CN108791269B - PHEV distributed control method applicable to power battery exchange modularization - Google Patents

PHEV distributed control method applicable to power battery exchange modularization Download PDF

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CN108791269B
CN108791269B CN201810682004.0A CN201810682004A CN108791269B CN 108791269 B CN108791269 B CN 108791269B CN 201810682004 A CN201810682004 A CN 201810682004A CN 108791269 B CN108791269 B CN 108791269B
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battery
system controller
feedback
phev
feedback gain
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CN108791269A (en
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林歆悠
任静
周坤城
苏炼
夏斌
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Fuzhou University
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Fuzhou University
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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
    • 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
    • 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
    • B60W2050/0001—Details of the control system
    • B60W2050/0002—Automatic control, details of type of controller or control system architecture
    • B60W2050/0008—Feedback, closed loop systems or details of feedback error signal
    • 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
    • Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02T90/10—Technologies relating to charging of electric vehicles
    • Y02T90/14—Plug-in electric vehicles

Abstract

The invention relates to a PHEV distributed control method applicable to power battery exchange modularization, which is characterized in that on the basis of a CS mode controller based on feedback, a controller distribution structure between a VSC and a BSC is provided by adopting an analysis method of sensitivity of a control signal relative to battery parameters, and finally, a distributed controller for realizing CSM of a battery is obtained by solving a double-layer optimization problem. The invention can reduce the coupling risk of a vehicle system, expand the pure electric mileage, and simultaneously meet the limitations of closed-loop system stability, battery charge sustainability and component reliability.

Description

PHEV distributed control method applicable to power battery exchange modularization
Technical Field
The invention relates to the field of automobile control, in particular to a PHEV distributed control method applicable to power battery exchange modularization.
Background
With the increase of electronic control unit components in the automobile control system, the standardization requirements of the design, manufacture and maintenance process of the control system are also improved. When a component in an automotive control system is changed, both the system controller and the component controller must be redesigned, including the updating of the calibration. When the control system is designed by adopting component swap-ping modularization (CSM), when the control system components are changed, the system controller does not need to be redesigned, and the recalibration work of the controller only needs to be carried out in the component controller, so that the CSM design of the automobile control system can increase the interchangeability between the components, shorten the development time and reduce the development cost.
The PHEV plug-in hybrid electric vehicle is used as a transition product from a traditional vehicle to an electric vehicle, and the continuation of the journey mileage and the fuel economy are both considered well. With the advancement of technology, the control strategy of PHEVs has shifted from traditional rule-based control to optimal control. One of the major obstacles to commercialization of PHEVs is the cost and reliability of the battery, and therefore it would be beneficial to develop a decoupled design of the vehicle and battery assembly that would become a swappable module if battery replacement could be accommodated by simply recalibrating the controller inside the battery module, thereby enabling vehicle performance to reach that which could be achieved by redesigning the entire centralized controller.
Disclosure of Invention
In view of this, the present invention provides a distributed PHEV control method applicable to power battery exchange modules, which can reduce the coupling risk of a vehicle system and expand the pure electric mileage.
The invention is realized by adopting the following scheme: a PHEV distributed control method applicable to power battery exchange modularization specifically comprises the following steps:
step S1: selecting a distributed control system structure of a plug-in hybrid electric vehicle (PHEV) based on a current vehicle control strategy;
step S2: constructing a feedback controller based on a battery CS mode applied to the PHEV according to an EPA US06 loop;
step S3: obtaining the feedback gain of the feedback controller in the step S2 by adopting a multi-constraint nonlinear optimization method;
step S4: performing fourth-order polynomial fitting on the optimal value of the feedback gain obtained in the step S3, and calculating the sensitivity of the vehicle control signal relative to the hardware parameter of the battery;
step S5: analyzing the sensitivity of the control signal relative to the hardware parameters of the battery, and determining the effective distribution of feedback gain between a vehicle system controller VSC and a battery system controller BSC;
step S6: a double-layer optimization method is adopted to coordinate feedback gains between a vehicle system controller VSC and a battery system controller BSC;
step S7: and (4) further optimizing the double-layer optimization method in the step S6 by adopting an augmented Lagrange decomposition method to realize the CSM of the battery.
