CN115675102A - Particle swarm algorithm optimized hybrid electric vehicle regenerative braking control method - Google Patents
Particle swarm algorithm optimized hybrid electric vehicle regenerative braking control method Download PDFInfo
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Abstract
The invention relates to the field of hybrid electric vehicles, and discloses a particle swarm algorithm optimized hybrid electric vehicle regenerative braking control method which mainly comprises three parts, namely front wheel and rear wheel braking force distribution, fuzzy controller design and particle swarm algorithm optimized fuzzy rule optimization. Firstly, realizing the distribution of the braking force of the front wheel and the braking force of the rear wheel by utilizing a braking force distribution curve of the front wheel and the rear wheel; then, the distribution of the mechanical braking force and the regenerative braking force of the front wheel is realized through a fuzzy controller; and finally, optimizing the fuzzy rule by using the particle swarm algorithm by taking the braking effect and the braking energy recovery as an optimization objective function, and applying the optimized fuzzy rule to the regenerative braking control of the hybrid electric vehicle. The invention reduces the design difficulty of fuzzy rules, improves the energy recovered by braking under the condition of meeting the braking effect, and is beneficial to energy conservation and emission reduction of the hybrid electric vehicle.
Description
Technical Field
The invention relates to the field of hybrid electric vehicles, in particular to a particle swarm algorithm optimized regenerative braking control method for a hybrid electric vehicle.
Background
With the decrease of fossil fuels and the increasing of global environmental problems, the reduction of petroleum consumption in the automobile industry and the improvement of energy utilization of automobiles have become important research points. At present, the energy recovery of the hybrid electric vehicle mainly has three modes: recovering energy by using waste heat of exhaust gas discharged by an engine; recovering energy in the vibration process of the suspension; energy recovery during braking. The invention focuses on energy recovery of a hybrid electric vehicle in the braking process, and the hybrid electric vehicle is provided with a motor and a lithium battery, so that kinetic energy can be converted into electric energy through power generation of the motor in the braking process, the obtained electric energy is stored in the lithium battery, and the hybrid electric vehicle can be used for energy consumption accessories of an HEV (hybrid electric vehicle) and can be used for driving the vehicle to run by the motor, and thus, the energy consumption is reduced.
The particle swarm algorithm is to simulate the foraging behavior of a bird swarm, wherein each bird is simplified into a particle. In a certain area, namely a range limited by the position x, each particle searches for a respective individual extreme value pBest, meanwhile, each particle carries out information sharing through the global optimal solution gBest, each particle continuously updates the respective position x and speed v in an iteration range, and iteration is terminated when the iteration times or the fitness function are met and reach a required range, so that a corresponding global optimal solution is obtained.
The traditional regenerative braking control method mainly considers a single evaluation index to achieve the maximum energy recovery or the best braking effect, and some methods still consider the driving style and the braking intention of a driver and take the single evaluation index to achieve the maximum energy recovery as the essence. Meanwhile, the design of the fuzzy rule of the fuzzy controller has high dependence on engineering experience of designers, and is not beneficial to maximizing the designed target.
Disclosure of Invention
The invention provides a particle swarm algorithm optimized hybrid electric vehicle regenerative braking control method, which aims to reduce the design difficulty of fuzzy rules, improve the energy recovered by braking under the condition of meeting the braking effect and further realize the energy conservation and emission reduction of a hybrid electric vehicle.
In order to solve the problems, the invention adopts the specific scheme that:
a regenerative braking control method of a hybrid electric vehicle optimized by a particle swarm algorithm mainly comprises three parts, namely front and rear wheel braking force distribution, a fuzzy controller design and a particle swarm algorithm optimization fuzzy rule;
according to the parameters of the whole vehicle and the required braking force, the braking force distribution of the front wheel and the rear wheel is realized through the designed braking force distribution curves of the front wheel and the rear wheel; based on the braking strength and the battery SOC, the distribution of the front wheel regenerative braking force and the front wheel mechanical braking force is realized by utilizing a designed fuzzy controller; and optimizing the fuzzy rule by using the particle swarm optimization algorithm by taking the braking effect and the braking energy recovery as an optimization objective function, and applying the optimized fuzzy rule to the regenerative braking control of the hybrid electric vehicle.
