CN110962834A - Electric automobile composite energy storage system and energy distribution method thereof - Google Patents

Electric automobile composite energy storage system and energy distribution method thereof Download PDF

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CN110962834A
CN110962834A CN201911280015.7A CN201911280015A CN110962834A CN 110962834 A CN110962834 A CN 110962834A CN 201911280015 A CN201911280015 A CN 201911280015A CN 110962834 A CN110962834 A CN 110962834A
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battery
motor
storage system
electric automobile
energy storage
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CN110962834B (en
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曹洪奎
孙福明
沈阳
岳城
彭冲
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Liaoning University of Technology
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • 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
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L50/00—Electric propulsion with power supplied within the vehicle
    • B60L50/50—Electric propulsion with power supplied within the vehicle using propulsion power supplied by batteries or fuel cells
    • B60L50/60—Electric propulsion with power supplied within the vehicle using propulsion power supplied by batteries or fuel cells using power supplied by batteries
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
    • B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
    • 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
    • B60W40/02—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 related to ambient conditions
    • B60W40/06—Road conditions
    • 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
    • B60W40/02—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 related to ambient conditions
    • B60W40/06—Road conditions
    • B60W40/076—Slope angle of the road
    • 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
    • B60W40/10—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 related to vehicle motion
    • B60W40/105—Speed
    • 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 discloses a composite energy storage system of an electric automobile, which comprises: the motor is connected with the hub of the electric automobile and used for driving the electric automobile to run; the first battery and the second battery are connected with the motor and are used for driving the motor to rotate; the motor monitoring module is used for acquiring vibration data and instantaneous rotating speed of the motor; the whole vehicle monitoring module is used for acquiring the running speed of a vehicle, the gradient of a road surface, the flatness of the road surface and the ambient temperature; and the main control computer is used for receiving the detection data of the motor monitoring module and the whole vehicle monitoring module and controlling the energy distribution of the first battery and the second battery. The invention also discloses an energy distribution method of the composite energy storage system of the electric automobile, which can determine the energy distribution states of the first battery and the second battery based on the BP neural network when the automobile runs; and the energy supply ratio of the first battery and the second battery can be accurately controlled, so that the automobile can keep sufficient power and long endurance time.

Description

Electric automobile composite energy storage system and energy distribution method thereof
Technical Field
The invention relates to the technical field of electric automobiles, in particular to a composite energy storage system of an electric automobile and an energy distribution method thereof.
Background
The traditional fuel oil automobile not only pollutes the environment, but also influences the health of human beings by the tail gas discharged by the automobile, so that the active reduction of the automobile tail gas pollution has a profound practical significance.
The clean energy vehicles appearing in the market at present mainly refer to hybrid vehicles and pure electric vehicles. The existing hybrid electric vehicle is simultaneously equipped with two power sources: a thermal power source (generated by a conventional gasoline or diesel engine) and an electric power source (battery and electric motor). The motor is used on the hybrid electric vehicle, so that the power system can be flexibly regulated and controlled according to the actual operation condition requirement of the vehicle, and the engine can work in an area with the best comprehensive performance, thereby reducing oil consumption and emission. Compared with the traditional fuel automobile, the pollution of the hybrid electric automobile to the environment is greatly reduced, but the problem is not thoroughly solved. The existing pure electric automobile mainly relies on a battery to provide power, but the existing battery generally has the defects of insufficient cruising ability, long charging time and insufficient power, so that the pure electric automobile cannot be popularized and used in a large range. Therefore, the battery and the power system are bottleneck problems restricting the development of the pure electric vehicle.
Disclosure of Invention
The invention aims to design and develop a composite energy storage system of an electric automobile, which adopts a first battery and a second battery to be matched for use, and has long endurance time and enough power.
Another object of the present invention is to design and develop an energy distribution method for a composite energy storage system of an electric vehicle, which is capable of determining an energy distribution state of a first battery and a second battery based on a BP neural network when the vehicle is running.