Further, in step S1, the distributed control architecture of the PHEV includes a vehicle system controller VSC and a battery system controller BSC, which are fixed to the vehicle; wherein the battery system controller BSC is fixed to a battery module of the vehicle, and is exchanged with the battery; the battery module is an intelligent component and is provided with an embedded microcontroller for executing a control function and communicating with the vehicle system controller VSC through a network; the embedded microcontroller is a battery pack controller; the whole vehicle system controller VSC and the hundred degree vehicle computing platform BCU are implemented in the same microprocessor, and the calibration of the BCU software may be able to be rearranged when the battery changes.
Further, in step S2, the feedback controller based on the battery CS mode configured for application in the PHEV is specifically: and the whole vehicle system controller VSC calculates the actual required power according to the working condition, distributes power between the engine and the battery module according to the state feedback information of the battery module, and respectively sends power requirement commands to the engine and the motor, and the motor and the engine drive the vehicle to move forward according to the distributed power requirement commands.
Further, in step S3, specifically, the pole of the feedback closed-loop control is disposed in the left half plane of the plane coordinates, and then the feedback gain of the feedback controller is obtained by using the multi-constraint nonlinear optimization method.
Further, the optimization constraint conditions in the multi-constraint nonlinear optimization method include:
the stability of the closed-loop system of the linear controller in the CS mode is determined by the position of a closed-loop pole; pole p of closed loop systemiIs located in the left half-plane real (p)i)＜0,i＝1,2,3；
Power P of engineeThe upper and lower limits satisfy: pemin＜Pe(t)＜Pemax(ii) a In the formula, PeminAnd PemaxSetting according to specific requirements;
limiting a rate of change of engine power to smooth engine power during continued engine operation; rate of change of engine powerThe requirements are as follows:in the formula (I), the compound is shown in the specification,threshold value for rate of change of engine power, typically based on engine startObtaining an external characteristic curve, wherein the external characteristic curve of the engine can be obtained according to the model and related parameters of the engine; may also be determined empirically;
power P of the batteryBThe upper and lower limits satisfy: pBmin＜PB(t)＜PBmax，PBminAnd PBmaxSetting according to specific requirements;
upper and lower limits of battery SOC: SOCmin＜SOC(t)＜SOCmaxIn the formula, SOCminAnd SOCmaxSetting according to specific requirements;
the battery SOC at the end of the driving cycle needs to satisfy: SOCfmin＜SOCf＜SOCfmaxIn the formula, SOCfIs the battery SOC value at the end of the driving cycle, SOCfminAnd SOCfmaxAccording to the specific requirements.
Preferably, in step S4, the effective allocation of feedback gain between the VSC and the BSC determines a compromise between performance and simplicity (in terms of calculation and calibration effort) of the feedback-based CS mode controller, which is generally closely linked to the order and gain of the controller, and the effective allocation of feedback gain between the VSC and the BSC is determined by fourth-order polynomial fitting of the optimal value of the feedback-based CS mode controller gain to calculate the sensitivity of the vehicle control signal to the battery parameters, analyzing the sensitivity of the control signal to the battery parameters.
Further, in step S4, the step of performing fourth order polynomial fitting on the optimal value of the feedback gain specifically includes the following steps:
step S41: and acquiring an optimal value of the feedback gain by adopting matlab software:
xi *(Bs)＝[xi,1(Bs),xi,2(Bs),...xi,n(Bs)]；
in the formula, xi *(Bs) For the optimum value of the feedback gain, xi,1(Bs)、xi,2(Bs) To xi,n(Bs) Respectively represent a certain variable in the battery moduleN feedback gains, some variable such as: variables such as current, voltage, temperature, battery SOC, etc.;
step S42: a fourth order polynomial fit to the optimal value of the feedback gain is performed by:
in the formula, BsAs a battery hardware parameter, cj,iAnd (j ═ 1,2,3 and 4) is a polynomial constant term coefficient.
