CN112231830A - Hybrid power vehicle multi-objective optimization control method based on adaptive equivalent factor - Google Patents

Hybrid power vehicle multi-objective optimization control method based on adaptive equivalent factor Download PDF

Info

Publication number
CN112231830A
CN112231830A CN202011065246.9A CN202011065246A CN112231830A CN 112231830 A CN112231830 A CN 112231830A CN 202011065246 A CN202011065246 A CN 202011065246A CN 112231830 A CN112231830 A CN 112231830A
Authority
CN
China
Prior art keywords
fuel cell
cell system
aging
lithium battery
battery system
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202011065246.9A
Other languages
Chinese (zh)
Other versions
CN112231830B (en
Inventor
陈剑
马延
朱晓媛
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Zhejiang University ZJU
Original Assignee
Zhejiang University ZJU
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Zhejiang University ZJU filed Critical Zhejiang University ZJU
Priority to CN202011065246.9A priority Critical patent/CN112231830B/en
Publication of CN112231830A publication Critical patent/CN112231830A/en
Application granted granted Critical
Publication of CN112231830B publication Critical patent/CN112231830B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/10—Geometric CAD
    • G06F30/15—Vehicle, aircraft or watercraft design
    • 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
    • B60W10/24—Conjoint control of vehicle sub-units of different type or different function including control of energy storage means
    • B60W10/26—Conjoint control of vehicle sub-units of different type or different function including control of energy storage means for electrical energy, e.g. batteries or capacitors
    • 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
    • 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/06—Multi-objective optimisation, e.g. Pareto optimisation using simulated annealing [SA], ant colony algorithms or genetic algorithms [GA]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
    • G06F2119/04—Ageing analysis or optimisation against ageing
    • 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
    • Y02T10/70—Energy storage systems for electromobility, e.g. batteries

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Geometry (AREA)
  • General Physics & Mathematics (AREA)
  • Automation & Control Theory (AREA)
  • Mechanical Engineering (AREA)
  • Transportation (AREA)
  • General Engineering & Computer Science (AREA)
  • Evolutionary Computation (AREA)
  • Computer Hardware Design (AREA)
  • Pure & Applied Mathematics (AREA)
  • Mathematical Optimization (AREA)
  • Mathematical Analysis (AREA)
  • Chemical & Material Sciences (AREA)
  • Computational Mathematics (AREA)
  • Aviation & Aerospace Engineering (AREA)
  • Combustion & Propulsion (AREA)
  • Mathematical Physics (AREA)
  • Electric Propulsion And Braking For Vehicles (AREA)
  • Fuel Cell (AREA)

Abstract

The invention discloses a hybrid vehicle multi-objective optimization control method based on adaptive equivalent factors. Establishing a hydrogen consumption model, a fuel cell system aging model and a lithium battery system aging model of the hybrid power vehicle, obtaining the hydrogen consumption and the aging state quantity of the fuel cell system and the lithium battery system, and converting the hydrogen consumption and the aging state quantity into energy consumption cost; establishing a semi-empirical fuel cell system aging model, setting an aging parameter, establishing a relation between the aging parameter and the resistance and limiting current of the fuel cell, solving to obtain the aging parameter, and calculating a self-adaptive equivalent factor by using the aging parameter; and establishing a multi-objective function consisting of energy consumption cost and containing self-adaptive equivalent factors, and solving a minimized objective to obtain optimized power distribution for control. The invention can still reasonably distribute the output power through the designed adaptive equivalent factor when the performance of the fuel cell system is attenuated, thereby realizing multi-objective optimization control and having good real-time performance.

