CN110962837A - Plug-in hybrid electric vehicle energy management method considering driving style - Google Patents

Plug-in hybrid electric vehicle energy management method considering driving style Download PDF

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CN110962837A
CN110962837A CN201911131914.0A CN201911131914A CN110962837A CN 110962837 A CN110962837 A CN 110962837A CN 201911131914 A CN201911131914 A CN 201911131914A CN 110962837 A CN110962837 A CN 110962837A
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driving style
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CN110962837B (en
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余世明
马包胜
何德峰
仇翔
宋秀兰
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Zhejiang University of Technology ZJUT
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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
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W2050/0062—Adapting control system settings
    • B60W2050/0075—Automatic parameter input, automatic initialising or calibrating means
    • 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
    • B60W2556/00—Input parameters relating to data
    • B60W2556/10—Historical data
    • 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
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/62—Hybrid vehicles

Abstract

The invention discloses a plug-in hybrid electric vehicle energy management method considering a driving style, which comprises three parts of working condition identification, driving style quantification and energy management. And the working condition identification part is used for constructing and identifying the working conditions by applying a K-means clustering algorithm. And a driving style quantification part which quantifies the driving style by taking the average impact degree of the automobile in the driving process as a driving style coefficient according to the collected data. And the energy management part relates to a control method using the minimum equivalent fuel consumption, wherein a reference track of the SOC of the battery is set according to a rule. And determining an initial value of the equivalent factor by considering four typical working conditions, correcting the equivalent factor according to the driving style coefficient, and finally obtaining the optimal equivalent factor by combining with the correction of the SOC of the battery. The method considers the driving style and the SOC consumption track of the battery, and has better fuel economy than the energy management method based on rules and better timeliness than the energy management method based on global optimum.

Description

Plug-in hybrid electric vehicle energy management method considering driving style
Technical Field
The invention relates to an energy optimization management method applied to a Plug-in Hybrid Electric Vehicle (PHEV), in particular to an instantaneous energy management method combining driving style quantification.
Background
With the aggravation of energy crisis and the stricter policy of energy conservation and emission reduction, pure fuel vehicles are gradually replaced by new energy vehicles. The current new energy automobile is mainly divided into a hybrid electric automobile and a pure electric automobile, and the hybrid electric automobile is still the mainstream of the new energy automobile before the battery endurance and the battery life are not obviously improved. Traditional Hybrid Electric Vehicles (HEVs) cannot be externally charged, and battery power comes from mechanical energy recovery; the Plug-in Hybrid Electric Vehicle (PHEV) can be charged externally, has larger battery capacity, can fully utilize the battery and reduce the fuel consumption.
The key of the PHEV energy management strategy is that the power of an engine and the power of a motor are reasonably distributed according to the power requirement of the whole vehicle, so that the engine and the motor work in a high-efficiency low-energy consumption area, and the minimum energy consumption is realized as far as possible. Rule-based energy management relies on the design experience of engineers and has low adaptability to actual operating condition changes. The energy management strategy based on global optimization depends on working condition circulation, the calculated amount is large, and the real-time performance of energy consumption optimization is poor. The energy management method based on minimum equivalent fuel consumption is a real-time optimization method, and the key for realizing the global optimization of the control strategy is to seek the optimal equivalent factor.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides an instantaneous energy management method which combines the driving style quantification and considers the change track of the SOC of a battery.
In order to achieve the purpose, the technical scheme of the invention is as follows:
1) and (5) working condition construction and parameter setting (offline). Extracting a plurality of 120s working condition segments from the existing running record working condition library, and selecting the average speed
Figure BDA0002277476570000011
Average acceleration
Figure BDA0002277476570000012
Average deceleration
Figure BDA0002277476570000013
And the idle speed time percentage r is used as a characteristic parameter, and a K-means clustering algorithm is used for clustering the working conditions to obtain four typical working conditions, namely congestion working conditions, urban working conditions, suburb working conditions and high-speed working conditions, and clustering centers mu of the four typical working conditionsi. And then calculating the optimal equivalent factor and driving style coefficient of each working condition.