Further, in step S4, the calculation of the sensitivity of the vehicle control signal with respect to the battery parameter employs the following equation:
in the formula, BsAs a battery hardware parameter, u (B)s,q,y,xi) For vehicle control signals, q is the input signal, y is the feedback signal, xiIs the feedback gain of the feedback controller in step S2.
Further, step S5 is specifically: distributing feedback gain which enables the sensitivity of vehicle control signals relative to battery hardware parameters to be high and corresponding calculation to a battery system controller BSC, and reserving the rest part of a feedback controller in a CS mode in a whole vehicle system controller VSC, and controlling the CD mode and regenerative braking; the battery system controller BSC is internally arranged in the battery module, and the control function related to the battery variable is distributed to the battery component controller in the battery system controller BSC, so that the bidirectional communication between the whole vehicle system controller VSC and the battery system controller BSC is introduced, and the effective distribution of the feedback gain between the VSC and the BSC is realized.
Further, step S6 includes two iterative stages: the first iteration stage is the outer stage: the outer layer master control problem generates feedback gain in a vehicle system controller VSC; the second iteration stage is an inner stage: the inner layer sub-problem refers to a feedback gain with higher sensitivity, which is generated by a feedback controller based on a CS mode of a battery and is related to battery parameter variables, in a battery system controller BSC, corresponding to different variables of a battery module; during each iteration, the feedback gain in the vehicle system controller VSC generated by the outer layer optimization problem is fixed and invariable as the parameter of the inner layer optimization problem, and the inner layer problems are mutually independent and can be solved in parallel.
Further, step S7 includes two stages, an outer stage, that, in each iteration, generates a new estimate of the feedback gain in the vehicle system controller VSCAnd as a parameter to solve each inner-layer phase problem; inner stage, by reassigning penalty weight viAnd wiGradually making the penalty function to zero to ensureAndapproach the same value; repeating the process until a maximum value of the penalty function estimate is reached; wherein the content of the first and second substances,an estimate value representing the feedback gain in the battery system controller BSC;
the penalty function phi is constructed by adopting a numerical algorithm of an augmented Lagrange decomposition method:
in the formula, xs、xs,iRespectively a feedback gain in the VSC of the vehicle system controller and a feedback gain in the BSC of the battery system controller,is a lagrange multiplier estimation vector;is a penalty weight vector, signRepresenting a Hadamard product.
In particular, in step S7, for all variables of the internal stage of the two-tier optimization, i.e., the feedback gain in the battery system controller BSC, the penalty function is constructed by using the augmented lagrange decomposition method and is made to approach 0, so as to allow the global variable, i.e., the feedback gain in the vehicle system controller VSC, to take different values for each different sub-problem (feedback gain in the BSC).
Compared with the prior art, the invention has the following beneficial effects: the invention provides a controller distribution structure between VSC and BSC by adopting an analysis method of control signal sensitivity relative to battery parameter based on feedback CS mode controller, and finally obtains a distributed controller for realizing battery CSM by solving a double-layer optimization problem, thereby reducing the coupling risk of vehicle system, expanding the pure electric mileage, and simultaneously meeting the restrictions of closed-loop system stability, battery charge sustainability and component reliability.
Drawings
Fig. 1 is a schematic diagram of a distributed control architecture according to an embodiment of the present invention.
Fig. 2 is a schematic diagram of a CS mode controller based on feedback according to an embodiment of the present invention.
Fig. 3 is a schematic flow chart of a two-layer optimization solution algorithm according to an embodiment of the present invention.
Fig. 4 is a diagram of a plug-in hybrid vehicle battery interchange modular distributed controller design architecture according to an embodiment of the present invention.
