CN111898726A - Parameter optimization method for electric vehicle control system, computer equipment and storage medium - Google Patents
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Abstract
The invention discloses a parameter optimization method for an electric vehicle control system, computer equipment and a storage medium. The invention comprises the following steps: 1) defining an initial bee colony, and constructing a dynamic evaluation index function 2) of an electric vehicle control system for optimizing to obtain bee fitness; 3) arranging bees in an ascending order according to fitness, and setting a plurality of bees before arrangement as detection bees and the rest as follower bees; 4) updating the positions of the two; in the position updating process, dynamically adjusting the optimizing area of the bees by adopting a self-adaptive search strategy, checking whether the maximum allowable optimizing step number is reached, and if the maximum allowable optimizing step number is not reached, returning to step 2); otherwise, go to 5); 5) and finding out the bee with the minimum fitness in the current population, wherein the value corresponding to each dimension of the bee is the parameter value of the optimized electric vehicle control system. The method solves the problems of complex parameter optimization modeling and large calculation amount of the electric automobile control system.
Description
Technical Field
The invention belongs to the field of parameter optimization of automobile control systems, and particularly relates to a parameter optimization method of an electric automobile control system, computer equipment and a storage medium.
Background
The electric automobile control system is a complex multi-input multi-output time-varying nonlinear system, and can be used for ensuring the stability and the maneuverability of an electric automobile, improving the task completion capability and the smoothness of the automobile, enhancing the safety of the automobile and reducing the burden of a driver. The design of control parameters of an electric vehicle control system becomes the most direct and important key link for ensuring the driving safety of the electric vehicle. Many engineers are faced with the parameter design and adjustment problem of complex system with internal implicit relationship in the actual design process of electric vehicle control system. Although the design of the control system can be performed by means of small-disturbance linearized equations, strong coupling often exists between the input quantities of the linearized equations, and no obvious mapping relation exists between the desired performance index and the controller parameters, which brings great difficulty to the selection of the controller parameters.
Under the general condition, when the traditional classical optimization method is applied to carry out parameter optimization on the electric automobile control system, the comprehensive consideration of the system pre-constraint is difficult to achieve. In the existing methods applying linear programming and quadratic programming, when system parameter optimization is performed by the methods, clear mathematical definition and structured design are required for an electric vehicle control system, however, final optimization results may be deteriorated due to uncertainty of a model in an actual operation process. In addition, methods using integer programming and hybrid programming need to consider the internal relation and possible coupling relation among parameters of the electric vehicle control system, so that a lot of time is consumed to take account of and balance the influence of each adjusted parameter on the multi-aspect performance of the system, and the process is time-consuming and labor-consuming. How to realize the rapid and accurate design of the parameters of the control system of the electric automobile according to the preset requirements under the condition of saving manpower and physical force as much as possible is a problem to be solved urgently at present.
The bee colony algorithm is an optimization method provided by simulating bee foraging behavior, is a specific application of a colony intelligent idea, and is mainly characterized in that special information of parameter optimization problems of an electric vehicle control system is not needed to be known, a plurality of design targets such as system overshoot, steady-state error, adjusting time and the like are only needed to be converted into a single optimization target in a weighted algebraic summation mode, and finally, a global optimum value is highlighted in a colony through local optimization behavior of each individual bee, so that the convergence speed is high. Each bee updates the position of the bee in the iterative calculation process of each step, and searches the optimal solution which possibly exists on the path in the moving process. Each iteration is the updating of the self state of the individual and is also the process of information interaction between the whole populations and gradual convergence to the global optimum. Because the prior knowledge is hardly needed for the problem to be processed and the preset condition is hardly provided, the bee colony algorithm is widely applied to the field of parameter optimization of a large class of control systems. However, in the process of performing parameter optimization on the electric vehicle control system by applying the swarm algorithm at present, the algorithm may fall into local optimization due to insufficient search of the target problem solution space, so that a global optimal solution cannot be finally obtained, and also may have a problem of slow later convergence speed due to an excessively large search range of the target problem solution space, which all affect the effect of performing parameter optimization on the electric vehicle control system by applying the swarm algorithm to a certain extent, thereby affecting the performance of the system.
Disclosure of Invention
The invention aims to overcome the defects that the parameter optimization of an electric vehicle control system by a swarm algorithm is easy to fall into local optimization or the convergence speed is low, and provides a parameter optimization method of the electric vehicle control system, computer equipment and a storage medium.
