CN117984988A - Hybrid power control method and system based on bionic dynamic system - Google Patents
Hybrid power control method and system based on bionic dynamic system Download PDFInfo
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Abstract
The invention relates to the technical field of bionic hybrid power control, in particular to a hybrid power control method and system based on a bionic dynamic system, wherein the method comprises the following steps: collecting operation data of the bionic hybrid power system, analyzing the operation rule of the bionic hybrid power system, and adaptively constructing a motor power-acting efficiency curve and an engine power-fuel efficiency curve by considering the bionic power control characteristic of the bionic hybrid power system under different requirements; and adaptively constructing an fitness function in a particle swarm algorithm based on the provided power change of the power source before and after power adjustment and a power distribution rule, and completing power distribution optimization of the hybrid power bionic system by combining the particle swarm algorithm. The invention aims to combine bionic dynamics with hybrid dynamics, improve the optimizing capability of a particle swarm algorithm, and further improve the efficiency and the energy utilization rate of hybrid power bionic control.
Description
Technical Field
The invention relates to the technical field of bionic hybrid power control, in particular to a hybrid power control method and system based on a bionic dynamic system.
Background
The bionic power system can obtain inspiration from organisms, and a more efficient power system and a transmission system are designed. By simulating the energy conversion mechanism inside the living body, the power distribution and the energy utilization efficiency of the vehicle can be optimized, the fuel efficiency can be improved, and more excellent power output characteristics can be realized.
The control system of the bionic hybrid electric vehicle can use the intelligent thinking mode to realize the coordination work among a plurality of power sources (such as an engine and a motor) in a similar way to information transmission, cooperation and division in the ant population. By simulating the distributed control, the self-adaption and the collaborative decision-making mechanism in the ant population, the intelligent and efficient energy management and power output are realized, and the performance of the whole vehicle is improved.
The particle swarm algorithm is a commonly used algorithm that optimizes the power distribution of the engine and motor. The fitness value of the particles in the particle swarm algorithm is the basis of the particle swarm algorithm and is used for measuring the quality degree of each particle in the current search space, so as to guide the movement and the position update of the particles. The method has direct influence on the searching performance and searching efficiency of the algorithm, and the good fitness function can enable the algorithm to find the optimal solution more easily, can converge more quickly in the iteration process, and can achieve the same optimization result with fewer iteration times or less calculation amount. Conversely, if the fitness function is poorly built, it may result in the algorithm falling into a locally optimal solution or failing to converge.
Disclosure of Invention
In order to solve the technical problems, the invention aims to provide a hybrid power control method and a hybrid power control system based on a bionic dynamic system, and the adopted technical scheme is as follows:
collecting bionic hybrid electric vehicle operation data at each moment, wherein the bionic hybrid electric vehicle operation data comprise operation speed data, acceleration data, power data of an engine, fuel consumption data, capacity data of a motor power supply and power data of a motor;
acquiring the linearity of the speed change at the current moment according to the distribution characteristics of the acceleration data at the continuous moment; acquiring the running condition stability of the current moment according to the speed change linearity of the current moment and the change characteristics of the running speed data; presetting a speed threshold value, and taking a speed range smaller than or equal to the speed threshold value as a low-speed interval; acquiring a motor power-acting efficiency curve according to the change characteristics of motor running power and capacity data of a motor power supply in all speed processes from starting to low-speed of the bionic hybrid electric vehicle; acquiring an engine power-fuel efficiency curve according to the change characteristics of the engine operating power and fuel consumption data in the process from a speed threshold to all speeds which are not in a low speed interval of the running speed of the bionic hybrid electric vehicle;
according to the motor power-acting efficiency curve and the engine power-fuel efficiency curve, the adaptability of particles in a particle swarm algorithm is adaptively built by combining the running state stability at the current moment, the particle swarm algorithm is adopted to obtain the optimal power distribution of the bionic hybrid electric vehicle engine and the motor at the current moment, and the hybrid power control is completed;
the motor power-working efficiency curve is specifically as follows:
For any low speed in a low speed interval, in the low speed process from starting to the low speed of the bionic hybrid electric vehicle, the ratio of the power consumption electric quantity to the total electric quantity of the power supply is recorded as a first ratio, and the difference value of 1 and the first ratio is used as a capacity ratio; calculating the result of one half of the product of the mass of the bionic hybrid electric vehicle, the square of the low speed, the linearity of the speed change corresponding to the low speed process and the capacity ratio; acquiring a motor time-power fitting curve in the low-speed process by adopting a least square method, and calculating an integral value of the fitting curve in the low-speed process; taking the ratio of the result to the integral value as a motor work efficiency factor in the process;
Taking the average value of the power data at all the moments in the low-speed process as the average power value of the low-speed process; and fitting the average power values of all the low-speed processes and the motor working efficiency factors by adopting a least square method to obtain a motor power-working efficiency curve. Preferably, the obtaining the linearity of the speed change at the current moment according to the distribution characteristics of the acceleration data at the continuous moments includes:
Taking a sequence formed by acceleration data in a preset duration of the bionic hybrid electric vehicle according to a time sequence as an acceleration time sequence;
For each data point of the acceleration time sequence, calculating a difference value between each data point and an adjacent following data point, and if the difference value is not equal to 0, storing the corresponding data point as a characteristic data point; counting the number of characteristic data points in the acceleration time sequence; calculating the sum of the number and 1; calculating an acceleration variance of the acceleration time sequence; calculating a product of the sum and the acceleration variance; taking the reciprocal of the product as the linearity of the speed change at the current moment.
Preferably, the obtaining the running condition stability at the current moment according to the speed change linearity at the current moment and the change feature of the running speed data specifically includes:
Taking a sequence formed by running speed data in a preset duration of the bionic hybrid electric vehicle according to a time sequence as a speed time sequence; calculating a velocity variance of the velocity time series; taking the product of the running speed at the current moment and the linearity of the speed change as a first product; taking the sum of all the differences of the acceleration time sequence as a difference sum; taking the product of the velocity variance and the difference sum as a second product; and taking the ratio of the first product to the second product as the running state stability at the current moment.
Preferably, the engine power-fuel efficiency curve is specifically:
for any speed greater than the speed threshold, acquiring an in-process motor time-power fitting curve by adopting the same method as the motor time-power fitting curve in the low-speed process in the process that the running speed of the bionic hybrid electric vehicle is from the speed threshold to the any speed Engine time-power fitting curve/>The expression of the fuel utilization rate of the engine in the process is as follows:
In the method, in the process of the invention, Representing the running speed of a bionic hybrid electric vehicleTo/>Fuel utilization rate of engine in running process,/>Representing the mass of a bionic hybrid electric vehicle,/>Any speed value indicating not in low speed interval,/>A speed threshold value is indicated and,Representing motor work efficiency factor corresponding to the current average power value on a motor power-work efficiency curve,/>、/>Respectively represent that the running speed of the bionic hybrid electric vehicle reaches/>、/>Time of/>Representing the running speed of the bionic hybrid electric vehicleTo/>Total fuel consumption during operation;
and obtaining an engine fuel oil-efficiency curve according to the engine fuel oil utilization rate of all the processes by adopting the same calculation method as the motor power-acting efficiency curve.
Preferably, the fitness of the particles in the particle swarm algorithm includes:
acquiring a power allocation judgment value of the particle at the current moment;
When the power distribution judgment value is more than or equal to 0, acquiring the power distribution coincidence degree of the particles at the current moment; calculating the product of the power distribution conformity, the motor acting efficiency factor at the current moment and the fuel utilization rate of the engine at the current moment; calculating the sum of the power distribution judgment value and a preset adjustment parameter larger than zero; taking the ratio of the product to the sum as the fitness of the particles at the current moment;
and when the power distribution judging value is smaller than 0, taking 0 as the fitness of the particles at the current moment.
Preferably, the power allocation judgment value of the particle at the current moment is obtained, and the expression is:
In the method, in the process of the invention, Power allocation judgment value representing particle at current time,/>、/>Motor power and motor work efficiency factor respectively representing particles at current moment,/>、/>Respectively representing the engine power and the engine fuel utilization rate of the particles at the current moment,/>、/>Respectively represent the motor power and motor work efficiency factor at the current moment,/>、/>Respectively representing the power and fuel utilization of the engine at the current moment.