Description

Hybrid power vehicle multi-objective optimization control method based on adaptive equivalent factor
Technical Field
The invention relates to a multi-objective optimization control method for a hybrid vehicle, which ensures multi-objective optimization and stability of a lithium battery system charge state by designing a self-adaptive equivalent factor based on an aging state of a fuel battery system under the condition of performance attenuation of the fuel battery system, and simultaneously ensures real-time performance of an algorithm.
Background
The hybrid vehicle has the advantages of zero emission, high energy conversion efficiency, long cruising range and the like, and is widely concerned by the industry and academia. At present, an energy system of a hybrid vehicle mainly comprises a fuel cell system and a lithium battery system or a super capacitor. It is a focus of most current energy control methods to study how to efficiently distribute the energy supply ratio of the two energy systems and improve the economy of the hybrid vehicle on the basis of satisfying the dynamic demand. However, the high cost and short service life of the fuel cell system are one of the main reasons for limiting the popularization and application of the hybrid vehicle. It is therefore also a necessary research effort to take into account the service life of fuel cell systems and lithium battery systems in hybrid vehicles in the energy control method. In addition, frequent start-stop of the fuel cell system of the hybrid vehicle may cause its performance to deteriorate, thereby reducing the operating efficiency of the fuel cell system. This tends to increase the use of the lithium battery system to meet the power demand, causing fluctuations in the state of charge of the lithium battery system to affect the performance of the hybrid vehicle. Therefore, it is necessary to study multi-objective optimization to improve the economy and durability of hybrid vehicles and to ensure stable state of charge of the lithium battery system.
Disclosure of Invention
Under the condition that the performance of a fuel cell system is degraded, in order to achieve multi-objective optimization of the economy and the durability of a hybrid vehicle and the stability of the state of charge of a lithium battery system, the technical scheme of the invention provides a multi-objective optimization control method of the hybrid vehicle based on adaptive equivalent factors.
The technical scheme adopted by the invention is as follows:
step 1, establishing a hydrogen consumption model, a fuel cell system aging model and a lithium battery system aging model of the hybrid vehicle:
the hydrogen consumption model of the hybrid vehicle mainly comprises hydrogen consumption of a fuel cell system and equivalent hydrogen consumption of a lithium cell system; the fuel cell system aging model is a model describing the aging state of the fuel cell system by the number of starts and the output power; the lithium battery system aging model is a model describing the aging state of the lithium battery system by the discharge rate and the operating temperature.
The hydrogen consumption of the fuel cell system is determined by the output power of the fuel cell system, while the equivalent hydrogen consumption of the lithium cell system depends on the output power and the equivalence factor of the lithium cell system.
The output power and equivalent factors of the fuel cell system and the lithium battery system are input into a hydrogen consumption model of the hybrid power vehicle and output to obtain the hydrogen consumption of the hybrid power vehicle, the output power and the start-stop times of the fuel cell system are input into an aging model of the fuel cell system and output to obtain an aging state of the fuel cell system, and the discharge rate and the battery temperature of the lithium battery system are input into the aging model of the lithium battery system and output to obtain the aging state of the lithium battery system.
Step 2, acquiring the hydrogen consumption of the hybrid power vehicle, the aging state quantity of the fuel cell system and the aging state quantity of the lithium battery system through the model established in the step 1, wherein the aging state of the fuel cell system and the aging state quantity of the lithium battery system are 1-100%, so that the hydrogen consumption of the hybrid power vehicle, the aging state quantity of the fuel cell system and the aging state quantity of the lithium battery system are converted into corresponding energy consumption costs;
step 3, establishing a semi-empirical fuel cell system aging model, setting an aging parameter of the fuel cell system, establishing a relation between the aging parameter and the resistance and limiting current of the fuel cell, and reflecting the aging state of the fuel cell system more accurately through the aging parameter; the unscented Kalman filtering algorithm is adopted to process the semi-empirical fuel cell system aging model to solve and estimate to obtain aging parameters, meanwhile, the covariance matching method is used to calculate the variance of process noise and measurement noise in the unscented Kalman filtering algorithm, and the estimation accuracy of the unscented Kalman filtering algorithm is improved. Finally, calculating a self-adaptive equivalent factor by using the estimated aging parameter, wherein the self-adaptive equivalent factor is used for ensuring the stability of the charge state of the lithium battery system;