In order to achieve the purpose, the invention adopts the following technical scheme to realize the purpose:
an electric vehicle control system parameter optimization method based on an improved bee colony algorithm comprises the following steps:
1) defining an initial bee colony, and constructing a dynamic evaluation index function of an electric vehicle control system;
2) optimizing the parameters of the electric vehicle control system by using the bee colony, and calculating by using a dynamic evaluation index function to obtain the fitness corresponding to each bee individual in the bee colony;
3) arranging the bee colony individuals according to the fitness of the bee colony individuals in an ascending order, and setting a plurality of former bees in the arrangement as detection bees and the rest bees as follower bees;
4) carrying out scout bee position updating and follower bee position updating;
in the position updating process, adjusting the optimizing area of the bee individuals after each step of searching by adopting a self-adaptive searching strategy, judging whether the maximum allowable optimizing step number is reached, and if the maximum allowable optimizing step number is not reached, returning to the step 2);
otherwise, go to step 5);
5) and finding out the bee individual with the minimum fitness in the current population, wherein the value corresponding to each dimension of the bee individual is the parameter value of the electric vehicle control system obtained through final optimization.
Further, the specific process of step 1) is as follows:
defining the dimension of the initial bee colony as NxD dimension, wherein N represents the total number of the bee colony, D represents the total number of parameters to be optimized of the electric vehicle control system stored by the individual bee, and the D (D is 1,2, …, D) dimension element B of the nth (N is 1,2, …, N) been,dGiven by the following equation:
Bn,d＝<x1,x2,…,xn>⊕<y1,y2,…,yd>
wherein < x >1,x2,…,xn>Is represented by x1,x2,…,xnA binary number of the composition;<y1,y2,…,yd>is represented by y1,y2,…,ydA binary number of the composition; x is the number ofi(i ═ 1.. times, n) is the ith bit in the binary representation of a positive integer X, X satisfying the condition: 2X>N；yi(i 1.., d) is the ith bit in the binary expression of another positive integer Y coprime to X, Y satisfying: 2Y>D; ≧ is XOR operator; b isn,dRepresents the nth bee pairThe d-th parameter requiring optimization of the electric vehicle control system is estimated.
Further, the step 1) of constructing a dynamic evaluation index function of the electric vehicle control system specifically comprises:
J＝ω1∫dt+ω2σ+ω3γ+ω4κ
wherein, sigma, gamma and kappa are four evaluation indexes of the performance of the control system of the electric automobile, namely tracking error, overshoot percentage, rise time and establishment time; omega1、ω2、ω3、ω4The weight coefficients are respectively corresponding to the four evaluation indexes; and t is the test time of the system.
Further, the optimizing process in the step 2) is as follows:
when the optimization is started, presetting an initial value of a weight coefficient:
in the process of optimizing, first, a first weight coefficient omega is given1Assigning values such that:
ω2＝ω3＝ω4＝0；
giving a second weight coefficient omega when a preset condition is satisfied2Assigning values such that:
ω3＝ω4＝0；
giving a third weight coefficient ω when the sum σ satisfies a preset condition3Assigning values such that:
ω4＝0；
giving a fourth weight coefficient when σ and γ satisfy a preset conditionω4Assigning values such that:
and then, for each bee B, calculating the corresponding J value, namely the fitness of the bee B.
Further, the process of updating the scout bee position in the step 4) comprises the following steps:
nth scout bee BnThe j-th movement of (a) is performed in the following manner:
Bn(j)＝Bn(j-1)+λn(j)Δn(j)
wherein λ isnIs BnStep size of movement of, ΔnIs BnDirection of movement of, omega0For the inertial weight to set the contribution of the first two steps of movement distance to the next step,
the geometric center, mu, of all the detected bee colonies at the (j-1) th step1U (0,1) and
are random variables with a uniform distribution.
Further, the process of updating the location of the follower bees in the step 4) is as follows:
m th follower bee BmThe j-th movement of (a) is performed in the following manner:
Bm(j)＝Bm(j-1)+λm(j)Δm(j)
wherein λ ismIs BmStep size of movement of, ΔmIs BmDirection of movement of, omega0For the inertial weight to set the contribution of the first two step movement distance to the next step, BnmIs a distance BmNearest nth scout bee, mu3U (0,1) and
are random variables with a uniform distribution.
