CN104933264A - Determining method for regenerative braking distribution coefficient of electric vehicle - Google Patents

Determining method for regenerative braking distribution coefficient of electric vehicle Download PDF

Info

Publication number
CN104933264A
CN104933264A CN201510395389.9A CN201510395389A CN104933264A CN 104933264 A CN104933264 A CN 104933264A CN 201510395389 A CN201510395389 A CN 201510395389A CN 104933264 A CN104933264 A CN 104933264A
Authority
CN
China
Prior art keywords
regenerative braking
braking
force
speed
partition factor
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN201510395389.9A
Other languages
Chinese (zh)
Other versions
CN104933264B (en
Inventor
郑宏
魏旻
杨圆圆
曹继申
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
University of Electronic Science and Technology of China
Original Assignee
University of Electronic Science and Technology of China
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by University of Electronic Science and Technology of China filed Critical University of Electronic Science and Technology of China
Priority to CN201510395389.9A priority Critical patent/CN104933264B/en
Publication of CN104933264A publication Critical patent/CN104933264A/en
Application granted granted Critical
Publication of CN104933264B publication Critical patent/CN104933264B/en
Expired - Fee Related legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Landscapes

  • Electric Propulsion And Braking For Vehicles (AREA)

Abstract

The invention discloses a determining method for the regenerative braking distribution coefficient of an electric vehicle. First, according to an ideal braking force distribution curvature, total braking force is distributed to front and back wheels, and then a fuzzy control algorithm is adopted for distributing braking force of the front wheels of the electric vehicle into regenerative braking force and friction braking force. In the concrete implementation process, the fuzzy control algorithm with vehicle speed, braking force strength and a battery SOC being input and a regenerative braking proportion coefficient being output is adopted, wherein the membership degree function of the battery SOC is determined through a fuzzy statistics method, and other input and output membership degree functions are determined through collecting a lot of experimental data in combination with actual experience and consideration for security.