the invention designs and constructs the adaptive equivalent factor contained in the multi-objective function to reasonably distribute the output power of the fuel cell system and the lithium cell system, thereby ensuring the stable charge state of the lithium cell system when the performance of the fuel cell system is attenuated and obtaining the more accurate aging state of the fuel cell system.
Wherein, the invention adopts a semi-empirical fuel cell system aging model. By utilizing the semi-empirical fuel cell system aging model, the resistance and the limiting current of the fuel cell can be obviously changed no matter in static load or dynamic load, and the estimation precision of the aging state of the fuel cell system is improved.
And 4, establishing a multi-objective function comprising a self-adaptive equivalent factor and consisting of the three energy consumption costs in the step 2, wherein the multi-objective function meets the requirement of vehicle dynamic property and limits the variation range of the output power of the fuel cell system and the output power of the lithium battery system, the multi-objective function is taken as a convex optimization problem with constrained quadratic programming, and then an effective set algorithm is utilized to solve by taking the minimized multi-objective function as a target to obtain optimized power distribution for controlling, so that the multi-objective optimization control is realized, the economy and the durability of the hybrid vehicle are improved, and the stability of the charge state of the lithium battery system is ensured.
The hybrid power vehicle is internally provided with a fuel cell system and a lithium cell system, and the fuel cell system and the lithium cell system are connected to jointly provide energy for the hybrid power vehicle.
The step 2 specifically comprises the following steps:
aging state quantity delta of fuel cell systemfcAnd the aging state quantity Delta of the lithium battery systembThe formula is adopted to calculate and obtain:
Figure BDA0002713566690000031
Figure BDA0002713566690000032
wherein N issIs the number of start-stops, δ, of the fuel cell systemsIs the coefficient of the number of times of starting and stopping of the fuel cell system, PfcIs the output power, P, of the fuel cell systemfc,rIs the rated power, delta, of the fuel cell system0And alpha0First and second damping coefficients for the fuel cell system, T is the operating time of the hybrid vehicle, IbatOutputting current for lithium battery system, and maximum service cycle N (c, T) of lithium battery systemc) Dependent on the discharge rate c and the operating temperature Tc，QnAnd t is the nominal capacity of the lithium battery system and time.
The step 3 specifically comprises the following steps:
the semi-empirical fuel cell system aging model is expressed as:
Figure BDA0002713566690000033
wherein N isfcNumber of fuel cells in the fuel cell system, EoIs the open circuit voltage of the fuel cell, IfcAnd VfcCurrent and voltage, A, respectively, of the fuel cell systemtAnd BcRespectively Tafel constant and concentration constant, ToAnd IoRespectively the operating temperature and the exchange current, R, of the fuel cellfcAnd IlRespectively representing the resistance and limiting current of the fuel cell;
the resistance and limiting current of the fuel cell are closely related to the aging state of the fuel cell system.
Then, an aging parameter alpha of the fuel cell system is set, and the fuel cell resistance R is described by using the aging parameter alphafcAnd limiting the current IlThe change of (2):
Rfc＝Rfco·(1-α)
Il＝Ilo·(1+α)
Figure BDA0002713566690000034
wherein R isfcoAnd IloRespectively initial fuel cell resistance and limiting current, beta is the change rate of the fuel cell system aging parameter alpha;
then, processing and estimating an aging parameter of the fuel cell system by adopting an unscented Kalman filtering algorithm to perform semi-empirical fuel cell system aging model, and updating the variances of process noise and measurement noise in the unscented Kalman filtering algorithm in real time by utilizing a covariance matching method;
finally, the obtained aging parameter of the fuel cell system is processed according to the following formula to obtain an adaptive equivalent factor lambdae：
λe＝λeo×(1+kbα)2
Wherein λ iseoIs an initial equivalence factor, kbRepresenting a normal number, to accommodate changes in the adaptive equivalence factor.
The step 4 specifically comprises the following steps:
constructing a multi-objective function representation as:
Figure BDA0002713566690000041
wherein, JeAs a function of multiple objectives, PbatFor the output power of lithium battery systems, AfAnd BfRespectively are a secondary term coefficient and a primary term coefficient of the output power of the fuel cell system; a. thebAnd BbRespectively representing a secondary coefficient and a primary coefficient of the output power of the lithium battery system; wherein the adaptive equivalence factor lambdaeContained in the coefficient of quadratic term BbIn (1).
Figure BDA0002713566690000042
Wherein, cbatCost of unit energy consumption of lithium battery system, Eb,rIs the rated capacity of the lithium battery system, AtolIs the total discharge capacity, VobIs the open circuit voltage of the lithium battery system,
Figure BDA0002713566690000043
for hydrogen lower heating value, sgn () is a sign function;
the constraints of the multi-objective function are expressed as:
Pfc+Pbat＝Pdem
Pfc，min≤Pfc≤Pfc,max
Pb,min≤Pbat≤Pb,max
