CN110696816B - Dynamic coordination hybrid electric vehicle energy management method based on working condition classification - Google Patents

Dynamic coordination hybrid electric vehicle energy management method based on working condition classification Download PDF

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CN110696816B
CN110696816B CN201911003322.0A CN201911003322A CN110696816B CN 110696816 B CN110696816 B CN 110696816B CN 201911003322 A CN201911003322 A CN 201911003322A CN 110696816 B CN110696816 B CN 110696816B
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working condition
torque
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hybrid electric
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CN110696816A (en
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李明明
吴静波
郭志军
卢耀真
王永巍
张印
田晨乐
付申振
肖金涛
杜自发
刘佳凯
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Henan University of Science and Technology
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    • 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
    • B60W20/15—Control strategies specially adapted for achieving a particular effect
    • 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
    • B60W10/00—Conjoint control of vehicle sub-units of different type or different function
    • B60W10/04—Conjoint control of vehicle sub-units of different type or different function including control of propulsion units
    • B60W10/06—Conjoint control of vehicle sub-units of different type or different function including control of propulsion units including control of combustion engines
    • 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
    • B60W10/00—Conjoint control of vehicle sub-units of different type or different function
    • B60W10/04—Conjoint control of vehicle sub-units of different type or different function including control of propulsion units
    • B60W10/08—Conjoint control of vehicle sub-units of different type or different function including control of propulsion units including control of electric propulsion units, e.g. motors or generators
    • 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
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • 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
    • B60W2520/00—Input parameters relating to overall vehicle dynamics
    • B60W2520/10—Longitudinal speed
    • 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
    • B60W2520/00—Input parameters relating to overall vehicle dynamics
    • B60W2520/10—Longitudinal speed
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    • 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
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    • B60W2710/06—Combustion engines, Gas turbines
    • B60W2710/0616—Position of fuel or air injector
    • 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
    • B60W2710/00—Output or target parameters relating to a particular sub-units
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    • 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
    • B60W2710/00—Output or target parameters relating to a particular sub-units
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    • 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
    • B60W2710/00—Output or target parameters relating to a particular sub-units
    • B60W2710/08—Electric propulsion units
    • B60W2710/083—Torque
    • 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
    • B60W2710/00—Output or target parameters relating to a particular sub-units
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    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/10—Internal combustion engine [ICE] based vehicles
    • Y02T10/40—Engine management systems
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
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Abstract

The invention provides a dynamic coordination hybrid electric vehicle energy management method based on working condition classification, which comprises the steps of utilizing an improved particle swarm optimization PSO (particle swarm optimization) to optimize a K-means clustering analysis algorithm K-means to divide driving working conditions and adjust the dynamic oil consumption of a hybrid electric vehicle when the driving working conditions face sudden change. In the aspect of driving condition division, optimizing a clustering center of a K-means algorithm by using an improved particle swarm algorithm, and then dividing the working conditions according to the extracted characteristic parameters of the typical working conditions; and when a sudden change working condition occurs, entering a throttle dynamic coordination program for reasonable torque distribution. The intelligent algorithm and the PID control technology are integrated to solve the problems of increased oil consumption, poor riding comfort and the like caused by sudden change of an accelerator when torque is improperly distributed and suddenly accelerated or decelerated at present, so that reasonable real-time control over power flow between an engine and a motor is realized, the fuel economy is improved, the emission of pollutants is reduced while the power performance is ensured, and the riding comfort is improved.

Description

Dynamic coordination hybrid electric vehicle energy management method based on working condition classification
Technical Field
The invention belongs to the technical field of automobile energy management, and particularly relates to a dynamic coordination hybrid electric vehicle energy management method based on working condition classification.
Background
With the increasing severity of environmental pollution and energy shortage, various countries take measures for energy conservation and emission reduction, so that various automobile manufacturers and scientific research institutions strengthen research and development of new energy automobiles, wherein hybrid electric vehicles have high endurance mileage and good emission effect, and are favored by people. The hybrid power mainly comprises two power sources, has multiple operation modes, and effectively improves the fuel economy and the performance of the whole vehicle system through the coordinated switching between the modes and the characteristic complementation of the energy sources. However, since the hybrid electric vehicle is a multivariable, discontinuous, and time-varying complex system, problems of poor smoothness, large impact, and the like can occur during mode switching, which not only increases dynamic fuel consumption, but also reduces riding comfort.
