CN112434463A - Energy management system for vehicle hybrid power supply - Google Patents

Energy management system for vehicle hybrid power supply Download PDF

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CN112434463A
CN112434463A CN202011167418.3A CN202011167418A CN112434463A CN 112434463 A CN112434463 A CN 112434463A CN 202011167418 A CN202011167418 A CN 202011167418A CN 112434463 A CN112434463 A CN 112434463A
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lithium battery
bat
charge
value
particle
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CN112434463B (en
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冯娜
马铁华
陈昌鑫
王晨斌
高伟涛
孟青
牛慧芳
张文
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North University of China
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    • 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
    • G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
    • H02J7/0047—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries with monitoring or indicating devices or circuits
    • H02J7/0048—Detection of remaining charge capacity or state of charge [SOC]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
    • H02J7/34—Parallel operation in networks using both storage and other dc sources, e.g. providing buffering
    • H02J7/345—Parallel operation in networks using both storage and other dc sources, e.g. providing buffering using capacitors as storage or buffering devices
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2207/00—Indexing scheme relating to details of circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
    • H02J2207/50—Charging of capacitors, supercapacitors, ultra-capacitors or double layer capacitors
    • 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

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Abstract

The invention relates to the technical field of energy management of a vehicle composite power supply system, in particular to an energy management system of a vehicle composite power supply, which mainly comprises a composite power supply management unit, a fuzzy logic controller and a particle swarm optimization algorithm, wherein the composite power supply management unit is used for acquiring the operation parameters of a lithium battery and a super capacitor, processing the operation parameters, outputting a charge state value to the fuzzy logic controller, and receiving an output signal of the fuzzy logic controller to control the output of the composite power supply; the fuzzy logic controller is used for inputting lithium batterySOCEstimation value, super capacitorSOCObtaining a charge and discharge control signal of the super capacitor through a logic relation with the required power; the particle swarm optimization algorithm is used for optimizing parameters of membership functions of the fuzzy logic controller. The invention can effectively reduce the charging and discharging times of the lithium battery, prolong the service life of the lithium battery and improve the power supply efficiency of the composite power supply system.

Description

Energy management system for vehicle hybrid power supply
Technical Field
The invention relates to the technical field of energy management of a vehicle hybrid power supply system, in particular to an energy management system of a vehicle hybrid power supply.
Background
With the increasing prominence of global energy crisis and environmental problems, the development of new energy automobiles becomes a necessary trend for the development of the automobile industry. The pure electric vehicle adopts a single power supply, so that the defects of weak cruising ability, insufficient accelerating power, short service life of a battery and the like are easy to occur, and the research and development of the hybrid electric vehicle are particularly important. The super capacitor and the storage battery are combined to be used as a power supply of the electric automobile, so that the quick response characteristic of the super capacitor can be fully utilized, the charging and discharging frequency of the storage battery is reduced, the service life of the storage battery is prolonged, and the driving range of the electric automobile is increased.
The performance of a hybrid electric vehicle is closely related to the energy management strategy adopted by the hybrid electric vehicle, and the most common energy management strategies at present are divided into two categories, namely a rule-based energy management strategy and an optimization-based energy management strategy. The fuzzy logic control belongs to a method for formulating rules to realize energy management in a human-simulated thinking mode, the membership function of a controller and the formulation basis of the rules are derived from the experience or theoretical knowledge of experts, the design is simple, the understanding is easy, and the situation of local optimization is easy to fall into.
When a fuzzy control rule is formulated, the SOC value of a battery needs to be considered, and the traditional ampere-hour integration method is difficult to be used in an actual vehicle power system because the SOC value obtained by the SOC initial value calculation, the measurement instrument error, the capacity change caused by current and temperature and the like is not real-time.