Preferably, the motor working efficiency factor is specifically a value corresponding to the motor efficiency on a motor power-working efficiency curve, and the engine fuel utilization rate is specifically a value corresponding to the engine efficiency on an engine power-fuel efficiency curve.
Preferably, the obtaining the power allocation compliance of the current time particle includes:
The ratio of the engine power to the motor power of the particles at the current moment is recorded as a second ratio; taking the absolute value of the difference between the second ratio and the residual capacity of the motor power supply at the current moment as a first absolute value of the difference; taking the absolute value of the difference value of the running speed at the current moment and the ratio as a second absolute value of the difference value; taking the absolute value of the difference between the running state stability at the current moment and the ratio as a third absolute value of the difference; calculating an inverse of the product of the second absolute value of the difference and the third absolute value of the difference; taking the opposite number as an exponent of an exponential function based on a natural constant; and taking the product of the calculation result of the exponential function and the absolute value of the first difference value as the power distribution conformity of particles.
In a second aspect, an embodiment of the present invention further provides a hybrid control system based on a bionic kinetic system, including a memory, a processor, and a computer program stored in the memory and running on the processor, where the processor executes the computer program to implement the steps of any one of the methods described above.
The invention has at least the following beneficial effects:
According to the invention, the running state of the bionic hybrid electric vehicle is analyzed to construct running state stability, and the running environment and the road condition complexity of the bionic hybrid electric vehicle are described; meanwhile, under the consideration of different running speeds, the power change characteristics of the motor and the engine in different running processes and the corresponding working efficiency are respectively analyzed, a motor power-working efficiency curve and an engine power-fuel efficiency curve are constructed, and the power control characteristic of the bionic hybrid electric vehicle is accurately depicted; based on the two curves and the running condition of the automobile, adaptively constructing an adaptability function in a particle swarm algorithm, and reflecting the power change provided by power sources before and after power optimization and the completion condition of a power distribution rule; the optimizing capability and efficiency of the particle swarm algorithm in hybrid power control are improved, and the efficiency and the energy utilization rate of hybrid power bionic control are further improved.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions and advantages of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of steps of a hybrid control method based on a biomimetic dynamics system according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a motor power-work efficiency curve;
FIG. 3 is a schematic illustration of an engine power-fuel efficiency curve;
FIG. 4 is a flowchart for obtaining fitness;
FIG. 5 is a schematic diagram of the 5 th iteration of the particle swarm optimization;
fig. 6 is a schematic diagram of the 9 th iteration of the particle swarm optimization process.
Detailed Description
In order to further describe the technical means and effects adopted by the present invention to achieve the preset purpose, the following detailed description refers to the specific implementation, structure, characteristics and effects of the hybrid power control method and system based on the bionic dynamic system according to the present invention, which are described in detail below with reference to the accompanying drawings and the preferred embodiments. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The following specifically describes a hybrid power control method and a system specific scheme based on a bionic dynamic system provided by the invention with reference to the accompanying drawings.
Referring to fig. 1, a flowchart of steps of a hybrid control method based on a bionic dynamic system according to an embodiment of the invention is shown, and the method includes the following steps:
step S001: and collecting the running related data of the bionic hybrid electric vehicle.
In hybrid bionic vehicles, hybrid bionic control systems play a vital role. The hybrid power bionic control system uses the biological characteristics of ants as a reference, utilizes information transmission, cooperation and division in ant groups, optimizes the control strategy of the hybrid power control system based on the idea of bionics, and realizes more efficient energy management and power distribution. The hybrid power bionic control system can realize self-adaptive adjustment, intelligent optimization and high-efficiency energy utilization, so that the overall performance and user experience of the hybrid power automobile are improved, and the output power of an engine and the output power of a motor can be intelligently adjusted according to the current running data of the automobile, so that the best fuel economy and performance are achieved.