The method comprises the following steps:
s1: according to the parameters of the whole vehicle and the required braking force, the braking force distribution curves of the front wheels and the rear wheels are utilized to realize the distribution of the braking force of the front wheels and the rear wheels, and the braking force of the front wheels and the braking force of the rear wheels are respectively obtained; the whole vehicle parameters comprise the whole vehicle preparation mass m of the vehicle, the radius r of wheels, the wheelbase L, the distance a from the center of mass to a front axle, the distance b from the center of mass to a rear axle and the height h from the center of mass to the ground;
s2: based on the braking strength and the battery SOC, a ratio of the front wheel regenerative braking force to the front wheel braking force is obtained by using a designed fuzzy controller, and then the front wheel braking force obtained in the step S1 is used for realizing the distribution of the front wheel regenerative braking force and the front wheel mechanical braking force, so that the front wheel regenerative braking force and the front wheel mechanical braking force are respectively obtained;
s3: optimizing the fuzzy rule of the fuzzy controller in the step S2 by utilizing a particle swarm algorithm, applying the optimized fuzzy rule to the fuzzy controller in the original regenerative braking control, and repeating the step S1 and the step S2 to obtain the optimized front wheel regenerative braking force, the optimized front wheel mechanical braking force and the optimized rear wheel braking force;
s4: and respectively distributing the front wheel regenerative braking force, the front wheel mechanical braking force and the rear wheel braking force which are optimized to the motor, the front wheel brake and the rear wheel brake to realize the recovery of energy in the braking process of the hybrid electric vehicle.
Advantageous effects
1. The invention comprehensively considers the braking effect and the braking energy recovery when designing the braking force distribution curves of the front wheel and the rear wheel, so that the braking force is distributed to the front wheel as much as possible on the premise of meeting the braking effect, the braking force distributed by the front wheel is improved, and the front wheel is used as a driving wheel, thereby being beneficial to improving the energy recovered by braking.
2. The method takes the braking effect and the braking energy recovery as optimization targets, optimizes the fuzzy rule of the fuzzy controller by utilizing the particle swarm optimization, applies the optimized fuzzy rule to the regenerative braking of the hybrid electric vehicle, is favorable for reducing the design difficulty of the fuzzy rule, simultaneously improves the energy recovered by the regenerative braking as much as possible under the condition of meeting the braking performance requirement of the hybrid electric vehicle, and is favorable for the energy conservation and emission reduction of the hybrid electric vehicle.
Drawings
FIG. 1 is a schematic diagram of the overall regenerative braking control architecture of a hybrid electric vehicle according to the present invention;
FIG. 2 is a graph of the braking force distribution of the front and rear wheels in the present invention;
FIG. 3 is a diagram of a fuzzy inference surface before optimization in the present invention;
FIG. 4 is a flow chart of the particle swarm optimization fuzzy rule of the present invention;
FIG. 5 is a diagram of the optimized fuzzy inference curve in the present invention;
FIG. 6 is a diagram showing the variation of the vehicle speed difference before and after optimization according to the present invention;
FIG. 7 is a diagram showing the variation of the SOC of the battery before and after the optimization in accordance with the present invention;
Detailed Description
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 obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention
Referring to fig. 1-7, the invention provides a particle swarm optimization-based regenerative braking control method for a hybrid electric vehicle, which mainly comprises three parts, namely front and rear wheel braking force distribution, fuzzy controller design and particle swarm optimization fuzzy rule optimization;
and the front and rear wheel brake distribution part realizes the distribution of the front and rear wheel brake force and the distribution of the brake force to the front wheel as far as possible by utilizing a designed front and rear wheel brake force distribution curve which comprehensively considers the brake effect and the recovery of brake energy according to the brake intensity and the whole vehicle parameters.