Disclosure of Invention
In order to solve the technical problems, the invention provides a vehicle hybrid power supply energy management system and a vehicle hybrid power supply energy management method, which aim to solve the problems of low lithium battery state of charge estimation precision, short service life of a lithium battery, low power supply efficiency of a hybrid power supply power system and the like.
The technical scheme adopted by the invention is as follows: the energy management system of the composite power supply of the vehicle is carried out according to the following steps
The method comprises the following steps: establishing a vehicle composite power supply power system model;
establishing a lithium battery circuit model:
Figure BDA0002745328410000021
Figure BDA0002745328410000022
UL＝Ubat-ibatRbat
therein, SOCbatThe real-time SOC value of the lithium battery is obtained; SOCbat.iniIs the initial SOC value of the lithium battery; qNThe rated capacity of the lithium battery; i.e. ibatThe method comprises the steps of representing charge and discharge current of the lithium battery, wherein the integral accumulated value in a period of time represents the used capacity of the lithium battery; u shapebatAnd RbatRespectively the open-circuit voltage and the ohmic internal resistance of the lithium battery; pbatIs the power of a lithium battery, ULIs the load voltage of the lithium battery,
the load voltage of the lithium battery is not allowed to exceed the open circuit voltage, so the maximum charge and discharge current of the lithium battery is:
Figure BDA0002745328410000023
wherein, ImaxIs the maximum charge-discharge current of the lithium battery, the charge-discharge current i of the batterybatMust be equal to the maximum charge-discharge current I before outputmaxIn comparison, if the charging and discharging current exceeds ImaxThen output Imax，
The Arrhenius model is adopted as the lithium battery capacity loss model, and the capacity accumulated loss is as follows:
Figure BDA0002745328410000031
wherein, CRateThe charge-discharge rate of the battery is,
Figure BDA0002745328410000032
i1cis 1C charge-discharge current; r is a gas constant, and 8.341J/(mol. K) is taken; t isbatIs the battery temperature in K; t (k +1) -t (k) is a simulation step time interval with the unit of s;
establishing super capacitor circuit model
Figure BDA0002745328410000033
Figure BDA0002745328410000034
Therein, SOCscIs the state of charge value, U, of the supercapacitorsc.maxAnd Usc.minMaximum and minimum voltages, U, respectively, of the supercapacitorscIs the real-time voltage of a super capacitor, IscIs the charging and discharging current of the super capacitor, RscAnd PscRespectively the internal resistance and the electric power of the super capacitor;
establishing a composite power supply system model
Preq＝Pbat+Psc
Wherein, PreqPower demand for the load, PbatAnd PscThe charging and discharging power of the lithium battery and the super capacitor is respectively, the power is positive during discharging, and the power is negative during charging;
designing a lithium battery SOC predictor, and estimating to obtain the state of charge SOC of the lithium battery by adopting a Bayesian-Monte Carlo method through a prediction algorithmbat.eThe value of (a) is,
applying a bayesian-monte carlo method to the estimation of the state of charge of a lithium battery, the method approximating a probability density function by a set of random samples with associated weights:
Figure BDA0002745328410000035
wherein the content of the first and second substances,
Figure BDA0002745328410000036
representing a random particle set generated at the k moment for a column vector formed by the charge state and the open-circuit voltage of the lithium battery at the any k moment; u shapebat.kRepresents the open-circuit voltage, SOC of the lithium battery at the time kbat.kRepresenting the state of charge of the lithium battery at the moment k;
Figure BDA0002745328410000041
is shown at Ubat.kUnder the condition of generating random particle set
Figure BDA0002745328410000042
A obeyed probability density function;
Figure BDA0002745328410000043
is a function of the probability density at time k
Figure BDA0002745328410000044
I (i-1 to N) extracted from the distribution showns) A random set of particles, NsRepresenting the number of random particle sets;
Figure BDA0002745328410000045
representing the weight of the ith particle set extracted at the time k; δ (·) denotes the Dirac function.