Further, the step 4) of dynamically adjusting the optimizing area of the bee by adopting a self-adaptive search strategy specifically comprises the following steps:
for nth bee Bn(N-1, …, N) after the j-th search is completed, the following adaptive search strategy is adopted to dynamically adjust its optimization area:
wherein,
and
respectively represent BnAt the upper and lower bounds of the j-th search,
is the geometric center of the whole bee colony in the j-1 step, | | | | represents the European distance, mu, is obtained5U (0,1) is a random variable with a uniform distribution.
A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method for optimizing parameters of an electric vehicle control system based on an improved bee colony algorithm when executing the computer program.
A computer-readable storage medium, which stores a computer program, wherein the computer program, when executed by a processor, implements the steps of the above method for optimizing parameters of an electric vehicle control system based on an improved bee colony algorithm.
Compared with the prior art, the invention has the following beneficial effects:
a parameter optimization method of an electric vehicle control system based on an improved bee colony algorithm is characterized in that a bee colony with low diversity factor is adopted to carry out self-adaptive search on a solution space, an optimization area of each bee is dynamically adjusted in the search process, and the global and local optimization capacities of the algorithm are considered, so that the algorithm can be prevented from falling into local optimization in the search process, the convergence speed of the algorithm can be ensured, and the overall optimal solution can be more quickly approached; the method solves the problems of complex modeling and large calculation amount in the parameter optimization process of the electric vehicle control system, and can quickly and accurately optimize the parameters of the electric vehicle control system; compared with the traditional linear programming method, the electric vehicle control system optimized by the optimization method provided by the invention has the advantages that the system has better stability and the lateral offset error is rapidly reduced when the double-shift-line task is completed.
Further, the inventive definition of the bee colony enables the generated bee colony to be distributed in the solution space as uniformly as possible, and the generated bee sequences have low diversity.
The invention provides computer equipment and a storage medium of an electric vehicle control system parameter optimization method based on an improved bee colony algorithm, which are used for realizing the specific steps of the optimization method.
Drawings
FIG. 1 is a schematic structural diagram of an electric vehicle control system;
FIG. 2 is a flow chart of the parameter optimization based on the improved bee colony algorithm of the present invention;
FIG. 3 is a diagram illustrating the yaw angle of an electric vehicle control system optimized by applying the linear programming method and the method of the present invention in a double-shift line condition;
FIG. 4 is a chart illustrating the yaw rate of the electric vehicle control system optimized using the linear programming method and the method of the present invention in a double-traverse mode;
FIG. 5 is a diagram illustrating the centroid slip angle of the electric vehicle control system optimized by the linear programming method and the method of the present invention under the double-shift condition;
FIG. 6 is a side overload of an electric vehicle control system optimized using the linear programming method and the method of the present invention in a double-traverse mode;
FIG. 7 is a graph illustrating lateral offset error of an electric vehicle control system optimized by applying the linear programming method and the method of the present invention in a double-shift line condition;
FIG. 8 is a schematic diagram of a computer device in an embodiment of the invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The invention is described in further detail below with reference to the accompanying drawings:
referring to fig. 1, fig. 1 shows a junction of a control system of an electric vehicleThe control system is constructed as a schematic diagram, and the control system passes 5 state parameters during the running process of the vehicle, namely a yaw angle phi and a lateral overload nyFeedback of the lateral offset y, the centroid lateral offset angle beta, and the yaw rate r to generate the desired yaw moment input and steering wheel angle input, phicAnd ycRespectively, command signals for yaw angle and lateral offset. Controlling gain k in each feedback channel1～k8I.e. the parameters that need to be optimized for the target system.