In the front wheel regenerative braking force and front wheel mechanical braking force distribution part, firstly, manually and preliminarily designing a fuzzy rule according to braking strength and battery SOC, realizing front wheel regenerative braking force and front wheel mechanical braking force distribution through a fuzzy controller, optimizing the fuzzy rule of the fuzzy controller by using a particle swarm optimization algorithm with the braking effect and braking energy recovery as optimization targets, and applying the optimized fuzzy rule to the regenerative braking of the hybrid electric vehicle.
The required braking force includes rolling resistance, air resistance, gradient resistance and acceleration resistance, and the required braking force F is a braking force irrespective of the gradient resistance b Comprises the following steps:
wherein, F b Is the required braking force, F bf For front wheel braking force, F br In order to provide a braking force for the rear wheels,m is the finished vehicle mass, g is the gravitational acceleration, f is the rolling resistance coefficient, C D Is the coefficient of air resistance, A is the frontal area, δ is the coefficient of rotating mass transfer, u is the vehicle speed, and du/dt during braking<0；
The front and rear braking force distribution curve is shown as follows:
when the braking strength z is between 0 and 0.1, the braking force required by the hybrid electric vehicle during braking is completely provided by the front wheels, the rear wheels are not divided into power, the braking force of the front wheels and the rear wheels is expressed as,
wherein G is gravity, z is braking strength,
z is between 0.1 and 0.10536, the front and rear wheel braking forces are expressed as,
z is between 0.10536 and 0.46, the front and rear wheel braking forces are expressed as,
wherein β is a ratio of the front wheel braking force to the required braking force, and β =0.94916;
z is between 0.46 and 0.6, the front and rear wheel braking forces are expressed as,
z is between 0.6 and 0.7, the front and rear wheel braking forces are expressed as,
whereinL is the wheelbase, b is the distance from the center of mass to the rear axle, h is the height from the center of mass to the ground,
is the road surface adhesion coefficient;
in emergency braking, when the braking intensity z is more than 0.7, the braking force of the front wheel and the rear wheel is distributed according to an I curve, the energy of the front wheel is not recovered through regenerative braking, the braking force of the front wheel and the rear wheel is calculated as,
the brake strength is a parameter for measuring the brake degree of the hybrid electric vehicle, and the formula of the brake strength z is as follows:
dudt＝zg
wherein t is time, and g is gravity acceleration;
the battery SOC is an important parameter for measuring the residual electric quantity of the battery, and the calculation formula of the battery SOC is as follows:
wherein, SOC o Is the initial state of charge value of the battery, I is the charging current, η chrg Is the battery charging efficiency and Q is the battery capacity.
Optimizing fuzzy rules of a fuzzy controller by a particle swarm algorithm, wherein the fuzzy rules mainly comprise fuzzy rule coding, fitness function design and particle updating speed and position; the fuzzy rule coding is to code the fuzzy linguistic variable into a corresponding positive integer; the fitness function is designed by taking the braking effect and the braking energy recovery as optimization targets; updating the particle speed and position is to search the corresponding particle position when the fitness function is minimum through continuous iteration, and obtain the corresponding optimized fuzzy rule after decoding.
