Real-time control method for power split type hybrid power bus

Abstract

The invention discloses a power split type hybrid power bus real-time control method, relates to the field of new energy automobiles, and solves the technical problem that torque distributions corresponding to different driving styles in an existing formulated energy management strategy are different. The method comprises the steps that the urban bus working condition and the initial SOC value are selected, the urban bus working condition and the initial SOC value are input into an MPGA algorithm to generate PI adjustment parameters, the PI adjustment parameters are adjusted through a fuzzy controller, optimal adjustment parameters are generated, and the energy of the whole vehicle is distributed in real time according to the optimal adjustment parameters. Compared with a fuzzy self-adaptive equivalent fuel consumption minimum strategy based on a driving style, the fuel economy of the whole vehicle and the sustainability of a vehicle battery are improved.

Description

Technical Field

[0001] The present invention relates to the field of new energy vehicles, and more specifically, it relates to a real-time control method for a power-split hybrid bus. Background Art

[0002] During the driving process of a vehicle on the road, its fuel consumption is not only affected by the current road conditions, but also inseparable from the driver's operation. Different people driving the same vehicle under the same road conditions will also result in different fuel consumptions of the vehicle. This is because different people have different driving styles due to different operations on the vehicle. When facing the current road conditions, the driver makes the vehicle present different motion states through operations on the steering wheel, accelerator / brake pedals, etc. Whether the vehicle can accurately respond to the driver's operation requires accurate identification of the driver's driving style. Therefore, to improve the performance of hybrid vehicles, it is necessary to consider not only the influence of the current road conditions, but also the current driver's driving style. Currently, experts at home and abroad generally classify driving styles into three categories: aggressive, standard, and calm. The characteristics of the aggressive style are that the driver steps on the accelerator pedal and the brake pedal with a large amplitude and high speed; the characteristics of the standard style are that the driver uses the accelerator pedal and the brake pedal relatively more reasonably; the characteristics of the calm style are that the driver steps on the accelerator pedal and the brake pedal with a small amplitude and low speed. In the existing energy management strategies, there are differences in torque distribution corresponding to different driving styles, which will reduce the fuel economy of hybrid buses. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a real-time control method for a power-split hybrid bus in view of the deficiencies of the prior art, and solve the technical problem that there are differences in torque distribution corresponding to different driving styles in the existing energy management strategies.

[0004] A real-time control method for a power-split hybrid bus according to the present invention includes selecting an urban bus driving condition and an initial SOC value, inputting the urban bus driving condition and the initial SOC value into an MPGA algorithm to generate PI adjustment parameters, adjusting the PI adjustment parameters through a fuzzy controller to generate optimal adjustment parameters, and allocating the vehicle energy in real time according to the optimal adjustment parameters.

[0005] For further improvement, the method of adjusting the PI adjustment parameters through a fuzzy controller to generate optimal adjustment parameters is as follows:

[0006] Obtain a correction proportional constant Δk p and a correction integral constant Δk i and the PI adjustment proportional parameter k of the PI adjustment parameter p and the PI adjustment integral parameter k of the PI adjustment parameteri , through the correction proportionality constant Δk p correct the PI adjustment proportionality parameter k p to obtain the optimal PI adjustment proportionality parameter k p1 , through the correction integral constant Δk i correct the PI adjustment integral parameter k i to obtain the optimal PI adjustment integral parameter k i1 , take the optimal PI adjustment integral parameter k i1 and the optimal PI adjustment proportionality parameter k p1 as the optimal adjustment parameters.

[0007] Furthermore, the method for obtaining the correction proportionality constant Δk p and the correction integral constant Δk i is as follows:

[0008] Obtain the battery reference SOC value and the battery real-time SOC value in the initial SOC value, subtract the battery reference SOC value from the battery real-time SOC value to obtain a difference ΔSOC, obtain a difference change rate dSOC based on the difference ΔSOC, and use the difference ΔSOC and the difference change rate dSOC as the two input quantities of a fuzzy controller. The two output quantities of the fuzzy controller are respectively the correction proportionality constant Δk p and the correction integral constant Δk i .