Weight of k time
Figure BDA0002745328410000046
Weight of normally distributed probability density function at k-1 moment
Figure BDA0002745328410000047
Is updated on the basis of the derivation formula of the update rule as：
Figure BDA0002745328410000048
Wherein, Ubat,kAnd
Figure BDA0002745328410000049
the measured value and the model output average value of the lithium battery open-circuit voltage at the moment k are respectively, and sigma is the standard deviation of the measured value and the model output average value.
Figure BDA00027453284100000410
Is shown in satisfying the particle set
Figure BDA00027453284100000411
Under the condition of Ubat.kThe obeyed probability density function accords with the normal distribution probability density function.
The weights of all particles are normalized:
Figure BDA00027453284100000412
the estimate after considering the total weight of all particles can be expressed as:
Figure BDA00027453284100000413
executing a Bayesian-Monte Carlo algorithm in the lithium battery SOC predictor, continuously performing iterative operation on the weight of the generated particle set, and finally obtaining a pre-estimated value of the state of charge of the lithium battery in a particle weighted summation mode, namely the vector
Figure BDA00027453284100000414
Is represented as:
Figure BDA00027453284100000415
step three, converting the required power P under different operation conditionsreqLithium battery state of charge (SOC) estimated valuebat.eAnd state of charge SOC of super capacitorscAs the input of the fuzzy logic controller, optimizing membership function parameters of the fuzzy logic controller by adopting a particle swarm optimization algorithm, and outputting a control signal scale factor K for charging and discharging the super capacitor through a logical relationscFurther obtain the charge-discharge control signal P of the super capacitorsc＝Ksc·PreqLithium battery charge and discharge control signal Pbat＝(1-Ksc)·Preq。
The fuzzy logic controller inputs the signal SOCbat.eAnd SOCscAre set to: low L, medium M, high H; will PreqAnd an output signal KscThe fuzzy subsets are respectively set as: the membership function of the input and output variables of the fuzzy logic controller adopts the combination of trapezoidal and triangular membership functions,
the expression of the triangular membership function is:
Figure BDA0002745328410000051
the expression of the trapezoidal membership function is:
Figure BDA0002745328410000052
the shape of the membership function curve is determined by parameters a, b, c and d, and the distance between parameter points is coded based on the membership function of the fuzzy logic controller in the step three to obtain a parameter m to be optimized1To m10All are real numbers.
According to the thought of a particle swarm optimization algorithm, the service life of a lithium battery is considered, an optimization target is designed to be the minimum accumulated capacity loss of the lithium battery, and the method comprises the following specific steps:
(1) determining a particle swarm solution spatial dimension to be optimizedNumber d 10, learning factor c1＝c2The particle swarm size is 30, and the inertia weight omega is linearly reduced between 2 and 0.5;
(2) initializing a particle swarm, wherein the particle swarm comprises the size, the random position and the speed of the particle swarm, the initial position value of the empirical particle is set as a parameter position code value before optimization in the graph 3, the initial position values of the rest 29 particles are randomly generated in a variation range, the iteration number is 50, and the maximum speed value is set as 0.08;
(3) calculating a corresponding fitness value of each particle according to f (x);
(4) the current fitness value of each particle is compared with the individual optimal fitness value pbestComparing, if better, updating pbest；
(5) The current fitness value of each particle is compared with the global optimal fitness value gbestIn comparison, if preferred, the g of the particle isbestUpdating the value to a global optimal value;
(6) updating the speed and the position of the particle according to a position updating formula and a speed updating formula;
(7) judging whether the maximum iteration number is reached, if so, continuing the step (8), otherwise, returning to the step (2) to continue execution;
(8) outputting the global optimal fitness value g of the whole particle swarmbestAnd finishing the optimizing operation.
The global optimal fitness value gbestAnd outputting the corresponding membership function parameters, and taking the result as a membership function of the new fuzzy logic controller.