The invention can also accurately control the energy supply ratio of the first battery and the second battery, so that the automobile can keep sufficient power and long endurance time.
The technical scheme provided by the invention is as follows:
an electric vehicle composite energy storage system comprising:
the motor is connected with the hub of the electric automobile and used for driving the electric automobile to run;
the first battery and the second battery are connected with the motor and are used for driving the motor to rotate;
the motor monitoring module is used for acquiring vibration data and instantaneous rotating speed of the motor;
the whole vehicle monitoring module is used for acquiring the running speed of a vehicle, the gradient of a road surface, the flatness of the road surface and the ambient temperature;
and the main control computer is used for receiving the detection data of the motor monitoring module and the whole vehicle monitoring module and controlling the energy distribution of the first battery and the second battery.
Preferably, the first battery is a high-capacity battery, and the second battery is a high-power battery.
Preferably, the motor monitoring module includes:
a rotation speed sensor for measuring the instantaneous rotation speed of the motor;
and the vibration acceleration sensor is used for measuring the vibration intensity of the motor.
Preferably, the vehicle monitoring module includes:
a temperature sensor for measuring an ambient temperature;
a flatness sensor for measuring the flatness of the road surface;
a speed sensor for measuring a vehicle running speed;
and a gradient sensor for measuring a gradient of a road surface on which the vehicle travels.
Preferably, a third battery is further included, which is a high capacity battery for starting when the first battery fails or runs out of charge.
An energy distribution method of a composite energy storage system of an electric automobile determines energy distribution states of a first battery and a second battery based on a BP neural network when the automobile runs, and comprises the following steps:
the method comprises the following steps: measuring the rotation speed fluctuation n of the motor by the sensor according to the sampling periodΔVibration intensity VrmsThe vehicle running speed upsilon, the road surface flatness R, the road surface gradient theta and the ambient temperature T;
step two, determining an input layer vector x ═ x of the three-layer BP neural network1,x2,x3,x4,x5,x6}; wherein x is1Is the amount of fluctuation of the motor speed, x2Is the vibration intensity of the motor, x3As the vehicle running speed, x4For road flatness, x5Is the road surface gradient, x6Is ambient temperature;
mapping the input layer vector to a hidden layer, wherein the number of neurons of the hidden layer is m;
step four, obtaining an output layer neuron vector o ═ o1}; wherein o is1For the energy distribution state of the first battery and the second battery, the output layer neuron value is
Figure BDA0002316476980000031
When o is1When the voltage is-1, the second battery drives the motor to rotate independently, and when the voltage is o11, the first battery alone drives the motor to rotate, when1When the voltage is 0, the first battery and the second battery drive the motor to rotate together.
Preferably, when o1When the power supply ratio of the first battery to the second battery is 0, controlling the power supply ratio of the first battery to the second battery to be:
Figure BDA0002316476980000032
wherein the content of the first and second substances,
Figure BDA0002316476980000033
the power supply ratio of the first battery and the second battery, the base number of the natural logarithm of the e position, nAIs unit rotational speed, V0Is the standard vibration intensity of the motor.
Preferably, the vibration intensity VrmsComprises the following steps:
Figure BDA0002316476980000034
wherein, ViFor the measured vibration velocity value, M is the measured vibration signal sample length.
Preferably, the rotation speed fluctuation amount n isΔComprises the following steps:
Figure BDA0002316476980000035
wherein n is expressed as the number of instantaneous rotation speed waveform representation in one working cycle, and nimaxAt the maximum of each fluctuation, niminIs the minimum value of each fluctuation.
Preferably, when the third battery is started, the energy of the third battery and the energy of the second battery are distributed by using the energy distribution method of the composite energy storage system of the electric vehicle.