[0009] Even further, the method for correcting the PI adjustment proportionality parameter k p through the correction proportionality constant Δk p and correcting the PI adjustment integral parameter k i through the correction integral constant Δk i is as follows:

[0010] When the difference ΔSOC is less than zero, subtract the correction proportionality constant Δk p from the PI adjustment proportionality parameter k p and subtract the correction integral constant Δk i from the PI adjustment integral parameter k i to reduce the PI compensation to lower the battery real-time SOC value; when the difference change rate dSOC is greater than zero after reducing the PI compensation, continue to subtract the correction proportionality constant Δk p from the PI adjustment proportionality parameter k p and subtract the correction integral constant Δk i from the PI adjustment integral parameter k i to reduce the PI compensation; when the difference change rate dSOC is less than zero after reducing the PI compensation, then by subtracting the correction proportionality constant Δk pAdd the correction proportional constant Δk p And adjust the PI integral parameter k i Add the correction integral constant Δk i To increase the said PI compensation;

[0011] When the difference ΔSOC is equal to zero, the said PI compensation is not performed;

[0012] When the difference ΔSOC is greater than zero, then adjust the PI proportional parameter k p Add the correction proportional constant Δk p And adjust the PI integral parameter k i Add the correction integral constant Δk i To increase the said PI compensation to improve the real-time SOC value of the battery; when the change rate dSOC of the difference is greater than zero after increasing the said PI compensation, then continue to adjust the PI proportional parameter k p Add the correction proportional constant Δk p And adjust the PI integral parameter k i Add the correction integral constant Δk i To increase the said PI compensation; when the change rate dSOC of the difference is less than zero after increasing the said PI compensation, then adjust the PI proportional parameter k p Subtract the correction proportional constant Δk p And the PI integral parameter k i Subtract the correction integral constant Δk i To reduce the PI compensation to lower the real-time SOC value of the battery.

[0013] Furthermore, the method for allocating the vehicle's total energy in real time according to the optimal adjustment parameters is as follows

[0014] Step 1: Obtain the initial mileage S of the vehicle 0 And the system's real-time output power P(t), and calculate the vehicle's real-time driving mileage S(t) according to the initial SOC value, the vehicle's initial mileage S 0 The optimal PI adjustment proportional parameter k p1 The optimal PI adjustment integral parameter k i1 Step 2: Obtain the motor torque T corresponding to the vehicle's real-time driving mileage S(t)

[0015] The motor speed ω Eng And the engine-to-motor torque ratio T Eng , and according to the motor torque T M / G The motor speed ω Eng The motor torque ratio T Eng M / G ​Determine the underlying constraint parameters of the vehicle, use the underlying constraint parameters that meet the preset underlying constraint range as the parameters for allocating the vehicle's overall energy, and allocate the vehicle's overall energy in real time according to the parameters for allocating energy.

[0016] Furthermore, the expression for calculating the real-time driving mileage S(t) of the vehicle is:

[0017]

[0018] where SOC(t) represents the real-time SOC value of the battery obtained, k p1 is the optimal PI adjustment ratio parameter, k i1 is the optimal PI adjustment integral parameter, SOC ref represents the reference SOC value of the battery obtained, S(t) represents the real-time driving mileage of the vehicle, S 0 represents the initial mileage of the vehicle, and P(t) represents the real-time output power of the system.

[0019] Furthermore, in step two, the underlying constraint parameters of the vehicle include the real-time output torque T ICE (t) of the engine, the real-time output torque ratio T M / G (t) of the engine and the motor, the real-time rotational speed ω ICE (t) of the engine, and the real-time rotational speed ratio ω M / G (t) of the engine and the motor and the SOC value;

[0020] The underlying constraint range includes a torque constraint range, a rotational speed constraint range, and a charge and discharge constraint range.

[0021] Furthermore, the expression for the torque constraint range is,

[0022]

[0023] where represents the preset minimum torque of the engine, T ICE (t) represents the real-time output torque of the engine, represents the preset maximum torque of the engine, represents the preset minimum torque ratio of the engine and the motor, T M / G (t) represents the real-time output torque ratio of the engine and the motor, represents the preset maximum torque ratio of the engine and the motor.