The input and output logic relation of the fuzzy logic controller adopts a Mamdami model reasoning method, the composite power supply power system model transmits a real-time value required by an optimization objective function to the particle swarm algorithm through a Matlab working space, and the particle swarm algorithm transmits updated particles to the system model for calculating the optimization objective.
The invention has the beneficial effects that: the invention solves the problems of low lithium battery state of charge estimation precision, short service life of the lithium battery, low power supply efficiency of a hybrid power supply power system and the like.
Drawings
FIG. 1 is a block diagram of the overall architecture of the system of the present invention;
FIG. 2 is a diagram of a control system of the present invention;
FIG. 3 is a diagram illustrating membership function of the discharging fuzzy controller and parameters to be optimized according to the present invention;
FIG. 4 is a flow chart of particle swarm optimization of membership function of fuzzy controller according to the present invention;
FIG. 5 is a diagram of an implementation of the PSO fuzzy controller system according to the present invention.
Detailed Description
The present invention will be further described with reference to the accompanying drawings. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention discloses a vehicle hybrid power supply energy management system, and the overall structure of the system is shown in figure 1. The system mainly comprises a composite power supply management unit, a fuzzy logic controller and a particle swarm optimization algorithm. The composite power supply management unit is used for acquiring the operation parameters of the lithium battery and the super capacitor, processing the operation parameters, outputting the state of charge value to the fuzzy logic controller, and receiving an output signal of the fuzzy logic controller to control the output of the composite power supply; the fuzzy logic controller is used for obtaining a charge and discharge control signal of the super capacitor through a logic relation between an input lithium battery SOC estimated value, the super capacitor SOC and the required power; the particle swarm optimization algorithm is used for optimizing parameters of membership functions of the fuzzy logic controller. The composite power management unit comprises a lithium battery management unit, a super capacitor management unit, a lithium battery and a super capacitor, wherein the lithium battery management unit is used for collecting temperature, current and voltage signals of the lithium battery, obtaining a state of charge estimated value of the lithium battery through an SOC predictor, receiving charge and discharge signals of the lithium battery output by the fuzzy controller and controlling overcharge and overdischarge of the lithium battery; the super capacitor management unit is used for collecting temperature, current and voltage signals of the super capacitor, receiving super capacitor charge and discharge signals output by the fuzzy controller and controlling the power output of the super capacitor. The control system diagram is shown in fig. 2.
The invention discloses a vehicle composite power supply energy management system, which comprises the following specific steps:
the method comprises the following steps: establishing a vehicle composite power supply power system model;
the vehicle hybrid power supply power system model established in the first step comprises the following steps:
(1) establishing a lithium battery circuit model:
Figure BDA0002745328410000081
Figure BDA0002745328410000082
UL＝Ubat-ibatRbat
therein, SOCbatThe real-time SOC value of the lithium battery is obtained; SOCbat.iniIs the initial SOC value of the lithium battery; qNThe rated capacity of the lithium battery; i.e. ibatThe method comprises the steps of representing charge and discharge current of the lithium battery, wherein the integral accumulated value in a period of time represents the used capacity of the lithium battery; u shapebatAnd RbatRespectively the open-circuit voltage and the ohmic internal resistance of the lithium battery; pbatIs the power of a lithium battery, ULIs the lithium battery load voltage.
The load voltage of the lithium battery is not allowed to exceed the open circuit voltage, so the maximum charge and discharge current of the lithium battery is:
Figure BDA0002745328410000083
wherein, ImaxThe maximum charge-discharge current of the lithium battery. Charging and discharging current i of batterybatMust be equal to the maximum charge-discharge current I before outputmaxIn comparison, if the charging and discharging current exceeds ImaxThen output Imax。
The Arrhenius model is adopted as the lithium battery capacity loss model, and the capacity accumulated loss is as follows:
Figure BDA0002745328410000091
wherein, CRateThe charge-discharge rate of the battery is,
Figure BDA0002745328410000092
i1cis 1C charge-discharge current; r is a gas constant, and 8.341J/(mol. K) is taken; t isbatIs the battery temperature in K; t (k +1) -t (k) is the simulation step time interval in units of s.