The invention has the following beneficial effects:
(1) the composite energy storage system of the electric automobile designed and developed by the invention adopts the first battery and the second battery to be matched for use, so that the endurance time is long and the power is sufficient; still be provided with the third battery to prevent that first battery from breaking down or the electric quantity from exhausting, make the vehicle can normally move and travel, further improve the time of endurance.
(2) The energy distribution method of the electric automobile composite energy storage system designed and developed by the invention can determine the energy distribution states of the first battery and the second battery based on the BP neural network when a vehicle runs. The invention can also accurately control the energy supply ratio of the first battery and the second battery, so that the automobile can keep sufficient power and long endurance time.
Drawings
FIG. 1 is a schematic diagram of a pure electric vehicle power system according to the present invention.
Detailed Description
The present invention is further described in detail below with reference to the attached drawings so that those skilled in the art can implement the invention by referring to the description text.
As shown in fig. 1, the present invention provides a pure electric vehicle power system, including: the motor 100 is connected with the hub of the electric automobile and used for driving the electric automobile to run; and a first battery 110 and a second battery 120, which are connected to the motor 100, for supplying energy to the motor 100 to drive the motor 100 to rotate; a motor monitoring module 130 for collecting motor vibration data and instantaneous rotational speed; the whole vehicle monitoring module 140 is used for acquiring the running speed of a vehicle, the gradient of a road surface, the flatness of the road surface and the ambient temperature; and the main control computer 150 is used for receiving the detection data of the motor monitoring module 130 and the vehicle monitoring module 140 and controlling the energy distribution of the first battery 110 and the second battery 120.
The motor monitoring module 130 includes: a rotation speed sensor 131 for measuring an instantaneous rotation speed of the motor 100; a vibration acceleration sensor 132 for measuring vibration severity of the motor 100; the entire vehicle monitoring module 140 includes: a temperature sensor 141 for measuring an ambient temperature; a flatness sensor 144 for measuring the flatness of the road surface; a speed sensor 142 for measuring a vehicle running speed; and a gradient sensor 143 for measuring a gradient of a road surface on which the vehicle travels.
In this embodiment, the first battery 110 is a high-capacity battery, and can provide more energy than a common battery under the same volume, the first battery 110 may be a sodium ion battery or a metal air battery, preferably a metal air battery, and the metal air battery has high energy density, and can provide more energy than a lithium ion battery, so as to ensure better cruising ability; the second battery 120 is a high power battery, which has low internal resistance and can be suitable for the situation of continuous charging and discharging with large current, and the second battery may be a lithium ion battery or a nickel-hydrogen battery, and preferably is a lithium ion battery. Because the metal air battery has long starting time, the high-power battery is adopted to provide energy required in the starting process of the vehicle for the motor in the starting stage of the vehicle, the high-capacity battery is used for assisting to start the vehicle and then drive the vehicle to run, the phenomenon that the vehicle is started too slowly due to long starting time of the battery is avoided, and the satisfaction degree of user experience is improved.
As another embodiment of the present invention, a third battery 160, which is a high-capacity battery, is further included for starting when the first battery 110 malfunctions or runs out of power.
The composite energy storage system of the electric automobile designed and developed by the invention adopts the first battery and the second battery to be matched for use, so that the endurance time is long and the power is sufficient; still be provided with the third battery to prevent that first battery from breaking down or the electric quantity from exhausting, make the vehicle can normally move and travel, further improve the time of endurance.
The invention also provides an energy distribution method of the composite energy storage system of the electric automobile, which determines the energy distribution states of the first battery and the second battery based on the BP neural network when the automobile runs, and comprises the following steps:
step one, establishing a BP neural network model.