[0024] Furthermore, the expression for the rotational speed constraint range is,

[0025]

[0026] where, is the preset minimum rotational speed of the engine, ωICE (t) is the real-time engine speed, is the preset maximum engine speed, is the preset minimum speed ratio of the engine to the motor, ω M / G (t) is the real-time speed ratio of the engine to the motor, is the preset maximum speed ratio of the engine to the motor.

[0027] Further, the expression of the charge and discharge constraint range is,

[0028] SOC min < SOC < SOC max ;

[0029] Among them, SOC min and SOC max respectively represent the preset minimum SOC value and the preset maximum SOC value.

[0030] Beneficial effects

[0031] The advantages of the present invention are as follows:

[0032] The present invention provides an improved adaptive equivalent fuel consumption minimum strategy optimized based on a multi-population genetic algorithm. By selecting the urban bus driving cycle and the initial SOC value, inputting the urban bus driving cycle and the initial SOC value into the MPGA algorithm to generate PI adjustment parameters, adjusting the PI adjustment parameters through a fuzzy controller to generate optimal adjustment parameters, and allocating the vehicle energy in real time according to the optimal adjustment parameters. Compared with the fuzzy adaptive equivalent fuel consumption minimum strategy based on driving style, the present invention improves the fuel economy of the whole vehicle and the sustainability of the vehicle battery. Description of the drawings

[0033] Figure 1 is the flow chart for obtaining the optimal adjustment parameters of the present invention;

[0034] Figure 2 is the flow chart for obtaining the real-time driving mileage S(t) of the vehicle of the present invention;

[0035] Figure 3 is the flow chart of the multi-population genetic algorithm structure of the present invention. Specific implementation manners

[0036] The following describes the present invention in further detail with reference to the embodiments, but it does not constitute any limitation to the present invention. Any limited modifications made by anyone within the scope of the claims of the present invention are still within the scope of the claims of the present invention.

[0037] Refer to Figures 1 - 3, a real-time control method for a power-split hybrid bus of the present invention. In order to improve the fuel economy of hybrid city buses, considering the complex and changeable but repetitive rules of urban bus working conditions, as well as the structural characteristics of the double planetary row hybrid system, such as Figure 3 shown, the present invention proposes a real-time control strategy for a power-split hybrid bus optimized by a multiple population genetic algorithm (MPGA) ( Figure 1 ).

[0038] This method includes selecting urban bus working conditions and an initial SOC value, inputting the urban bus working conditions and the initial SOC value into the MPGA algorithm to generate PI adjustment parameters, adjusting the PI adjustment parameters through a fuzzy controller to generate optimal adjustment parameters, and allocating the vehicle energy in real time according to the optimal adjustment parameters.

[0039] First, select representative urban bus working conditions and different initial SOC values as inputs, and use the MPGA algorithm to perform offline iterative optimization on the PI adjustment parameters of the equivalent factor. Thus, the optimal PI adjustment parameters under the selected urban bus working conditions are obtained. However, in the traditional PI adjustment method, when adjusting at a certain state, it is debugged one by one for an interval, and the control parameters of the working points within the interval are not directly obtained, but are replaced by the method of linear interpolation, which cannot ensure good control effects of the PI parameters within the interval.

[0040] The present invention uses a fuzzy controller to replace the method of linear interpolation, so as to realize the continuity of the PI parameter look-up table. The output is the corrected proportional constant Δkp and integral constant Δki. The operation of the fuzzy control rules in the system is as Figure 2 shown; then apply the optimization result to the online ECMS energy management strategy, use the equivalent fuel consumption minimum strategy to solve the optimal instantaneous energy allocation problem, and realize the real-time strategy of the power-split hybrid city bus. The optimization problem of the equivalent factor PI adjustment parameters under known driving conditions can be transformed into a non-linear global optimal solution search problem. At the same time, when the power-split hybrid bus runs on a fixed bus line, its working conditions have the characteristics of repetitive statistics. The initial solution randomly generated by the static optimization algorithm can be used for search, and the optimal solution can be obtained through the repeated iterative optimization of the algorithm to improve the fuel economy of the whole vehicle.