Description

A kind of defining method of electric automobile regenerative braking partition factor
Technical field
The invention belongs to electric vehicle engineering field, more specifically say, relate to a kind of defining method of electric automobile regenerative braking partition factor.
Background technology
In recent years, electric automobile obtains and develops fast, but the problem of its flying power deficiency, cause the extensive concern of Chinese scholars.Regenerative braking controls the important channel of the distance travelled number being raising electric motor car.
Existing regenerative braking moment allocation strategy mainly contains two kinds: tandem Control Strategy for Regenerative Braking and parallel Strategy for Regeneration Control.Tandem Control Strategy for Regenerative Braking has the feature that the braking energy recovery is high, but its shortcoming also clearly, and that is exactly system complex, and technical difficulty is large, is not easy to realize.Parallel Strategy for Regeneration Control refers to when severity of braking is less, adopts regenerative braking separately; When severity of braking is medium, regenerative braking force and mechanical braking force is adopted to distribute the parallel strategy braked by fixed proportion; During large severity of braking, only adopt mechanical braking.Parallel have the feature that reliable operation, structure are simple, be convenient to Project Realization, but its energy recovery efficiency is not as tandem regenerative braking allocation strategy.
Summary of the invention
The object of the invention is to overcome the deficiencies in the prior art, a kind of defining method of electric automobile regenerative braking partition factor is provided, electric automobile regenerative braking partition factor is estimated by FUZZY ALGORITHMS FOR CONTROL, under the prerequisite ensureing electric vehicle brake safety, better can recover energy like this.
For achieving the above object, the defining method of a kind of electric automobile regenerative braking of the present invention partition factor, is characterized in that, comprise the following steps:
(1) membership function inputting in fuzzy control, export, is determined
(1.1), by pedal sensor gather lot of experimental data, construct the membership function of severity of braking Z;
Z＝{Low Middle High}；
Wherein, Low represents light brake, and Middle represents that moderate is braked, and High represents that severe is braked;
(1.2), Negotiation speed sensor gather lot of experimental data, construct the membership function of speed v;
Wherein, speed v divides Three Estate, represent that the speed of a motor vehicle is in the first estate, represent that the speed of a motor vehicle is in the second grade, represent that the speed of a motor vehicle is in the tertiary gradient;
(1.3) under, counting different state-of-charge SOC by fuzzy statistical method, the probability of charging current when large current charge state can be accepted and can not accept large current charge state, recycling MATLAB carries out matching, determines the membership function of state-of-charge SOC;
SOC＝{Acceptable Unacceptable}；
Wherein, Acceptable is for can accept large current charge state, and Unacceptable is for can not accept large current charge state;
(1.4), the membership function exporting regenerative braking partition factor β is determined;
β＝{Low'Middle'High'}；
Wherein, export regenerative braking partition factor β and divide Three Estate, Low' represents that exporting regenerative braking partition factor β is in the first estate, and Middle' represents that exporting regenerative braking partition factor β is in the second grade, and High' represents that exporting regenerative braking partition factor β is in the tertiary gradient;
(2), fuzzy control rule is determined
(2.1), when exporting regenerative braking partition factor β and belonging to Low', the initial conditions of fuzzy control rule is: severity of braking Z belongs to High, or SOC belongs to Unacceptable;
(2.2), when exporting regenerative braking partition factor β and belonging to Middle', the initial conditions of fuzzy control rule is: severity of braking Z belongs to Middle and SOC belongs to Acceptable and speed v belongs to or
(2.3), when exporting regenerative braking partition factor β and belonging to High', the initial conditions of fuzzy control rule is: severity of braking Z belongs to Middle and SOC belongs to Acceptable and speed v belongs to or severity of braking Z belongs to Low and SOC belongs to Acceptable;
(3), fuzzy control rule in step (2) is incorporated in the Brake force distribution strategy of electric automobile model
(3.1) front and back wheel damping force and the total braking force relation of electric automobile, is determined
(3.1.1), to front and back wheel earth point place carry out force analysis, obtain the relation between each stressed and electric automobile parameter, as follows:
F z f L = G b + m d v d t h g
F z r L = G a - m d v d t h g
Wherein, G represents automobile general assembly (TW); M represents automobile gross mass; L represents wheelbase; A represents barycenter front axle distance; B represents barycenter rear axle distance; h grepresent that barycenter is high; F zfrepresent the reacting force in front-wheel normal direction; F zrrepresent the reacting force in trailing wheel normal direction; V represents the speed of automobile, represent ground attaching coefficient, wherein, when front and back wheel locking simultaneously, ground attaching coefficient meet:
(3.1.2), when front and back wheel locking simultaneously, the relation of front and back wheel damping force and total braking force meets:
Again abbreviation arrangement is carried out to above formula, the relation of front and back wheel damping force and total braking force can be obtained:
F μ r = 1 L ( a F - F 2 h g G ) F μ f = F - F μ r
Wherein, F urfor rear-wheel braking force, F uffor front wheel brake power;
(3.2), utilize MATLAB to carry out matching to the speed v under electric automobile current time, severity of braking Z and state-of-charge SOC, obtain the regenerative braking partition factor β under current time 0;
(3.3), according to the regenerative braking partition factor β under current time 0, by front wheel brake power F urregenerative braking force F is distributed into regenerative braking fuzzy control rwith friction brake force F u;