The method comprises the steps of firstly acquiring historical data of running of the bionic hybrid electric vehicle, then acquiring the data of the running of the bionic hybrid electric vehicle, and acquiring running states of the bionic hybrid electric vehicle, specifically running speed data, acceleration data, power data of an engine, fuel consumption data, power data of a motor and capacity data of a motor power supply, of the vehicle through a vehicle-mounted sensor. In this embodiment, the sampling interval is set to 10ms, which can be adjusted by the practitioner.
So far, the related operation data of the bionic hybrid electric vehicle are obtained.
Step S002: and analyzing the power change characteristics of the motor and the engine according to the speed data change characteristics of the bionic hybrid electric vehicle in the running process, and adaptively constructing the fitness of particles in the particle swarm algorithm.
Hybrid power of the bionic hybrid power automobile is based on the mutual cooperation of a motor and an engine so as to provide a more efficient and environment-friendly power source. When the vehicle is started, the hybrid power bionic control system uses a battery to provide power for the motor to start the vehicle. When the battery level is low, the engine may charge the motor while providing power, ensuring that the motor has sufficient energy to continue to operate. When the vehicle runs, the motor and the engine in the hybrid bionic control system simultaneously provide power, so that the vehicle can keep stable speed and power under different conditions.
The hybrid power bionic control system can fully utilize the performances of the motor and the engine through a reasonable power distribution strategy, and the optimization efficiency of the system is realized. When the bionic hybrid electric vehicle runs, the allocated power of the bionic hybrid electric vehicle is different due to different road conditions and different running conditions of vehicle equipment. Therefore, the embodiment optimizes the power output proportion of the engine and the motor by adaptively constructing the adaptability of particles and adopting a particle swarm algorithm, thereby realizing the optimal control of the system.
Specifically, firstly, analyzing the running condition of the vehicle according to the running condition of the bionic hybrid electric vehicle at the current moment, acquiring vehicle running speed data and acceleration data of the bionic hybrid electric vehicle at each moment in ten minutes of history, forming a speed time sequence of the bionic hybrid electric vehicle according to a time sequence, calculating the difference value between each data point and the adjacent next data point in the acceleration time sequence, and if the difference value is not equal to 0, storing the corresponding data point as a characteristic data point; conversely, the corresponding data point is not a characteristic data point. By analyzing the speed time sequence, the acceleration time sequence and the characteristic data points, the running state stability at the current moment is constructed, and the expression is as follows:
In the method, in the process of the invention, Representing the stability of the running condition at the current moment,/>Indicating the linearity of the speed change at the current time,Acceleration variance representing acceleration time series,/>Representing the number of characteristic data points in the acceleration time series,/>Velocity variance representing velocity time series,/>Indicating the/>, within the acceleration time seriesData points and/>Acceleration difference of data points,/>Representing the running speed of the bionic hybrid electric vehicle at the current moment,/>The number of data points in the acceleration time series is shown. Will/>As a first product, will/>As a second product.
The greater the running condition stability of the bionic hybrid electric vehicle at the current moment is, the smaller the possibility that the speed change of the vehicle occurs in a short period is, the simpler the running road condition of the current bionic hybrid electric vehicle is, when the speed is greater, the more power provided by the engine is often, the greater the power distributed by the engine is, the smaller the stability is, the more complex the running road condition of the current bionic hybrid electric vehicle is, at the moment, the more frequent start and stop operations, acceleration and deceleration operations and the like are needed, the energy of the engine is wasted, the fuel utilization rate is lower, and the greater the power distributed by the motor is.