(2) Establishing a super-capacitor circuit model:
Figure BDA0002745328410000093
Figure BDA0002745328410000094
therein, SOCscIs the state of charge value, U, of the supercapacitorsc.maxAnd Usc.minMaximum and minimum voltages, U, respectively, of the supercapacitorscIs the real-time voltage of a super capacitor, IscIs the charging and discharging current of the super capacitor, RscAnd PscRespectively the internal resistance and the electric power of the super capacitor.
(3) Establishing a composite power supply system model:
Preq＝Pbat+Psc
wherein, PreqPower demand for the load, PbatAnd PscThe charging and discharging power of the lithium battery and the super capacitor is respectively positive during discharging and negative during charging.
Step two: designing a lithium battery SOC predictor, wherein an ampere-hour integration method and a Bayesian-Monte are adopted in a prediction algorithmEstimating the state of charge (SOC) of the lithium battery by a Tecarol methodbat.eThe value of (c).
In the second step, the Bayes-Monte Carlo method is applied to the estimation of the state of charge of the lithium battery. The method approximates the probability density function by a set of random samples with associated weights:
Figure BDA0002745328410000101
wherein the content of the first and second substances,
Figure BDA0002745328410000102
representing a random particle set generated at the k moment for a column vector formed by the charge state and the open-circuit voltage of the lithium battery at the any k moment; u shapebat.kRepresents the open-circuit voltage, SOC of the lithium battery at the time kbat.kRepresenting the state of charge of the lithium battery at the moment k;
Figure BDA0002745328410000103
is shown at Ubat.kUnder the condition of generating random particle set
Figure BDA0002745328410000104
A obeyed probability density function;
Figure BDA0002745328410000105
is a function of the probability density at time k
Figure BDA0002745328410000106
I (i-1 to N) extracted from the distribution showns) A random set of particles, NsRepresenting the number of random particle sets;
Figure BDA0002745328410000107
representing the weight of the ith particle set extracted at the time k; δ (·) denotes the Dirac function.
Weight of k time
Figure BDA0002745328410000108
Weight of normally distributed probability density function at k-1 moment
Figure BDA0002745328410000109
Updating on the basis, wherein the derivation formula of the updating rule is as follows:
Figure BDA00027453284100001010
wherein, Ubat,kAnd
Figure BDA00027453284100001011
the measured value and the model output average value of the lithium battery open-circuit voltage at the moment k are respectively, and sigma is the standard deviation of the measured value and the model output average value.
Figure BDA00027453284100001012
Is shown in satisfying the particle set
Figure BDA00027453284100001013
Under the condition of Ubat.kThe obeyed probability density function accords with the normal distribution probability density function.
The weights of all particles are normalized:
Figure BDA00027453284100001014
the estimate after considering the total weight of all particles can be expressed as:
Figure BDA00027453284100001015
executing a Bayesian-Monte Carlo algorithm in the lithium battery SOC predictor, continuously performing iterative operation on the weight of the generated particle set, and finally obtaining a pre-estimated value of the state of charge of the lithium battery in a particle weighted summation mode, namely the vector
Figure BDA0002745328410000111
Is represented as:
Figure BDA0002745328410000112
step three: based on the vehicle composite power supply power system model established in the step one, the required power P under different operation conditions is obtainedreqLithium battery state of charge (SOC) estimated valuebat.eAnd state of charge SOC of super capacitorscAs the input of the fuzzy logic controller, optimizing membership function parameters of the fuzzy logic controller by adopting a particle swarm optimization algorithm, and outputting a control signal scale factor K for charging and discharging the super capacitor through a logical relationscFurther obtain the charge-discharge control signal P of the super capacitorsc。
The fuzzy logic controller inputs the signal SOCbat.eAnd SOCscAre set to: low L, medium M, high H; will PreqAnd an output signal KscThe fuzzy subsets are respectively set as: smaller TS, small S, medium M, large B, large TB. The membership function of the input and output variables of the fuzzy logic controller is combined by adopting trapezoidal and triangular membership functions, and the discourse domain and the membership function are shown in figure 3.
