diag(K s1 ,K s2 )∈R 2×2 is a positive definite matrix, K s1 and K s2 is the error coefficient, e=XX d ,λ s =[λ s1 ,λ s2 ]T∈R 2 ,λ s1 and λ s2 is the symbol coefficient, h s =[h s1 ,h s2 ] T ∈R 2 ,h s1 and h s2 is the integral coefficient, sgn() is the sign function, and the operator is the matrix Hadamard product, is the estimated value of Γ, Ф a is the adaptive compensation amount.

[0075] The comprehensive performance index functional of the multi-objective optimization algorithm, which includes the lumped disturbance estimation error, variable state error, and impact error, is:

[0076]

[0077] q 1 ,q2 ,q 3 is the weight coefficient of the comprehensive performance functional, j is the longitudinal impact degree of vehicle mode switching, and j d The impact target value is generally 10m / s 3 On this basis, combined with the executable domain of the power source, a genetic algorithm is used to iteratively search for the optimal solution of the adaptive compensation amount. a The constraints in solving the comprehensive performance index functional using genetic algorithm are:

[0078]

[0079] Where ω is the corresponding component speed, T E is the engine output torque, T E-max is the maximum engine torque, T MG1 and T MG2 are the output torques of motors MG1 and MG2, T MG1-min and T MG1-max are the minimum and maximum output torques of motor MG1, T MG2-min and T MG2-max are the minimum torque and maximum torque of motor MG2.

[0080] Control Parameter λ in Super Helical Sliding Mode Controller s and h s Derived from Lyapunov stability theory, the Lyapunov function V is:

[0081]

[0082] Take the derivative of V and make it satisfy the following conditions,

[0083]

[0084] P, Q are the control parameters λ s and h s As long as the parameter matrices are all positive definite matrices, the Lyapunov stability of the entire control system can be guaranteed.

[0085] Combining the above UDE filter and adaptive compensation controller design, the output U of the super-helical sliding mode controller can be converted into the secondary target torque (T E-2 ,T MG1-2 ,T MG2-2 ).

[0086] The final target torque command (T E-d ,T MG1-d ,T MG2-d) is transmitted to the corresponding actuator, as shown in the following formula, and each actuator receives and completes the command. At the same time, the vehicle speed sensor collects the current actual vehicle speed in real time, and updates the torque command at the next moment by reducing the error with the target speed, thus forming a complete hybrid vehicle closed-loop coordinated control system. When the vehicle working mode enters a steady state, the power of the engine and MG1 converges and outputs at the front planetary gear ring, combined with the power transmission from MG2 in the rear planetary carrier, and finally the three power flows are output together to drive the wheels, so far, the mode switching process is over.

[0087]

[0088] Through the design of the super-helical sliding mode controller integrating the UDE filter, the anti-interference ability of the vehicle mode switching system can be effectively improved. At the same time, an adaptive compensation controller module based on genetic algorithm is added to lay a solid foundation for compensating the filter estimation error and improving the adaptability of the switching system to a wide range of road conditions.

[0089] This embodiment realizes real-time estimation of the lumped interference term during the switching process of the whole vehicle system through the design of the UDE filter based on the Butterworth transfer function. In order to further reduce the estimation error of the filter, a multi-objective optimization algorithm is used to obtain the adaptive torque compensation amount, and the super-helical sliding mode control algorithm and the basic motor torque compensation control are combined to derive the final target torque command of the power source, thereby achieving smooth and efficient mode switching quality.

[0090] Example 3

[0091] This embodiment is based on the above two embodiments. This embodiment uses Matlab / Simulink simulation technology to verify the technical effect of smooth and efficient mode switching of the present application. Specifically, according to the power transmission route of the whole vehicle, a transient switching model of the whole vehicle is built in the Matlab / Simulink simulation platform, and the engine execution noise and vehicle parameter uncertainty are simulated by adding white noise and 5% parameter perturbation band. Figure 4 The longitudinal dynamic performance curves of the vehicle are given when the vehicle switches from pure electric to hybrid drive mode with engine execution noise and model uncertainty, using robust switching control and basic motor torque compensation control (i.e. without robust switching control).

[0092] Depend on Figure 4 and Figure 5 It can be seen that compared with the basic motor torque control, the robust switching control can reduce the switching shock by nearly 80%, and reduce the vehicle speed tracking error to below 0.06m / s, thereby improving the stability of vehicle speed tracking.