And respectively analyzing the output conditions of the power of the motor and the power of the engine under different running speeds according to the running speed of the current bionic hybrid electric vehicle, the current distribution power of the motor and the current distribution power of the engine. Firstly, the motor is analyzed, according to priori knowledge, the bionic hybrid electric vehicle usually only provides power for the motor when running at low speed, and the maximum value of the speed of the motor only when running at low speed is taken as a speed threshold valueSpeed threshold in this embodimentWhen the speed of the bionic hybrid electric vehicle is 30km/h, namely, the bionic hybrid electric vehicle is considered to be running at a low speed, and an operator can adjust the bionic hybrid electric vehicle according to actual conditions. In this embodiment, analysis is performed according to the relevant driving data of the bionic hybrid electric vehicle from starting to running at any low speed v, wherein/>，/>For a low-speed interval, only the motor is powered in the process, so that the motor acting efficiency factor is obtained, and the expression is as follows:
In the method, in the process of the invention, Representing the running speed of the bionic hybrid electric vehicle from 0 to/>Motor work efficiency factor in low speed process of/>Representing the mass of a bionic hybrid electric vehicle,/>Any low speed value representing a low speed interval,/>Representing the running speed of the bionic hybrid electric vehicle from 0 to/>Time count in low-speed process of (2)/>Representing the running speed of the bionic hybrid electric vehicle from 0 to/>Motor time-power fitting curve during low speed of (2)/>Representing the running speed of the bionic hybrid electric vehicle from 0 to/>Linearity of speed change during low speed of (i)/>Representing the running speed of the bionic hybrid electric vehicle from 0 to/>Capacity ratio of the power supply during low speed. The capacity ratio obtaining method includes: running speed of bionic hybrid electric vehicle is changed from 0 to/>The ratio of the power consumption after the low-speed process divided by the total power of the power supply is recorded as a first ratio, and the difference between 1 and the first ratio is used as a capacity ratio; the running speed of the bionic hybrid electric vehicle is from 0 to/>, by adopting a least square methodAnd (3) fitting the motor power data at all times in the low-speed process to obtain a motor time-power fitting curve. The least square method is a known technique, and will not be described in detail in this embodiment.
Because the bionic hybrid electric vehicle only uses the motor to provide power when running at a low speed, the greater the linearity of the speed change of the process is, the more stable the running of the bionic hybrid electric vehicle is, the more effectively the vehicle can utilize energy sources in the running process, the energy waste is reduced, and the greater the acting efficiency of the motor is.
Therefore, in the low speed regionAnd selecting any one low speed, repeating the steps, and obtaining the average power value of the motor and the corresponding motor work efficiency factor. Taking the average power value of the motor in low-speed operation as an abscissa and the corresponding motor work efficiency factor as an ordinate; in the embodiment, the data points are fitted based on a least square method, so that a motor power-work efficiency curve is obtained. It should be noted that, in consideration of that one average power value corresponds to a plurality of motor work efficiency factors, at this time, a data point with the largest work efficiency factor is selected for fitting. A schematic of the motor power-work efficiency curve is shown in fig. 2.
The engine is continuously analyzed, and according to the operation rule of the engine, when the operation speed of the bionic hybrid electric vehicle is not operated in a low-speed interval, the power of the bionic hybrid electric vehicle is provided by the engine and the motor together, and the historical operation data of the bionic hybrid electric vehicle is analyzed to obtain a speed threshold value which is larger than the speed threshold valueAny one of the speeds/>At this time, the engine starts to provide power for the bionic hybrid electric vehicle, the process is analyzed, the average power values of the engine and the motor in the process are respectively obtained, the working efficiency factor corresponding to the average power value in the process is obtained through the motor power-working efficiency curve, and the engine fuel utilization rate corresponding to the average power value of the engine can be obtained, wherein the expression is as follows:
In the method, in the process of the invention, Representing the running speed of a bionic hybrid electric vehicleTo/>Fuel utilization rate of engine in running process,/>Representing the mass of a bionic hybrid electric vehicle,/>Any speed value indicating not in low speed interval,/>A speed threshold value is indicated and,Representing motor work efficiency factor corresponding to the current average power value on a motor power-work efficiency curve,/>、/>Respectively represent that the running speed of the bionic hybrid electric vehicle reaches/>、/>Time of/>、/>Respectively represent the running speed of the bionic hybrid electric vehicleTo/>Motor time-power fitting curve, engine time-power fitting curve during operation,/>Representing the running speed of the bionic hybrid electric vehicleReach/>Total fuel consumption in the process.
The engine power-fuel efficiency curve is obtained by adopting the same method as the motor power-work efficiency curve. Wherein the engine power-fuel efficiency curve is shown in fig. 3.
Thus, a motor power-work efficiency curve and an engine power-fuel efficiency curve are obtained.
