CN110239355A - Hybrid vehicle regenerating brake control method - Google Patents
Hybrid vehicle regenerating brake control method Download PDFInfo
- Publication number
- CN110239355A CN110239355A CN201910560657.6A CN201910560657A CN110239355A CN 110239355 A CN110239355 A CN 110239355A CN 201910560657 A CN201910560657 A CN 201910560657A CN 110239355 A CN110239355 A CN 110239355A
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- particle
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- optimal solution
- speed
- hybrid vehicle
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- 238000000034 method Methods 0.000 title claims abstract description 36
- 230000001172 regenerating effect Effects 0.000 title claims abstract description 30
- 239000002245 particle Substances 0.000 claims description 81
- 238000005457 optimization Methods 0.000 claims description 17
- 230000003044 adaptive effect Effects 0.000 claims description 6
- 230000000694 effects Effects 0.000 abstract description 2
- 230000005611 electricity Effects 0.000 description 1
- 230000005284 excitation Effects 0.000 description 1
- 238000010248 power generation Methods 0.000 description 1
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L7/00—Electrodynamic brake systems for vehicles in general
- B60L7/10—Dynamic electric regenerative braking
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W20/00—Control systems specially adapted for hybrid vehicles
- B60W20/10—Controlling the power contribution of each of the prime movers to meet required power demand
- B60W20/13—Controlling the power contribution of each of the prime movers to meet required power demand in order to stay within battery power input or output limits; in order to prevent overcharging or battery depletion
- B60W20/14—Controlling the power contribution of each of the prime movers to meet required power demand in order to stay within battery power input or output limits; in order to prevent overcharging or battery depletion in conjunction with braking regeneration
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- Engineering & Computer Science (AREA)
- Transportation (AREA)
- Mechanical Engineering (AREA)
- Power Engineering (AREA)
- Automation & Control Theory (AREA)
- Regulating Braking Force (AREA)
- Electric Propulsion And Braking For Vehicles (AREA)
Abstract
Hybrid vehicle regenerating brake control method.In emergency braking, needing severity of braking larger, and regenerative braking device not can guarantee braking effect, will will affect braking safety in this way.Present invention composition include: severity of braking z be divided into 0≤z< 0.1,0.1≤z< 0.8,0.8≤zThree ranges, the division for carrying out specific curve to front and back wheel brake force according to specific range, under urban traffic situation, severity of braking is not more than 0.3, rear-wheel distributes brake force by fixed proportion in electric vehicle brake, and front-wheel friction catch and regenerative braking are distributed according to fuzzy algorithmic approach.Regenerative braking of the present invention for hybrid vehicle controls.
Description
Technical field:
The present invention relates to a kind of hybrid vehicle regenerating brake control methods.
Background technique:
The regenerative braking device being widely used in electric car or hybrid vehicle can lead to during vehicle braking
Motor of overdriving converts electric energy for the kinetic energy of vehicle and stores in the battery, and regenerative braking device is by driving motor
Excitation makes driving motor generate electricity, and brake force is generated in power generation process, and the electric energy stored into battery can be in vehicle driving
When supply driving motor use, improve the continual mileage of vehicle；But single its severity of braking of regenerative braking device compared with
It is low, therefore, at present there is still a need for a set of mechanical brake device is arranged in electric car or hybrid vehicle, in electric regenerative
When brake apparatus is not enough to provide enough brake force, mechanical brake device can provide brake force, to guarantee to have for vehicle
Enough brake force needs severity of braking larger to guarantee the braking safety of vehicle, and in emergency braking, and electric power
Regenerative braking device not can guarantee braking effect, will will affect braking safety in this way.Therefore, in electric car or hybrid power
When automobile brake, how can make regenerative braking device to greatest extent by vehicle energy be converted into electric energy and
Braking safety is a current problem urgently to be solved.
Summary of the invention:
The object of the present invention is to provide a kind of hybrid vehicle regenerating brake control methods.
Above-mentioned purpose is realized by following technical scheme:
A kind of hybrid vehicle regenerating brake control method, this method comprises the following steps:
Step 1: severity of braking z is divided into 0≤z< 0.1,0.1≤z< 0.8,0.8≤zThree ranges, according to tool
The range of body carries out the division of specific curve to front and back wheel brake force, and under urban traffic situation, severity of braking is not more than 0.3, rear-wheel
Brake force is distributed by fixed proportion in electric vehicle brake, front-wheel friction catch and regenerative braking divide according to fuzzy algorithmic approach
Match；
Step 2: using particle group optimizing fuzzy rule, and particle swarm optimization algorithm initializes first generates a group random particles, so
In choosing generation, finds optimal solution afterwards, and in each choosing generation, particle is constantly updated by two extreme values of tracking, i.e., individual extreme value history is optimal
SolutionAnd globally optimal solution, then according to the speed of speed and location formula more new particle and position.
Step 3: according to brake pedal depth, battery charge state and speed, carrying out particle coding, excellent using population
Change algorithm to optimize the rule base of fuzzy controller.
The hybrid vehicle regenerating brake control method, the detailed process of the particle swarm optimization algorithm are as follows:
Initialization generates a group random particles first, and then choosing generation finds optimal solution, and in each choosing generation, particle passes through two poles of tracking
Value updates oneself, and one is that the optimal solution that particle itself is found is individual extreme value, the other is entire population is current
The optimal solution found is global extremum, particle updates the speed of oneself according to formula after finding above-mentioned two extreme value
And position, formula are as follows:
In formula: V is the speed of particle；
P is the current location of particle；
For the random number between (0,1)；
、For Studying factors, usually==2；
For inertia weight；
Particle is updated by constantly study, finally flies the position into solution space where optimal solution, and search process terminates, last defeated
OutIt is exactly globally optimal solution, at no point in the update process, particle is restricted to per one-dimensional maximum rate, particle is every
One-dimensional coordinate is also limited in the range of permission；
Larger then algorithm has stronger ability of searching optimum,Smaller, algorithm tends to local search, can incite somebody to actionLinearly reduce with the number of iterations, then:
(3)
K is current iteration number,For total the number of iterations；
For weight limit,For minimal weight.
The hybrid vehicle regenerating brake control method, the particle cataloged procedure based on particle group optimizing
Are as follows: the fuzzy rule of optimization is 120, is adjusted using the optimization that particle swarm algorithm carries out fuzzy rule, the mould that needs are optimized
Paste control parameter is encoded into particle sequence, each variable integer of particle
It indicates, variable-value range is 1-11.
The hybrid vehicle regenerating brake control method, the particle Optimization Steps are as follows:
(1) 120 variables are encoded, and determines search range and the maximum speed of particle；
(2) speed and the position of each particle are initialized；
(3) the history optimal value of each particle is stored inIn, the optimal value of each iteration is stored inIn, make
For globally optimal solution；
(4) each dimension speed of more new particle and position；
(5) inertia weight is updated；
(6) each particle is encoded, updates fuzzy rule, calculate adaptive value, and determine whether to update according to adaptive valueWith；
(7) step (3) is gone to be iterated, until be optimal the number of iterations orIt updates step-length and is less than specified threshold, it willDecode the optimal fuzzy rule as fuzzy controller.
The utility model has the advantages that
1. fuzzy controller of the invention can control motor braking power lower than battery peak charge power well, avoiding can
It can be due to the higher damage to electromagnetism of charge power；
2. fuzzy controller energy regenerating of the present invention under different speeds is significantly improved.
Braking force distribution of the invention meets ECE laws and regulations requirement, is the mould of fuzzy controller using particle swarm optimization algorithm
The customization of paste rule proposes objective, scientific method.
Specific embodiment:
Embodiment 1:
A kind of hybrid vehicle regenerating brake control method, this method comprises the following steps:
Step 1: severity of braking z is divided into 0≤z< 0.1,0.1≤z< 0.8,0.8≤zThree ranges, according to tool
The range of body carries out the division of specific curve to front and back wheel brake force, and under urban traffic situation, severity of braking is not more than 0.3, rear-wheel
Brake force is distributed by fixed proportion in electric vehicle brake, front-wheel friction catch and regenerative braking divide according to fuzzy algorithmic approach
Match；
Step 2: using particle group optimizing fuzzy rule, and particle swarm optimization algorithm initializes first generates a group random particles, so
In choosing generation, finds optimal solution afterwards, and in each choosing generation, particle is constantly updated by two extreme values of tracking, i.e., individual extreme value history is optimal
SolutionAnd globally optimal solution, then according to the speed of speed and location formula more new particle and position.
Step 3: according to brake pedal depth, battery charge state and speed, carrying out particle coding, excellent using population
Change algorithm to optimize the rule base of fuzzy controller.
Embodiment 2:
According to hybrid vehicle regenerating brake control method described in embodiment 1, the particle swarm optimization algorithm it is specific
Process are as follows: initialization generates a group random particles first, and then choosing generation finds optimal solution, in each choosing generation, particle by with
Track two extreme values update oneself, and one is that the optimal solution that particle itself is found is individual extreme value, the other is entirely
The optimal solution that population is found at present is global extremum, particle after finding above-mentioned two extreme value, updated according to formula from
Oneself speed and position, formula are as follows:
In formula: V is the speed of particle；
P is the current location of particle；
For the random number between (0,1)；
、For Studying factors, usually==2；
For inertia weight；
Particle is updated by constantly study, finally flies the position into solution space where optimal solution, and search process terminates, last defeated
OutIt is exactly globally optimal solution, at no point in the update process, particle is restricted to per one-dimensional maximum rate, particle is every
One-dimensional coordinate is also limited in the range of permission；
Larger then algorithm has stronger ability of searching optimum,Smaller, algorithm tends to local search, can incite somebody to actionLinearly reduce with the number of iterations, then:
(3)
K is current iteration number,For total the number of iterations；
For weight limit,For minimal weight.
Embodiment 3:
The hybrid vehicle regenerating brake control method according to embodiment 1 or 2, the grain based on particle group optimizing
Sub- cataloged procedure are as follows: the fuzzy rule of optimization is 120, is adjusted using the optimization that particle swarm algorithm carries out fuzzy rule, need to
The fuzzy control parameter to be optimized is encoded into particle sequence, each of particle
Variable integer representation, variable-value range are 1-11.
Embodiment 4:
According to hybrid vehicle regenerating brake control method described in embodiment 1 or 2 or 3, the particle Optimization Steps are as follows:
(1) 120 variables are encoded, and determines search range and the maximum speed of particle；
(2) speed and the position of each particle are initialized；
(3) the history optimal value of each particle is stored inIn, the optimal value of each iteration is stored inIn, make
For globally optimal solution；
(4) each dimension speed of more new particle and position；
(5) inertia weight is updated；
(6) each particle is encoded, updates fuzzy rule, calculate adaptive value, and determine whether to update according to adaptive valueWith；
(7) step (3) is gone to be iterated, until be optimal the number of iterations orIt updates step-length and is less than specified threshold, it willDecode the optimal fuzzy rule as fuzzy controller.
Claims (4)
1. a kind of hybrid vehicle regenerating brake control method, it is characterized in that: this method comprises the following steps:
Step 1: severity of braking z is divided into 0≤z< 0.1,0.1≤z< 0.8,0.8≤zThree ranges, according to tool
The range of body carries out the division of specific curve to front and back wheel brake force, and under urban traffic situation, severity of braking is not more than 0.3, rear-wheel
Brake force is distributed by fixed proportion in electric vehicle brake, front-wheel friction catch and regenerative braking divide according to fuzzy algorithmic approach
Match；
Step 2: using particle group optimizing fuzzy rule, and particle swarm optimization algorithm initializes first generates a group random particles, so
In choosing generation, finds optimal solution afterwards, and in each choosing generation, particle is constantly updated by two extreme values of tracking, i.e., individual extreme value history is optimal
SolutionAnd globally optimal solution, then according to the speed of speed and location formula more new particle and position.
Step 3: according to brake pedal depth, battery charge state and speed, particle coding is carried out, Particle Swarm Optimization is utilized
Method optimizes the rule base of fuzzy controller.
2. hybrid vehicle regenerating brake control method according to claim 1, it is characterized in that: the population is excellent
Change the detailed process of algorithm are as follows: initialization generation a group random particles first, then choosing generation finds optimal solution, selects from generation to generation each
In, particle updates oneself by two extreme values of tracking, and one is that the optimal solution that particle itself is found is individual extreme value
, the other is the optimal solution that entire population is found at present is global extremum, particle is after finding above-mentioned two extreme value, root
Oneself speed and position, formula are updated according to formula are as follows:
In formula: V is the speed of particle；
P is the current location of particle；
For the random number between (0,1)；
、For Studying factors, usually==2；
For inertia weight；
Particle is updated by constantly study, finally flies the position into solution space where optimal solution, and search process terminates, last defeated
OutIt is exactly globally optimal solution, at no point in the update process, particle is restricted to per one-dimensional maximum rate, particle is every
One-dimensional coordinate is also limited in the range of permission；
Larger then algorithm has stronger ability of searching optimum,Smaller, algorithm tends to local search, can incite somebody to actionLinearly reduce with the number of iterations, then:
(3)
K is current iteration number,For total the number of iterations；
For weight limit,For minimal weight.
3. hybrid vehicle regenerating brake control method according to claim 2, it is characterized in that: described based on particle
The particle cataloged procedure of group's optimization are as follows: the fuzzy rule of optimization is 120, and the optimization of fuzzy rule is carried out using particle swarm algorithm
Adjusting will need the fuzzy control parameter optimized to be encoded into particle sequence,
Each variable integer representation of particle, variable-value range are 1-11.
4. hybrid vehicle regenerating brake control method according to claim 3, it is characterized in that:
The particle Optimization Steps are as follows:
(1) 120 variables are encoded, and determines search range and the maximum speed of particle；
(2) speed and the position of each particle are initialized；
(3) the history optimal value of each particle is stored inIn, the optimal value of each iteration is stored inIn, as
Globally optimal solution；
(4) each dimension speed of more new particle and position；
(5) inertia weight is updated；
(6) each particle is encoded, updates fuzzy rule, calculate adaptive value, and determine whether to update according to adaptive valueWith；
(7) step (3) is gone to be iterated, until be optimal the number of iterations orIt updates step-length and is less than specified threshold, it willDecode the optimal fuzzy rule as fuzzy controller.
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| CN201910560657.6A CN110239355A (en) | 2019-06-26 | 2019-06-26 | Hybrid vehicle regenerating brake control method |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116278803A (en) * | 2023-03-30 | 2023-06-23 | 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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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116278803A (en) * | 2023-03-30 | 2023-06-23 | 吉林大学 | Energy-saving torque distribution system of electric automobile driven by four-wheel hub motor and control method thereof |
| 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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