2) And (4) identifying the working condition (online). And extracting the last 120 seconds of working conditions from the running working conditions recorded by the vehicle to identify the working conditions, wherein the identification is carried out once every 3 seconds. At an average speed
Figure BDA0002277476570000014
Average acceleration
Figure BDA0002277476570000015
Average deceleration
Figure BDA0002277476570000016
And taking the idle time percentage r as a characteristic parameter of the working condition identification, calculating the Euclidean distance from the sample center to the centers of four working conditions, and considering the working condition with the minimum distance as the working condition type of nearly 120 seconds.
3) Driving style quantification (online). And extracting the latest 9s of working conditions from the driving working conditions recorded by the vehicle to quantize the driving style, wherein the driving style coefficient is gamma once every 3 seconds.
4) Estimating and inputting the total mileage S of a journey to be performed by a driver, and determining a battery SOC reference value SOC according to the traveled mileage S (t) of the journeyref(t)。
5) The equivalence factor is determined (on-line). Obtaining the initial value of the corresponding equivalent factor according to the identified working condition, and then obtaining the initial value of the equivalent factor according to the current driving style coefficient and the reference value SOC of the battery SOCref(t) correcting to obtain the current running conditionMost equivalent factor lambda*(t) of (d). According to the required power of the whole vehicle, the output power P of the battery is determined by the equivalent fuel consumption minimum control methodbattAnd the engine output power Pfuel。
In the step 1, the average impact J is used as the driving style coefficient gamma,
Figure BDA0002277476570000021
the degree of impact is defined as
Figure BDA0002277476570000022
Where v (t) is the current running speed.
In said step 4, SOCref(t) the acquisition mode is rule-based. Specifying maximum value SOC of battery SOCmaxAt 90% of full capacity, minimum value SOC of battery SOCminAt 15% of full capacity, if the driver does not charge the vehicle after the trip is over, the battery SOC needs to be dynamically balanced during the trip, at which point the SOCref(t) is
SOCref(t)＝(SOCmax+SOCmin)/2
Therein, SOCmaxIs 90% of full capacity of the battery, SOCmin15% of the full capacity of the battery, if the driver charges the automobile after the journey is finished, the SOC is linearly reduced in relation to the mileage during the journey, and at the moment, the SOC isref(t) is
Figure BDA0002277476570000023
Wherein, SOC (t)0) Is the battery SOC at the beginning of the tripend30% of the full capacity of the battery. If the travel exceeds the preset mileage S, the SOC of the battery is in the SOC in the subsequent travelendDynamic balance is maintained nearby, and SOC is maintained at the momentref(t) is
SOCref(t)＝20％
In the step 5, an equivalent oil consumption model is established according to the extremely small Pontrieya principle
Figure BDA0002277476570000024
Wherein, x (t) is a state variable of the system, which is referred to as a battery SOC state; u (T) is a control variable of the system, here motor torque Tm。
Figure BDA0002277476570000026
Lambda (t) & f (x (t), u (t), t) is the equivalent fuel consumption of the motor, f (x (t),
Figure BDA0002277476570000025
λ (t) is an equivalence factor. The goal is to minimize the Hamiltonian H (x), (t), u (t), λ (t), t).
In an off-line environment, calculating to obtain an initial value lambda of the equivalent factor under four types of typical working conditions according to an existing driving record working condition library0Calculating equivalent factor lambda of drivers with different driving styles under four working conditions1(t)，
Figure BDA0002277476570000031
Wherein x is1＝γ/γ0The correction factors a, b, c, d are determined by least squares fitting.
In real-time, on-line, a penalty function ψ (SOC (t)) is set for the battery SOC variation to ensure that the battery SOC varies around the reference curve,
Figure BDA0002277476570000032
wherein Δ SOC (t) SOCref(t), the value range of delta SOC (t) is (-0.1,0.1), and the equivalent factor after adding the penalty function is
λ*(t)＝λ1(t)+ψ(SOC(t))
Finally, the Hamiltonian becomes
Figure BDA0002277476570000033
After the optimal equivalent factor under the current driving state is determined, the power P is required according to the whole vehiclerefCalculating the output power P of the batterybattAnd the engine output power PfuelThe relationship between the three is
Pref＝Pbatt+Pfuel
Compared with the prior art, the invention has the following advantages: the invention provides a plug-in hybrid electric vehicle energy management method considering a driving style, which quantifies the driving style by taking average impact as a driving style coefficient, and improves the fuel economy of the vehicle by combining the recognition of a driving condition and the control of the SOC change of a battery.