wherein, PdemPower demand for hybrid vehicles, Pfc,minAnd Pfc,maxMinimum and maximum output power, P, respectively, of the fuel cell systemb,minAnd Pb,maxRespectively the minimum and maximum output power of the lithium battery system;
finally, the multi-objective function is regarded as a secondary planning problem with constraint, and the secondary planning problem is solved and optimized by using an active set algorithm to obtain the optimal output power P of the fuel cell systemfcAnd the output power P of the lithium battery systembatThe real-time performance of the algorithm can be ensured.
The invention can design the adaptive equivalent factor based on the unscented Kalman filtering algorithm, reasonably and efficiently distribute the output power of the fuel cell system and the lithium cell system under the condition of performance degradation of the fuel cell system, and realize multi-objective optimization.
The invention has the beneficial effects that:
the invention can improve the economy and the durability of the hybrid power and ensure the stability of the charge state of the lithium battery system. The utilization rate of the energy and the performance of the lithium battery system can be effectively improved, and the service lives of the fuel battery system and the lithium battery system are prolonged.
Drawings
FIG. 1 is a block diagram of a multi-objective optimization algorithm method;
FIG. 2 illustrates UDDS driving conditions;
FIG. 3 illustrates HWFET driving conditions;
FIG. 4UDDS operating mode output power variation of fuel cell system
FIG. 5UDDS operating mode output power change of lithium battery system
FIG. 6 variation of fuel cell system output power under HWFET operating conditions
FIG. 7 lithium battery system output power variation under HFET conditions
FIG. 8 shows the change of the state of charge of the lithium battery system under the UDDS working condition;
FIG. 9 shows the change of the state of charge of the lithium battery system under the HWFET operating condition;
table 1 shows the comparison of the cost consumed by each method under UDDS conditions;
table 2 compares the cost of each process under HWFET conditions.
Detailed Description
The invention is further illustrated below with reference to specific examples. It should be understood that these examples are for illustrative purposes only and are not intended to limit the scope of the present invention.
The invention is embodied in a simulation environment to verify the effectiveness of a multi-objective optimization control method for an aged fuel cell system. The specific embodiment and the implementation process thereof are as follows:
FIG. 1 is a system diagram of a multi-objective optimization control method provided by the present invention, and the work flow of the multi-objective optimization control method is as follows: the required power may be calculated based on the vehicle speed of the hybrid vehicle and a dynamic model of the vehicle. The indexes for measuring the economical efficiency and the durability can be obtained as the total cost through the established hydrogen consumption model of the hybrid power vehicle, the fuel cell system aging model and the lithium battery system aging model. The calculation expression is as follows
C＝Ce+Cfc+Cbat
Wherein, CeThe hydrogen consumption cost is composed of the hydrogen consumption of the fuel cell system and the equivalent hydrogen consumption of the lithium cell system, wherein the equivalent hydrogen consumption of the lithium cell system is obtained by equivalently converting the consumed electric energy into the hydrogen consumption by utilizing an adaptive equivalence factor; cfcA loss penalty for aging of the fuel cell system, CbatThe loss cost of the lithium battery system aging is solved.
The economy and durability of the hybrid vehicle is then improved by minimizing a multi-objective function, which is expressed as:
Figure BDA0002713566690000061
wherein the content of the first and second substances,
Figure BDA0002713566690000062
in order to provide a rate of change in the hydrogen consumption cost,
Figure BDA0002713566690000063
for the rate of change of the fuel cell system aging consumption cost,
Figure BDA0002713566690000064
the rate of change of the consumption cost is the aging of the fuel cell system. The multi-objective function is composed of the change rate of the energy consumption cost, and aims to convert multi-objective optimization into a quadratic programming problem finally and improve the instantaneity of the multi-objective optimization control method.
In order to ensure the stability of the state of charge of the lithium battery system under the condition of performance attenuation of the fuel battery system, the aging parameters of the fuel battery system are accurately estimated by adopting an unscented Kalman filtering algorithm based on a semi-empirical fuel battery system aging model, so that a self-adaptive equivalent factor is obtained to maintain the stability of the state of charge of the lithium battery system.
The semi-empirical fuel cell system aging model described above is expressed as:
Figure BDA0002713566690000065
wherein N isfcNumber of fuel cells in the fuel cell system, EoIs the open circuit voltage of the fuel cell, IfcAnd VfcCurrent and voltage, A, respectively, of the fuel cell systemtAnd BcRespectively Tafel constant and concentration constant, ToAnd IoRespectively the operating temperature of the fuel cell and the exchange current.
Wherein the resistance R of the fuel cellfcAnd limiting the current IlHas close relation with the aging state of the fuel cell system.