[0041] Take the urban bus working conditions and the initial SOC value as the two input quantities of the MPGA algorithm. The output quantities of the MPGA algorithm are S 0 and the PI adjustment ratio parameter and the PI adjustment integral parameter.

[0042] The method of adjusting the PI adjustment parameters through a fuzzy controller to generate optimal adjustment parameters is

[0043] The PI adjustment ratio parameter of the PI adjustment parameter, the PI adjustment integral parameter of the PI adjustment parameter, the correction proportional constant Δk p and the correction integral constant Δk i , through the correction proportional constant Δk p correct the PI adjustment ratio parameter k p to obtain the optimal PI adjustment ratio parameter k p1 , through the correction integral constant Δk i correct the PI adjustment integral parameter k i to obtain the optimal PI adjustment integral parameter k i1 . Take the optimal PI adjustment integral parameter k i1 and the optimal PI adjustment ratio parameter k p1 as the optimal adjustment parameters.

[0044] The method for obtaining the correction proportional constant Δk p and the correction integral constant Δk i is as follows:

[0045] Obtain the battery reference SOC value and the battery real-time SOC value of the vehicle, subtract the battery reference SOC value from the battery real-time SOC value to get the difference ΔSOC, obtain the difference change rate dSOC according to the difference ΔSOC, and take the difference ΔSOC and the difference change rate dSOC as the two input quantities of the fuzzy controller. The two output quantities of the fuzzy controller are respectively the correction proportional constant Δk p and the correction integral constant Δk i .

[0046] The method for correcting the PI adjustment ratio parameter through the correction proportional constant Δk p and correcting the PI adjustment integral parameter through the correction integral constant Δk i is as follows: When the difference ΔSOC is less than zero, reduce the PI compensation to lower the battery real-time SOC value; when the PI compensation is reduced and the difference change rate dSOC is greater than zero, continue to reduce the PI compensation; when the PI compensation is reduced and the difference change rate dSOC is less than zero, increase the PI compensation;

[0047] When the difference ΔSOC is equal to zero, no PI compensation is performed;

[0048] When the difference ΔSOC is greater than zero, increase the PI compensation to increase the battery real-time SOC value; when the PI compensation is increased and the difference change rate dSOC is greater than zero, continue to increase the PI compensation; when the PI compensation is increased and the difference change rate dSOC is less than zero, reduce the PI compensation.

[0049] The method for allocating the vehicle's total energy in real time according to the optimal adjustment parameters is:

[0050] Step 1: Obtain the initial vehicle mileage S 0 and the system's real-time output power P(t). Based on the initial SOC value, the initial vehicle mileage S 0 , the optimal PI adjustment ratio parameter k p1 , and the optimal PI adjustment integral parameter k i1 , calculate the real-time driving mileage S(t) of the vehicle;

[0051] Step 2: Obtain the motor torque T corresponding to the real-time driving mileage S(t) of the vehicle Eng , the motor speed ω Eng , and the engine-to-motor torque ratio T M / G . Based on the motor torque T Eng , the motor speed ω Eng , and the motor torque ratio T M / G , determine the underlying constraint parameters of the vehicle, and use the underlying constraint parameters that meet the preset underlying constraint range as the optimal adjustment parameters.

[0052] As Figure 2 shown, the expression for calculating the real-time driving mileage S(t) of the vehicle is:

[0053]

[0054] where SOC(t) represents the real-time SOC value of the battery, k p1 is the optimal PI adjustment ratio parameter, k i1 is the optimal PI adjustment integral parameter, SOC ref represents the reference SOC value of the battery, S(t) represents the real-time driving mileage of the vehicle, and S 0 represents the initial vehicle mileage, and P(t) represents the system's real-time output power.

[0055] In Step 3, the underlying constraint parameters of the vehicle include the engine's real-time output torque T ICE (t), the real-time output torque ratio T M / G (t) of the engine and the motor, the engine's real-time speed ω ICE (t), and the real-time speed ratio ω M / G (t) of the engine and the motor, and the SOC value;

[0056] The underlying constraint range includes a torque constraint range, a speed constraint range, and a charge-discharge constraint range.