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FIG. 1 is a schematic diagram of a coaxial parallel hybrid power system configuration.
FIG. 2 is a flow chart of a method for managing energy of a plug-in hybrid electric vehicle in consideration of driving style according to the present invention.
Detailed Description
The invention is applicable to a plug-in hybrid electric vehicle, taking a coaxial parallel plug-in hybrid electric vehicle as an example (as shown in figure 1), the electric motor of the vehicle with the structure is positioned between a clutch and a transmission, and the engine and the electric motor are coaxial so that the rotating speeds of the electric motor and the engine are equal. The motor can work in a generator mode or a motor mode as required, and the driving mode of the whole vehicle is divided into four modes: the motor drive, the engine drive, engine and motor jointly drive, and the engine drive drives and drives the motor electricity generation.
Fig. 2 is a flow chart of a method for managing energy of a plug-in hybrid electric vehicle considering driving style according to the present invention, and the following describes the technical scheme of the present invention in detail with reference to fig. 2.
Step 1: and (5) working condition construction and parameter setting (offline). Selecting effective strokes from the existing running record working condition library, and dividing the strokes intoA plurality of groups of 120s running condition segments, and the average speed of each group of the working condition segments is calculated
Figure BDA0002277476570000034
Average acceleration
Figure BDA0002277476570000041
Average deceleration
Figure BDA0002277476570000042
And the idle speed time percentage r is used as a characteristic parameter, and the working condition segments are clustered by using a K-means clustering algorithm to obtain four typical working conditions, namely congestion working conditions, urban working conditions, suburban working conditions and high-speed working conditions, and clustering centers mu of the four typical working conditionsi. And then calculating the optimal equivalent factor and the driving style coefficient of each working condition, and establishing a mapping relation between the equivalent factor and the driving style coefficient.
And respectively selecting 600s driving data of four typical working conditions from the working condition library, wherein the SOC reference value of the battery is 60% and is kept constant. Obtaining optimal equivalent factor lambda of four typical working conditions by a fixed step length exhaustive method01，λ02，λ03，λ04And its corresponding driving style coefficient gamma01，γ02，γ03，γ04。
Quantifying driving style by taking average impact degree as driving style coefficient
Figure BDA0002277476570000043
The impact is the rate of change of acceleration, as shown in formula 2
Figure BDA0002277476570000044
Drivers who do not have the driving style drive the automobile under the same working condition, and the equivalent factors are slightly different. So it is necessary to combine the driving style coefficient with the equivalence factor lambda01，λ02，λ03，λ04Make modifications for the purpose of descriptionThe method is convenient, and city working conditions are taken as an example. And selecting driving records (600s) of a plurality of different drivers under urban working conditions from a working condition library, respectively calculating a driving style coefficient gamma and an equivalent factor lambda, and fitting and determining parameters a, b, c and d by a least square method.
Figure BDA0002277476570000045
Wherein x1＝γ/γ0The other working conditions are the same.
Step 2: and (4) identifying the working condition (online). And extracting the vehicle running state of the last 120 seconds from the running record of the vehicle to identify the working condition, wherein the identification is carried out once every 3 seconds. At an average speed
Figure BDA0002277476570000046
Average acceleration
Figure BDA0002277476570000047
Average deceleration
Figure BDA0002277476570000048
And taking the idle time percentage r as a characteristic parameter for identifying the working condition, calculating the Euclidean distance from the sample center to the centers of four typical working conditions, and considering the working condition with the minimum distance as the working condition type of nearly 120 seconds.
And step 3: driving style quantification (online). And extracting the latest 9s working condition from the driving working conditions recorded by the vehicle to calculate the driving style coefficient, and identifying once every 3 seconds.