Fully interconnected connections are formed among neurons of each layer on the BP model, the neurons in each layer are not connected, and the output and the input of neurons in an input layer are the same, namely oi＝xi. The operating characteristics of the neurons of the intermediate hidden and output layers are
Figure BDA0002316476980000051
opj＝fj(netpj)
Where p represents the current input sample, ωjiIs the connection weight from neuron i to neuron j, opiIs the current input of neuron j, opjIs the output thereof; f. ofjIs a non-linear, slightly non-decreasing function, generally taken as a sigmoid function, i.e. fj(x)＝1/(1+e-x)。
The BP network system structure adopted by the invention consists of three layers, wherein the first layer is an input layer, n nodes are provided in total, n detection signals representing the working state of the equipment are correspondingly provided, and the signal parameters are given by a data preprocessing module; the second layer is a hidden layer, and has m nodes which are determined by the training process of the network in a self-adaptive mode; the third layer is an output layer, p nodes are provided in total, and the output is determined by the response actually needed by the system.
The mathematical model of the network is:
inputting a vector: x ═ x1,x2,...,xn)T
Intermediate layer vector: y ═ y1,y2,...,ym)T
Outputting a vector: o ═ o (o)1,o2,...,op)T
In the invention, the number of nodes of an input layer is n-6, the number of nodes of an output layer is p-1, and the number of nodes of a hidden layer is m-6.
The input layer 6 parameters are respectively expressed as: x is the number of1Is the amount of fluctuation of the motor speed, x2Is the vibration intensity of the motor, x3As the vehicle running speed, x4For road flatness, x5Is the road surface gradient, x6Is ambient temperature;
output layer 1 parameters are expressed as: o1For the energy distribution state of the first battery and the second battery, the output layer neuron value is
Figure BDA0002316476980000061
When o is1When the voltage is-1, the second battery drives the motor to rotate independently, and when the voltage is o11, the first battery alone drives the motor to rotate, when1When the voltage is 0, the first battery and the second battery drive the motor to rotate together.
And step two, training the BP neural network.
After the BP neural network node model is established, the training of the BP neural network can be carried out. And obtaining a training sample according to historical experience data of the product, and giving a connection weight between the input node i and the hidden layer node j and a connection weight between the hidden layer node j and the output layer node k.
(1) Training method
Each subnet adopts a separate training method; when training, firstly providing a group of training samples, wherein each sample consists of an input sample and an ideal output pair, and when all actual outputs of the network are consistent with the ideal outputs of the network, the training is finished; otherwise, the ideal output of the network is consistent with the actual output by correcting the weight; the output samples for each subnet training are shown in table 1.
TABLE 1 output samples for network training
Figure BDA0002316476980000062
(2) Training algorithm
The BP network is trained by using a back Propagation (Backward Propagation) algorithm, and the steps can be summarized as follows:
the first step is as follows: and selecting a network with a reasonable structure, and setting initial values of all node thresholds and connection weights.
The second step is that: for each input sample, the following calculations are made:
(a) forward calculation: for j unit of l layer
Figure BDA0002316476980000071
In the formula (I), the compound is shown in the specification,
Figure BDA0002316476980000072
for the weighted sum of the j unit information of the l layer at the nth calculation,
Figure BDA0002316476980000073
is the connection weight between the j cell of the l layer and the cell i of the previous layer (i.e. the l-1 layer),
Figure BDA0002316476980000074
is the previous layer (i.e. l-1 layer, node number n)l-1) The operating signal sent by the unit i; when i is 0, order
Figure BDA0002316476980000075
Figure BDA0002316476980000076
Is the threshold of the j cell of the l layer.
If the activation function of the unit j is a sigmoid function, then
Figure BDA0002316476980000077
And is
Figure BDA0002316476980000078
If neuron j belongs to the first hidden layer (l ═ 1), then there are
Figure BDA0002316476980000079
If neuron j belongs to the output layer (L ═ L), then there are
Figure BDA00023164769800000710
And ej(n)＝xj(n)-oj(n)；
(b) And (3) calculating the error reversely:
for output unit
Figure BDA00023164769800000711
Pair hidden unit
Figure BDA00023164769800000712
(c) Correcting the weight value:
Figure BDA00023164769800000713
η is the learning rate.