F r = β 0 F u f F u = ( 1 - β 0 ) F u f
Wherein, F uffor front wheel brake power.
Goal of the invention of the present invention is achieved in that
The defining method of electric automobile regenerative braking partition factor of the present invention, first according to ideal braking force distribution curve, total braking force is assigned in front and back wheel, then adopts FUZZY ALGORITHMS FOR CONTROL that electric automobile front wheel brake power is assigned as regenerative braking force and friction brake force.In specific implementation process, this patent adopts with the speed of a motor vehicle, and damping force intensity and battery SOC are as input, and regenerative braking scale-up factor is as the FUZZY ALGORITHMS FOR CONTROL exported.Wherein, the membership function of battery SOC adopts fuzzy statistical method to determine, the membership function of other input and output is then by gathering lot of experimental data, in conjunction with practical experience and determine the consideration of security.
Meanwhile, the defining method of electric automobile regenerative braking partition factor of the present invention also has following beneficial effect:
The fuzzy control Control Strategy for Regenerative Braking that the present invention adopts, and in conjunction with the advantage of tandem and parallel Strategy for Regeneration Control, make energy recovery efficiency higher than parallel control strategy for regenerative braking, but comparatively tandem control strategy for regenerative braking is easy to realize; Secondly, the fuzzy control control strategy for regenerative braking that the present invention adopts employs the distribution that severity of braking, battery charge state, the speed of a motor vehicle decide regenerative braking force jointly, make Torque distribution more reasonable, further increase energy recovery rate, under the prerequisite ensureing driving safety, capacity usage ratio can be improved.
Accompanying drawing explanation
Fig. 1 is the determination method flow diagram of electric automobile regenerative braking partition factor of the present invention;
Fig. 2 is the membership function process flow diagram determining fuzzy control input and output;
Fig. 3 is the membership function schematic diagram of severity of braking Z;
Fig. 4 is the membership function schematic diagram of speed v;
Fig. 5 is the membership function schematic diagram of state-of-charge SOC;
Fig. 6 is the membership function schematic diagram exporting regenerative braking partition factor β;
Fig. 7 is regenerative braking force factor beta and the relation surface chart between Z-SOC, Z-v, SOC-v;
Fig. 8 is the force analysis figure of the electric automobile that the present invention selects.
Embodiment
Below in conjunction with accompanying drawing, the specific embodiment of the present invention is described, so that those skilled in the art understands the present invention better.Requiring particular attention is that, in the following description, when perhaps the detailed description of known function and design can desalinate main contents of the present invention, these are described in and will be left in the basket here.
Embodiment
Fig. 1 is the determination method flow diagram of electric automobile regenerative braking partition factor of the present invention.
In the present embodiment, as shown in Figure 1, the defining method of regenerative braking partition factor of the present invention, mainly comprises following three steps:
T1, the membership function determined input in fuzzy control, export;
T2, determine fuzzy control rule;
T3, fuzzy control rule to be incorporated in the Brake force distribution strategy of electric automobile model.
Respectively above-mentioned three steps are elaborated below:
T1, determine the membership function of fuzzy control input and output
In the present embodiment, as shown in Figure 2, determine that the membership function of fuzzy control input and output comprises the following steps:
T1.1), by pedal sensor gather lot of experimental data, and in conjunction with practical experience and the consideration to security, construct the membership function of severity of braking Z;
In the present embodiment, lot of experimental data is gathered by pedal sensor, and in conjunction with practical experience and the consideration to security, to the division methods of severity of braking Z, the membership function of severity of braking Z as shown in Figure 3, the membership function of severity of braking Z is: Z={Low Middle High}, and wherein: Low represents light brake, scope is roughly 0 ~ 0.15; Middle represents that moderate is braked, and scope is roughly 0.15 ~ 0.65; High represents that severe is braked, and scope is roughly 0.65 ~ 1;
T1.2), Negotiation speed sensor gathers lot of experimental data, and in conjunction with practical experience and the consideration to security, constructs the membership function of speed v;
In the present embodiment, Negotiation speed sensor gathers lot of experimental data, and in conjunction with practical experience and the consideration to security, constructs the membership function of speed v, and as shown in Figure 4, the membership function of speed v is: wherein: represent that the speed of a motor vehicle is in the first estate, scope is roughly at 0 ~ 20km/h; represent that the speed of a motor vehicle is in the second grade, scope is roughly at 20 ~ 45km/h; represent that the speed of a motor vehicle is in the tertiary gradient, scope is roughly at more than 45km/h;
T1.3) membership function of state-of-charge SOC, is determined
The state-of-charge of accumulator of electric car, i.e. battery SOC, its size directly affects the ability to accept of accumulator to charging current; SOC value is larger, and the acceptable maximum current of accumulator will be less.And the size of regenerative braking force can change the size of current of electric power generation, therefore under different battery SOC states, rational regenerative braking force coefficient should be adopted to control electric power generation size of current, so just can make the charging current of battery within rational scope.