[0057] The expression for the torque constraint range is

[0058]

[0059] where represents the preset minimum torque of the engine, and T ICE(t) represents the real-time output torque of the engine, represents the preset maximum torque of the engine, represents the preset minimum torque ratio of the engine and the motor, T M / G (t) represents the real-time output torque ratio of the engine and the motor, represents the preset maximum torque ratio of the engine and the motor;

[0060] The expression of the speed constraint range is,

[0061]

[0062] where, is the minimum speed of the engine, ω ICE (t) is the real-time speed of the engine, is the maximum speed of the engine, is the minimum speed ratio of the engine and the motor, ω M / G (t) is the real-time speed ratio of the engine and the motor, is the maximum speed ratio of the engine and the motor,

[0063] The expression of the charge and discharge constraint range is,

[0064] SOC min <SOC<SOC max ;

[0065] where, SOC min and SOC max respectively represent the preset minimum SOC value and the preset maximum SOC value.

[0066] This invention selects a multi-population genetic algorithm to globally optimize the equivalent factor of a power-split hybrid bus running in real time with the minimum equivalent fuel consumption strategy for fixed bus routes, so as to obtain the real-time optimal energy distribution of the whole vehicle under given working conditions.

[0067] By correcting the proportional constant Δk p to correct the PI adjustment proportional parameter k p and by correcting the integral constant Δk i to correct the PI adjustment integral parameter k i The method is,

[0068] When the difference ΔSOC is less than zero, subtract the correction proportional constant Δk p from the PI adjustment proportional parameter k p and subtract the correction integral constant Δk i from the PI adjustment integral parameter k iReduce the PI compensation to lower the real-time SOC value of the battery; when the PI compensation is reduced and the difference change rate dSOC is greater than zero, continue to adjust the PI ratio parameter k p Subtract the correction ratio constant Δk p And make the PI adjustment integral parameter k i Subtract the correction integral constant Δk i To reduce the PI compensation; when the PI compensation is reduced and the difference change rate dSOC is less than zero, then by adjusting the PI ratio parameter k p Add the correction ratio constant Δk p And the PI adjustment integral parameter k i Add the correction integral constant Δk i To increase the PI compensation;

[0069] When the difference ΔSOC is equal to zero, no PI compensation is performed;

[0070] When the difference ΔSOC is greater than zero, then adjust the PI ratio parameter k p Add the correction ratio constant Δk p And the PI adjustment integral parameter k i Add the correction integral constant Δk i To increase the PI compensation to increase the real-time SOC value of the battery; when the PI compensation is increased and the difference change rate dSOC is greater than zero, continue to adjust the PI ratio parameter k p Add the correction ratio constant Δk p And the PI adjustment integral parameter k i Add the correction integral constant Δk i To increase the PI compensation to increase the PI compensation; when the PI compensation is increased and the difference change rate dSOC is less than zero, then adjust the PI ratio parameter k p Subtract the correction ratio constant Δk p And the PI adjustment integral parameter k i Subtract the correction integral constant Δk i To reduce the PI compensation to lower the real-time SOC value of the battery.

[0071] To improve the fuel economy of the vehicle and the charging sustainability of the battery, the fuzzy controller adopts a double-input and double-output system. The two inputs are the difference ΔSOC between the battery reference SOC value and the battery real-time SOC value and its difference change rate dSOC, and the two outputs are the PI adjustment parameters pk and ik of the equivalent factor. If the difference ΔSOC is less than zero, it means that the battery real-time SOC value is greater than the battery reference SOC value, and the PI compensation should be reduced, and the battery real-time SOC value will drop. If the difference change rate dSOC is greater than zero at this time, it means that the deviation is continuing to increase, and the PI compensation should be further reduced appropriately. On the contrary, the PI compensation should be increased.

[0072] If the difference ΔSOC is zero, it indicates that the real-time SOC value of the battery is the same as the reference SOC value of the battery, and PI compensation does not need to intervene. If the change rate dSOC of the difference has a changing trend at this time, when the change rate dSOC of the difference decreases, increase the PI compensation; when the change rate dSOC of the difference increases, decrease the PI compensation.