Step S003: and constructing a fitness function based on a fitting curve of the motor and the engine, and distributing and optimizing the power of the bionic hybrid electric vehicle by adopting a particle swarm algorithm.
In the particle swarm algorithm, each particle corresponds to motor power and engine power, a corresponding motor work efficiency factor can be found according to the motor power on a motor power-work efficiency curve, and a corresponding engine fuel utilization rate can be found according to the engine power on an engine power-fuel efficiency curve.
Based on the difference between the power corresponding to the particles and the initial power and the working efficiency of the motor and the engine under the particle power, the corresponding power ratio, the fuel utilization and other self-adaptive construction particle fitness in the particle swarm algorithm, the expression is as follows:
In the method, in the process of the invention, Indicating the fitness of the particle at the current moment,/>Power allocation judgment value representing particle at current time,/>Indicating the coincidence degree of the power distribution of particles at the current moment,/>、/>Respectively representing the motor power and motor work efficiency factor of the particles at the current moment, and/>、/>Respectively representing the engine power and the engine fuel utilization rate of the particles at the current moment,/>、/>Respectively represent the motor power and motor work efficiency factor at the current moment,/>、/>Respectively representing the power and the fuel utilization rate of the engine at the current momentRepresenting the remaining capacity of the motor power supply at the current moment,/>Representing the running speed of the bionic hybrid electric vehicle at the current moment,/>Representing the stability of the running condition at the current moment,/>Representing an exponential function based on natural constants,/>Representing an adjustment parameter greater than zero. Will/>The second ratio is noted. In this embodiment, the value of the adjustment parameter is 0.001. In the calculation of the fitness, the fuel utilization rate of the engine is specifically a value corresponding to the efficiency of the engine on an engine power-fuel efficiency curve, and the work efficiency of the motor is specifically a value corresponding to the efficiency of the motor on a motor power-work efficiency curve. The procedure for obtaining the fitness is shown in fig. 4.
The power distribution judgment value can reflect whether the power distribution corresponding to the particle can meet the power requirement of the bionic hybrid electric vehicle or not, ifIf 0 or more, it can be satisfied that if/>If the power is smaller than 0, the power requirement of the bionic hybrid electric vehicle cannot be met; /(I)The larger the difference is, the more the particle power distribution accords with the law that the smaller the power supply electric quantity is, the smaller the distributed power value is, and the larger the adaptability value is. Automobile running speed, stability and/>, at current momentThe smaller the difference, the more consistent the particle power distribution is to the speed, the more the engine is powered, and the larger the fitness value is.
According to the steps, the construction of the fitness of the particles in the particle swarm algorithm is completed in a self-adaptive manner, the acquisition of the optimal power of the motor and the engine at the current moment is completed through the particle swarm algorithm, namely, the normalized fitness value of the particles acquired by the particle swarm algorithm is more than or equal to a set threshold value of 0.8, or the number of iterations is up to 200, at the moment, the particle with the largest fitness value is selected as the optimal solution, and the corresponding power is the optimal power. Wherein the threshold implementer may adjust itself. The specific optimization process is a well-known technology and will not be described in detail herein. The 5 th iteration schematic diagram of the particle swarm optimization process is shown in fig. 5, and the 9 th iteration schematic diagram showing the particle swarm optimization process is shown in fig. 6.
According to the obtained optimal power, the power of the motor and the power of the engine of the bionic hybrid electric vehicle in operation are regulated, wherein the power regulating process is a known technology and is not repeated here.
Based on the same inventive concept as the above method, the embodiment of the invention further provides a hybrid power control system based on a bionic dynamic system, which comprises a memory, a processor and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to realize the steps of any one of the hybrid power control methods based on the bionic dynamic system.
In summary, the embodiment of the invention analyzes the running state of the bionic hybrid electric vehicle, constructs the running state stability, and characterizes the running environment and the road condition complexity of the bionic hybrid electric vehicle; meanwhile, under the consideration of different running speeds, the power change characteristics of the motor and the engine in different running processes and the corresponding working efficiency are respectively analyzed, a motor power-working efficiency curve and an engine power-fuel efficiency curve are constructed, and the power control characteristic of the bionic hybrid electric vehicle is accurately depicted; based on the two curves and the running condition of the automobile, adaptively constructing an adaptability function in a particle swarm algorithm, and reflecting the power change provided by power sources before and after power optimization and the completion condition of a power distribution rule; the optimizing capability and efficiency of the particle swarm algorithm in hybrid power control are improved, and the efficiency and the energy utilization rate of hybrid power bionic control are further improved.