Then, an aging parameter alpha of the fuel cell system is set, and the fuel cell resistance R is described by using the aging parameter alphafcAnd limiting the current IlThe change of (2):
Figure BDA0002713566690000066
Il＝Ilo×(1+a)
Figure BDA0002713566690000067
wherein R isfcoAnd IloInitial fuel cell resistance and limiting current, respectively, and β is the rate of change of the fuel cell system aging parameter α. And then estimating the aging parameters of the fuel cell system by adopting an unscented Kalman filtering algorithm, and updating the variances of process noise and measurement noise in the unscented Kalman filtering algorithm in real time by utilizing a covariance matching method. Finally, an adaptive equivalence factor λ is adapted based on the estimated fuel cell system aging parametereExpressed as:
λe＝λeo×(1+kba)2
wherein λ iseoIs an initial equivalent factor of the number of the active elements,normal number kbTo accommodate changes in the adaptive equivalence factor. The multi-objective function is then simplified to obtain the following expression:
Figure BDA0002713566690000071
wherein, PbatFor the output power of lithium battery systems, AfAnd BfRespectively are a secondary term coefficient and a primary term coefficient of the output power of the fuel cell system; a. thebAnd BbRespectively representing a secondary coefficient and a primary coefficient of the output power of the lithium battery system; wherein the adaptive equivalence factor lambdaeContained in the coefficient of quadratic term BbIn (1).
Figure BDA0002713566690000072
Wherein, cbatCost of unit energy consumption of lithium battery system, Eb,rIs the rated capacity of the lithium battery system, AtolIs the total discharge capacity, VobIs the open circuit voltage of the lithium battery system,
Figure BDA0002713566690000073
is hydrogen gas with low heating value.
In order to meet the dynamic requirements of vehicles and ensure that the output power of a fuel cell system and a lithium cell system is limited within an allowable variation range, the equation and inequality constraint of a multi-objective function is established and expressed as follows:
Pfc+Pbat＝Pdem
Pfc，min≤Pfc≤Pfc,max
Pb,min≤Pbat≤Pb,max
wherein, Pfc,minAnd Pfc,maxMinimum and maximum output power, P, respectively, of the fuel cell systemb,minAnd Pb,maxRespectively, the minimum and maximum output power of the lithium battery system.
Finally, the multi-objective function is regarded as a secondary planning problem with constraint, and the secondary planning problem is solved and optimized by using an active set algorithm to obtain the optimal output power P of the fuel cell systemfcAnd the output power P of the lithium battery systembatMeanwhile, the real-time performance of the algorithm can be guaranteed.
The method of the invention is verified on a simulation platform, and the algorithm is verified under two standard working conditions. The initial lithium battery system state of charge is set to 0.7 and the initial equivalence factor is determined from the new fuel cell system. The maximum output power of the fuel cell system and the lithium cell system was 50kW and 25kW, respectively. Then comparing the simulation results of the energy control methods based on the constant equivalent factor and the adaptive equivalent factor when the fuel cell system is aged, the energy control optimization methods of the two methods only differ in whether the equivalent factor is updated or not. Meanwhile, the effectiveness of the designed multi-target energy control method is compared by a common equivalent energy consumption minimum method. Two simulated operating conditions are shown in fig. 2 and 3. The power distribution under both conditions is as shown in fig. 4-7. The state of charge change of the battery under the two conditions is shown in fig. 8 and fig. 9. In addition, the energy control cost ratio of each method under two working conditions is shown in table 1 and table 2. As can be seen from the simulation results, the equivalent energy consumption minimizing method reduces the aging loss of the lithium battery system, but increases the aging loss of the fuel cell system. Compared with the multi-objective optimization control method, the method with the minimum equivalent energy consumption has the largest total energy consumption cost. Compared with a multi-objective optimization control method of constant equivalent factors, the adaptive equivalent factors can maintain the state of charge of the lithium battery system to be close to an initial value.
TABLE 2 comparison of the cost of consumption of each method under UDDS conditions
Method of producing a composite material Minimum equivalent energy consumption Constant equivalent factor Adaptive equivalence factor
Ce($) 0.301 0.2625 0.229
Cbat($) 6.909e-08 1.673e-08 1.427e-07
Cfc($) 1.672 1.65 1.641
Total of 1.973 1.913 1.87
TABLE 3 comparison of cost consumption for each method under HWFET conditions
Method of producing a composite material Equivalent energyConsumption is minimum Constant equivalent factor Adaptive equivalence factor
Ce($) 0.3316 0.2771 0.2462
Cbat($) 4.678e-08 1.397e-07 1.164e-07
Cfc($) 0.7694 0.7549 0.7469
Total of 1.101 1.032 0.9931
Therefore, compared with other control methods, the method disclosed by the invention has the lowest total consumption cost under different working conditions, which means that the designed multi-objective optimization control method can balance the economy and durability of the hybrid vehicle, and meanwhile, the method can also keep the stability of the charge state of the lithium battery system.

