CN109849693B - Electric vehicle hybrid energy management system based on adaptive wavelet transformation and control method thereof - Google Patents

Electric vehicle hybrid energy management system based on adaptive wavelet transformation and control method thereof Download PDF

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CN109849693B
CN109849693B CN201910180965.6A CN201910180965A CN109849693B CN 109849693 B CN109849693 B CN 109849693B CN 201910180965 A CN201910180965 A CN 201910180965A CN 109849693 B CN109849693 B CN 109849693B
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super capacitor
current
lithium battery
charge
load
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CN109849693A (en
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彭军
王瑞
黄志武
李恒
杨迎泽
蒋富
刘伟荣
张晓勇
周艳辉
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Central South University
CERNET Corp
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CERNET Corp
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    • YGENERAL 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
    • Y02TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02TCLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00Road transport of goods or passengers
    • Y02T10/60Other road transportation technologies with climate change mitigation effect
    • Y02T10/70Energy storage systems for electromobility, e.g. batteries
    • YGENERAL 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
    • Y02TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02TCLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00Road transport of goods or passengers
    • Y02T10/60Other road transportation technologies with climate change mitigation effect
    • Y02T10/72Electric energy management in electromobility

Abstract

The invention discloses an electric automobile hybrid energy management system based on self-adaptive wavelet transformation and a control method thereof, wherein the system comprises the following steps: the system comprises a super capacitor, a lithium battery, two DC/DC converters, a driving module, an acquisition circuit and a control module. The control method comprises the steps that firstly, the total reference current required by the load is obtained through voltage loop control; performing wavelet transformation on the total reference current required by the load by using a self-adaptive wavelet transformation algorithm to obtain a high-frequency current component and a low-frequency current component, wherein the low-frequency current component is used as the reference current of the lithium battery, and the high-frequency current component is used as the reference current of the super capacitor; and then, the reference current of the lithium battery and the reference current of the super capacitor are tracked in real time through a current loop of the control module, so that the real-time distribution of the power required by the load is realized. The invention fully utilizes the super capacitor and effectively protects the lithium battery.

Description

Electric vehicle hybrid energy management system based on adaptive wavelet transformation and control method thereof
Technical Field
The invention relates to the technical field of hybrid energy management of electric vehicles, in particular to an electric vehicle hybrid energy management system based on adaptive wavelet transformation and a control method thereof.
Background
In the world, the situation of energy shortage and environmental deterioration is severe day by day, and electric vehicles have rich energy sources and no exhaust pollution, and are very beneficial to environmental protection and air cleanness. In the face of the changed output power in the actual energy supply process, the battery energy storage system needs to provide changed current, and the high-frequency rapid charging and discharging current change can cause great damage to the performance of the battery, so that the service life of the battery is shortened. Therefore, considering the power density and discharge characteristics of the battery has become a key issue and research focus in hybrid energy systems of electric vehicles.
The super capacitor can provide enough high peak current during acceleration and store recovered energy during braking, so the application of the battery-super capacitor hybrid energy storage system in the field of electric vehicles is more popular. On one hand, the battery has the advantages of high energy density and low self-discharge, but also has the defects of long charge-discharge time, low power transmission capability, short service life and the like; on the other hand, the super capacitor has the advantages of high power density, wide working temperature range, rapid charge and discharge and long service cycle, but has the defect of low energy density. Therefore, the super capacitor is used for supplying or storing peak power or rapidly-changed power in the hybrid energy storage system, so that the energy supply quality can be improved, the current pressure of the battery can be reduced, and the service life of the battery can be prolonged.