In FIG. 1, q1、q2Representing the input signal, y1、y2Representing a feedback signal u1、u2Representing a control signal, S1、S2Represents: a communication network signal;
in FIG. 2, PreIndicating power demand, P, of the engineeHair with indicationOutput power of the engine, PrwRepresenting the actual power demand, P, of the vehiclerbIndicating the power demand of the motor, PbRepresents motor output power (power output from the motor to the battery), PwRepresenting the actual output power of the vehicle, and y representing feedback information;
in FIG. 4, PrwRepresenting the actual power demand, SOC of the vehiclerRepresenting the battery SOC reference value and q representing the input signal.
Detailed Description
The invention is further explained below with reference to the drawings and the embodiments.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
As shown in fig. 4, the present embodiment provides a distributed PHEV control method applicable to power battery exchange modularization, which specifically includes the following steps:
step S1: selecting a distributed control architecture for a plug-in hybrid electric vehicle PHEV based on current vehicle control strategies, as shown in FIG. 1;
step S2: a feedback controller based on the battery CS mode applied in PHEV was constructed according to the EPA US06 loop, as shown in fig. 2;
step S3: obtaining the feedback gain of the feedback controller in the step S2 by adopting a multi-constraint nonlinear optimization method;
step S4: performing fourth-order polynomial fitting on the optimal value of the feedback gain obtained in the step S3, and calculating the sensitivity of the vehicle control signal relative to the hardware parameter of the battery;
step S5: analyzing the sensitivity of the control signal relative to the hardware parameters of the battery, and determining the effective distribution of feedback gain between a vehicle system controller VSC and a battery system controller BSC;
step S6: a double-layer optimization method is adopted to coordinate feedback gains between a vehicle system controller VSC and a battery system controller BSC;
step S7: and (4) further optimizing the double-layer optimization method in the step S6 by adopting an augmented Lagrange decomposition method to realize the CSM of the battery.
In this embodiment, in step S1, the distributed control architecture of the PHEV includes a vehicle system controller VSC and a battery system controller BSC that are fixed to the vehicle; wherein the battery system controller BSC is fixed to a battery module of the vehicle, and is exchanged with the battery; the battery module is an intelligent component and is provided with an embedded microcontroller for executing a control function and communicating with the vehicle system controller VSC through a network; the embedded microcontroller is a battery pack controller; the whole vehicle system controller VSC and the hundred degree vehicle computing platform BCU are implemented in the same microprocessor, and the calibration of the BCU software may be able to be rearranged when the battery changes.
In this embodiment, in step S2, the feedback controller based on the CS mode of the battery configured for use in the PHEV is specifically: and the whole vehicle system controller VSC calculates the actual required power according to the working condition, distributes power between the engine and the battery module according to the state feedback information of the battery module, and respectively sends power requirement commands to the engine and the motor, and the motor and the engine drive the vehicle to move forward according to the distributed power requirement commands.
In this embodiment, in step S3, specifically, the poles of the feedback closed-loop control are arranged in the left half plane of the plane coordinates, and then the feedback gain of the feedback controller is obtained by using the multi-constraint nonlinear optimization method.
In this embodiment, the optimization constraint conditions in the multi-constraint nonlinear optimization method include:
the stability of the closed-loop system of the linear controller in the CS mode is determined by the position of a closed-loop pole; pole p of closed loop systemiIs located in the left half-plane real (p)i)＜0,i＝1,2,3；
Power P of engineeThe upper and lower limits satisfy: pemin＜Pe(t)＜PemaxIn the formula, PeminAnd PemaxSetting according to specific requirements;
limiting a rate of change of engine power to smooth engine power during continued engine operation; rate of change of engine powerThe requirements are as follows:in the formula (I), the compound is shown in the specification,is a threshold value for the rate of change of engine power.
Power P of the batteryBThe upper and lower limits satisfy: pBmin＜PB(t)＜PBmax，PBminAnd PBmaxSetting according to specific requirements;
upper and lower limits of battery SOC: SOCmin＜SOC(t)＜SOCmaxIn the formula, SOCminAnd SOCmaxSetting according to specific requirements;
the battery SOC at the end of the driving cycle needs to satisfy: SOCfmin＜SOCf＜SOCfmaxIn the formula, SOCfIs the battery SOC value at the end of the driving cycle, SOCfminAnd SOCfmaxAccording to the specific requirements.