[0093] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A hybrid electric vehicle robust switching coordination control method based on a super-helical sliding mode algorithm is characterized by: It includes the following steps, The S1 on-board speed sensor collects the actual vehicle speed signal and transmits it to the driver model. By comparing it with the target speed, the required torque of the vehicle is calculated and transmitted to the basic mode switching controller. The energy management strategy based on the target mode is derived and calculated to calculate the initial target torque of the power source that is used by the basic motor to compensate for the engine response lag. S2 designs a super-helical sliding mode controller that integrates adaptive compensator and uncertainty interference filter estimation, and derives the power source input expression that guarantees the robustness of the switching system. The output U of the super-helical sliding mode controller can be converted into the secondary target torque of the engine and the motor. S3 transmits the final target torque command after the initial target torque of the power source and the secondary target torque are superimposed to the corresponding actuator. Each actuator receives and completes the command. The speed sensor collects the current actual speed of the vehicle in real time and updates the torque command at the next moment by reducing the error between the actual speed and the target speed. In step S2, the power source input expression is: Among them, B is the coefficient matrix of the state space model of the dual planetary gear dynamic coupling mechanism, and the state variable X = [ω e ,ω out ]T,ω e is the engine speed, ω out is the speed of the output shaft of the planetary coupling mechanism, X d is the expected value of X, Ks = diag(K s1 ,K s2 )∈R 2×2 is a positive definite matrix, K s1 and K s2 is the error coefficient, e=XX d ,λ s =[λ s1 ,λ s2 ]T∈R 2 ,λ s1 and λ s2 is the symbol coefficient, h s =[h s1 ,h s2 ] T ∈R 2 ,h s1 and h s2 is the integral coefficient, sgn() is the sign function, and the operator is the matrix Hadamard product, is the estimated value of Γ, Ф a is the adaptive compensation, which integrates the nonlinearity of the vehicle system, external disturbance and model uncertainty into the lumped disturbance term Γ; Design the following uncertainty interference filter, T is the time constant of the first-order Butterworth filter, that is Design the following sliding surface: s=K s e1+ee1 is the integral of the tracking error; An adaptive compensator based on a multi-objective optimization algorithm is added. The comprehensive performance index functional of the multi-objective optimization algorithm, which includes the lumped disturbance estimation error, variable state error, and impulse error, is: q1, q2, q3 are the weight coefficients of the comprehensive performance functional, j is the longitudinal impact degree of vehicle mode switching, j d is the impact target value. On this basis, combined with the executable domain of the power source, the genetic algorithm is used to iteratively search for the optimal solution of the adaptive compensation amount. a .

2. The hybrid electric vehicle robust switching coordinated control method based on the super spiral sliding mode algorithm according to claim 1, characterized in that: The constraints in the process of using genetic algorithm to solve the comprehensive performance index functional are: Where ω is the corresponding component speed, T E is the engine output torque, T E-max is the maximum engine torque, T MG1 and T MG2 are the output torques of motors MG1 and MG2, T MG1-min and T MG1-max are the minimum and maximum output torques of motor MG1, T MG2-min and T MG2-max are the minimum torque and maximum torque of motor MG2.

3. The hybrid electric vehicle robust switching coordinated control method based on the super spiral sliding mode algorithm as claimed in claim 1, characterized in that: In step S2, the control parameter λ in the super-helical sliding mode controller is s and h s Derived from Lyapunov stability theory, the Lyapunov function V is: Take the derivative of V and make it satisfy the following conditions, That is, P and Q are both about the control parameter λ s and h s A positive definite matrix can be used.

4. The hybrid electric vehicle robust switching coordinated control method based on the super spiral sliding mode algorithm as claimed in claim 1, characterized in that: According to the speed torque balance equation of the dual planetary gear power coupling mechanism, the following state space model including the nonlinearity of the vehicle system, external interference and model uncertainty can be obtained: Among them, k1 and k2 are the characteristic parameters of the front planetary gear and the rear planetary gear in the power coupling mechanism, respectively, T E 、T MG1 、T MG2 are the actual output torques of the engine, motor MG1, and motor MG2, respectively, T out is the load at the output end of the power coupling mechanism, △B is the uncertainty term of the model parameter, and f i is a nonlinear term, and d is external interference.

5. The hybrid electric vehicle robust switching coordinated control method based on the super spiral sliding mode algorithm according to any one of claims 1 to 4, characterized in that: In step S3, the final target torque instruction is: Among them, T E-d is the final target torque of the engine, T MG1-d is the final target torque of motor MG1, T MG2-d is the final target torque of motor MG2, T E-0 is the initial target torque of the engine, T MG1-0 is the initial target torque of the motor MG1, T MG2-0 is the initial target torque of motor MG2, T E-2 is the secondary target torque of the engine, T MG1-2 is the secondary target torque of motor MG1, T MG2-2 is the secondary target torque of the motor MG2.

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