And 4, step 4: the equivalence factor is determined (on-line). Determining the initial value of the equivalent factor according to the identified working condition, and then determining the initial value according to the current driving style coefficient and the reference value SOC of the battery SOCref(t) correcting to obtain the optimal equivalent factor lambda under the current running condition*(t) of (d). According to the required power of the whole vehicle, the output power P of the battery is determined by the equivalent fuel consumption minimum control methodbattAnd the engine output power Pfuel。
The minimum equivalent fuel consumption control method is shown in formula 4
Figure BDA0002277476570000049
Wherein, x (t) is a state variable of the system, which is referred to as a battery SOC state; u (T) is a control variable of the system, here motor torque Tm。
Figure BDA0002277476570000051
The instantaneous oil consumption of the engine is lambda (t) & f (x (t), u (t), and t), the equivalent oil consumption of the motor is t,
Figure BDA0002277476570000052
λ (t) is an equivalence factor. The goal is to minimize the Hamiltonian H (x), (t), u (t), λ (t), t).
The equivalent factor initial value lambda corresponding to the identified real-time working condition0Substituting the real-time driving style coefficient gamma into a formula 3 to obtain a new equivalent factor lambda1. Adding a penalty function considering that the SOC needs to change near the reference trajectory
Figure BDA0002277476570000053
Wherein Δ SOC (t) SOCref(t), Δ SOC (t) is in the range of (-0.1, 0.1). Estimating and inputting the total mileage S of a journey to be started by a driver, and determining a battery SOC reference value SOC according to the traveled mileage S (t) of the journeyref(t), the reference values are specified as follows:
a) if the driver does not charge the automobile after the journey is finished, the SOC reference value of the battery is
SOCref(t)＝(SOCmax+SOCmin)/2 (6)
Therein, SOCmaxIs 90% of full capacity of the battery, SOCmin15% of the full capacity of the battery.
b) If the driver charges the automobile after the journey is finished, and when the travelled distance exceeds the preset total mileage S, the SOC reference value of the battery is
Figure BDA0002277476570000054
Wherein, SOC (t)0) Is the battery SOC at the beginning of the tripend30% of the full capacity of the battery.
c) If the stroke exceeds the preset mileage S, the SOC reference value of the battery in the subsequent stroke is
SOCref(t)＝20％ (8)
Obtaining real-time optimal equivalent factor lambda after adding penalty function*(t)
λ*(t)＝λ1(t)+ψ(SOC(t)) (9)
Finally, the Hamiltonian is written as
Figure BDA0002277476570000055
Wherein
Figure BDA0002277476570000056
For instantaneous fuel consumption of the engine, in determining engine torque TfuelAnd a rotational speed nfuelThen obtaining the engine oil consumption through table lookup; x (t) is the state variable of the system, here the battery SOC state; u (T) is the control variable of the system, here the motor torque TmTherefore, f (x (t), u (t), and t) are the inverse values of the rate of change of the SOC of the battery,
Figure BDA0002277476570000059
Figure BDA0002277476570000057
Figure BDA0002277476570000058
Figure BDA0002277476570000061
wherein V (SOC) is the voltage of the battery pack, R0(SOC) is the resistance of the battery pack and Q is the charge of the battery pack. After the optimal equivalent factor under the current driving state is determined, the power P is required according to the whole vehiclerefCalculating the output power P of the batterybattAnd the engine output power PfuelThe relationship between the three is
Pref＝Pbatt+Pfuel(14)
The running mode of the coaxial parallel type plug-in hybrid electric vehicle is divided into a driving mode and a braking mode, and the driving modes comprise the following modes: in the pure electric mode, the clutch is separated, and only the motor works; the engine and the motor are driven in a combined mode, the clutch is combined, and the engine and the motor work cooperatively; the engine is driven independently, the clutch is combined, only the engine works, and the motor follows up; the engine drives the motor to generate electricity, and the clutch is combined. For the braking mode, the clutch is separated, the engine stops working, and the mechanical motion drives the motor to generate electricity. In the driving mode, except the pure electric mode, the clutches are all in a combined state in other modes, and the rotating speeds of the engine and the motor are equal, so that the required torque T of the whole vehicle can be obtained after the required power of the whole vehicle is knownrefAnd speed N, so that the division of power is equivalent to the division of torque
Tref＝Tbatt+Tfuel(15)
When the torque distribution is optimal, the Hamilton function is minimum, and the oil consumption of the whole vehicle is minimum.