Preferably, in step S4, the effective allocation of feedback gain between the VSC and the BSC determines a compromise between performance and simplicity (in terms of calculation and calibration effort) of the feedback-based CS mode controller, which is generally closely linked to the order and gain of the controller, and the effective allocation of feedback gain between the VSC and the BSC is determined by fourth-order polynomial fitting of the optimal value of the feedback-based CS mode controller gain to calculate the sensitivity of the vehicle control signal to the battery parameters, analyzing the sensitivity of the control signal to the battery parameters.
In this embodiment, the step S4 of performing fourth order polynomial fitting on the optimal value of the feedback gain specifically includes the following steps:
step S41: and acquiring an optimal value of the feedback gain by adopting matlab software:
xi *(Bs)＝[xi,1(Bs),xi,2(Bs),...xi,n(Bs)]；
in the formula, xi *(Bs) For the optimum value of the feedback gain, xi,1(Bs)、xi,2(Bs) To xi,n(Bs) Respectively represents n feedback gains under a certain variable in the battery module, such as: variables such as current, voltage, temperature, battery SOC, etc.;
step S42: a fourth order polynomial fit to the optimal value of the feedback gain is performed by:
in the formula, BsAs a battery hardware parameter, cj,iAnd (j ═ 1,2,3 and 4) is a polynomial constant term coefficient.
In the present embodiment, in step S4, the calculation of the sensitivity of the vehicle control signal with respect to the battery parameter employs the following equation:
in the formula, BsAs a battery hardware parameter, u (B)s,q,y,xi) For vehicle control signals, q is the input signal, y is the feedback signal, xiIs feedback of the feedback controller in step S2And (4) gain.
In this embodiment, step S5 specifically includes: distributing feedback gain which enables the sensitivity of vehicle control signals relative to battery hardware parameters to be high and corresponding calculation to a battery system controller BSC, and reserving the rest part of a feedback controller in a CS mode in a whole vehicle system controller VSC, and controlling the CD mode and regenerative braking; the battery system controller BSC is internally arranged in the battery module, and the control function related to the battery variable is distributed to the battery component controller in the battery system controller BSC, so that the bidirectional communication between the whole vehicle system controller VSC and the battery system controller BSC is introduced, and the effective distribution of the feedback gain between the VSC and the BSC is realized.
In the present embodiment, step S6 includes two iterative stages: the first iteration stage is the outer stage: the outer layer master control problem generates feedback gain in a vehicle system controller VSC; the second iteration stage is an inner stage: the inner layer sub-problem refers to feedback gains which are generated by a feedback controller based on a CS mode of the battery and are related to parameter variables of the battery, corresponding to different variables of the battery module, in a battery system controller BSC; during each iteration, the feedback gain in the vehicle system controller VSC generated by the outer layer optimization problem is fixed and invariable as the parameter of the inner layer optimization problem, and the inner layer problems are mutually independent and can be solved in parallel.
In the present embodiment, as shown in fig. 3, step S7 includes two stages, an outer stage, in each iteration, generating a new estimate of the feedback gain in the vehicle system controller VSCAnd as a parameter to solve each inner-layer phase problem; inner stage, by reassigning penalty weight viAnd wiGradually making the penalty function to zero to ensureAndapproach to the sameA value of (d); repeating the process until a maximum value of the penalty function estimate is reached; wherein the content of the first and second substances,representing an estimate of the feedback gain in the battery system controller BSC.
The penalty function phi is constructed by adopting a numerical algorithm of an augmented Lagrange decomposition method:
in the formula, xs、xs,iRespectively a feedback gain in the VSC of the vehicle system controller and a feedback gain in the BSC of the battery system controller,is a lagrange multiplier estimation vector;is a penalty weight vector, signRepresenting a Hadamard product.