Fig. 2 shows a flow chart of a parameter optimization method for an electric vehicle control system based on an improved bee colony algorithm, which is directed to 8 parameters k to be optimized of the electric vehicle control system in fig. 11～k8Defining the dimension of the initial bee colony as NxD dimension, wherein N-100 represents the total number of the bee colony, D-8 represents the total number of parameters to be optimized of the electric vehicle control system stored in each bee individual, and the maximum allowable optimization step number of the algorithm is 500 steps;
in order to distribute the generated bee colony as uniformly as possible in the solution space, a bee sequence with a low degree of diversity is generated by using a method in which the D (D) th dimension element B of the nth (N-1, 2, …, N) th bee is 1,2, …, Dn,dGiven by the following equation:
Bn,d＝<x1,x2,…,xn>⊕<y1,y2,…,yd>
wherein,<x1,x2,…,xn>is represented by x1,x2,…,xnThe number of binary digits that are formed,<y1,y2,…,yd>is represented by y1,y2,…,ydFormed binary number, xi(i 1.., n) is the ith bit in a binary representation of a positive integer X, where X is chosen to satisfy the condition: 2X>N，yi(i 1.. d) is the ith bit in a binary expression of another positive integer Y coprime to X, where Y is chosen to satisfy the condition: 2Y>D; ≧ is XOR operator; b isn,dRepresenting the estimated value of the nth bee to the parameter needing to be optimized of the electric vehicle control system;
step 2: constructing a dynamic evaluation index function J of the electric vehicle control system as follows:
J＝ω1∫dt+ω2σ+ω3γ+ω4κ
wherein, sigma, gamma and kappa are four evaluation indexes of the performance of the control system of the electric automobile respectively: tracking error, overshoot percentage, rise time and settling time; omega1、ω2、ω3、ω4The weight coefficients are respectively corresponding to the four evaluation indexes; t is the test time of the system;
when the parameters of the electric automobile control system are optimized, presetting an initial value of a weight coefficient:
in the process of algorithm optimization, a first weight coefficient omega is given first1Assigning values such that:
ω2＝ω3＝ω 40; giving a second weight coefficient omega when a preset condition is satisfied2Assigning values such that:
ω3＝ω 40; giving a third weight coefficient ω when the sum σ satisfies a preset condition3Assigning values such that:
ω 40; giving the fourth weight coefficient ω when σ and γ satisfy the preset condition4Assigning values such that:
then, for each bee B, calculating the corresponding J value, namely the fitness of the bee B;
and step 3: arranging individuals of the whole population in an ascending order according to the corresponding J values, and setting the first 10% of bees as detection bees and the last 90% of bees as follower bees; during the search, the nth scout bee
The j-th motion of (1) is performed as follows:
Bn(j)＝Bn(j-1)+λn(j)Δn(j)
wherein λ isnIs BnStep size of movement of, ΔnIs BnDirection of movement of, omega0For the inertial weight to set the contribution of the first two steps of movement distance to the next step,
the geometric center, mu, of all the detected bee colonies at the step j-11U (0,1) and
random variables with uniform distribution;
the mth follower bee
The j-th motion of (1) is performed as follows:
Bm(j)＝Bm(j-1)+λm(j)Δm(j)
wherein λ ismIs BmStep size of movement of, ΔmIs BmDirection of movement of, omega0For setting inertial weightContribution of the first two movement distances to the next step, BnmIs a distance BmNearest nth scout bee, mu3U (0,1) and
random variables with uniform distribution;
and 4, step 4: in order to take account of the global and local optimization abilities of the algorithm, the nth bee Bn(N-1, …, N) at step j, the following adaptive search strategy will be used to dynamically adjust its seek area:
wherein,
and
respectively represent BnAt the upper and lower bounds of the j-th search,
is the geometric center of the whole bee colony in the j-1 step, | | | | represents the European distance, mu, is obtained5U (0,1) is a random variable with a uniform distribution;
and 5: after the search in the jth step is finished, checking whether J reaches the maximum allowable optimization step number, if not, returning to the step 2 to continue the optimization, otherwise, stopping the calculation, and finding out the bee individual B with the minimum J value in the current populationminThe value (B) corresponding to each dimension of the beemin,1,Bmin,2,…,Bmin,D) Namely, the parameter values of the electric vehicle control system obtained by final optimization.
Testing and verifying by using data obtained under Simulink simulation environment:
examples
8 final optimization parameters k of the electric vehicle control system obtained by the method1～k8＝[2.43780.0080-0.0377-45.6130-83.0195-3.511751.5439-0.1968]And carrying out double-wire-shifting working condition test.