The expression of the triangular membership function is:
Figure BDA0002745328410000113
the expression of the trapezoidal membership function is:
Figure BDA0002745328410000114
the shape of the membership function curve is determined by parameters a, b, c and d, and the distance between parameter points is coded based on the membership function of the fuzzy logic controller in the step three to obtain a parameter m to be optimized1To m10All are real numbers as indicated by the labels in the membership function curves of FIG. 3.
According to the thinking of the particle swarm optimization algorithm, the service life of the lithium battery is considered, the optimization target is designed to be the minimum loss of the lithium battery, the flow of the particle swarm optimization membership function is shown in figure 4, and the specific steps are as follows:
(1) determining a particle swarm solution space dimension d to be optimized as 10 and a learning factor c1＝c2The particle swarm size is 30, and the inertia weight omega is linearly reduced between 2 and 0.5;
(2) initializing a particle swarm, wherein the particle swarm comprises the size, the random position and the speed of the particle swarm, the initial position value of the empirical particle is set as a parameter position code value before optimization in the graph 3, the initial position values of the rest 29 particles are randomly generated in a variation range, the iteration number is 50, and the maximum speed value is set as 0.08;
(3) calculating a corresponding fitness value of each particle according to f (x);
(4) the current fitness value of each particle is compared with the individual optimal fitness value pbestComparing, if better, updating pbest；
(5) The current fitness value of each particle is compared with the global optimal fitness value gbestIn comparison, if preferred, the g of the particle isbestUpdating the value to a global optimal value;
(6) updating the speed and the position of the particle according to a position updating formula and a speed updating formula;
(7) judging whether the maximum iteration number is reached, if so, continuing the step (8), otherwise, returning to the step (2) to continue execution;
(8) outputting the global optimal fitness value g of the whole particle swarmbestAnd finishing the optimizing operation.
The global optimal fitness value gbestAnd outputting the corresponding membership function parameters, and taking the result as a membership function of the new fuzzy logic controller.
Further, the input and output logic relationship of the fuzzy logic controller adopts a Mamdami model inference method, and a rule table is shown as the following table:
Figure BDA0002745328410000131
the m file transmits a real-time value required by an optimization target function to the particle swarm algorithm through a Matlab working space, the particle swarm algorithm transmits updated particles to the system model for calculating the optimization target, and the system execution process is shown in FIG. 5.
The output parameter of the fuzzy logic controller is KscThe charging and discharging control signal of the super capacitor is expressed as:
Psc＝Ksc·Preq
the lithium battery charge and discharge control signal is expressed as:
Pbat＝(1-Ksc)·Preq
the above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be able to cover the technical solutions and the inventive concepts of the present invention within the technical scope of the present invention.
The invention relates to the technical field of energy management of a vehicle composite power supply system, in particular to a vehicle composite power supply energy management system and a method, which mainly comprise a composite power supply management unit, a fuzzy logic controller and a particle swarm optimization algorithm, wherein the composite power supply management unit is used for acquiring the operation parameters of a lithium battery and a super capacitor, processing the operation parameters, outputting a charge state value to the fuzzy logic controller, and receiving an output signal of the fuzzy logic controller to control the output of a composite power supply; the fuzzy logic controller is used for obtaining a charging and discharging control signal of the super capacitor through a logic relation between an input lithium battery SOC estimated value, the super capacitor SOC and the required power.

