CN113212415B - Combined optimization method for component parameters and control parameters of P2 hybrid electric vehicle - Google Patents

Combined optimization method for component parameters and control parameters of P2 hybrid electric vehicle Download PDF

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CN113212415B
CN113212415B CN202110623003.0A CN202110623003A CN113212415B CN 113212415 B CN113212415 B CN 113212415B CN 202110623003 A CN202110623003 A CN 202110623003A CN 113212415 B CN113212415 B CN 113212415B
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CN113212415A (en
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曾小华
段朝胜
陈建新
宋大凤
梁伟智
钱琦峰
李敦迈
黄钰峰
向远贵
郑琦
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Jilin 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
    • B60W20/10—Controlling the power contribution of each of the prime movers to meet required power demand
    • B60W20/11—Controlling the power contribution of each of the prime movers to meet required power demand using model predictive control [MPC] strategies, i.e. control methods based on models predicting performance
    • 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
    • B60W2510/00—Input parameters relating to a particular sub-units
    • B60W2510/06—Combustion engines, Gas turbines
    • 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
    • B60W2510/00—Input parameters relating to a particular sub-units
    • B60W2510/08—Electric propulsion units
    • B60W2510/083—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
    • B60W2510/00—Input parameters relating to a particular sub-units
    • B60W2510/08—Electric propulsion units
    • B60W2510/085—Power
    • 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
    • B60W2510/00—Input parameters relating to a particular sub-units
    • B60W2510/10—Change speed gearings
    • 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
    • B60W2510/00—Input parameters relating to a particular sub-units
    • B60W2510/10—Change speed gearings
    • B60W2510/1005—Transmission ratio engaged
    • 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
    • B60W2510/00—Input parameters relating to a particular sub-units
    • B60W2510/24—Energy storage means
    • B60W2510/242—Energy storage means for electrical energy
    • B60W2510/244—Charge state
    • 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
    • Y02T10/80—Technologies aiming to reduce greenhouse gasses emissions common to all road transportation technologies
    • Y02T10/84—Data processing systems or methods, management, administration

Abstract

The invention discloses a combined optimization method of component parameters and control parameters of a P2 hybrid electric vehicle, which aims to solve the problems that the design result cannot reach the optimum due to the fact that the component parameters and the control parameters are separately considered in the design of the conventional P2 hybrid electric system. According to the method, the optimal component parameters and control parameter values comprehensively considering the component cost and the fuel cost are obtained through combined optimization, the component parameters and the control design period of the P2 hybrid electric vehicle are shortened, and the optimality of the design result is ensured.