[0073] If the difference ΔSOC is greater than zero, it indicates that the real-time SOC value of the battery is less than the reference SOC value of the battery, and the PI compensation should be increased. As the real-time SOC value of the battery increases, if the change rate dSOC of the difference is still greater than zero after increasing the PI compensation, it indicates that the deviation will continue to increase, and the PI compensation should be increased appropriately again. On the contrary, if the change rate dSOC of the difference is less than zero after increasing the PI compensation, the PI compensation should be decreased.

[0074] The multi-population genetic algorithm in the present invention is a prior art. The structural schematic diagram of the adopted multi-population genetic algorithm is as Figure 3 shown. The PI adjustment parameters are optimized by using the algorithm to make the PI adjustment parameters optimal. On the basis of the multi-population genetic algorithm, a binary Gray code encoding method with higher search efficiency is adopted, and the objective function is directly converted into a fitness function by means of crossover operation, mutation operation and immigration operation.

[0075] Compared with the fuzzy adaptive equivalent fuel consumption minimization strategy based on driving style, the present invention improves the fuel economy of the whole vehicle and the sustainability of the vehicle battery.

[0076] The above are only the preferred embodiments of the present invention. It should be pointed out that for those skilled in the art, without departing from the structure of the present invention, several deformations and improvements can be made, and these will not affect the implementation effect of the present invention and the practicability of the patent.

Claims

1. A real-time control method for a power-split hybrid bus, characterized in that: The method includes selecting an urban bus operating condition and an initial SOC value, inputting the urban bus operating condition and the initial SOC value into an MPGA algorithm to generate a PI adjustment parameter, adjusting the PI adjustment parameter through a fuzzy controller and generating an optimal adjustment parameter, and allocating vehicle energy in real time according to the optimal adjustment parameter.

2. A real-time control method for a power-split hybrid bus according to claim 1, characterized in that: The method of adjusting the PI adjustment parameters and generating the optimal adjustment parameters by using a fuzzy controller is: Get the correction proportional constant Δk p , corrected integral constant Δk i , the PI adjustment ratio parameter k of the PI adjustment parameter p and PI adjustment parameter PI adjustment integral parameter k i , through the correction proportional constant Δk p Adjust the proportional parameter k for PI p Correction is performed to obtain the optimal PI adjustment ratio parameter k p1 , through the correction integration constant Δk i Adjust the integral parameter k for PI i Correction is performed to obtain the optimal PI adjustment integral parameter k i1 , adjust the optimal PI integral parameter k i1 and the optimal PI adjustment ratio parameter k p1 as the optimal adjustment parameter.

3. A real-time control method for a power-split hybrid bus according to claim 2, characterized in that: Get the correction proportional constant Δk p and the modified integration constant Δk i The method is, Obtain a battery reference SOC value and a battery real-time SOC value in the initial SOC value, make a difference between the battery reference SOC value and the battery real-time SOC value to obtain a difference ΔSOC, obtain a difference change rate dSOC according to the difference ΔSOC, use the difference ΔSOC and the difference change rate dSOC as two input quantities of a fuzzy controller, and the two output quantities of the fuzzy controller are respectively a correction proportional constant Δk p and the modified integration constant Δk i .