Claims (4)

1. A vehicle hybrid power supply energy management system, characterized by: the method comprises the following steps: establishing a vehicle composite power supply power system model;
establishing a lithium battery circuit model:
Figure FDA0002745328400000011
Figure FDA0002745328400000012
UL＝Ubat-ibatRbat
therein, SOCbatThe real-time SOC value of the lithium battery is obtained; SOCbat.iniIs the initial SOC value of the lithium battery; qNThe rated capacity of the lithium battery; i.e. ibatThe method comprises the steps of representing charge and discharge current of the lithium battery, wherein the integral accumulated value in a period of time represents the used capacity of the lithium battery; u shapebatAnd RbatRespectively the open-circuit voltage and the ohmic internal resistance of the lithium battery; pbatIs the power of a lithium battery, ULIs the load voltage of the lithium battery,
the load voltage of the lithium battery is not allowed to exceed the open circuit voltage, so the maximum charge and discharge current of the lithium battery is:
Figure FDA0002745328400000013
wherein, ImaxIs the maximum charge-discharge current of the lithium battery, the charge-discharge current i of the batterybatMust be equal to the maximum charge-discharge current I before outputmaxIn comparison, if the charging and discharging current exceeds ImaxThen output Imax，
The Arrhenius model is adopted as the lithium battery capacity loss model, and the capacity accumulated loss is as follows:
Figure FDA0002745328400000014
wherein, CRateThe charge-discharge rate of the battery is,
Figure FDA0002745328400000015
i1cis 1C charge-discharge current; r is a gas constant, and 8.341J/(mol. K) is taken; t isbatIs the battery temperature in K; t (k +1) -t (k) is a simulation step time interval with the unit of s;
establishing super capacitor circuit model
Figure FDA0002745328400000021
Figure FDA0002745328400000022
Therein, SOCscIs the state of charge value, U, of the supercapacitorsc.maxAnd Usc.minMaximum and minimum voltages, U, respectively, of the supercapacitorscIs the real-time voltage of a super capacitor, IscIs the charging and discharging current of the super capacitor, RscAnd PscRespectively the internal resistance and the electric power of the super capacitor;
establishing a composite power supply system model
Preq＝Pbat+Psc
Wherein, PreqPower demand for the load, PbatAnd PscThe charging and discharging power of the lithium battery and the super capacitor is respectively, the power is positive during discharging, and the power is negative during charging;
designing a lithium battery SOC predictor, and estimating to obtain the state of charge SOC of the lithium battery by adopting a Bayesian-Monte Carlo method through a prediction algorithmbat.eThe value of (a) is,
applying a bayesian-monte carlo method to the estimation of the state of charge of a lithium battery, the method approximating a probability density function by a set of random samples with associated weights:
Figure FDA0002745328400000023
wherein the content of the first and second substances,
Figure FDA0002745328400000024
representing a random particle set generated at the k moment for a column vector formed by the charge state and the open-circuit voltage of the lithium battery at the any k moment; u shapebat.kRepresents the open-circuit voltage, SOC of the lithium battery at the time kbat.kRepresenting the state of charge of the lithium battery at the moment k;
Figure FDA0002745328400000025
is shown at Ubat.kUnder the condition of generating random particle set
Figure FDA0002745328400000026
A obeyed probability density function;
Figure FDA0002745328400000031
is a function of the probability density at time k
Figure FDA0002745328400000032
I (i-1 to N) extracted from the distribution showns) A random set of particles, NsRepresenting the number of random particle sets;
Figure FDA0002745328400000033
representing the weight of the ith particle set extracted at the time k; δ (·) denotes the Dirac function.
Weight of k time
Figure FDA0002745328400000034
Weight of normally distributed probability density function at k-1 moment
Figure FDA0002745328400000035
Updating on the basis, wherein the derivation formula of the updating rule is as follows:
Figure FDA0002745328400000036
wherein, Ubat,kAnd
Figure FDA0002745328400000037
the measured value and the model output average value of the lithium battery open-circuit voltage at the moment k are respectively, and sigma is the standard deviation of the measured value and the model output average value.