The energy distribution strategy in the hybrid energy system of the electric automobile mainly comprises a rule-based control method, an energy efficiency index-based optimization method and a frequency division-based control method. The rule-based control method is simple and easy to implement, cannot cope with high-frequency components in load required power, and can damage the lithium battery. The optimal method based on the energy efficiency index generally converts the current fluctuation of the lithium battery and the loss in the whole hybrid energy management system into a uniform performance index, constructs a corresponding optimization problem and a target function thereof, and adopts an evolutionary algorithm or an optimization algorithm to solve. When the wavelet conversion method is directly adopted for frequency division, the wavelet method is equivalent to that a filter divides the frequency of power. The control method based on frequency division generally adopts a filter to distribute high-frequency components and low-frequency components to load power, the method can realize real-time energy distribution, but the charge state of a mixed energy source and the frequency distribution condition in the load power requirements under different road conditions are not considered, after the super capacitor works for a long time, the overcharge or overdischarge condition is easy to occur, and the performance of the whole system is reduced.
For some prior patent applications, such as patent application publication No. CN105480101A, published as 2016, 4, 13, entitled "power distribution method and device for hybrid power source electric vehicle", the present invention first calculates the charge states of a lithium battery and a super capacitor according to their temperature and voltage characteristic curves, and then performs power distribution according to the charge states of the lithium battery and the super capacitor, but no specific power distribution method is provided. The invention discloses a patent application with the publication number of CN103072492A and the publication date of 2013, 5.1.titled as an active control type composite power supply for a pure electric bus and a control method thereof.A lithium ion storage battery pack is controlled to charge a super capacitor in a starting stage, the super capacitor is used for supplying power independently in a starting stage, the lithium ion storage battery pack is used for supplying power independently in a steady driving stage, the lithium ion storage battery pack and the super capacitor are connected in parallel for supplying power together in an emergency acceleration stage after the steady driving, the voltage of the super capacitor tends to an expected value according to different vehicle speeds, whether the lithium battery needs to charge the super capacitor is judged according to the actual voltage of the super capacitor, different energy sources are adopted under different road conditions, and the characteristics of low power density and high energy density of the lithium battery are not fully considered. In the stable driving stage, the invention also uses the lithium battery and the super capacitor at the same time, and the super capacitor is still adopted to provide high-frequency components in the load power as much as possible. The method fully uses the super capacitor and more effectively protects the battery. The invention discloses a patent application with the publication number of CN107947211A and the publication date of 2018, 20.4.2018 and the name of an island-type microgrid energy storage optimization configuration method adopting wavelet packet decomposition and considering frequency response, which comprehensively considers the characteristics of an energy storage battery, a super capacitor and system frequency deviation absorption capacity, carries out Lay layer wavelet packet decomposition on unbalanced power to obtain wavelet packet components, divides the unbalanced power into three parts, namely low-frequency components, medium-frequency components and high-frequency components by using fixed frequency dividing points nL and nH, and carries out stabilization by the three parts respectively.
In view of the foregoing, it is desirable to design a hybrid energy management system for an electric vehicle and a control method thereof with better applicability.
Disclosure of Invention
The invention aims to solve the technical problem that aiming at the defects of the prior art, the invention provides the electric automobile hybrid energy management system based on the adaptive wavelet transformation and the control method thereof, which can not only protect the service life of a lithium battery, but also improve the utilization rate of a super capacitor.
The technical scheme provided by the invention is as follows:
a hybrid energy management system of an electric vehicle based on adaptive wavelet transformation comprises a lithium battery, a super capacitor, a first DC/DC converter, a second DC/DC converter, a control module, a driving module and an acquisition module;
one side of the first DC/DC converter is connected with the lithium battery, and the other side of the first DC/DC converter is connected with the bus load; one side of the second DC/DC converter is connected with the super capacitor, and the other side of the second DC/DC converter is connected with the bus load; one sides of the first DC/DC converter and the second DC/DC converter, which are connected with the bus load, are connected in parallel, and an output filter capacitor is also connected in parallel;
the acquisition module is used for acquiring the current I of the lithium battery in real timeBat(t) Voltage V of the super capacitorSC(t) and current ISC(t) and the bus voltage V at the load sideBus(t), t denotes time, t is 1,2, …;
the control module calculates to obtain a control signal by adopting a self-adaptive wavelet transform algorithm according to the signal acquired by the acquisition module;
and the driving module receives the control signal of the control module and controls the on-off of the power tubes in the first DC/DC converter and the second DC/DC converter.