The parameters of the electric vehicle control system are optimized by using a traditional linear programming method and the improved bee colony algorithm provided by the invention, and then the optimized system is subjected to a double-shift-line working condition test, wherein the test time is 10 seconds. Compared with the 5 state parameters of the system optimized by the two methods, namely the yaw angle, the yaw rate, the mass center side deviation angle, the lateral overload and the lateral offset error, referring to fig. 3-7, it can be seen from fig. 3-6 that compared with the traditional linear programming method, the control system optimized by applying the method of the invention can enable the electric automobile to have smaller variation ranges of the yaw angle, the yaw rate, the mass center side deviation angle and the lateral overload under the condition of double-line-shifting work, and improve the stability of the system; it can be seen from fig. 7 that the method of the present invention can rapidly reduce the lateral offset error to zero in the driving process of the electric vehicle, and improve the rapidity of the vehicle for command tracking. Therefore, the working state in the double-shift line condition can be seen as follows: compared with the traditional linear programming method, the electric vehicle control system optimized by the method disclosed by the invention has better stability and quickly reduced lateral offset error when the double-shift-line task is completed.
In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as shown in fig. 8. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The computer program is executed by a processor to implement a method for optimizing parameters of an electric vehicle control system based on an improved bee colony algorithm.
In one embodiment, a computer device is provided, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the following steps when executing the computer program: 1) defining an initial bee colony, and constructing a dynamic evaluation index function of an electric vehicle control system;
2) optimizing the parameters of the electric vehicle control system by using the bee colony, and calculating by using a dynamic evaluation index function to obtain the fitness corresponding to each bee individual in the bee colony;
3) arranging the bee colony individuals according to the fitness of the bee colony individuals in an ascending order, and setting a plurality of former bees in the arrangement as detection bees and the rest bees as follower bees;
4) carrying out scout bee position updating and follower bee position updating;
in the position updating process, adjusting the optimizing area of the bee individuals after each step of searching by adopting a self-adaptive searching strategy, judging whether the maximum allowable optimizing step number is reached, and if the maximum allowable optimizing step number is not reached, returning to the step 2);
otherwise, go to step 5);
5) and finding out the bee individual with the minimum fitness in the current population, wherein the value corresponding to each dimension of the bee individual is the parameter value of the electric vehicle control system obtained through final optimization.
In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of: 1) defining an initial bee colony, and constructing a dynamic evaluation index function of an electric vehicle control system;
2) optimizing the parameters of the electric vehicle control system by using the bee colony, and calculating by using a dynamic evaluation index function to obtain the fitness corresponding to each bee individual in the bee colony;
3) arranging the bee colony individuals according to the fitness of the bee colony individuals in an ascending order, and setting a plurality of former bees in the arrangement as detection bees and the rest bees as follower bees;
4) carrying out scout bee position updating and follower bee position updating;
in the position updating process, adjusting the optimizing area of the bee individuals after each step of searching by adopting a self-adaptive searching strategy, judging whether the maximum allowable optimizing step number is reached, and if the maximum allowable optimizing step number is not reached, returning to the step 2);
otherwise, go to step 5);
5) and finding out the bee individual with the minimum fitness in the current population, wherein the value corresponding to each dimension of the bee individual is the parameter value of the electric vehicle control system obtained through final optimization.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, databases, or other media used in embodiments provided herein may include non-volatile and/or volatile memory. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The above-mentioned contents are only for illustrating the technical idea of the present invention, and the protection scope of the present invention is not limited thereby, and any modification made on the basis of the technical idea of the present invention falls within the protection scope of the claims of the present invention.
Claims (9)
1. An electric vehicle control system parameter optimization method based on an improved bee colony algorithm is characterized by comprising the following steps:
1) defining an initial bee colony, and constructing a dynamic evaluation index function of an electric vehicle control system;
2) optimizing the parameters of the electric vehicle control system by using the bee colony, and calculating by using a dynamic evaluation index function to obtain the fitness corresponding to each bee individual in the bee colony;
3) arranging the bee colony individuals according to the fitness of the bee colony individuals in an ascending order, and setting a plurality of former bees in the arrangement as detection bees and the rest bees as follower bees;
4) carrying out scout bee position updating and follower bee position updating;
in the position updating process, adjusting the optimizing area of the bee individuals after each step of searching by adopting a self-adaptive searching strategy, judging whether the maximum allowable optimizing step number is reached, and if the maximum allowable optimizing step number is not reached, returning to the step 2);
otherwise, go to step 5);
5) and finding out the bee individual with the minimum fitness in the current population, wherein the value corresponding to each dimension of the bee individual is the parameter value of the electric vehicle control system obtained through final optimization.