The third step: inputting a new sample or a new period sample until the network converges, and randomly re-ordering the input sequence of the samples in each period during training.
The BP algorithm adopts a gradient descent method to solve the extreme value of a nonlinear function, and has the problems of local minimum, low convergence speed and the like. A more effective algorithm is a Levenberg-Marquardt optimization algorithm, which enables the network learning time to be shorter and can effectively inhibit the network from being locally minimum. The weight adjustment rate is selected as
Δω＝(JTJ+μI)-1JTe
Wherein J is a Jacobian (Jacobian) matrix of error to weight differentiation, I is an input vector, e is an error vector, and the variable mu is a scalar quantity which is self-adaptive and adjusted and is used for determining whether the learning is finished according to a Newton method or a gradient method.
When the system is designed, the system model is a network which is only initialized, the weight needs to be learned and adjusted according to data samples obtained in the using process, and therefore the self-learning function of the system is designed. Under the condition of appointing learning samples and quantity, the system can carry out self-learning so as to continuously improve the network performance.
When o is1When the power supply ratio of the first battery to the second battery is 0, controlling the power supply ratio of the first battery to the second battery to be:
Figure BDA0002316476980000081
wherein the content of the first and second substances,
Figure BDA0002316476980000082
the power supply ratio of the first battery and the second battery, the base number of the natural logarithm of the e position, nAIs unit rotational speed, V0Is the standard vibration intensity of the motor.
The vibration intensity VrmsComprises the following steps:
Figure BDA0002316476980000083
wherein, ViFor the measured vibration velocity value, M is the measured vibration signal sample length.
The rotational speed fluctuation nΔComprises the following steps:
Figure BDA0002316476980000084
wherein n is expressed as the number of instantaneous rotation speed waveform representation in one working cycle, and nimaxAt the maximum of each fluctuation, niminIs the minimum value of each fluctuation.
When the third battery is started, the energy of the third battery and the energy of the second battery are distributed by adopting the energy distribution method of the composite energy storage system of the electric automobile.
The energy distribution method of the composite energy storage system provided by the invention is further described below with reference to specific embodiments.
10 groups of different road conditions, vehicle conditions and environmental conditions are simulated for testing, and specific test data are shown in table 2.
TABLE 2 test data
Figure BDA0002316476980000091
The energy distribution method of the composite energy storage system provided by the invention is adopted for regulation and control, and the regulation and control results are shown in table 3.
TABLE 3 Regulation and control results
Serial number State of the art Function ratio
1 1 ∞
2 0 15/2
3 0 13/2
4 -1 0
5 0 23/2
6 -1 0
7 0 4/3
8 0 7/1
9 0 1/1
10 0 23/1
Adopt compound energy storage system, first battery and second battery cooperation are used, and the time of endurance is long and power is sufficient, has improved the travelling comfort.
The energy distribution method of the electric automobile composite energy storage system designed and developed by the invention can determine the energy distribution states of the first battery and the second battery based on the BP neural network when a vehicle runs. The invention can also accurately control the energy supply ratio of the first battery and the second battery, so that the automobile can keep sufficient power and long endurance time.
While embodiments of the invention have been described above, it is not limited to the applications set forth in the description and the embodiments, which are fully applicable in various fields of endeavor to which the invention pertains, and further modifications may readily be made by those skilled in the art, it being understood that the invention is not limited to the details shown and described herein without departing from the general concept defined by the appended claims and their equivalents.

Claims (10)

1. The utility model provides a compound energy storage system of electric automobile which characterized in that includes:
the motor is connected with the hub of the electric automobile and used for driving the electric automobile to run;
the first battery and the second battery are connected with the motor and are used for driving the motor to rotate;
the motor monitoring module is used for acquiring vibration data and instantaneous rotating speed of the motor;
the whole vehicle monitoring module is used for acquiring the running speed of a vehicle, the gradient of a road surface, the flatness of the road surface and the ambient temperature;
and the main control computer is used for receiving the detection data of the motor monitoring module and the whole vehicle monitoring module and controlling the energy distribution of the first battery and the second battery.