A control module calculates to obtain a control signal according to a signal acquired by an acquisition module by adopting a self-adaptive wavelet transform algorithm, and specifically comprises the following steps:
step 1, inputting a given bus reference voltage and a collected bus voltage into a voltage closed-loop circuit, wherein the voltage closed-loop circuit firstly makes a difference between the bus reference voltage and the collected bus voltage, and then inputs the obtained difference into a PI control module (the control parameter is k)p-voltage loop=3,ki-voltage loop10) to obtain the total reference current I of the load demandLoad
Step 2, performing wavelet transformation on the total reference current required by the load by using a self-adaptive wavelet transformation algorithm to obtain a high-frequency current component and a low-frequency current component, wherein the low-frequency current component is used as the reference current of the lithium battery, and the high-frequency current component is used as the reference current of the super capacitor;
and 3, tracking the reference current of the lithium battery and the super capacitor in real time through a current loop of the control module to realize real-time distribution of the power required by the load:
inputting the reference current of the lithium battery and the collected lithium battery current into a first current closed-loop circuit, wherein the first current closed-loop circuit firstly makes a difference between the reference current of the lithium battery and the collected lithium battery current, and then sequentially inputs the obtained difference into a PI control module (the control parameter is k)p-current loop=0.05,ki-current loop3) and PWM module, resulting in duty cycle DBAT
Inputting the reference current of the super capacitor and the collected super capacitor current into a second current closed loop circuit, and secondly, subtracting the reference current of the super capacitor battery and the collected super capacitor battery current in the current closed loop circuit, and then sequentially inputting the obtained difference value into PI control Module (control parameter: k)p-current loop=0.05,ki-current loop3) and PWM module, resulting in output duty cycle DSC
Step 4, duty ratio DBATAnd DSCThe output signals are input into a driving module, two paths of complementary driving signals with dead time are respectively generated and are respectively used as control signals of two power tubes of the first DC/DC converter and the second DC/DC converter.
Further, in step 2, the adaptive wavelet transform algorithm determines the number L of decomposition layers of wavelet transform by the following stepswt
Step 2.1, based on the collected super capacitor voltage VSC(t) calculating the state of charge SOC of the super capacitorSC(t); lithium battery current I based on collectionBat(t) calculating the state of charge SOC of the lithium batteryBat(t);
Step 2.2, according to the load power P and the SOC of the super capacitorSC(t) determining the charge/discharge level L of the supercapacitorSC
Step 2.3, according to the load power P and the state of charge SOC of the lithium batteryBat(t) and the charging/discharging level L of the supercapacitorSCAdaptively obtaining the number L of decomposition layers of wavelet transformwt
Further, in step 2.1, the state of charge SOC of the super capacitorSCThe formula for calculation of (t) is:
wherein, VSC(t) represents the voltage, V, of the supercapacitor collected at time tSC_normRepresents the nominal voltage of the supercapacitor;
the calculation formula of the state of charge SOC of the lithium battery is as follows:
SOCBat(0)=(Q0/Q)·100%
therein, SOCBat(0) Is the initial state of charge, Q, of the lithium battery0Is the initial charge of the lithium battery, Q is the nominal capacity of the lithium battery, IBatAnd (t) the lithium battery current collected at the moment t.