In particular, in step S7, for all variables of the internal stage of the two-tier optimization, i.e., the feedback gain in the battery system controller BSC, the penalty function is constructed by using the augmented lagrange decomposition method and is made to approach 0, so as to allow the global variable, i.e., the feedback gain in the vehicle system controller VSC, to take different values for each different sub-problem (feedback gain in the BSC).
The above description is only a preferred embodiment of the present invention, and all equivalent changes and modifications made in accordance with the claims of the present invention should be covered by the present invention.

Claims (9)

1. A PHEV distributed control method applicable to power battery exchange modularization is characterized in that: the method comprises the following steps:
step S1: selecting a distributed control system structure of a plug-in hybrid electric vehicle (PHEV) based on a current vehicle control strategy;
step S2: constructing a feedback controller based on a CS mode of a battery applied to the PHEV according to an EPAUS06 cycle;
step S3: obtaining the feedback gain of the feedback controller in the step S2 by adopting a multi-constraint nonlinear optimization method;
step S4: performing fourth-order polynomial fitting on the optimal value of the feedback gain obtained in the step S3, and calculating the sensitivity of the vehicle control signal relative to the hardware parameter of the battery;
step S5: analyzing the sensitivity of the control signal relative to the hardware parameters of the battery, and determining the effective distribution of feedback gain between a vehicle system controller VSC and a battery system controller BSC;
step S6: a double-layer optimization method is adopted to coordinate feedback gains between a vehicle system controller VSC and a battery system controller BSC;
step S7: an augmented Lagrange decomposition method is adopted to further optimize the double-layer optimization method in the step S6, and CSM of the battery is achieved;
in step S4, the sensitivity of the vehicle control signal with respect to the battery parameter is calculated by the following formula:
in the formula, BsAs a battery hardware parameter, u (B)s,q,y,xi) For vehicle control signals, q is the input signal, y is the feedback signal, xiIs the feedback gain of the feedback controller in step S2.
2. The distributed control method for the PHEV applicable to the power battery exchange modularization as claimed in claim 1, wherein: in step S1, the distributed control architecture of the PHEV includes a vehicle system controller VSC and a battery system controller BSC, which are fixed to the vehicle; wherein the battery system controller BSC is fixed to a battery module of the vehicle, and is exchanged with the battery; the battery module is an intelligent component and is provided with an embedded microcontroller for executing a control function and communicating with the vehicle system controller VSC through a network; the embedded microcontroller is a battery pack controller; the whole vehicle system controller VSC and the hundred-degree vehicle-mounted computing platform BCU are realized in the same microprocessor, and when the battery is changed, the calibration of the BCU software can be rearranged.
3. The distributed control method for the PHEV applicable to the power battery exchange modularization as claimed in claim 1, wherein: in step S2, the feedback controller based on the battery CS mode configured for application in the PHEV is specifically: and the whole vehicle system controller VSC calculates the actual required power according to the working condition, distributes power between the engine and the battery module according to the state feedback information of the battery module, and respectively sends power requirement commands to the engine and the motor, and the motor and the engine drive the vehicle to move forward according to the distributed power requirement commands.
4. The distributed control method for the PHEV applicable to the power battery exchange modularization as claimed in claim 1, wherein: in step S3, specifically, the pole of the feedback closed-loop control is disposed in the left half plane of the plane coordinate, and then the feedback gain of the feedback controller is obtained by using the multi-constraint nonlinear optimization method.