The particle swarm optimization fuzzy controller fuzzy rule comprises the following specific steps:
s1, setting iteration times, particle number, learning factors and inertia weight;
s2, fuzzy rule coding, namely coding the fuzzy rule into a positive integer;
s3, initializing the particle speed and initializing the positions of the particles according to the encoded fuzzy rule, so that the optimization speed of the particle swarm optimization is accelerated;
s4, building a whole hybrid electric vehicle model, and calculating the SOC and the speed difference of the battery;
s5, designing a fitness function, wherein the formula is as follows:
J＝λ(max(|u-u ref |))+(SOC o -SOC end )
the constraint conditions are as follows:
therein, SOC end The SOC value and SOC value of the battery at the end of the standard Driving mode (UDDS mode) o Is an initial value of the SOC of the battery, u ref The vehicle speed is the vehicle speed under the standard driving condition, u is the collected actual vehicle speed of the whole vehicle, lambda is the learning factor, T m Motor torque, T m_min Is the allowable generating torque of the motor and is a negative value, n m_min Is the minimum rotational speed of the motor, n, which allows recovery of braking energy m_max Is the maximum rotational speed, SOC, of the motor max Maximum battery SOC to allow recovery of braking energy;
s6, updating the position and the speed of the particles, wherein the formula is as follows:
where vi d (k + 1) represents the d-dimensional velocity of the ith particle in the (k + 1) th iteration, w is the inertial weight, c 1 、c 2 Local and global learning factors, x, respectivelyid (k + 1) represents the position of the ith particle in the d dimension for the (k + 1) th iteration;
s7, finishing the particle swarm optimization fuzzy controller fuzzy rule when the iteration times are reached;
and S8, obtaining a coding fuzzy rule after optimization, and obtaining the fuzzy rule through decoding.
The optimized fuzzy rule replaces the fuzzy rule in the fuzzy controller before optimization, the distribution of the front wheel regenerative braking force and the front wheel mechanical braking force is realized, and the control of the regenerative braking of the hybrid electric vehicle is finally realized.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (5)
1. A particle swarm algorithm optimized hybrid electric vehicle regenerative braking control method is characterized in that: mainly comprises front and rear wheel braking force distribution, fuzzy controller design and particle swarm algorithm optimization fuzzy rules;
according to the parameters of the whole vehicle and the required braking force, the braking force distribution of the front wheel and the rear wheel is realized through the designed braking force distribution curves of the front wheel and the rear wheel; based on the braking strength and the SOC of the battery, the distribution of the front wheel regenerative braking force and the front wheel mechanical braking force is realized by utilizing a designed fuzzy controller; and optimizing the fuzzy rule by using the particle swarm algorithm by taking the braking effect and the braking energy recovery as an optimization objective function, and applying the optimized fuzzy rule to the regenerative braking control of the hybrid electric vehicle.
2. The particle swarm algorithm-optimized hybrid vehicle regenerative braking control method according to claim 1, wherein: the method comprises the following steps:
s1: according to the parameters of the whole vehicle and the required braking force, the braking force distribution curves of the front wheels and the rear wheels are utilized to realize the distribution of the braking force of the front wheels and the rear wheels, and the braking force of the front wheels and the braking force of the rear wheels are respectively obtained; the whole vehicle parameters comprise the whole vehicle preparation mass m of the vehicle, the radius r of wheels, the wheelbase L, the distance a from the center of mass to a front axle, the distance b from the center of mass to a rear axle and the height h from the center of mass to the ground;
s2: based on the braking strength and the battery SOC, a ratio of the front wheel regenerative braking force to the front wheel braking force is obtained by using a designed fuzzy controller, and then the front wheel braking force obtained in the step S1 is used for realizing the distribution of the front wheel regenerative braking force and the front wheel mechanical braking force, so that the front wheel regenerative braking force and the front wheel mechanical braking force are respectively obtained;
s3: optimizing the fuzzy rule of the fuzzy controller in the step S2 by utilizing a particle swarm algorithm, applying the optimized fuzzy rule to the fuzzy controller in the original regenerative braking control, and repeating the step S1 and the step S2 to obtain the optimized front wheel regenerative braking force, the optimized front wheel mechanical braking force and the optimized rear wheel braking force;
s4: and respectively distributing the front wheel regenerative braking force, the front wheel mechanical braking force and the rear wheel braking force which are optimized to the front and the rear to the motor, the front wheel brake and the rear wheel brake to realize the control of the regenerative braking of the hybrid electric vehicle.