Further, in step 2.2, the load power P is calculated by the following formula:
Fr=CrMg
Fd=0.5ρACdV2
wherein M is the mass of the electric vehicle, a is the acceleration of the electric vehicle, FrAnd FdIs an intermediate variable, V is the speed of the electric vehicle, CrIs the rolling resistance coefficient of the electric automobile, g is the gravity acceleration, and rho is the air density (1.29 kg/m)3) A is the front area of the electric vehicle, CdIs the aerodynamic drag coefficient, eta, of the electric vehicledrAnd ηfbRespectively the electric energy conversion efficiency and the kinetic energy feedback efficiency of the electric automobile; m, C thereinr、A、Cd、ηdrAnd ηfbBoth parameters are intrinsic to the electric vehicle, and a and V can be obtained through sensors on the vehicle.
The following table shows the relevant parameters of an electric vehicle:
TABLE 1 parameters of electric vehicles
Further, in the step 2.2, the charging/discharging level L of the super capacitorSCDetermined according to the following method:
1) when P is more than or equal to 0.9Pbat_max,Then, the charge/discharge grade L of the super capacitor is obtainedSC=1;
2) When P is more than or equal to 0.9Pbat_max,Then, the charge/discharge grade L of the super capacitor is obtainedSC=2;
3) When in useThen, the charge/discharge grade L of the super capacitor is obtainedSC=3;
4) When in useThen, the charge/discharge grade L of the super capacitor is obtainedSC=4;
5) When in useThen, the charge/discharge grade L of the super capacitor is obtainedSC=5;
6) When in useThen, the charge/discharge grade L of the super capacitor is obtainedSC=6;
7) When in useThen, the charge/discharge grade L of the super capacitor is obtainedSC=7;
8) When P is present<0,Then, the charge/discharge grade L of the super capacitor is obtainedSC=8;
Wherein the content of the first and second substances,5 thresholds of SOC for super capacitor based onThe method of fuzzy control yields, Pbat_maxThe maximum power which can be output by the battery; if P<0,The feedback energy is directly absorbed by the lithium battery without wavelet decomposition. When P is present<At 0, LSCTo charge level, P>At 0, LSCIs the discharge level. When P is 0, charging and discharging are not necessary.
Further, in step 2.3, the number of decomposition layers L of wavelet transform is setwtDetermined according to the following method:
1) when P is more than or equal to 0.9Pbat_max,LSCAdaptively finding the number of decomposition layers L of wavelet transform when 1wt=1;
2) When in useSelf-adaptively obtaining the number L of decomposition layers of wavelet transformwt=2;
3) When in useSelf-adaptively obtaining the number L of decomposition layers of wavelet transformwt=3;
4) When in useSelf-adaptively obtaining the number L of decomposition layers of wavelet transformwt=4;
5) When in useSelf-adaptively obtaining the number L of decomposition layers of wavelet transformwt=5。
Wherein the content of the first and second substances,andthe two threshold values of the charge state of the super capacitor are obtained based on a fuzzy control method.
According to the invention, firstly, the mapping relation between the charge and discharge grade of the super capacitor and the P and charge states is determined based on a rule method (according to experience), and the charge and discharge grade of the super capacitor is divided. Then, under the condition of considering the complementary characteristics of high energy density of the lithium battery and high power density of the super capacitor, the super capacitor is fully utilized as an energy buffer device, in the process of energy distribution, the power which can be provided by the super capacitor has a direct relation with the SOC thereof, and meanwhile, in order to ensure that the SOC of the super capacitor can absorb the energy fed back under the condition of possible future braking in space, the SOC of the super capacitor is maintained as far as possibleWithin the range. When the energy of the lithium battery is too low, the super capacitor plays a role as an energy buffer device, and energy is provided as much as possible, so that the decomposition layer number of the wavelet conversion is higher (the high-frequency component obtained by the wavelet conversion is larger, and the super capacitor bears higher load power). Therefore, the SOC of the super capacitor can be maintained, so that the system is fully confronted with the traction or braking condition, and meanwhile, the super capacitor is used as an energy buffer device as much as possible to assist the lithium battery to provide power for the load together. The charge state threshold division of the lithium battery and the super capacitor is obtained by a fuzzy control-based method.