4. A real-time control method for a power-split hybrid bus according to claim 3, characterized in that: The correction proportional constant Δk p Adjust the proportional parameter k for PI p Correction and correction of the integral constant Δk i Adjust the integral parameter k for PI i The correction method is: When the difference ΔSOC is less than zero, the PI adjustment ratio parameter k p Subtract the correction proportional constant Δk p And PI adjusts the integral parameter k i Subtract the correction integration constant Δk i To reduce the PI compensation to reduce the real-time SOC value of the battery; When the PI compensation is reduced and the difference change rate dSOC is greater than zero, the PI adjustment ratio parameter k is continued to be p Subtract the correction proportional constant Δk p And PI adjusts the integral parameter k i Subtract the correction integration constant Δk i to reduce the PI compensation; when the difference change rate dSOC is less than zero after reducing the PI compensation, the PI adjustment ratio parameter k p Add the correction proportional constant Δk p And adjust the PI integral parameter k i Add the correction integration constant Δk i To increase the PI compensation; When the difference ΔSOC is equal to zero, the PI compensation is not performed; When the difference ΔSOC is greater than zero, the PI adjustment ratio parameter k p Add the correction proportional constant Δk p And adjust the PI integral parameter k i Add the correction integration constant Δk i To increase the PI compensation to improve the real-time SOC value of the battery; When the difference change rate dSOC is greater than zero after adding the PI compensation, the PI adjustment ratio parameter k is further adjusted. p Add the correction proportional constant Δk p And adjust the PI integral parameter k i Add the correction integration constant Δk i to increase the PI compensation; when the difference change rate dSOC is less than zero after the PI compensation is increased, the PI adjustment ratio parameter k p Subtract the correction proportional constant Δk p And PI adjusts the integral parameter k i Subtract the correction integration constant Δk i To reduce the PI compensation to lower the real-time SOC value of the battery.

5. The real-time control method of a power-split hybrid bus according to claim 1, characterized in that: The method for real-time allocation of vehicle energy according to the optimal adjustment parameters is: Step 1: Obtain the vehicle's initial mileage S0 and the system's real-time output power P(t), and adjust the proportional parameter k according to the initial SOC value, the vehicle's initial mileage S0, and the optimal PI p1 , optimal PI adjustment integral parameter k i1 Calculate the real-time mileage S(t) of the vehicle; Step 2: Obtain the motor torque T corresponding to the real-time mileage S(t) of the vehicle Eng 、Motor speedω Eng and the engine to motor torque ratio T M / G , according to the motor torque T Eng 、Motor speedω Eng , motor torque ratio T M / G The underlying constraint parameters of the vehicle are determined, the underlying constraint parameters that meet the preset underlying constraint range are used as parameters for allocating the energy of the entire vehicle, and the energy of the entire vehicle is allocated in real time according to the parameters for allocating the energy.

6. A real-time control method for a power-split hybrid bus according to claim 5, characterized in that: The expression for calculating the real-time mileage S(t) of the vehicle is: Among them, SOC(t) represents the real-time SOC value of the battery, k p1 is the optimal PI adjustment ratio parameter, k i1 Adjust the integral parameters for optimal PI, SOC ref represents the obtained battery reference SOC value, S(t) represents the vehicle's real-time mileage, S0 represents the vehicle's initial mileage, and P(t) represents the system's real-time output power.

7. A real-time control method for a power-split hybrid bus according to claim 4, characterized in that: In step 2, the underlying constraint parameters of the vehicle include the real-time output torque T ICE (t), real-time output torque ratio of the engine and the motor T M / G (t), real-time engine speed ω ICE (t) and the real-time speed ratio of the engine and the motor ω M / G (t) and SOC value; The bottom constraint range includes a torque constraint range, a rotation speed constraint range and a charge and discharge constraint range.

8. A real-time control method for a power-split hybrid bus according to claim 7, characterized in that: The expression of the torque constraint range is: in Indicates the preset minimum engine torque, T ICE (t) represents the real-time output torque of the engine, Indicates the preset maximum engine torque. Indicates the preset minimum torque ratio between the engine and the motor, T M / G (t) represents the real-time output torque ratio of the engine and the motor, Indicates the preset maximum torque ratio between the engine and the motor.

9. A real-time control method for a power-split hybrid bus according to claim 7, characterized in that: The expression of the speed constraint range is: in, is the preset minimum engine speed, ω ICE (t) is the real-time engine speed, is the preset maximum engine speed, is the preset minimum speed ratio between the engine and the motor, ω M / G (t) is the real-time speed ratio between the engine and the motor, It is the preset maximum speed ratio between the engine and the motor.

10. A real-time control method for a power-split hybrid bus according to claim 7, characterized in that: The expression of the charge and discharge constraint range is: SOCIETY min <SOC<SOC max ; Among them, SOC min and SOC max They respectively represent the preset SOC minimum value and the preset SOC maximum value, and SOC is the SOC value of the vehicle.

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