3. The particle swarm algorithm-optimized hybrid vehicle regenerative braking control method according to claim 2, wherein:
in S1, the required braking force comprises rolling resistance, air resistance, gradient resistance and acceleration resistance, and the required braking force F does not consider the gradient resistance b Comprises the following steps:
wherein, F b Is the required braking force, F bf For front wheel braking force, F br For rear wheel braking force, m is the overall vehicle trim mass, g is the gravitational acceleration, f is the rolling resistance coefficient, C D Is the coefficient of air resistance, A is the frontal area, δ is the coefficient of rotating mass transfer, u is the vehicle speed, and du/dt during braking<0；
The formula of the front and rear braking force distribution curve is as follows:
when the braking strength z is between 0 and 0.1, the braking force required by the hybrid electric vehicle during braking is completely provided by the front wheels, the rear wheels are not divided into power, the braking force of the front wheels and the rear wheels is expressed as,
wherein G is gravity, z is braking strength,
z is between 0.1 and 0.10536, the front and rear wheel braking forces are expressed as,
z is between 0.10536 and 0.46, the front and rear wheel braking forces are expressed as,
wherein β is a ratio of the front wheel braking force to the required braking force, and β =0.94916;
z is between 0.46 and 0.6, the front and rear wheel braking forces are expressed as,
z is between 0.6 and 0.7, the front and rear wheel braking forces are expressed as,
wherein L is the wheelbase, b is the distance from the center of mass to the rear axle, h is the height from the center of mass to the ground,
is the road adhesion coefficient;
in emergency braking, when the braking strength z is greater than 0.7, the braking forces of the front wheel and the rear wheel are distributed according to an I curve, the energy of the front wheel is not recovered through regenerative braking, the braking forces of the front wheel and the rear wheel are calculated as,
4. the particle swarm algorithm-optimized hybrid vehicle regenerative braking control method according to claim 2, wherein: in S2, the braking strength is a parameter for measuring the braking degree of the hybrid electric vehicle, and the formula of the braking strength z is as follows:
du/dt＝zg
wherein t is time, and g is gravity acceleration;
the battery SOC is an important parameter for measuring the residual electric quantity of the battery, and the calculation formula of the battery SOC is as follows:
therein, SOC o Is the initial state of charge value of the battery, I is the charging current, η chrg Is the battery charging efficiency, and Q is the battery capacity.
5. A particle swarm algorithm optimized hybrid vehicle regenerative braking control method as recited in claim 2, wherein: s3, optimizing fuzzy rules of the fuzzy controller by the particle swarm algorithm, wherein the fuzzy rules mainly comprise fuzzy rule coding, fitness function designing and particle updating speed and position; the fuzzy rule coding is to code the fuzzy linguistic variable into a corresponding positive integer; the fitness function is designed by taking the braking effect and the braking energy recovery as optimization targets; updating the particle speed and the particle position comprises the steps of searching the corresponding particle position when the fitness function is minimum through continuous iteration, and obtaining a corresponding optimized fuzzy rule after decoding;
the designed fitness function comprehensively considers the braking effect and the braking energy recovery, and the formula is that J = lambda (max (| u-u) ref |))+(SOC o -SOC end )
The constraint conditions are as follows:
therein, SOC end The SOC value and SOC value of the battery at the end of the standard Driving mode (UDDS mode) o Is an initial value of the SOC of the battery, u ref The vehicle speed is the vehicle speed under the standard driving condition, u is the acquired actual vehicle speed of the whole vehicle, lambda is a learning factor, and T m Motor torque, T m_min Is the allowable generating torque of the motor and is a negative value, n m_min Is the minimum rotational speed of the motor, n, which allows recovery of braking energy m_max Is the maximum rotational speed, SOC, of the motor max The maximum value of the battery SOC for allowing the recovery of braking energy.
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| CN116278803B (en) * | 2023-03-30 | 2024-03-08 | 吉林大学 | Energy-saving torque distribution system of electric automobile driven by four-wheel hub motor and control method thereof |
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