It should be noted that: the sequence of the embodiments of the present invention is only for description, and does not represent the advantages and disadvantages of the embodiments. And the foregoing description has been directed to specific embodiments of this specification. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments.
The foregoing description of the preferred embodiments of the present invention is not intended to be limiting, but rather, any modifications, equivalents, improvements, etc. that fall within the principles of the present invention are intended to be included within the scope of the present invention.
Claims (9)
1. The hybrid power control method based on the bionic dynamic system is characterized by comprising the following steps of:
collecting bionic hybrid electric vehicle operation data at each moment, wherein the bionic hybrid electric vehicle operation data comprise operation speed data, acceleration data, power data of an engine, fuel consumption data, capacity data of a motor power supply and power data of a motor;
Acquiring the linearity of the speed change at the current moment according to the distribution characteristics of the acceleration data at the continuous moment; acquiring the running condition stability of the current moment according to the speed change linearity of the current moment and the change characteristics of the running speed data; presetting a speed threshold value, and taking a speed range smaller than or equal to the speed threshold value as a low-speed interval; acquiring a motor power-acting efficiency curve according to the change characteristics of motor running power and capacity data of a motor power supply in all speed processes from starting to low-speed of the bionic hybrid electric vehicle;
acquiring an engine power-fuel efficiency curve according to the change characteristics of the engine operating power and fuel consumption data in the process from a speed threshold to all speeds which are not in a low speed interval of the running speed of the bionic hybrid electric vehicle;
according to the motor power-acting efficiency curve and the engine power-fuel efficiency curve, the adaptability of particles in a particle swarm algorithm is adaptively built by combining the running state stability at the current moment, the particle swarm algorithm is adopted to obtain the optimal power distribution of the bionic hybrid electric vehicle engine and the motor at the current moment, and the hybrid power control is completed;
the motor power-working efficiency curve is specifically as follows:
For any low speed in a low speed interval, in the low speed process from starting to the low speed of the bionic hybrid electric vehicle, the ratio of the power consumption electric quantity to the total electric quantity of the power supply is recorded as a first ratio, and the difference value of 1 and the first ratio is used as a capacity ratio; calculating the result of one half of the product of the mass of the bionic hybrid electric vehicle, the square of the low speed, the linearity of the speed change corresponding to the low speed process and the capacity ratio; acquiring a motor time-power fitting curve in the low-speed process by adopting a least square method, and calculating an integral value of the fitting curve in the low-speed process; taking the ratio of the result to the integral value as a motor work efficiency factor in the process;
Taking the average value of the power data at all the moments in the low-speed process as the average power value of the low-speed process; and fitting the average power values of all the low-speed processes and the motor working efficiency factors by adopting a least square method to obtain a motor power-working efficiency curve.
2. The hybrid power control method based on a bionic dynamic system according to claim 1, wherein the obtaining the linearity of the speed change at the current time according to the distribution characteristics of the acceleration data at the continuous time comprises:
Taking a sequence formed by acceleration data in a preset duration of the bionic hybrid electric vehicle according to a time sequence as an acceleration time sequence;
For each data point of the acceleration time sequence, calculating a difference value between each data point and an adjacent following data point, and if the difference value is not equal to 0, storing the corresponding data point as a characteristic data point; counting the number of characteristic data points in the acceleration time sequence; calculating the sum of the number and 1; calculating an acceleration variance of the acceleration time sequence; calculating a product of the sum and the acceleration variance; taking the reciprocal of the product as the linearity of the speed change at the current moment.