2. The improved bee colony algorithm-based parameter optimization method for the electric vehicle control system according to claim 1, wherein the specific process of the step 1) is as follows:
defining the dimension of the initial bee colony as NxD dimension, wherein N represents the total number of the bee colony, D represents the total number of parameters to be optimized of the electric vehicle control system stored by the individual bee, and the D (D is 1,2, …, D) dimension element B of the nth (N is 1,2, …, N) been,dGiven by the following equation:
wherein,<x1,x2,…,xn>is represented by x1,x2,…,xnA binary number of the composition;<y1,y2,…,yd>is represented by y1,y2,…,ydA binary number of the composition; x is the number ofi(i ═ 1.. times, n) is the ith bit in the binary representation of a positive integer X, X satisfying the condition: 2X>N；yi(i 1.., d) is the ith bit in the binary expression of another positive integer Y coprime to X, Y satisfying: 2Y>D；
Is an XOR operator; b isn,dRepresents the estimated value of the nth bee to the d-th parameter needing to be optimized of the electric vehicle control system.
3. The parameter optimization method for the electric vehicle control system based on the improved bee colony algorithm according to claim 2, wherein the step 1) of constructing the dynamic evaluation index function of the electric vehicle control system specifically comprises the following steps:
J＝ω1∫dt+ω2σ+ω3γ+ω4κ
wherein, sigma, gamma and kappa are four evaluation indexes of the performance of the control system of the electric automobile, namely tracking error, overshoot percentage, rise time and establishment time; omega1、ω2、ω3、ω4The weight coefficients are respectively corresponding to the four evaluation indexes; and t is the test time of the system.
4. The improved bee colony algorithm-based parameter optimization method for the electric vehicle control system according to claim 3, wherein the optimization process in the step 2) is as follows:
when the optimization is started, presetting an initial value of a weight coefficient:
in the process of optimizing, first, a first weight coefficient omega is given1Assigning values such that:
ω2＝ω3＝ω4＝0；
giving a second weight coefficient omega when a preset condition is satisfied2Assigning values such that:
ω3＝ω4＝0；
giving a third weight coefficient ω when the sum σ satisfies a preset condition3Assigning values such that:
ω4＝0；
giving the fourth weight coefficient ω when σ and γ satisfy the preset condition4Assigning values such that:
and then, for each bee B, calculating the corresponding J value, namely the fitness of the bee B.
5. The improved bee colony algorithm-based parameter optimization method for the electric vehicle control system according to claim 4, wherein the scout bee position updating process in the step 4) comprises the following steps:
nth scout bee BnThe j-th movement of (a) is performed in the following manner:
Bn(j)＝Bn(j-1)+λn(j)Δn(j)
wherein λ isnIs BnStep size of movement of, ΔnIs BnDirection of movement of, omega0For the inertial weight to set the contribution of the first two steps of movement distance to the next step,
the geometric center, mu, of all the detected bee colonies at the (j-1) th step1U (0,1) and
are random variables with a uniform distribution.
6. The improved bee colony algorithm-based parameter optimization method for the electric vehicle control system according to claim 5, wherein the process of updating the position of the follower bees in the step 4) is as follows:
m th follower bee BmThe j-th movement of (a) is performed in the following manner:
Bm(j)＝Bm(j-1)+λm(j)Δm(j)
wherein λ ismIs BmStep size of movement of, ΔmIs BmDirection of movement of, omega0For the inertial weight to set the contribution of the first two step movement distance to the next step, BnmIs a distance BmNearest nth scout bee, mu3U (0,1) and
are random variables with a uniform distribution.
7. The improved bee colony algorithm-based parameter optimization method for the electric vehicle control system as claimed in claim 4, wherein the step 4) of dynamically adjusting the optimization area of the bees by using the adaptive search strategy specifically comprises:
for nth bee Bn(N-1, …, N) after the j-th search is completed, the following adaptive search strategy is adopted to dynamically adjust its optimization area:
wherein,
and
respectively represent BnAt the upper and lower bounds of the j-th search,
is the geometric center of the whole bee colony in the j-1 step, | | | | represents the European distance, mu, is obtained5U (0,1) is a random variable with a uniform distribution.
8. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor when executing the computer program implements the steps of the improved bee colony algorithm based parameter optimization method for an electric vehicle control system according to any one of claims 1-7.
9. A computer-readable storage medium, in which a computer program is stored, which, when being executed by a processor, carries out the steps of the improved bee colony algorithm-based parameter optimization method for an electric vehicle control system according to any one of claims 1 to 7.
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