The energy management method of the hybrid electric vehicle directly influences the fuel economy, the dynamic property, the operation stability and the like of the whole vehicle, and has a very key effect. The driving condition directly affects torque distribution between the engine and the motor of the vehicle control unit VCU, and the vehicle control strategy is also greatly affected. If the driving conditions can be reasonably and accurately classified by using the history and the current data of the vehicle, and then the reasonable torque distribution is carried out according to different driving conditions, the optimization effect of the energy management strategy can be further improved.
Disclosure of Invention
The invention aims to provide a dynamic coordination hybrid electric vehicle energy management method based on working condition classification, which constructs a working condition division model by utilizing a particle swarm algorithm and a K-means algorithm, classifies driving working conditions, reasonably distributes torque to different working conditions through a throttle dynamic coordination program, integrates an intelligent algorithm and an advanced PID control technology to solve the problems of oil consumption increase, poor riding comfort and the like caused by improper torque distribution and sudden accelerator change when a vehicle suddenly accelerates or decelerates, reasonably controls the power flow between an engine and a motor in real time after driving working condition division and dynamic oil consumption regulation, improves the fuel economy and riding comfort while ensuring the dynamic property, and reduces the emission of pollutants to the maximum extent.
In order to achieve the purpose, the invention adopts the technical scheme that: the dynamic coordination hybrid electric vehicle energy management method based on the working condition classification comprises the following steps:
s1: adopting a clustering center based on an improved PSO optimized K-means algorithm to identify working conditions and establishing a working condition division model;
s2: extracting characteristic parameters of typical working conditions when the PHEV whole vehicle runs, and judging the type of the current working condition through the working condition division model;
s3: the VCU of the vehicle controller obtains the type of the driving condition according to the condition division model, and then carries out reasonable torque distribution;
s4: when the current driving working condition is identified as a stable working condition by the working condition division model, reasonably adjusting and controlling the power flow between the engine and the motor by adopting an equivalent fuel minimum control strategy; when the current running working condition is identified to be a sudden change working condition by the working condition division model, entering a throttle opening dynamic regulation program, and avoiding excessive fuel injection of the engine during sudden acceleration or deceleration by limiting the opening change rate delta beta of the throttle of the engine.
Further, after the opening degree change rate Δ β of the throttle valve of the engine is limited, the limited engine output torque T is outpute_actAnd current motor output torque
Figure BDA0002241978140000031
And (4) after the PHEV whole vehicle is started, correcting the opening change rate delta beta of the throttle valve through PID control.
Further, in the step S1, in the process of identifying the operating conditions, two cluster center points are first calculated according to the previous driving data, the cluster center of the K-means algorithm is optimized by using the improved PSO, and then the characteristic parameters of the typical operating conditions are extracted, which are: maximum vehicle speed vmaxMinimum vehicle speed vminAveraging vehicleVelocity vavgMaximum acceleration amaxMaximum deceleration dmaxAcceleration time ratio paVehicle speed standard deviation km/h and acceleration standard deviation m/s2。
Further, the improved PSO for optimizing the clustering center of the K-means algorithm means that the improved PSO algorithm is combined with the K-means algorithm, and the global search capability of the improved PSO algorithm is utilized to optimize the initial clustering center of the K-means algorithm.
Further, the improved PSO algorithm monitors the optimal values of all the particles and the particle swarm in real time in the particle iteration process, timely varies the particles trapped in premature convergence, increases the diversity of the particle swarm and enables the particles to jump out of the local optimal solution in time.
Further, in step S2, the characteristic parameters of the driving condition include: maximum vehicle speed vmaxMinimum vehicle speed vminAverage vehicle speed vavgMaximum acceleration amaxMaximum deceleration dmaxAcceleration time ratio paVehicle speed standard deviation km/h and acceleration standard deviation m/s2。
Further, in step S3, the torque is reasonably distributed according to the following formula:
Figure BDA0002241978140000041
wherein, Te_tar’Is a target torque of the engine; t isrewThe torque required by the whole vehicle;
Figure BDA0002241978140000043
outputting torque for the current motor; t iseIs the output torque of the engine; delta beta is the opening change rate of the throttle valve of the engine; Δ α is a limit value of the rate of change of the throttle opening of the engine.