Figure FDA0002745328400000038
Is shown in satisfying the particle set
Figure FDA0002745328400000039
Under the condition of Ubat.kThe obeyed probability density function accords with the normal distribution probability density function.
The weights of all particles are normalized:
Figure FDA00027453284000000310
the estimate after considering the total weight of all particles can be expressed as:
Figure FDA00027453284000000311
executing a Bayesian-Monte Carlo algorithm in the lithium battery SOC predictor, continuously performing iterative operation on the weight of the generated particle set, and finally obtaining a pre-estimated value of the state of charge of the lithium battery in a particle weighted summation mode, namely the vector
Figure FDA00027453284000000312
Is represented as:
Figure FDA00027453284000000313
step three, converting the required power P under different operation conditionsreqLithium battery state of charge (SOC) estimated valuebat.eAnd state of charge SOC of super capacitorscAs the input of the fuzzy logic controller, optimizing membership function parameters of the fuzzy logic controller by adopting a particle swarm optimization algorithm, and outputting a control signal scale factor K for charging and discharging the super capacitor through a logical relationscFurther obtain the charge-discharge control signal P of the super capacitorsc＝Ksc·PreqLithium battery charge and discharge control signal Pbat＝(1-Ksc)·Preq。
2. The vehicle hybrid power supply energy management system of claim 1, wherein: the fuzzy logic controller inputs the signal SOCbat.eAnd SOCscAre set to: low L, medium M, high H; will PreqAnd an output signal KscThe fuzzy subsets are respectively set as: the membership function of the input and output variables of the fuzzy logic controller adopts the combination of trapezoidal and triangular membership functions,
the expression of the triangular membership function is:
Figure FDA0002745328400000041
the expression of the trapezoidal membership function is:
Figure FDA0002745328400000042
the shape of the membership function curve is determined by parameters a, b, c and d, and the distance between parameter points is coded based on the membership function of the fuzzy logic controller in the step three to obtain a parameter m to be optimized1To m10All are real numbers.
3. The vehicle hybrid power supply energy management system of claim 2, wherein: according to the thought of a particle swarm optimization algorithm, the service life of a lithium battery is considered, an optimization target is designed to be the minimum accumulated capacity loss of the lithium battery, and the method comprises the following specific steps:
(1) determining a particle swarm solution space dimension d to be optimized as 10 and a learning factor c1＝c2The particle swarm size is 30, and the inertia weight omega is linearly reduced between 2 and 0.5;
(2) initializing a particle swarm, wherein the particle swarm comprises the size, the random position and the speed of the particle swarm, the initial position value of the empirical particle is set as a parameter position code value before optimization in the graph 3, the initial position values of the rest 29 particles are randomly generated in a variation range, the iteration number is 50, and the maximum speed value is set as 0.08;
(3) calculating a corresponding fitness value of each particle according to f (x);
(4) the current fitness value of each particle is compared with the individual optimal fitness value pbestComparing, if better, updating pbest；
(5) The current fitness value of each particle is compared with the global optimal fitness value gbestIn comparison, if preferred, the g of the particle isbestUpdating the value to a global optimal value;
(6) updating the speed and the position of the particle according to a position updating formula and a speed updating formula;
(7) judging whether the maximum iteration number is reached, if so, continuing the step (8), otherwise, returning to the step (2) to continue execution;
(8) outputting the global optimal fitness value g of the whole particle swarmbestAnd finishing the optimizing operation.
The global optimal fitness value gbestAnd outputting the corresponding membership function parameters, and taking the result as a membership function of the new fuzzy logic controller.
4. A vehicle hybrid power supply energy management system according to claim 3, characterized in that: the input and output logic relation of the fuzzy logic controller adopts a Mamdami model reasoning method, the composite power supply power system model transmits a real-time value required by an optimization objective function to the particle swarm algorithm through a Matlab working space, and the particle swarm algorithm transmits updated particles to the system model for calculating the optimization objective.
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