In the present embodiment, the membership function by using fuzzy statistical method to determine input parameter SOC.Idiographic flow is: first obtained the charging current under all SOC states by ADVISOR emulation, then under counting different state-of-charge SOC by fuzzy statistical method, the probability of charging current when large current charge state can be accepted and can not accept large current charge state, we recycle MATLAB and carry out matching, determine the membership function of state-of-charge SOC, as shown in Figure 5, the membership function of state-of-charge SOC is: SOC={Acceptable Unacceptable}, wherein, Acceptable is for can accept large current charge state, scope is roughly 0 ~ 0.8, Unacceptable is for can not accept large current charge state, and scope is roughly 0.8 ~ 1,
T1.4), the membership function exporting regenerative braking partition factor β is determined;
According to a large amount of emulation experiments, and in conjunction with practical experience and the consideration to security, determine the membership function of output parameter regenerative braking ratio beta, as shown in Figure 6, the membership function exporting regenerative braking partition factor β is: β={ Low'Middle'High'}, wherein: Low' represents that β is in the first estate, scope is roughly 0 ~ 0.3; Middle' represents that β is in the second grade, and scope is roughly 0.3 ~ 0.7; High' represents that β is in the tertiary gradient, and scope is roughly 0.7 ~ 1;
T2, determine fuzzy control rule
When formulating regenerative braking fuzzy control rule, security and the comfortableness of car load should be ensured as far as possible, reclaiming more energy as far as possible simultaneously.And the reasoning of Sugeno Fuzzy has very high operation efficiency, can linear control theory be worked in coordination with, use and optimize and adaptive technique, ensure the advantages such as the continuity of output plane, therefore adopt Sugeno reasoning in the present embodiment.
In the present embodiment, can formulate regenerative braking fuzzy control rule according to output regenerative braking partition factor β, specific rules is as follows:
(T2.1), when exporting regenerative braking partition factor β and belonging to Low', the initial conditions of fuzzy control rule is: severity of braking Z belongs to High, or SOC belongs to Unacceptable;
(T2.2), when exporting regenerative braking partition factor β and belonging to Middle', the initial conditions of fuzzy control rule is: severity of braking Z belongs to Middle and SOC belongs to Acceptable and speed v belongs to or
(T2.3), when exporting regenerative braking partition factor β and belonging to High', the initial conditions of fuzzy control rule is: severity of braking Z belongs to Middle and SOC belongs to Acceptable and speed v belongs to or severity of braking Z belongs to Low and SOC belongs to Acceptable;
Can fuzzy control rule table be generated according to (T2.1) ~ (T2.3), as shown in table 1, have 18 rules.
Table 1 is fuzzy control rule table;
Table 1
According to above-mentioned membership function and fuzzy control rule, curved surface as shown in Figure 7 can be obtained, this curved surface is obtained by MATLAB matching, by after setting input and output membership function and respective rule between the regenerative braking force factor beta that calculates and Z-SOC, between β and Z-v, the relation curved surface between β and SOC-v.
T3, fuzzy control rule to be incorporated in the Brake force distribution strategy of electric automobile model
(T3.1) front and back wheel damping force and the total braking force relation of electric automobile, is determined
(T3.1.1), as shown in Figure 8, to front and back wheel earth point, place carries out force analysis, obtains the relation between each stressed and electric automobile parameter, as follows:
F z f L = G b + m d v d t h g
F z r L = G a - m d v d t h g
Wherein, G represents automobile general assembly (TW); M represents automobile gross mass; L represents wheelbase; A represents barycenter front axle distance; B represents barycenter rear axle distance; h grepresent that barycenter is high; F zfrepresent the reacting force in front-wheel normal direction; F zrrepresent the reacting force in trailing wheel normal direction; V represents the speed of automobile, represent ground attaching coefficient, wherein, when front and back wheel locking simultaneously, ground attaching coefficient meet:
In the present embodiment, the partial parameters of the electric automobile model selected is as shown in table 2:
Table 2 is parameter lists of electric automobile model;
Project Data
Electric automobile gross mass m (kg) 1200
Physical dimension (mm*mm*mm) 3900*1555*1670
Barycenter front axle distance a (m) 1.452
Height of C.G. h g(m) 0.8
Wheelbase L (m) 2.420
Table 2
Wherein, the reacting force F in front-wheel normal direction zfmeet:
Reacting force F in trailing wheel normal direction zrmeet:
(T3.1.2), when front and back wheel locking simultaneously, the relation of front and back wheel damping force and total braking force meets:
Again abbreviation arrangement is carried out to above formula, the relation of front and back wheel damping force and total braking force can be obtained:
F μ r = 1 L ( a F - F 2 h g G ) F μ f = F - F μ r
Wherein, F urfor rear-wheel braking force, F uffor front wheel brake power;
(T3.2), utilize MATLAB to carry out matching to the speed v under electric automobile current time, severity of braking Z and state-of-charge SOC, obtain the regenerative braking partition factor β under current time 0;
Wherein, the computing method of the severity of braking Z under current time are:
Z = d v g d t
Wherein, g represents acceleration of gravity, and t represents the time;
(T3.3), according to the regenerative braking partition factor β under current time 0, by front wheel brake power F urregenerative braking force F is distributed into regenerative braking fuzzy control rwith friction brake force F u;
F r = β 0 F u f F u = ( 1 - β 0 ) F u f
Wherein, F uffor front wheel brake power.
Although be described the illustrative embodiment of the present invention above; so that those skilled in the art understand the present invention; but should be clear; the invention is not restricted to the scope of embodiment; to those skilled in the art; as long as various change to limit and in the spirit and scope of the present invention determined, these changes are apparent, and all innovation and creation utilizing the present invention to conceive are all at the row of protection in appended claim.

Claims (3)

1. a defining method for electric automobile regenerative braking partition factor, is characterized in that, comprises the following steps:
(1) membership function inputting in fuzzy control, export, is determined
(1.1), by pedal sensor gather lot of experimental data, construct the membership function of severity of braking Z;
Z＝{Low Middle High}；
Wherein, Low represents light brake, and Middle represents that moderate is braked, and High represents that severe is braked;
(1.2), Negotiation speed sensor gather lot of experimental data, construct the membership function of speed v;
Wherein, speed v divides Three Estate, represent that the speed of a motor vehicle is in the first estate, Middle represents that the speed of a motor vehicle is in the second grade, represent that the speed of a motor vehicle is in the tertiary gradient;
(1.3) under, counting different state-of-charge SOC by fuzzy statistical method, the probability of charging current when large current charge state can be accepted and can not accept large current charge state, recycling MATLAB carries out matching, determines the membership function of state-of-charge SOC;
SOC＝{Acceptable Unacceptable}；
Wherein, Acceptable is for can accept large current charge state, and Unacceptable is for can not accept large current charge state;
(1.4), the membership function exporting regenerative braking partition factor β is determined;
β＝{Low'Middle'High'}；
Wherein, export regenerative braking partition factor β and divide Three Estate, Low' represents that exporting regenerative braking partition factor β is in the first estate, and Middle' represents that exporting regenerative braking partition factor β is in the second grade, and High' represents that exporting regenerative braking partition factor β is in the tertiary gradient;
(2), fuzzy control rule is determined
(2.1), when exporting regenerative braking partition factor β and belonging to Low', the initial conditions of fuzzy control rule is: severity of braking Z belongs to High, or SOC belongs to Unacceptable;
(2.2), when exporting regenerative braking partition factor β and belonging to Middle', the initial conditions of fuzzy control rule is: severity of braking Z belongs to Middle and SOC belongs to Acceptable and speed v belongs to or
(2.3), when exporting regenerative braking partition factor β and belonging to High', the initial conditions of fuzzy control rule is: severity of braking Z belongs to Middle and SOC belongs to Acceptable and speed v belongs to or severity of braking Z belongs to Low and SOC belongs to Acceptable;
(3), fuzzy control rule in step (2) is incorporated in the Brake force distribution strategy of electric automobile model
(3.1) front and back wheel damping force and the total braking force relation of electric automobile, is determined
(3.1.1), to front and back wheel earth point place carry out force analysis, obtain the relation between each stressed and electric automobile parameter, as follows:
Wherein, G represents automobile general assembly (TW); M represents automobile gross mass; L represents wheelbase; A represents barycenter front axle distance; B represents barycenter rear axle distance; h grepresent that barycenter is high; F zfrepresent the reacting force in front-wheel normal direction; F zrrepresent the reacting force in trailing wheel normal direction; V represents the speed of automobile, represent ground attaching coefficient, wherein, when front and back wheel locking simultaneously, ground attaching coefficient meet:
(3.1.2), when front and back wheel locking simultaneously, the relation of front and back wheel damping force and total braking force meets:
Again abbreviation arrangement is carried out to above formula, the relation of front and back wheel damping force and total braking force can be obtained:
Wherein, F urfor rear-wheel braking force, F uffor front wheel brake power;
(3.2), utilize MATLAB to carry out matching to the speed v under electric automobile current time, severity of braking Z and state-of-charge SOC, obtain the regenerative braking partition factor β under current time 0;
(3.3), according to the regenerative braking partition factor β under current time 0, by front wheel brake power F urregenerative braking force F is distributed into regenerative braking fuzzy control rwith friction brake force F u;
Wherein, F uffor front wheel brake power.
2. the defining method of a kind of electric automobile regenerative braking partition factor according to claim 1, is characterized in that, the reacting force F in described front-wheel normal direction zfmeet:
Reacting force F in described trailing wheel normal direction zrmeet:
Wherein, G represents automobile general assembly (TW); L represents wheelbase; A represents barycenter front axle distance; B represents barycenter rear axle distance; h grepresent that barycenter is high; V represents the speed of automobile, represent front and back wheel locking simultaneously ground attaching coefficient constantly.
3. the defining method of a kind of electric automobile regenerative braking partition factor according to claim 1, is characterized in that, in described step (3.2), the severity of braking Z under current time is:
Wherein, g represents acceleration of gravity, and t represents the time.
CN201510395389.9A 2015-07-07 2015-07-07 A kind of determination method of electric automobile regenerative braking distribution coefficient Expired - Fee Related CN104933264B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201510395389.9A CN104933264B (en) 2015-07-07 2015-07-07 A kind of determination method of electric automobile regenerative braking distribution coefficient

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201510395389.9A CN104933264B (en) 2015-07-07 2015-07-07 A kind of determination method of electric automobile regenerative braking distribution coefficient

Publications (2)

Publication Number Publication Date
CN104933264A true CN104933264A (en) 2015-09-23
CN104933264B CN104933264B (en) 2017-10-31

Family

ID=54120430

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201510395389.9A Expired - Fee Related CN104933264B (en) 2015-07-07 2015-07-07 A kind of determination method of electric automobile regenerative braking distribution coefficient

Country Status (1)

Country Link
CN (1) CN104933264B (en)

Cited By (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105083026A (en) * 2015-08-21 2015-11-25 奇瑞汽车股份有限公司 Control method and apparatus of charging current
CN109878480A (en) * 2019-03-06 2019-06-14 哈尔滨理工大学 A kind of electric car coefficient of friction prediction mode switching regenerating brake control method
CN110254239A (en) * 2019-06-28 2019-09-20 福州大学 A kind of Torque distribution method during electric car regenerative braking transient response
CN111196164A (en) * 2020-01-22 2020-05-26 辽宁工业大学 Control method for distributed electric automobile brake system
CN111983471A (en) * 2020-08-24 2020-11-24 哈尔滨理工大学 Lithium ion power battery safety degree estimation method and estimation device based on double Kalman filtering
CN111983472A (en) * 2020-08-24 2020-11-24 哈尔滨理工大学 Lithium ion power battery safety degree estimation method and estimation device based on adaptive Kalman filtering
CN112078371A (en) * 2020-08-13 2020-12-15 江苏理工学院 Energy recovery method and energy recovery device of hybrid power supply electric vehicle
CN112297860A (en) * 2020-10-27 2021-02-02 吉林大学 Method for distributing regenerative braking force of pure electric vehicle
CN112677771A (en) * 2020-12-31 2021-04-20 吉林大学 Method for controlling regenerative braking of forerunner electric vehicle based on fuzzy control

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20110184602A1 (en) * 1998-09-14 2011-07-28 Paice Llc Hybrid vehicles
CN202499132U (en) * 2012-03-05 2012-10-24 浙江大学城市学院 New type Plug_in hybrid electric vehicle energy management controller
CN104553817A (en) * 2014-12-26 2015-04-29 芜湖市汽车产业技术研究院有限公司 Regeneration braking control method and system

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20110184602A1 (en) * 1998-09-14 2011-07-28 Paice Llc Hybrid vehicles
CN202499132U (en) * 2012-03-05 2012-10-24 浙江大学城市学院 New type Plug_in hybrid electric vehicle energy management controller
CN104553817A (en) * 2014-12-26 2015-04-29 芜湖市汽车产业技术研究院有限公司 Regeneration braking control method and system

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
孙鹏龙等: "CVT 混合动力汽车再生制动模糊控制策略", 《计算机仿真》 *