Claims (6)

1. A plug-in hybrid electric vehicle energy management method considering driving style is characterized in that: in an off-line environment, extracting effective driving segments from an existing working condition library, selecting characteristic parameters for clustering to construct four typical working conditions and determining an initial value of an equivalent factor; determining a mapping relation between the driving style coefficient and the equivalent factor by taking the average impact degree in a section of travel as the driving style coefficient; under the real-time online condition, extracting characteristic parameters from the driving record to identify the driving condition, calculating the driving style coefficient and determining an equivalent factor; and then, the equivalent factor is corrected by referring to the state SOC of the battery, and the output power of the engine and the output power of the battery are distributed according to an equivalent fuel consumption minimum method.
2. The method as claimed in claim 1, wherein the method comprises extracting a plurality of 120s operating condition segments from an existing driving record operating condition library under an off-line condition, and selecting the average speed
Figure FDA0002277476560000011
Average acceleration
Figure FDA0002277476560000012
Average deceleration
Figure FDA0002277476560000013
And the idle speed time percentage r is used as a characteristic parameter, and a K-means clustering algorithm is used for clustering the driving working conditions to obtain the clustering centers of the four typical working conditions.
3. The method as claimed in claim 1, wherein in the off-line environment, the driving data of 600s for each of the four typical working conditions is selected and constructed from the working condition library, the equivalent factor λ (t) and the driving style coefficient γ (t) of the driving data are calculated, and one pair of data λ (t) is selected according to the type of the working condition0And gamma0As a reference. Taking the average impact degree in a section of stroke as a driving style coefficient
Figure FDA0002277476560000014
The degree of impact is defined as
Figure FDA0002277476560000015
Where v (t) is the current running speed. Respectively fitting the mapping relation between the equivalent factors and the driving style coefficients under four types of typical working conditions by using a least square method, and determining parameters a, b, c and d;
Figure FDA0002277476560000016
x1(t)＝γ(t)/γ0。
4. the method as claimed in claim 1, wherein the characteristic parameters are extracted from the last 120s driving history, and the characteristic parameters include average speed
Figure FDA0002277476560000017
Average acceleration
Figure FDA0002277476560000018
Average deceleration
Figure FDA0002277476560000019
And the idle time percentage r is used for identifying the driving working condition according to the nearest Euclidean distance from the sample center to the four types of working condition centers, and the driving working condition is identified every 3 s; the driving style was quantified with the average jerk of the latest 9s as the driving style coefficient, once every 3 s.
5. The method of claim 1, wherein the total mileage S of the trip to be started is estimated and inputted by the driver, and the reference value SOC of the battery SOC is determined according to the mileage S (t) traveled during the tripref(t), the reference values are specified as follows:
a) if the driver does not charge the automobile after the journey is finished, the SOC reference value of the battery is
SOCref(t)＝(SOCmax+SOCmin)/2
Therein, SOCmaxIs 90% of full capacity of the battery, SOCmin15% of the full capacity of the battery;
b) if the driver charges the automobile after the journey is finished, and when the travelled distance S (t) exceeds the preset total mileage S, the SOC reference value of the battery is
Figure FDA0002277476560000021
Wherein, SOC (t)0) Is the battery SOC at the beginning of the tripend30% of the full capacity of the battery;
c) if the stroke exceeds the preset mileage S, the SOC reference value of the battery in the subsequent stroke is
SOCref(t)＝20％
Therein, SOCmaxTaking 90% as the upper limit of the SOC of the battery; SOCminTaking 15% as the lower limit of the SOC of the battery; in order to change the battery SOC around the reference trajectory, a penalty function ψ (SOC (t))
Figure FDA0002277476560000022
6. The method of claim 1, wherein the final equivalence factor is determined according to the driving condition type, the driving style coefficient, and the battery state SOC,
λ*(t)＝λ1(t)+ψ(SOC(t))
according to the required power of the whole vehicle, the output power P of the battery is determined by the equivalent fuel consumption minimum control methodbattAnd the engine output power Pfuel。
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