2. The composite energy storage system of claim 1, wherein the first battery is a high capacity battery and the second battery is a high power battery.
3. The composite energy storage system of the electric vehicle as claimed in claim 1 or 2, wherein the motor monitoring module comprises:
a rotation speed sensor for measuring the instantaneous rotation speed of the motor;
and the vibration acceleration sensor is used for measuring the vibration intensity of the motor.
4. The composite energy storage system of the electric automobile of claim 3, wherein the entire automobile monitoring module comprises:
a temperature sensor for measuring an ambient temperature;
a flatness sensor for measuring the flatness of the road surface;
a speed sensor for measuring a vehicle running speed;
and a gradient sensor for measuring a gradient of a road surface on which the vehicle travels.
5. The composite energy storage system of the electric vehicle as claimed in claim 1, 2 or 4, further comprising a third battery, which is a high-capacity battery, for starting when the first battery fails or runs out.
6. The energy distribution method of the composite energy storage system of the electric automobile is characterized in that when a vehicle runs, the energy distribution state of a first battery and a second battery is determined based on a BP neural network, and the method comprises the following steps:
the method comprises the following steps: measuring the rotation speed fluctuation n of the motor by the sensor according to the sampling periodΔVibration intensity VrmsThe vehicle running speed upsilon, the road surface flatness R, the road surface gradient theta and the ambient temperature T;
step two, determining an input layer vector x ═ x of the three-layer BP neural network1,x2,x3,x4,x5,x6}; wherein x is1Is the amount of fluctuation of the motor speed, x2Is the vibration intensity of the motor, x3As the vehicle running speed, x4For road flatness, x5Is the road surface gradient, x6Is ambient temperature;
mapping the input layer vector to a hidden layer, wherein the number of neurons of the hidden layer is m;
step four, obtaining an output layer neuron vector o ═ o1}; wherein o is1For the energy distribution state of the first battery and the second battery, the output layer neuron value is
Figure FDA0002316476970000021
When o is1When the voltage is-1, the second battery drives the motor to rotate independently, and when the voltage is o11, the first battery alone drives the motor to rotate, when1When the voltage is 0, the first battery and the second battery drive the motor to rotate together.
7. The energy distribution method of the composite energy storage system of the electric automobile as claimed in claim 6, characterized in that when o is1When the power supply ratio of the first battery to the second battery is 0, controlling the power supply ratio of the first battery to the second battery to be:
Figure FDA0002316476970000022
wherein the content of the first and second substances,
Figure FDA0002316476970000025
the power supply ratio of the first battery and the second battery, the base number of the natural logarithm of the e position, nAIs unit rotational speed, V0Is the standard vibration intensity of the motor.
8. The energy distribution method of the composite energy storage system of the electric automobile according to claim 7, wherein the vibration intensity V isrmsComprises the following steps:
Figure FDA0002316476970000023
wherein, ViFor measured vibration velocity values, M is the measured vibration signal sample lengthAnd (4) degree.
9. The method for distributing energy in the hybrid energy storage system of an electric vehicle according to claim 8, wherein the rotation speed fluctuation amount n isΔComprises the following steps:
Figure FDA0002316476970000024
wherein n is expressed as the number of instantaneous rotation speed waveform representation in one working cycle, and nimaxAt the maximum of each fluctuation, niminIs the minimum value of each fluctuation.
10. The energy distribution method of the composite energy storage system of the electric automobile according to claim 9, wherein when the third battery is started, the energy of the third battery and the energy of the second battery are distributed by using the energy distribution method of the composite energy storage system of the electric automobile according to any one of claims 6 to 9.
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