Description

Combined optimization method for component parameters and control parameters of P2 hybrid electric vehicle
Technical Field
The invention relates to the technical field of power transmission and control of hybrid electric vehicles, in particular to a method for jointly optimizing component parameters and control parameters of a P2 hybrid electric vehicle.
Background
The design of the component parameters and the control parameters plays a decisive role in whether the hybrid electric vehicle can fully exert the advantages of energy conservation and emission reduction, but the design problems given by a hybrid electric system become very complicated due to the mutual coupling restriction of the two aspects of the component parameters and the control parameters, and in order to simplify the complexity of the design problems, most of the current documents separately consider the two aspects, so that the finally obtained design result is usually suboptimal. With the development of hybrid theory and application technology, the combined consideration of component parameters and control parameters is the development trend of the design problem of the hybrid power system.
In the prior patents, for example, patent number CN 110667566B, a cooperative combination optimization method for matching parameters and control strategies of a hybrid electric vehicle is provided, in which five gear speed ratios of a transmission are used as matching parameters, an engine working torque and a starting rotation speed, and an SOC threshold when an engine starts to work are used as control parameters to perform combination optimization, and a combination optimization algorithm is established in Isight software to complete optimization to obtain optimal values of the matching parameters and the control parameters. Although the invention carries out combined optimization on the component parameters and the control parameters, the component parameters only consider the single component of the transmission, the design problems of the key component parameters of an engine, a motor and a battery in the hybrid power system are ignored, and the finally obtained design result does not consider the component cost.
Disclosure of Invention
The invention mainly solves the technical problem of overcoming the prior technical difficulty, and discloses a combined optimization method of component parameters and control parameters of a P2 hybrid electric vehicle, which establishes an embedded double-layer optimization framework, takes the control parameters as inner layer optimization to be embedded into outer layer component parameter optimization, for each group of component parameters given by the outer layer optimization, the inner layer optimization traverses all control parameter groups to calculate the minimum fuel oil cost meeting simulation initial and final electric quantity balance constraint, further obtains the comprehensive cost of the components and the fuel oil under the group of component parameters, traverses all component parameters to obtain the optimal comprehensive cost and the corresponding optimal component parameters and control parameters, shortens the design optimization period of the P2 hybrid electric vehicle, and ensures the optimality of the design result.
In order to solve the technical problems, the invention is realized by adopting the following technical scheme:
a method for jointly optimizing component parameters and control parameters of a P2 hybrid electric vehicle comprises the following steps:
s1: establishing a whole vehicle model of a P2 hybrid electric vehicle, and determining component parameter design variables;
s2: establishing a P2 hybrid electric vehicle control strategy, and determining control parameter design variables;
s3: establishing an embedded double-layer optimization framework, embedding control parameters serving as inner layer optimization into outer layer component parameter optimization, calculating an outer layer objective function J by using a formula (1) by using the complete vehicle dynamic property as a constraint condition in the outer layer optimizationouter
Jouter＝α1Pcomp+α2Pfuel (1)
In the formula, alpha1-part parameter total cost weight coefficient
Pcomp-total cost of part parameters
α2-fuel cost weighting factor
PfuelCost of fuel
The inner layer optimization takes the simulation initial and final electric quantity balance as a constraint condition, and the simulation initial and final electric quantity balance constraint is specifically
ΔSOC＝|SOCend-SOCini|≤T (2)
In the formula, Δ SOC-simulation starting and ending SOC difference
SOCend-SOC value at the end of the simulation
SOCini-SOC value at the beginning of the simulation
T-simulation beginning and end electric quantity balance threshold value
Inner layer objective function JinnerIs the fuel oil cost Pfuel。
In a preferred technical scheme, the component parameter design variables of step S1 include an engine model, a maximum motor power, a maximum motor torque, a maximum transmission speed ratio, a minimum transmission speed ratio, a transmission gear number, and a final reduction ratio, and different design variables are discretized in their value ranges to obtain n sets of component parameters.
In a preferred technical solution, the control parameter design variables of step S2 include an electric-only mode SOC threshold SOC _ EV and an electric-only mode power threshold Pow _ EV, and the two design variables are discretized in their value ranges to obtain m sets of control parameters.
In a preferred technical solution, the embedded double-layer optimization process of step S3 specifically includes:
s31: the outer optimization algorithm gives a set of component parameters Ai(i＝1,2,...,n)，AiInputting the data into the whole hybrid electric vehicle model when the dynamic constraint condition is met and entering the step S32, otherwise entering the step S34 and returning to the step AiLower outer optimal objective function Jouter_iIs empty;
s32: the inner optimization algorithm gives a set of control parameters Cj(j ═ 1, 2.. times, m), mixing CjControl strategy of hybrid electric vehicle and step S31AiThe whole vehicle model is combined to carry out economic simulation, and the fuel cost P obtained by each simulation calculationfuel_ijAnd Δ SOCijReturning and storing the control parameters to the inner-layer optimization algorithm, giving another set of control parameters (j ═ j +1) by the inner-layer optimization algorithm, circulating the step S32 until all the control parameters (j ═ m) are traversed, and finally obtaining A by the inner-layer optimizationiMinimum fuel cost P satisfying the formula (2) belowfuel_iAnd corresponding control parameterCounting, advances to step S33;
s33: the outer optimization algorithm passes the component parameter AiCalculating a component parameter total cost Pcomp_iAnd loads P output in step S32fuel_iCalculating A using equation (1)iOuter layer objective function ofouter_iAnd saving the calculation result, and proceeding to step S34;
s34: and (3) setting i to i +1, judging whether i is greater than n, finishing embedded double-layer optimization if i is greater than n, and outputting an optimal outer layer objective function Jouter_optAnd corresponding component parameter AoptAnd a control parameter CoptOptimal outer objective function Jouter_optIs shown as
Jouter_opt＝min{α1Pcomp_i+α2Pfuel_i} (3)
Otherwise, another set of component parameters is given by the outer layer optimization algorithm, and the step returns to the steps S31, S32, S33 and S34 for iterative calculation.
Compared with the prior art, the invention has the advantages that:
1. the method for jointly optimizing the component parameters and the control parameters of the P2 hybrid electric vehicle simultaneously jointly optimizes the component parameters and the control parameters, and avoids the defect that the design result is suboptimal due to independent optimization of the two aspects;
2. according to the combined optimization method for the component parameters and the control parameters of the P2 hybrid electric vehicle, parameter matching and control strategies are combined, the problem that the independent optimization design process is complicated and easy to get wrong due to the fact that the two aspects are mutually coupled and restricted is avoided, and the period of optimization of the component parameters and the control strategies of the P2 hybrid electric vehicle is shortened;
3. according to the method for jointly optimizing the component parameters and the control parameters of the P2 hybrid electric vehicle, key components in a hybrid power system such as an engine, a motor and a transmission are used as external optimization design variables, the cost of the components and the fuel cost are comprehensively considered, the component parameters and the control parameters under the optimal comprehensive cost are obtained, on the premise that the energy-saving advantage of the P2 hybrid power system is fully exerted, the cost input of the components at the early stage is reduced as much as possible, and the method has important guiding significance on the design of the P2 hybrid power system.
Drawings
The invention is further described with reference to the following figures and examples:
FIG. 1 is a flow chart of a method for jointly optimizing component parameters and control parameters of a P2 hybrid electric vehicle according to the invention;
FIG. 2 is a flowchart of a control strategy in a method for jointly optimizing component parameters and control parameters of a P2 hybrid electric vehicle according to the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings in conjunction with the following detailed description. It should be understood that the description is intended to be exemplary only, and is not intended to limit the scope of the present invention. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present invention.
The invention is further explained by taking a heavy-duty commercial vehicle P2 hybrid power system as an embodiment and combining the attached drawings.
Referring to fig. 1, the invention provides a method for jointly optimizing component parameters and control parameters of a P2 hybrid electric vehicle, which specifically comprises the following steps:
establishing a hybrid power vehicle model, and determining component parameter design variables including an engine model, a motor maximum power, a motor maximum torque, a transmission maximum speed ratio, a transmission minimum speed ratio, a transmission gear number and a main reduction ratio. The value ranges and the discretization values of the parameter design variables of different parts of the heavy commercial vehicle in the embodiment are shown in table 1.
TABLE 1 value range and discretization value table for P2 heavy commercial vehicle parts and control parameters
Figure BDA0003100740680000041
Establishing a P2 hybrid electric vehicle control strategy: during driving, when the battery SOC is larger than a pure electric mode SOC threshold SOC _ EV and the power demand Pow _ req is smaller than the pure electric mode power threshold Pow _ EV, adopting the pure electric mode, otherwise, judging whether the power demand exceeds the maximum power Pe _ max of the engine under the current engine rotating speed, if so, adopting a motor power-assisted mode, otherwise, adopting an engine single driving mode; during braking, when the battery capacity does not exceed the allowable SOC upper limit SOC _ high, a regenerative braking mode is adopted, otherwise, a mechanical braking mode is entered, and control parameters are determined to be SOC _ EV and Pow _ EV. The value ranges and the discretization values of the two design variables of the heavy commercial vehicle in the embodiment are shown in table 1.
Establishing an embedded double-layer optimization framework, realizing an optimization process in Isight software, selecting an NSGA II algorithm as an outer-layer optimization algorithm, and setting the maximum creep gradient to be not less than 30% under an outer-layer dynamic constraint condition; the inner layer optimization algorithm selects a sequential quadratic programming method, the inner layer constraint condition is that delta SOC is less than or equal to 0.5 percent, and the specific optimization process comprises the following steps: the outer NSGA II algorithm gives a group of discretized component parameters in a table 1, the component parameters are input into a vehicle dynamics model when meeting dynamic constraint conditions, the inner sequence quadratic programming method gives a group of discretized control parameters in the table 1 and inputs the discretized control parameters into a control strategy, economic simulation is carried out by combining the vehicle dynamics model to calculate the fuel cost and the delta SOC, the lowest fuel cost meeting the condition that the delta SOC is less than or equal to 0.5 percent and the corresponding control parameters under the current component parameters can be obtained by traversing all control parameter combinations, the result is returned and stored into the outer NSGA II algorithm, the outer NSGA II algorithm gives another group of component parameters after calculating the current component cost and the outer target function, the iteration is carried out in the way until all component parameter combinations are traversed, and finally different weight coefficients (alpha is obtained1And alpha2) A lower optimal outer layer objective function and corresponding component parameters and control parameters.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.

Claims (4)

1. A method for jointly optimizing component parameters and control parameters of a P2 hybrid electric vehicle is characterized by comprising the following steps:
s1: establishing a P2 hybrid electric vehicle model, and determining component parameter design variables;
s2: establishing a P2 hybrid electric vehicle control strategy, and determining control parameter design variables;
s3: establishing an embedded double-layer optimization framework, embedding control parameters serving as inner layer optimization into outer layer component parameter optimization, calculating an outer layer objective function J by using a formula (1) by using the complete vehicle dynamic property as a constraint condition in the outer layer optimizationouter
Jouter＝α1Pcomp+α2Pfuel (1)
In the formula, alpha1-part parameter total cost weight coefficient
Pcomp-total cost of part parameters
α2-fuel cost weighting factor
PfuelCost of fuel
The inner layer optimization takes the simulation initial and final electric quantity balance as a constraint condition, and the simulation initial and final electric quantity balance constraint is specifically
ΔSOC＝|SOCend-SOCini|≤T (2)
In the formula, delta SOC-simulation starting and ending SOC difference
SOCend-SOC value at the end of the simulation
SOCini-SOC value at the beginning of the simulation
T-simulation beginning and end electric quantity balance threshold value
Inner layer objective function JinnerIs the fuel oil cost Pfuel。
2. The method for jointly optimizing the component parameters and the control parameters of the P2 hybrid electric vehicle according to claim 1, wherein the component parameter design variables of step S1 include an engine model, a maximum motor power, a maximum motor torque, a maximum transmission speed ratio, a minimum transmission speed ratio, a transmission gear number and a final reduction ratio, and different design variables are discretized in a value range to obtain n sets of component parameters.
3. The method for jointly optimizing component parameters and control parameters of a P2 hybrid electric vehicle according to claim 1, wherein the control parameter design variables of step S2 include an electric-only mode SOC threshold SOC _ EV and an electric-only mode power threshold Pow _ EV, and the two design variables are discretized in their value ranges to obtain m sets of control parameters.
4. The method for jointly optimizing component parameters and control parameters of a P2 hybrid electric vehicle according to claim 1, wherein the embedded double-layer optimization procedure of step S3 specifically comprises:
s31: the outer optimization algorithm gives a set of component parameters Ai(i＝1,2,...,n)，AiInputting the data into the whole hybrid electric vehicle model when the dynamic constraint condition is met and entering the step S32, otherwise entering the step S34 and returning to the step AiLower outer optimal objective function Jouter_iIs empty;
s32: the inner optimization algorithm gives a set of control parameters Cj(j ═ 1, 2.. times, m), mixing CjControl strategy of hybrid electric vehicle and step S31AiThe whole vehicle model is combined to carry out economic simulation, and the fuel cost P obtained by each simulation calculationfuel_ijAnd Δ SOCijReturning and storing the control parameters to the inner-layer optimization algorithm, giving another set of control parameters (j ═ j +1) by the inner-layer optimization algorithm, circulating the step S32 until all the control parameters (j ═ m) are traversed, and finally obtaining A by the inner-layer optimizationiMinimum fuel cost P satisfying the formula (2) belowfuel_iAnd corresponding control parameters, and the process proceeds to step S33;
s33: the outer optimization algorithm passes the component parameter AiCalculating a component parameter total cost Pcomp_iAnd loads P output in step S32fuel_iCalculating A using equation (1)iOuter layer objective function ofouter_iAnd saves the calculation result, and the process proceeds to step S34;
s34: and (3) setting i to i +1, judging whether i is greater than n, finishing embedded double-layer optimization if i is greater than n, and outputting an optimal outer layer objective function Jouter_optAnd corresponding component parameter AoptAnd a control parameter CoptOptimal outer objective function Jouter_optIs shown as
Jouter_opt＝min{α1Pcomp_i+α2Pfuel_i} (3)
Otherwise, another set of component parameters is given by the outer layer optimization algorithm, and the step returns to the steps S31, S32, S33 and S34 for iterative calculation.
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