3. The hybrid power control method based on a bionic dynamic system according to claim 1, wherein the obtaining the running state stability at the current moment according to the linearity of the speed change at the current moment and the change characteristic of the running speed data specifically comprises:
Taking a sequence formed by running speed data in a preset duration of the bionic hybrid electric vehicle according to a time sequence as a speed time sequence; calculating a velocity variance of the velocity time series; taking the product of the running speed at the current moment and the linearity of the speed change as a first product; taking the sum of all the differences of the acceleration time sequence as a difference sum; taking the product of the velocity variance and the difference sum as a second product; and taking the ratio of the first product to the second product as the running state stability at the current moment.
4. The hybrid control method based on a biomimetic kinetic system according to claim 2, wherein the engine power-fuel efficiency curve is specifically:
for any speed greater than the speed threshold, acquiring an in-process motor time-power fitting curve by adopting the same method as the motor time-power fitting curve in the low-speed process in the process that the running speed of the bionic hybrid electric vehicle is from the speed threshold to the any speed Engine time-power fitting curve/>The expression of the fuel utilization rate of the engine in the process is as follows:
In the method, in the process of the invention, Representing the running speed of a bionic hybrid electric vehicleTo/>Fuel utilization rate of engine in running process,/>Representing the mass of a bionic hybrid electric vehicle,/>Any speed value indicating not in low speed interval,/>Representing a speed threshold,/>Representing motor work efficiency factor corresponding to the current average power value on a motor power-work efficiency curve,/>、/>Respectively represent that the running speed of the bionic hybrid electric vehicle reaches/>、/>Time of/>Representing the running speed of the bionic hybrid electric vehicleTo/>Total fuel consumption during operation;
and obtaining an engine fuel oil-efficiency curve according to the engine fuel oil utilization rate of all the processes by adopting the same calculation method as the motor power-acting efficiency curve.
5. The hybrid control method based on a biomimetic kinetic system according to claim 1, wherein the fitness of particles in the particle swarm algorithm comprises:
acquiring a power allocation judgment value of the particle at the current moment;
When the power distribution judgment value is more than or equal to 0, acquiring the power distribution coincidence degree of the particles at the current moment; calculating the product of the power distribution conformity, the motor acting efficiency factor at the current moment and the fuel utilization rate of the engine at the current moment; calculating the sum of the power distribution judgment value and a preset adjustment parameter larger than zero; taking the ratio of the product to the sum as the fitness of the particles at the current moment;
and when the power distribution judging value is smaller than 0, taking 0 as the fitness of the particles at the current moment.
6. The hybrid power control method based on a bionic kinetic system according to claim 5, wherein the power distribution judgment value of the particle at the current moment is obtained by the following expression:
In the method, in the process of the invention, Power allocation judgment value representing particle at current time,/>、/>Motor power and motor work efficiency factor respectively representing particles at current moment,/>、/>Respectively representing the engine power and the engine fuel utilization rate of the particles at the current moment,/>、/>Respectively represent the motor power and motor work efficiency factor at the current moment,/>、/>Respectively representing the power and fuel utilization of the engine at the current moment.
7. The hybrid power control method based on a bionic dynamic system according to claim 6, wherein the motor work efficiency factor is a value corresponding to motor efficiency on a motor power-work efficiency curve, and the engine fuel utilization is a value corresponding to engine efficiency on an engine power-fuel efficiency curve.
8. The hybrid power control method based on a bionic kinetic system according to claim 5, wherein the obtaining the power distribution conformity of the particles at the current moment includes:
The ratio of the engine power to the motor power of the particles at the current moment is recorded as a second ratio; taking the absolute value of the difference between the second ratio and the residual capacity of the motor power supply at the current moment as a first absolute value of the difference; taking the absolute value of the difference value of the running speed at the current moment and the ratio as a second absolute value of the difference value; taking the absolute value of the difference between the running state stability at the current moment and the ratio as a third absolute value of the difference; calculating an inverse of the product of the second absolute value of the difference and the third absolute value of the difference; taking the opposite number as an exponent of an exponential function based on a natural constant; and taking the product of the calculation result of the exponential function and the absolute value of the first difference value as the power distribution conformity of particles.
9. Hybrid control system based on a biomimetic kinetic system, comprising a memory, a processor and a computer program stored in the memory and running on the processor, characterized in that the processor implements the steps of the method according to any one of claims 1-8 when executing the computer program.
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