Claims (5)

1. A hybrid vehicle multi-objective optimization control method based on adaptive equivalent factors is characterized in that:
step 1, establishing a hydrogen consumption model, a fuel cell system aging model and a lithium battery system aging model of the hybrid vehicle:
step 2, acquiring the hydrogen consumption, the fuel cell system aging state quantity and the lithium battery system aging state quantity of the hybrid power vehicle through the model established in the step 1, so as to convert the hydrogen consumption, the fuel cell system aging state quantity and the lithium battery system aging state quantity of the hybrid power vehicle into corresponding energy consumption cost;
step 3, establishing an aging model of the semi-empirical fuel cell system, setting an aging parameter of the fuel cell system, establishing a relation between the aging parameter and the resistance and the limiting current of the fuel cell, processing the aging model of the semi-empirical fuel cell system by adopting an unscented Kalman filtering algorithm to solve and estimate to obtain the aging parameter, meanwhile, calculating the variance of process noise and measurement noise in the unscented Kalman filtering algorithm by using a covariance matching method, and finally calculating an adaptive equivalent factor by using the aging parameter obtained by estimation;
and 4, establishing a multi-objective function comprising the self-adaptive equivalent factors and formed by the three energy consumption costs in the step 2, and solving by using an active set algorithm with the minimized multi-objective function as a target to obtain optimized power distribution for control.
2. The hybrid vehicle multi-objective optimization control method based on the adaptive equivalence factor according to claim 1, characterized in that: the hybrid power vehicle is internally provided with a fuel cell system and a lithium cell system, and the fuel cell system and the lithium cell system are connected to jointly provide energy for the hybrid power vehicle.
3. The hybrid vehicle multi-objective optimization control method based on the adaptive equivalence factor according to claim 1, characterized in that: the step 2 specifically comprises the following steps:
aging state quantity delta of fuel cell systemfcAnd the aging state quantity Delta of the lithium battery systembThe formula is adopted to calculate and obtain:
Figure FDA0002713566680000011
Figure FDA0002713566680000012
wherein N issIs the number of start-stops, δ, of the fuel cell systemsIs the coefficient of the number of times of starting and stopping of the fuel cell system, PfcIs the output power, P, of the fuel cell systemfc，rIs the rated power, delta, of the fuel cell system0And alpha0First and second damping coefficients for the fuel cell system, T is the operating time of the hybrid vehicle, IbatFor outputting current, Q, of lithium battery systemnAnd t is the nominal capacity of the lithium battery system and time.
4. The hybrid vehicle multi-objective optimization control method based on the adaptive equivalence factor according to claim 1, characterized in that: the step 3 specifically comprises the following steps:
the semi-empirical fuel cell system aging model is expressed as:
Figure FDA0002713566680000021
wherein N isfcNumber of fuel cells in the fuel cell system, EoIs the open circuit voltage of the fuel cell, IfcAnd VfcCurrent and voltage, A, respectively, of the fuel cell systemtAnd BcRespectively Tafel constant and concentration constant, ToAnd IoRespectively the operating temperature and the exchange current, R, of the fuel cellfcAnd IlRespectively representing the resistance and limiting current of the fuel cell;
then, an aging parameter alpha of the fuel cell system is set, and the fuel cell resistance R is described by using the aging parameter alphafcAnd limiting the current IlThe change of (2):
Rfc＝Rfco·(1-α)
Il＝Ilo·(1+α)
Figure FDA0002713566680000022
wherein R isfcoAnd IloRespectively initial fuel cell resistance and limiting current, beta is the change rate of the fuel cell system aging parameter alpha;
then, processing and estimating an aging parameter of the fuel cell system by adopting an unscented Kalman filtering algorithm to perform semi-empirical fuel cell system aging model, and updating the variances of process noise and measurement noise in the unscented Kalman filtering algorithm in real time by utilizing a covariance matching method;
finally, the obtained aging parameter of the fuel cell system is processed according to the following formula to obtain an adaptive equivalent factor lambdae：
λe＝λeo·(1+kbα)2
Wherein λ iseoIs an initial equivalence factor, kbIndicating a normal number.
5. The hybrid vehicle multi-objective optimization control method based on the adaptive equivalence factor according to claim 1, characterized in that: the step 4 specifically comprises the following steps:
constructing a multi-objective function representation as:
Figure FDA0002713566680000023
wherein, JeAs a function of multiple objectives, PbatFor the output power of lithium battery systems, AfAnd BfRespectively are a secondary term coefficient and a primary term coefficient of the output power of the fuel cell system; a. thebAnd BbRespectively representing a secondary coefficient and a primary coefficient of the output power of the lithium battery system;
Figure FDA0002713566680000031
wherein, cbatCost of unit energy consumption of lithium battery system, Eb，rIs the rated capacity of the lithium battery system, AtolIs the total discharge capacity, VobIs the open circuit voltage of the lithium battery system,
Figure FDA0002713566680000032
for hydrogen lower heating value, sgn () is a sign function;
the constraints of the multi-objective function are expressed as:
Pfc+Pbat＝Pdem
Pfc，min≤Pfc≤Pfc，max
Pb，min≤Pbat≤Pb，max
wherein, PdemPower demand for hybrid vehicles, Pfc，minAnd Pfc，maxMinimum and maximum output power, P, respectively, of the fuel cell systemb，minAnd Pb，maxRespectively the minimum and maximum output power of the lithium battery system;
finally, the multi-objective function is regarded as a secondary planning problem with constraint, and the secondary planning problem is solved and optimized by using an active set algorithm to obtain the optimal output power P of the fuel cell systemfcAnd the output power of the lithium battery system.
CN202011065246.9A 2020-09-30 2020-09-30 Hybrid power vehicle multi-objective optimization control method based on adaptive equivalent factor Active CN112231830B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202011065246.9A CN112231830B (en) 2020-09-30 2020-09-30 Hybrid power vehicle multi-objective optimization control method based on adaptive equivalent factor