Further, in the step S6, the following of the target torque by the engine is realized by using the opening degree change rate Δ β of the throttle valve as the output of the PID controller, which can be expressed as:
Figure BDA0002241978140000042
wherein: e is the engine torque deviation control quantity, and e is Te_tar’－Te_act，Te_tar’Is the target torque of the engine, Te_actIs the actual torque of the engine;
kp、ki、kdrespectively proportional, integral and differential gain coefficients.
Compared with the prior art, the invention has the beneficial effects that: the invention provides a dynamic coordination hybrid electric vehicle energy management method based on working condition classification, aiming at the division of driving working conditions, a cluster center of a K-means cluster analysis algorithm is optimized by adopting an improved particle swarm algorithm, and the cluster division is further carried out; aiming at the sudden change working condition in the driving process, the method for limiting the throttle opening change rate is adopted, so that the dynamic oil consumption is greatly reduced, and the riding comfort is improved.
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FIG. 1 is a schematic diagram of a working condition identification process based on an intelligent algorithm according to the present invention;
FIG. 2 is a schematic flow chart of the improved PSO-based optimized K-means cluster center of the present invention;
FIG. 3 is a schematic flow chart illustrating the present invention employing limiting throttle opening rate for abrupt conditions;
FIG. 4 is a schematic diagram of the engine and motor dynamic torque control architecture after limiting of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be described clearly and completely with reference to the accompanying drawings in the present invention, and it is obvious that the described embodiments are some embodiments of the present invention, but not all embodiments, and all other embodiments obtained by a person of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the protection scope of the present invention.
The dynamic coordination hybrid electric vehicle energy management method based on the working condition classification comprises an online working condition classification part and a torque dynamic coordination part, wherein the online working condition classification part comprises the following steps: firstly, optimizing a clustering center of a K-means algorithm by using an improved Particle Swarm Optimization (PSO) algorithm in an off-line state, dividing the vehicle running condition into two categories of a stable working condition and a sudden change working condition around the clustering center according to extracted characteristic parameters of a typical working condition, continuously acquiring current running data in the running process, comparing the current running data with the divided data in the cluster, judging the current working condition of the vehicle on line in real time, and reasonably adjusting and controlling power flow between an engine and a motor by adopting an equivalent fuel minimum control strategy (ECMS) when the vehicle runs in the stable working condition. When the vehicle runs under the sudden change working condition, the dynamic coordination control program of the throttle valve is entered, and the fuel injection concentration of the engine is limited by controlling the opening change rate of the throttle valve, so that the dynamic fuel consumption increase caused by sudden change of the throttle valve of the engine during mode switching or sudden acceleration is avoided.
The invention provides an intelligent algorithm based on running condition classification, which comprises an improved particle swarm optimization algorithm PSO and a K-means cluster analysis algorithm K-means.
According to the dynamic coordination hybrid electric vehicle energy management method based on working condition classification, aiming at the phenomenon that premature convergence possibly occurs due to the fact that a basic particle swarm optimization algorithm PSO falls into a local extreme value in the particle iteration process, the optimal values of all particles and particle swarms are monitored in real time, the particles falling into premature convergence are timely varied, the diversity of the particle swarms is increased, and the local optimal solution is timely skipped; meanwhile, the inertia weight parameters and the flight time factors are dynamically adjusted when the speed of the particles is updated, so that the searching performance of the particle swarm optimization algorithm is enhanced.
The PSO algorithm updates the particle states using the following formula:
vid(t+1)＝ωvid(t)+c1r1(pi-xid)+c2r2(pg-xid)
xid(t+1)＝xid(t)+vid(t+1)，i＝1，2，…，n；d＝1，2，…，D
in the formula: v. ofid(t) and xid(t) respectively representing the velocity and position of the particle i at the tth iteration; p is a radical ofiAnd pgRespectively representing the individual optimal position and the group optimal position of the particle i; omega is an inertia weight coefficient and represents the influence of the last iteration speed of the particles on the current iteration speed; c. C1And c2The expression learning factor expresses the influence degree of the individual optimal position and the group optimal position on the speed; r is1And r2Random weights are provided for different parts of the particle velocity definitions, which are uniformly distributed in a closed interval [0, 1 ]]Random numbers within a range.
According to the dynamic coordination hybrid electric vehicle energy management method based on working condition classification, the K-means algorithm is a classic algorithm based on division, and is widely applied due to the characteristics of simple algorithm, easy realization, high convergence speed, relative scalability and high efficiency when a large data set is processed. The collected basic working condition data are classified by using a K-means clustering algorithm, so that the data in the same class have larger characteristic similarity, and the data in different classes have larger difference, thereby carrying out the on-line classification of the working conditions.
The criterion function of the K-means algorithm is defined as:
Figure BDA0002241978140000071
where x is a point in space that represents a given data object;
Figure BDA0002241978140000072
is a cluster CiAverage value of (a). This criterion may ensure that the generated clusters are as compact and independent as possible.
Aiming at the defects that the K-means algorithm is sensitive to the selection of the clustering center and the algorithm is easy to fall into the local optimal solution, the improved PSO algorithm is combined with the K-means algorithm, the global search capability of the improved PSO algorithm is utilized to optimize the initial clustering center of the K-means algorithm, and the division precision of the working condition is greatly improved.
The invention discloses a dynamic coordination hybrid electric vehicle energy management method based on working condition classification, which utilizes an intelligent algorithm to divide working conditions and comprises the following specific steps:
firstly, obtaining typical cycle condition data, and comparing the highest vehicle speed v in the cycle condition datamaxMinimum vehicle speed vminAverage vehicle speed vavgMaximum acceleration amaxMaximum deceleration dmaxAcceleration time ratio paVehicle speed standard deviation km/h and acceleration standard deviation m/s2And clustering the similar points into segments, extracting similar characteristic parameters in each segment, and clustering around two given clustering centers.
Further, an improved particle swarm optimization algorithm is used for optimizing an initial clustering center, K-means clustering analysis is carried out on the working condition data to be identified, and a final clustering center c can be obtained1＝[c11，c12，…，c1m]，c2＝[c21，c22，…，c2m]。
Further, a cluster c is calculated1Cluster c2The intra-class difference w (C) and the inter-class difference b (C) of (a).
Figure BDA0002241978140000081
Figure BDA0002241978140000082
Where x is a point in space, representing a given data object,
Figure BDA0002241978140000084
is a cluster CiMean value of (1), definitionThese functions may ensure that the clusters generated are as compact and independent as possible.
Further, continuously adjusting the clustering center until the criterion functions E, w (C), b (C) do not change any more, then considering the classification to be stable, and finally obtaining the clustering center C under stable working conditions1And c, clustering center of sudden change working condition2。
Further, the extracted characteristic parameters are used as an array, and the Euclidean distance between the array and each final clustering center is calculated according to the following formula:
Figure BDA0002241978140000083
the cluster center closest to the array is the cluster to which the array belongs, and the working condition to which the current moment belongs is the working condition class represented by the cluster.
The dynamic coordination hybrid electric vehicle energy management method based on working condition classification adopts a method of limiting the throttle opening change rate aiming at sudden change working conditions, when the throttle opening change rate delta beta of an engine is larger than a preset value delta alpha, a throttle control regulation program is entered, regulated engine torque is fed back to a vehicle control unit VCU through an engine controller ECU, and then the vehicle control unit VCU carries out reasonable distribution on motor torque according to the magnitude of required torque.
The dynamic coordination hybrid electric vehicle energy management method based on the working condition classification aims at controlling the throttle opening change rate of an engine as follows: and when the engine controller ECU receives the distributed target torque, analyzing the target torque into a command of a throttle opening beta, and realizing that the engine smoothly follows the target torque by adjusting the throttle opening. The increment of the throttle opening in unit time is a limited value, namely, the throttle opening change rate, and according to the integral property, if the throttle opening change rate delta beta in a future period is integrated, a specific throttle opening value can be obtained. Therefore, using the throttle opening change rate Δ β as the output of the PID controller, the following of the target torque by the engine is achieved, which can be expressed as:
Figure BDA0002241978140000091
wherein: e is the engine torque deviation control quantity, and e is Te_tar’－Te_act，te_tar’Is the target torque of the engine, Te_actIs the actual torque of the engine;
kp、ki、kdrespectively proportional, integral and differential gain coefficients.
The engine is dynamically and coordinately controlled, the opening change rate of the throttle is limited within a set value, the reduced torque difference is compensated by utilizing the quick response characteristic of the motor, the sum of the torques of the throttle and the motor is ensured not to generate large fluctuation, the deterioration of fuel economy and emission of the gasoline engine when the throttle is suddenly increased or decreased is avoided, and the riding comfort is improved.
The invention has the advantages that: the dynamic coordination hybrid electric vehicle energy management method based on working condition classification is provided, aiming at the division of driving working conditions, the improved particle swarm optimization is adopted to optimize the clustering center of a K-means clustering analysis algorithm, and further clustering division is carried out; aiming at the sudden change working condition in the driving process, the method for limiting the throttle opening change rate is adopted, so that the dynamic oil consumption is greatly reduced, and the riding comfort is improved.
The invention is described in detail below with reference to the accompanying drawings:
as shown in fig. 1 to 4, the dynamic coordination hybrid electric vehicle energy management method based on working condition classification provided by the present invention includes two parts of online working condition classification and torque dynamic coordination, including two parts of online working condition classification and torque dynamic coordination, wherein the online working condition classification part is as follows: firstly, optimizing the clustering center of a K-means algorithm by utilizing an improved particle swarm optimization algorithm PSO in an off-line state, then dividing the vehicle running working conditions into two main categories of stable working conditions and abrupt working conditions according to the extracted characteristic parameters of the typical working conditions around a clustering center, continuously acquiring current running data in the running process, comparing the current running data with the divided data in the clusters, judging the current working condition of the vehicle on line in real time, when the vehicle runs under a stable working condition, the equivalent fuel minimum control strategy (ECMS) is adopted to reasonably regulate and control the power flow between the engine and the motor, when the vehicle runs under an abrupt working condition, entering a throttle valve dynamic coordination control program, limiting the fuel injection concentration of the engine by controlling the opening change rate of the throttle valve, therefore, the dynamic oil consumption increase caused by sudden change of the engine accelerator during mode switching or sudden acceleration is avoided.
As shown in fig. 1, the working condition recognition model recognizes the current working condition type according to the characteristic parameters of the typical working conditions, and adopts different torque distribution modes for different working conditions.
As shown in FIG. 2, the invention provides a dynamic coordination hybrid electric vehicle energy management method based on working condition classification, wherein a clustering center for optimizing the working conditions comprises an improved PSO algorithm and a K-means clustering analysis algorithm:
firstly, aiming at the defects that a K-means algorithm is sensitive to cluster center selection and the algorithm is easy to fall into a local optimal solution, combining a PSO algorithm and the K-means algorithm, and optimizing an initial cluster center of the K-means algorithm by using the global search capability of the PSO algorithm; and then, classifying the acquired basic working condition data by using a K-means clustering algorithm to realize that the data in the same class has larger characteristic similarity and the difference between different classes is larger, thereby carrying out the on-line classification of the working conditions.
As shown in fig. 3, a method for limiting the throttle opening change rate is adopted for a sudden change condition, when the throttle opening change rate of the engine is greater than a preset value Δ α, a throttle control adjustment procedure is entered, the adjusted engine torque is fed back to a vehicle control unit VCU through an Engine Controller (ECU), and then the vehicle control unit VCU reasonably distributes the motor torque according to the magnitude of the required torque.
The control for the engine throttle opening change rate is as follows: and after the engine controller ECU receives the distributed target torque, analyzing the target torque into a command of the throttle opening, and realizing that the engine smoothly follows the target torque by adjusting the throttle opening. The increment of the throttle opening in unit time is a limited value, namely, the throttle opening change rate, and according to the integral property, if the throttle opening change rate in a period of time is integrated, a specific throttle opening value can be obtained.
As shown in fig. 4, the engine is dynamically controlled in a coordinated manner, and the torque difference that decreases while limiting the throttle opening change rate is compensated for by the quick response characteristic of the motor. Meanwhile, the sum of the torques of the two can not generate large fluctuation, and the deterioration of fuel economy and emission of the gasoline engine when the throttle valve is suddenly increased or decreased is avoided.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims (5)

1. The dynamic coordination hybrid electric vehicle energy management method based on the working condition classification is characterized by comprising the following steps of:
s1: adopting a clustering center based on an improved PSO optimized K-means algorithm to identify working conditions and establishing a working condition division model;
s2: extracting characteristic parameters of typical working conditions when the PHEV whole vehicle runs, and judging the type of the current working condition through the working condition division model;
s3: the VCU of the vehicle controller obtains the type of the driving condition according to the condition division model, and then carries out reasonable torque distribution;
s4: when the current driving working condition is identified as a stable working condition by the working condition division model, reasonably adjusting and controlling the power flow between the engine and the motor by adopting an equivalent fuel minimum control strategy; when the current running working condition is identified as a sudden change working condition by the working condition division model, entering a throttle opening dynamic regulation program, and avoiding excessive fuel injection of the engine during sudden acceleration or deceleration by limiting the opening change rate delta beta of the throttle of the engine;
in the step S1, in the process of identifying the operating conditions, two clustering center points are first calculated according to the previous driving data, the clustering center of the K-means algorithm is optimized by using the improved PSO, and then the characteristic parameters of the typical operating conditions are extracted, which are: maximum vehicle speed vmaxMinimum vehicle speed vminAverage vehicle speed vavgMaximum acceleration amaxMaximum deceleration dmaxAcceleration time ratio paVehicle speed standard deviation km/h and acceleration standard deviation m/s2；
The improved PSO optimization of the clustering center of the K-means algorithm refers to the combination of the improved PSO algorithm and the K-means algorithm, and the global search capability of the improved PSO algorithm is utilized to optimize the initial clustering center of the K-means algorithm;
the improved PSO algorithm monitors the optimal values of all particles and particle swarms in real time in the particle iteration process, varies the particles falling into premature convergence in time, increases the diversity of the particle swarms and enables the particles to jump out of local optimal solutions in time.
2. The method for dynamically coordinating hybrid electric vehicle energy management based on operating condition classification as claimed in claim 1, wherein: after limiting the opening degree change rate Delta beta of the throttle valve of the engine, the limited engine output torque T is outpute_actAnd current motor output torque
Figure FDA0002707640070000021
And (4) after the PHEV whole vehicle is started, correcting the opening change rate delta beta of the throttle valve through PID control.
3. The operating condition-based system of claim 1The classified dynamic coordination hybrid electric vehicle energy management method is characterized by comprising the following steps of: in step S2, the characteristic parameters of the driving condition include: maximum vehicle speed vmaxMinimum vehicle speed vminAverage vehicle speed vavgMaximum acceleration amaxMaximum deceleration dmaxAcceleration time ratio paVehicle speed standard deviation km/h and acceleration standard deviation m/s2。
4. The method for dynamically coordinating hybrid electric vehicle energy management based on operating condition classification as claimed in claim 1, wherein: in step S3, the torque is distributed reasonably by the following formula:
Figure FDA0002707640070000022
wherein, Te_tar’Is a target torque of the engine; t isrewThe torque required by the whole vehicle;
Figure FDA0002707640070000023
outputting torque for the current motor; t iseIs the output torque of the engine; delta beta is the opening change rate of the throttle valve of the engine; Δ α is a limit value of the rate of change of the throttle opening of the engine.
5. The method for dynamically coordinating hybrid electric vehicle energy management based on operating condition classification as claimed in claim 1, wherein: in step S6, the following of the target torque by the engine is achieved by using the opening degree change rate Δ β of the throttle valve as the output of the PID controller, which can be expressed as:
Figure FDA0002707640070000031
wherein: e is the engine torque deviation control quantity, and e is Te_tar’－Te_act，Te_tar’Is the target torque of the engine, Te_actIs the actual torque of the engine; k is a radical ofp、ki、kdRespectively proportional, integral and differential gain coefficients.
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