Cited By (14)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105083026A (en) * 2015-08-21 2015-11-25 奇瑞汽车股份有限公司 Control method and apparatus of charging current
CN109878480A (en) * 2019-03-06 2019-06-14 哈尔滨理工大学 A kind of electric car coefficient of friction prediction mode switching regenerating brake control method
CN109878480B (en) * 2019-03-06 2021-07-09 哈尔滨理工大学 Regenerative braking control method for switching friction coefficient prediction modes of electric automobile
CN110254239A (en) * 2019-06-28 2019-09-20 福州大学 A kind of Torque distribution method during electric car regenerative braking transient response
CN111196164B (en) * 2020-01-22 2021-06-04 辽宁工业大学 Control method for distributed electric automobile brake system
CN111196164A (en) * 2020-01-22 2020-05-26 辽宁工业大学 Control method for distributed electric automobile brake system
CN112078371A (en) * 2020-08-13 2020-12-15 江苏理工学院 Energy recovery method and energy recovery device of hybrid power supply electric vehicle
CN111983472A (en) * 2020-08-24 2020-11-24 哈尔滨理工大学 Lithium ion power battery safety degree estimation method and estimation device based on adaptive Kalman filtering
CN111983471A (en) * 2020-08-24 2020-11-24 哈尔滨理工大学 Lithium ion power battery safety degree estimation method and estimation device based on double Kalman filtering
CN111983471B (en) * 2020-08-24 2022-11-22 哈尔滨理工大学 Lithium ion power battery safety degree estimation method and estimation device based on double Kalman filtering
CN111983472B (en) * 2020-08-24 2022-11-25 哈尔滨理工大学 Lithium ion power battery safety degree estimation method and estimation device based on adaptive Kalman filtering
CN112297860A (en) * 2020-10-27 2021-02-02 吉林大学 Method for distributing regenerative braking force of pure electric vehicle
CN112297860B (en) * 2020-10-27 2022-02-08 吉林大学 Method for distributing regenerative braking force of pure electric vehicle
CN112677771A (en) * 2020-12-31 2021-04-20 吉林大学 Method for controlling regenerative braking of forerunner electric vehicle based on fuzzy control

Also Published As

Publication number Publication date
CN104933264B (en) 2017-10-31

Similar Documents

Publication Publication Date Title
CN104933264A (en) Determining method for regenerative braking distribution coefficient of electric vehicle
Heydari et al. Maximizing regenerative braking energy recovery of electric vehicles through dynamic low-speed cutoff point detection
Peng et al. Rule based energy management strategy for a series–parallel plug-in hybrid electric bus optimized by dynamic programming
Tao et al. A regenerative braking system for electric vehicle with four in-wheel motors based on fuzzy control
CN104108316B (en) Electrohydraulic-combined brake control method of battery electric vehicle
CN106218419A (en) A kind of antero posterior axis drives the control method of electric automobile process of regenerative braking
Ma et al. Energy recovery strategy based on ideal braking force distribution for regenerative braking system of a four-wheel drive electric vehicle
CN105034817B (en) A kind of electric automobile regenerating brake control method based under multi-constraint condition
CN110304044A (en) PHEV 4 wheel driven torque distribution method based on ECMS
He et al. Forward power-train energy management modeling for assessing benefits of integrating predictive traffic data into plug-in-hybrid electric vehicles
CN106394259A (en) Implementation method for electric vehicle braking force redistribution
Zhang et al. Regenerative braking for electric vehicle based on fuzzy logic control strategy
CN113547928B (en) Dual-motor four-wheel drive electric vehicle torque distribution method considering tire slippage
CN102897041B (en) A kind of four-wheel-drive hybrid power system regenerative braking torque distribution method
Heydari et al. Regenerative braking performance of different electric vehicle configurations considering dynamic low speed cutoff point
Wang et al. Research on control strategy of regenerative braking and anti-lock braking system for electric vehicle
Ye et al. A fast Q-learning energy management strategy for battery/supercapacitor electric vehicles considering energy saving and battery aging
Heydari et al. Optimal blending of regenerative and friction braking at low speeds for maximizing energy extraction in electric vehicles
Chu et al. Braking force distribution strategy for HEV based on braking strength
Heydari et al. Maximizing harvested energy through regenerative braking process in dual-motor all-wheel drive electric vehicles
Ling et al. Braking force distribution research in electric vehicle regenerative braking strategy
CN104290747B (en) Composite power energy distributing method of hybrid vehicle
Zhang et al. Regenerative braking control strategy of electric vehicle based on composite power supply
CN105691212A (en) Regenerative braking control strategy of hybrid electric vehicle hybrid power supply and vehicle
Wu et al. Braking force distribution of electric vehicles based on fuzzy control

Legal Events

Date Code Title Description
C06 Publication
PB01 Publication
C10 Entry into substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant
CF01 Termination of patent right due to non-payment of annual fee
CF01 Termination of patent right due to non-payment of annual fee

Granted publication date: 20171031

Termination date: 20200707