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202011065246.9A CN112231830B (en) 2020-09-30 2020-09-30 Hybrid power vehicle multi-objective optimization control method based on adaptive equivalent factor

Publications (2)

Publication Number Publication Date
CN112231830A true CN112231830A (en) 2021-01-15
CN112231830B CN112231830B (en) 2022-04-08

Family

ID=74120581

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202011065246.9A Active CN112231830B (en) 2020-09-30 2020-09-30 Hybrid power vehicle multi-objective optimization control method based on adaptive equivalent factor

Country Status (1)

Country Link
CN (1) CN112231830B (en)

Cited By (16)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112918330A (en) * 2021-03-18 2021-06-08 北京交通大学 Method for calculating optimal working state control strategy of fuel cell vehicle
CN113022384A (en) * 2021-05-26 2021-06-25 北京理工大学 Fuel cell automobile energy management method based on convex optimization
CN113043917A (en) * 2021-04-15 2021-06-29 西南交通大学 Layered control method for multi-stack fuel cell hybrid power system
CN113085665A (en) * 2021-05-10 2021-07-09 重庆大学 Fuel cell automobile energy management method based on TD3 algorithm
CN113158429A (en) * 2021-03-23 2021-07-23 东南大学 Battery feasible domain modeling method and device
CN113222387A (en) * 2021-04-30 2021-08-06 北京理工新源信息科技有限公司 Multi-objective scheduling and collaborative optimization method for hydrogen fuel vehicle
CN113434962A (en) * 2021-07-05 2021-09-24 南京航空航天大学 Optimization method of series-parallel hybrid power unmanned aerial vehicle power system
CN113533991A (en) * 2021-07-13 2021-10-22 浙江大学 Aging diagnosis method based on fuel cell self-adaptive mixed aging characteristics
CN114379386A (en) * 2022-03-25 2022-04-22 北京理工大学 Fuel cell and lithium battery hybrid system collaborative recession control method and system
CN114824370A (en) * 2022-04-08 2022-07-29 金龙联合汽车工业（苏州）有限公司 Whole vehicle energy control method of double-pile fuel cell system
CN114843561A (en) * 2022-05-13 2022-08-02 中国第一汽车股份有限公司 Method and device for controlling fuel cell
CN114889498A (en) * 2022-05-07 2022-08-12 苏州市华昌能源科技有限公司 Power optimization distribution method of hydrogen-electricity hybrid power system
CN115107538A (en) * 2022-06-30 2022-09-27 安徽华菱汽车有限公司 Energy management method and device for automobile
CN115339330A (en) * 2022-09-07 2022-11-15 武汉理工大学 Energy output management method of hybrid electric vehicle based on battery aging
CN115422764A (en) * 2022-09-22 2022-12-02 西北工业大学太仓长三角研究院 Passivity-based lateral vehicle speed estimation method
CN118099487A (en) * 2024-04-28 2024-05-28 中科嘉鸿(佛山市)新能源科技有限公司 Energy management control method of high-temperature methanol fuel cell

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20130158755A1 (en) * 2011-12-14 2013-06-20 GM Global Technology Operations LLC Optimizing system performance using state of health information
US20160093927A1 (en) * 2014-09-26 2016-03-31 Ford Global Technologies, Llc Battery capacity degradation resolution methods and systems
CN107351701A (en) * 2017-06-07 2017-11-17 东莞市德尔能新能源股份有限公司 Based on the multiple target self-adaptation control method that aging is balanced
CN108490365A (en) * 2018-04-18 2018-09-04 北京理工大学 A method of the remaining life of the power battery of estimation electric vehicle
WO2018209038A1 (en) * 2017-05-12 2018-11-15 Ohio State Innovation Foundation Real-time energy management strategy for hybrid electric vehicles with reduced battery aging

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20130158755A1 (en) * 2011-12-14 2013-06-20 GM Global Technology Operations LLC Optimizing system performance using state of health information
US20160093927A1 (en) * 2014-09-26 2016-03-31 Ford Global Technologies, Llc Battery capacity degradation resolution methods and systems
WO2018209038A1 (en) * 2017-05-12 2018-11-15 Ohio State Innovation Foundation Real-time energy management strategy for hybrid electric vehicles with reduced battery aging
CN107351701A (en) * 2017-06-07 2017-11-17 东莞市德尔能新能源股份有限公司 Based on the multiple target self-adaptation control method that aging is balanced
CN108490365A (en) * 2018-04-18 2018-09-04 北京理工大学 A method of the remaining life of the power battery of estimation electric vehicle

Non-Patent Citations (5)

* Cited by examiner, † Cited by third party
Title
JIAN CHEN等: "《Control of regenerative braking systems for four-wheel-independently-actuated electric vehicles》", 《MECHATRONICS》 *
YAN MA等: "《Lateral stability integrated with energy efficiency control for electric vehicles》", 《MECHANICAL SYSTEMS AND SIGNAL PROCESSING》 *
YAN MA等: "《Simultaneous Lateral Stability and Energy Efficiency Control of Over-Actuated Electric Vehicles》", 《2018 ANNUAL AMERICAN CONTROL CONFERENCE (ACC)》 *
张风奇等: "《混合动力汽车模型预测能量管理研究现状与展望》", 《机械工程学报》 *
徐陈锋: "《基于自适应模糊策略的燃料电池车混合动力系统控制》", 《中国优秀博硕士学位论文全文数据库(硕士)》 *