5. The distributed control method for the PHEV applicable to the power battery exchange modularization as claimed in claim 4, wherein: the optimization constraint conditions in the multi-constraint nonlinear optimization method comprise the following steps:
pole p for feedback closed-loop controliArranged in the left half plane of the plane coordinates: real (p)i)＜0,i＝1,2,3；
Power P of engineeThe upper and lower limits satisfy: pemin＜Pe(t)＜PemaxIn the formula, PeminAnd PemaxSetting according to specific requirements;
during continued engine operation, limiting the rate of change of engine power to smooth engine power: rate of change of engine powerThe requirements are as follows:in the formula (I), the compound is shown in the specification,a threshold value for the rate of change of engine power;
power P of the batteryBThe upper and lower limits satisfy: pBmin＜PB(t)＜PBmax(ii) a Wherein, PBminAnd PBmaxSetting according to specific requirements;
upper and lower limits of battery SOC: SOCmin＜SOC(t)＜SOCmaxIn the formula, SOCminAnd SOCmaxSetting according to specific requirements;
the battery SOC at the end of the driving cycle needs to meet SOCfmin＜SOCf＜SOCfmax(ii) a In the formula, SOCfIs the battery SOC value at the end of the driving cycle, SOCfminAnd SOCfmaxAccording to the specific requirements.
6. The distributed control method for the PHEV applicable to the power battery exchange modularization as claimed in claim 1, wherein: in step S4, the fourth order polynomial fitting of the optimal value of the feedback gain specifically includes the following steps:
step S41: and acquiring an optimal value of the feedback gain by adopting matlab software:
xi *(Bs)＝[xi,1(Bs),xi,2(Bs),...xi,n(Bs)]；
in the formula, xi *(Bs) For the optimum value of the feedback gain, xi,1(Bs)、xi,2(Bs) To xi,n(Bs) Representing n feedback gains under a certain variable in the battery module;
step S42: a fourth order polynomial fit to the optimal value of the feedback gain is performed by:
in the formula, BsAs a battery hardware parameter, cj,iAnd (j ═ 1,2,3 and 4) is a polynomial constant term coefficient.
7. The distributed control method for the PHEV applicable to the power battery exchange modularization as claimed in claim 2, wherein: step S5 specifically includes: distributing feedback gain which enables the sensitivity of vehicle control signals relative to battery hardware parameters to be high and corresponding calculation to a battery system controller BSC, and reserving the rest part of a feedback controller in a CS mode in a whole vehicle system controller VSC, and controlling the CD mode and regenerative braking; the battery system controller BSC is internally arranged in the battery module, and the control function related to the battery variable is distributed to the battery component controller in the battery system controller BSC, so that the bidirectional communication between the whole vehicle system controller VSC and the battery system controller BSC is introduced, and the effective distribution of the feedback gain between the VSC and the BSC is realized.
8. The distributed control method for the PHEV applicable to the power battery exchange modularization as claimed in claim 1, wherein: step S6 includes two iteration stages: the first iteration stage is the outer stage: the outer layer master control problem generates feedback gain in a vehicle system controller VSC; the second iteration stage is an inner stage: the inner layer sub-problem refers to a feedback gain with higher sensitivity, which is generated by a feedback controller based on a CS mode of a battery and is related to battery parameter variables, in a battery system controller BSC, corresponding to different variables of a battery module; during each iteration, the feedback gain in the vehicle system controller VSC generated by the outer layer optimization problem is fixed and invariable as the parameter of the inner layer optimization problem, and the inner layer problems are mutually independent and can be solved in parallel.
9. The distributed control method for the PHEV applicable to the power battery exchange modularization as claimed in claim 1, wherein: step S7 includes two stages, an outer stage, that during each iteration, generates a new estimate of the feedback gain in the vehicle system controller VSCAnd as a parameter to solve each inner-layer phase problem; inner stage, by reassigning penalty weight viAnd wiGradually making the penalty function to zero to ensureAndapproach the same value; repeating the process until a maximum value of the penalty function estimate is reached; wherein the content of the first and second substances,an estimate value representing the feedback gain in the battery system controller BSC;
the penalty function phi is constructed by adopting a numerical algorithm of an augmented Lagrange decomposition method:
in the formula, xs、xs,iRespectively a feedback gain in the VSC of the vehicle system controller and a feedback gain in the BSC of the battery system controller,is a lagrange multiplier estimation vector;is a penalty weight vector, signRepresenting a Hadamard product.
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