The invention designs the wavelet conversion algorithm of the self-adaptive layer number by utilizing the self-adaptive characteristic of the wavelet conversion to the signal frequency and considering the adaptability of the algorithm to different road conditions, realizes the real-time distribution of the power required by the load by the wavelet conversion of the self-adaptive decomposition layer number, controls the output current of the lithium battery and the super capacitor according to the distribution result, not only meets the load requirement, but also ensures that the SOC of the super capacitor is maintained at about 50 percent by separating out the high-frequency component in the load requirement, fully utilizes the super capacitor and effectively protects the lithium battery.
The development of the invention is partially supported by national science foundation projects 61672539, 61672537, 61803394 and 61873353.
Compared with the prior art, the invention has the following beneficial effects:
1. the method calculates the charge state of the current super capacitor through the super capacitor voltage acquired in real time, determines the charge and discharge level of the super capacitor according to the charge state and the load power of the super capacitor, and further adaptively determines the decomposition layer number L of the wavelet transform according to the load power and the charge and discharge level of the super capacitorwt. Therefore, excessive charging and discharging of the lithium battery are effectively avoided, the super capacitor can bear larger peak current or high-frequency power, the utilization rate of the super capacitor is greatly improved, and fluctuation of bus voltage is reduced.
2. Under different road conditions, such as high speed, low speed, frequent acceleration and deceleration, starting, stopping and the like, when the load power is decomposed by adopting the wavelet conversion method with the same decomposition layer number, the obtained high-frequency component and the obtained low-frequency component are different, namely the high-frequency part and the low-frequency part are related to the load power and change along with the frequency change of the load power. The invention designs the wavelet transform algorithm of the adaptive layer number on the basis, and fully utilizes the adaptive characteristic of the wavelet transform to the signal frequency. The power distribution is carried out by combining the charge state and the wavelet transformation of the super capacitor, the acceleration and emergency brake conditions which may appear in future road conditions are fully considered, and the charge state SOC of the super capacitor is kept on the whole road condition time scale on the premise of meeting the real-time load power requirementSCMaintained at an intermediate level (fluctuating around 50%), SOC of the supercapacitor was maintained at [ 45%, 55% ]at each time]Within the range, the utilization rate of the super capacitor is greatly improved, sufficient capacity preparation is provided for high-frequency positive and negative power requirements which possibly appear in the future, the super capacitor is used for providing high-frequency components in load power as far as possible, the frequency of low-frequency components borne by the lithium battery is lower, current fluctuation of the lithium battery is smaller, the lithium battery is effectively protected, energy consumption of the whole hybrid energy system is reduced, and the relative stability of the SOC of the super capacitor has a positive effect on the stability of bus voltage。
3. The invention relates to a self-adaptive wavelet conversion control method for hybrid energy source of an electric vehicle, which is used for controlling the charging/discharging level L of a super capacitorSCAnd the number of decomposition layers L of wavelet transformwtAnd (6) mapping. The decomposing layer number of the wavelet conversion is determined in a self-adaptive mode in the face of the load power P and the charge state of the super capacitor under different road conditions, the wavelet conversion of different layer numbers is carried out, and the method has good applicability to different road conditions and has the advantage of being expandable. The wavelet conversion with different layers is adopted, so that the calculation amount of the online algorithm is reduced, and the calculation cost and the time cost are saved.
4. The self-adaptive wavelet transformation control method for the hybrid energy source of the electric automobile has the advantages of simple control method, small calculated amount and ideal control effect, and can effectively improve the energy efficiency of the hybrid energy system of the electric automobile.
Drawings
FIG. 1 is a schematic block diagram of the system of the present invention;
FIG. 2 is a diagram of the topology of the main circuit of the system of the present invention;
FIG. 3 is a block diagram of a driving circuit in the system of the present invention;
fig. 4 shows an acquisition circuit module in the system of the present invention.
FIG. 5 is a control flow diagram of a control module in the system of the present invention;
Detailed Description
The invention will be further described with reference to the accompanying drawings in which:
fig. 1 shows a schematic block diagram of the system of the present invention, and fig. 2 shows a main circuit topology structure diagram of the system of the present invention. The two DC/DC converters are respectively connected with the lithium battery and the super capacitor at the low-voltage input end. The two DC/DC converters are connected in parallel at a high-voltage output end, then connected in parallel with an output filter capacitor and finally connected to a bus load. The current is connected in parallel at the high-voltage output end, and a common parallel output filter capacitor is adopted to be connected to the bus load.
The hybrid energy source is output to the load in a boosting mode to be in a forward direction, and the load is output to the load in a decompression mode to be fed back to the hybrid energy source in a reverse direction. The output terminal voltage is the bus voltage.
Fig. 3 shows a driving circuit module in the system of the present invention, which has two sets of driving circuits for respectively performing voltage boosting and voltage reducing for a lithium battery and a super capacitor. And inputting the duty ratio obtained by the controller into a driving circuit, outputting the duty ratio into two paths of complementary signals through a signal complementary control unit, and respectively inputting the two paths of complementary signals into two tubes of the DC/DC converter.
Fig. 4 shows an acquisition circuit module in the system of the present invention. The bus voltage and the super capacitor voltage are collected through the voltage sensor, and the current of the lithium battery and the super capacitor is collected through the current sensor. And a high-precision acquisition module is adopted to acquire through a sensor, and acquired signals are converted into 0-3V signals and input into a control module.
FIG. 5 shows a control flow diagram of the control module in the system of the present invention. And giving a target bus reference voltage, performing adaptive wavelet transform control according to the fed-back voltage and current of the super capacitor, the current of the lithium battery and the bus voltage, and adaptively dividing the load power into a high-frequency part and a low-frequency part which are respectively distributed to the super capacitor and the lithium battery. Through power distribution, the requirement of load power can be met in real time, the charge state of the super capacitor is maintained to fluctuate around the middle level, the utilization rate of the super capacitor is improved, and the stability of bus voltage is continuously kept.
After the voltage and the current are collected, the process of performing the self-adaptive wavelet transformation control is as follows:
a. a given bus reference voltage and the collected bus voltage form a voltage loop, and a load reference total current is output;
b. in combination with the collected supercapacitor voltage according toCalculating the state of charge of the supercapacitor, wherein VSC_normRepresents the nominal voltage of the supercapacitor;
in combination with the collected current of the lithium battery according toCalculating the charge state of the lithium battery; in which the starting of the lithium batteryState of charge SOCBat(0)=(Q0/Q)·100%,Q0The initial charge quantity of the lithium battery is Q, and the nominal capacity of the lithium battery is Q;
c. and according to the calculated charge state of the super capacitor, the charge state of the lithium battery and the load power, performing power distribution on the load reference total current through a self-adaptive wavelet transform algorithm, and taking the high-frequency part as the reference current of the super capacitor and the low-frequency part as the reference current of the lithium battery.
d. A current loop is formed by the reference current of the lithium battery and the collected lithium battery current, and the duty ratio is output to a DC/DC converter at the lithium battery end; and a current loop is formed by the reference current of the super capacitor and the collected super capacitor current, and the duty ratio is output to a DC/DC converter at the end of the super capacitor.
e. The obtained duty ratio is input into a driving module to generate two paths of complementary driving signals and output to a power tube.
Wherein, the number of decomposition layers L in the self-adaptive wavelet conversion algorithmwtIs determined by the following steps:
a. combining the load power P and according to the charge state of the super capacitor and the charge-discharge grade L of the super capacitorSCObtaining the charging/discharging grade L of the super capacitorSCWherein L isSC∈{1,2,3,4,5,6,7,8}。
b. Combining the load power P and the charge state of the lithium battery and according to the charge/discharge level L of the super capacitorSCAnd the number of decomposition layers L of wavelet transformwtTo obtain the decomposition layer number L of the wavelet conversion of the super capacitorwtWherein L iswt∈{1,2,3,4,5}。
Number of decomposition layers L according to wavelet transformwtAnd performing wavelet conversion on the load reference total current by corresponding layers to respectively obtain a low-frequency component and a high-frequency component.
The invention can solve the problems of overlarge charging and discharging current and fluctuation of the battery and low utilization rate of the super capacitor in the prior art.

Claims (6)

1. A control method of an electric vehicle hybrid energy management system based on adaptive wavelet transformation is characterized in that the electric vehicle hybrid energy management system comprises a lithium battery, a super capacitor, a first DC/DC converter, a second DC/DC converter, a control module, a driving module and an acquisition module;
one side of the first DC/DC converter is connected with the lithium battery, and the other side of the first DC/DC converter is connected with the bus load; one side of the second DC/DC converter is connected with the super capacitor, and the other side of the second DC/DC converter is connected with the bus load; one sides of the first DC/DC converter and the second DC/DC converter, which are connected with the bus load, are connected in parallel, and an output filter capacitor is also connected in parallel;
the acquisition module is used for acquiring the current I of the lithium battery in real timeBat(t) Voltage V of the super capacitorSC(t) and current ISC(t) and the bus voltage V at the load sideBus(t), t represents time, t ═ 1,2, ·;
the control module calculates to obtain a control signal by adopting a self-adaptive wavelet transform algorithm according to the signal acquired by the acquisition module;
the driving module receives a control signal of the control module and controls the on-off of power tubes in the first DC/DC converter and the second DC/DC converter;
the control module calculates to obtain a control signal by adopting a self-adaptive wavelet transform algorithm according to the signal acquired by the acquisition module, and comprises the following steps of:
step 1, inputting a given bus reference voltage and a collected bus voltage into a voltage closed-loop circuit, wherein the voltage closed-loop circuit firstly makes a difference between the bus reference voltage and the collected bus voltage, and then inputs the obtained difference into a PI (proportional-integral) control module to obtain a total reference current I required by a loadLoad
Step 2, performing wavelet transformation on the total reference current required by the load by using a self-adaptive wavelet transformation algorithm to obtain a high-frequency current component and a low-frequency current component, wherein the low-frequency current component is used as the reference current of the lithium battery, and the high-frequency current component is used as the reference current of the super capacitor;
step 3, inputting the reference current of the lithium battery and the collected lithium battery current into a first current closed-loop circuit, wherein the first current closed-loop circuit firstly refers to the lithium batteryThe difference is made between the current and the collected lithium battery current, and the obtained difference is sequentially input into a PI control module and a PWM module to obtain a duty ratio DBAT
Inputting the reference current of the super capacitor and the collected super capacitor current into a second current closed-loop circuit, and secondly, subtracting the reference current of the super capacitor battery and the collected super capacitor battery current in the current closed-loop circuit, and then sequentially inputting the obtained difference value into a PI control module and a PWM module to obtain an output duty ratio DSC
Step 4, duty ratio DBATAnd DSCThe output signals are input into a driving module, two paths of complementary driving signals with dead time are respectively generated and are respectively used as control signals of two power tubes of the first DC/DC converter and the second DC/DC converter.
2. The method for controlling the hybrid energy management system for electric vehicles based on adaptive wavelet transformation as claimed in claim 1, wherein in step 2, the adaptive wavelet transformation algorithm determines the number of decomposition layers L of wavelet transformation by the following stepswt
Step 2.1, based on the collected super capacitor voltage VSC(t) calculating the state of charge SOC of the super capacitorSC(t); lithium battery current I based on collectionBat(t) calculating the state of charge SOC of the lithium batteryBat(t);
Step 2.2, according to the load power P and the SOC of the super capacitorSC(t) determining the charge/discharge level L of the supercapacitorSC
Step 2.3, according to the load power P and the state of charge SOC of the lithium batteryBat(t) and the charging/discharging level L of the supercapacitorSCAdaptively obtaining the number L of decomposition layers of wavelet transformwt
3. The method for controlling the hybrid energy management system of the electric vehicle based on the adaptive wavelet transform of claim 2, wherein in step 2.2, the calculation formula of the load power P is:
Fr=CrMg
Fd=0.5ρACdV2
wherein M is the mass of the electric vehicle, a is the acceleration of the electric vehicle, FrAnd FdIs an intermediate variable, V is the speed of the electric vehicle, CrIs the rolling resistance coefficient of the electric automobile, g is the gravity acceleration, and rho is the air density (1.29 kg/m)3) A is the front area of the electric vehicle, CdIs the aerodynamic drag coefficient, eta, of the electric vehicledrAnd ηfbRespectively the electric energy conversion efficiency and the kinetic energy feedback efficiency of the electric automobile; m, C thereinr、A、Cd、ηdrAnd ηfbBoth are intrinsic parameters of the electric vehicle, and a and V are obtained through sensor data on the vehicle.
4. The method for controlling the hybrid energy management system of the electric vehicle based on the adaptive wavelet transform as claimed in claim 2, wherein in step 2.1, the state of charge SOC of the super capacitorSCThe formula for calculation of (t) is:
wherein, VSC_normRepresents the nominal voltage of the supercapacitor;
the calculation formula of the state of charge SOC of the lithium battery is as follows:
SOCBat(0)=(Q0/Q)·100%
therein, SOCBat(0) Is the initial state of charge, Q, of the lithium battery0Is the initial charge of the lithium battery and Q is the nominal capacity of the lithium battery.
5. The method for controlling the hybrid energy management system of the electric vehicle based on the adaptive wavelet transform as claimed in claim 2, wherein in step 2.2, the charging/discharging level L of the super capacitor is determinedSCDetermined according to the following method:
1) when P is more than or equal to 0.9Pbat_max,Then, the charge/discharge grade L of the super capacitor is obtainedSC=1;
2) When P is more than or equal to 0.9Pbat_max,Then, the charge/discharge grade L of the super capacitor is obtainedSC=2;
3) When in useThen, the charge/discharge grade L of the super capacitor is obtainedSC=3;
4) When in useThen, the charge/discharge grade L of the super capacitor is obtainedSC=4;
5) When in useThen, the charge/discharge grade L of the super capacitor is obtainedSC=5;
6) When in useThen, the charge/discharge grade L of the super capacitor is obtainedSC=6;
7) When in useThen, the charge/discharge grade L of the super capacitor is obtainedSC=7;
8) When the P is less than 0, the content of the P,then, the charge/discharge grade L of the super capacitor is obtainedSC=8;
Wherein the content of the first and second substances,is 5 threshold values of the charge state of the super capacitor and is obtained based on a fuzzy control method, Pbat_maxThe maximum power which can be output by the battery; if P is less than 0, the first step is to obtain a P-,the feedback energy is directly absorbed by the lithium battery without wavelet decomposition.
6. The method for controlling the hybrid energy management system for electric vehicles based on adaptive wavelet transformation as claimed in claim 5, wherein in step 2.3, the number of decomposition layers L of wavelet transformation is setwtDetermined according to the following method:
1) when P is more than or equal to 0.9Pbat_max,LSCAdaptively finding the number of decomposition layers L of wavelet transform when 1wt=1;
2) When in useSelf-adaptively obtaining the number L of decomposition layers of wavelet transformwt=2;
3) When in useSelf-adaptively obtaining the number L of decomposition layers of wavelet transformwt=3;
4) When in useSelf-adaptively obtaining the number L of decomposition layers of wavelet transformwt=4;
5) When in useSelf-adaptively obtaining the number L of decomposition layers of wavelet transformwt=5;
Wherein the content of the first and second substances,andthe two threshold values of the charge state of the super capacitor are obtained based on a fuzzy control method.
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