Cited By (22)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112918330A (en) * 2021-03-18 2021-06-08 北京交通大学 Method for calculating optimal working state control strategy of fuel cell vehicle
CN113158429A (en) * 2021-03-23 2021-07-23 东南大学 Battery feasible domain modeling method and device
CN113043917A (en) * 2021-04-15 2021-06-29 西南交通大学 Layered control method for multi-stack fuel cell hybrid power system
CN113222387A (en) * 2021-04-30 2021-08-06 北京理工新源信息科技有限公司 Multi-objective scheduling and collaborative optimization method for hydrogen fuel vehicle
CN113222387B (en) * 2021-04-30 2023-11-24 北京理工新源信息科技有限公司 Multi-objective scheduling and collaborative optimization method for hydrogen fuel vehicle
CN113085665A (en) * 2021-05-10 2021-07-09 重庆大学 Fuel cell automobile energy management method based on TD3 algorithm
CN113022384A (en) * 2021-05-26 2021-06-25 北京理工大学 Fuel cell automobile energy management method based on convex optimization
CN113434962A (en) * 2021-07-05 2021-09-24 南京航空航天大学 Optimization method of series-parallel hybrid power unmanned aerial vehicle power system
CN113533991B (en) * 2021-07-13 2023-08-15 浙江大学 Aging diagnosis method based on self-adaptive mixed aging characteristics of fuel cell
CN113533991A (en) * 2021-07-13 2021-10-22 浙江大学 Aging diagnosis method based on fuel cell self-adaptive mixed aging characteristics
CN114379386A (en) * 2022-03-25 2022-04-22 北京理工大学 Fuel cell and lithium battery hybrid system collaborative recession control method and system
CN114824370B (en) * 2022-04-08 2024-05-03 金龙联合汽车工业（苏州）有限公司 Whole vehicle energy control method for double-stack fuel cell system
CN114824370A (en) * 2022-04-08 2022-07-29 金龙联合汽车工业（苏州）有限公司 Whole vehicle energy control method of double-pile fuel cell system
CN114889498B (en) * 2022-05-07 2023-12-15 苏州市华昌能源科技有限公司 Power optimization distribution method of hydrogen-electricity hybrid power system
CN114889498A (en) * 2022-05-07 2022-08-12 苏州市华昌能源科技有限公司 Power optimization distribution method of hydrogen-electricity hybrid power system
CN114843561A (en) * 2022-05-13 2022-08-02 中国第一汽车股份有限公司 Method and device for controlling fuel cell
CN115107538A (en) * 2022-06-30 2022-09-27 安徽华菱汽车有限公司 Energy management method and device for automobile
CN115107538B (en) * 2022-06-30 2024-04-26 安徽华菱汽车有限公司 Energy management method and device for automobile
CN115339330A (en) * 2022-09-07 2022-11-15 武汉理工大学 Energy output management method of hybrid electric vehicle based on battery aging
CN115422764A (en) * 2022-09-22 2022-12-02 西北工业大学太仓长三角研究院 Passivity-based lateral vehicle speed estimation method
CN115422764B (en) * 2022-09-22 2023-11-24 西北工业大学太仓长三角研究院 Passive-based lateral vehicle speed estimation method
CN118099487A (en) * 2024-04-28 2024-05-28 中科嘉鸿(佛山市)新能源科技有限公司 Energy management control method of high-temperature methanol fuel cell

Also Published As

Publication number Publication date
CN112231830B (en) 2022-04-08

Similar Documents

Publication Publication Date Title
CN112231830B (en) Hybrid power vehicle multi-objective optimization control method based on adaptive equivalent factor
Xu et al. Multi-objective component sizing based on optimal energy management strategy of fuel cell electric vehicles
CN105978016A (en) Optimization control method based on optimal power flow for multi-terminal flexible direct current transmission system
CN114336702A (en) Wind-solar energy storage station group power distribution collaborative optimization method based on double-layer stochastic programming
Lu et al. Online optimization of energy management strategy for FCV control parameters considering dual power source lifespan decay synergy
CN113682203B (en) Energy regulation and control method based on full life cycle state of fuel cell tramcar
CN112069600A (en) Multi-power-source hybrid power system and energy management method thereof
Sun et al. Energy management strategy for FCEV considering degradation of fuel cell
CN114944661B (en) Micro-grid three-stage optimization control method based on rolling optimization of energy storage system
CN114919752A (en) ECMS-MPC-based energy management method for hydrogen fuel hybrid unmanned aerial vehicle
Monden et al. Charging and discharging control of a hybrid battery energy storage system using different battery types in order to avoid degradation
CN110380404B (en) Power transmission network excitation system adjustment coefficient optimization setting method considering high energy consumption point load
Quan et al. Health-aware model predictive energy management for fuel cell electric vehicle based on hybrid modeling method
CN110470993B (en) SOC algorithm for starting and stopping battery
CN111071073A (en) Random load-based fuel cell hybrid power system and control method thereof
Zuo et al. A deterioration-aware energy management strategy for the lifetime improvement of a multi-stack fuel cell system subject to a random dynamic load
CN114488821B (en) Method and system for predicting and controlling interval economic model of fuel cell oxygen passing ratio
Tang et al. Degradation adaptive energy management strategy for FCHEV based on the Rule-DDPG method: tailored to the current SOH of the powertrain
CN111564868B (en) Off-grid type optical storage micro-grid system capacity configuration evaluation method and device
CN114415040A (en) Energy storage power station energy management method and device based on SOC real-time estimation
Wu Real-time energy management and transient power control for fuel cell electrified vehicles
CN118082630B (en) Multi-stack fuel cell hybrid system energy management strategy and system for hydrogen electric vehicle
Lian et al. Real‐time energy management strategy for fuel cell plug‐in hybrid electric bus using short‐term power smoothing prediction and distance adaptive state‐of‐charge consumption
CN115017735B (en) Multi-agent probability voltage stability calculation method for high-dimensional system
CN114368320B (en) Control method and system for actively managing whole vehicle SOC according to weather forecast

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant