CN111301223A - Electric vehicle battery management system and management method - Google Patents

Electric vehicle battery management system and management method Download PDF

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CN111301223A
CN111301223A CN202010193662.0A CN202010193662A CN111301223A CN 111301223 A CN111301223 A CN 111301223A CN 202010193662 A CN202010193662 A CN 202010193662A CN 111301223 A CN111301223 A CN 111301223A
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battery pack
battery
motor
automobile
vehicle
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曹洪奎
孙福明
沈阳
王文静
李春树
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Liaoning University of Technology
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    • 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
    • B60L58/12—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries responding to state of charge [SoC]
    • 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
    • B60L15/00—Methods, circuits, or devices for controlling the traction-motor speed of electrically-propelled vehicles
    • B60L15/20—Methods, circuits, or devices for controlling the traction-motor speed of electrically-propelled vehicles for control of the vehicle or its driving motor to achieve a desired performance, e.g. speed, torque, programmed variation of speed
    • 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
    • B60L58/18—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries of two or more battery modules
    • 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
    • B60L2240/00—Control parameters of input or output; Target parameters
    • B60L2240/10—Vehicle control parameters
    • B60L2240/12—Speed
    • 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
    • B60L2240/00—Control parameters of input or output; Target parameters
    • B60L2240/40—Drive Train control parameters
    • B60L2240/42—Drive Train control parameters related to electric machines
    • B60L2240/421—Speed
    • 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
    • B60L2240/00—Control parameters of input or output; Target parameters
    • B60L2240/60—Navigation input
    • B60L2240/64—Road conditions
    • B60L2240/642—Slope of road
    • 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
    • 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
    • Y02T10/72—Electric energy management in electromobility

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  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Sustainable Development (AREA)
  • Sustainable Energy (AREA)
  • Electric Propulsion And Braking For Vehicles (AREA)

Abstract

The invention discloses a battery management system of an electric automobile, which comprises: the motor is used for driving the electric automobile to run; the first battery pack and the second battery pack are respectively connected with the motor and used for supplying energy to the motor; the motor monitoring module is used for acquiring motor vibration data and instantaneous rotating speed; the whole vehicle monitoring module is used for acquiring the running speed of a vehicle, the gradient of a road surface, the ambient temperature and the residual electric quantity of the first battery pack and the second battery pack; the main control machine is used for receiving the detection data of the motor monitoring module and the whole vehicle monitoring module and controlling the first battery pack and the second battery pack to work; the first battery pack is a high-capacity battery, and the second battery pack is a high-power battery. The invention also provides a battery management method of the electric automobile, which is used for determining the working states of the first battery pack and the second battery pack based on the BP neural network when the electric automobile runs. The dual battery packs are used in a matched mode, the endurance time is long, and the power is sufficient.

Description

Electric vehicle battery management system and management method
Technical Field
The invention relates to the technical field of electric vehicle battery management, in particular to an electric vehicle battery management system and a management method.
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, battery management of the electric vehicle is a bottleneck problem restricting the development of the pure electric vehicle.
Disclosure of Invention
The invention aims to design and develop a battery management system of an electric automobile, which is provided with a double battery pack, and has long endurance time and enough power by the combined use of the double battery packs.
Another object of the present invention is to devise and develop a battery management method for an electric vehicle, which determines the operating states of a first battery pack and a second battery pack based on a BP neural network when the electric vehicle is running.
The invention can also accurately control the working power of the first battery pack and the second battery pack, and has long endurance time and enough power by matching the double battery packs.
The technical scheme provided by the invention is as follows:
an electric vehicle battery management system comprising:
the motor is used for driving the electric automobile to run; and
the first battery pack and the second battery pack are respectively connected with the motor and used for supplying energy to the motor;
the motor monitoring module is used for acquiring motor vibration data and instantaneous rotating speed;
the whole vehicle monitoring module is used for acquiring the running speed of a vehicle, the gradient of a road surface, the ambient temperature and the residual electric quantity of the first battery pack and the second battery pack;
the main control machine is used for receiving the detection data of the motor monitoring module and the whole vehicle monitoring module and controlling the first battery pack and the second battery pack to work;
the first battery pack is a high-capacity battery, and the second battery pack 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 speed sensor for measuring a vehicle running speed;
a gradient sensor for measuring a gradient of a road surface on which the vehicle travels;
and a remaining power sensor for detecting remaining power of the first battery pack and the second battery pack.
A battery management method for an electric vehicle, which determines the working states of a first battery pack and a second battery pack based on a BP neural network when the electric vehicle is driven, comprises the following steps:
step one, measuring the rotation speed fluctuation amount of a motor, the residual electric quantity coefficient of a first battery pack, the residual electric quantity coefficient of a second battery pack, the vibration intensity of an automobile, the driving speed of the automobile and the road surface gradient through a sensor according to a sampling period;
step two, determining an input layer neuron vector x ═ x of the three-layer BP neural network1，x2，x3，x4，x5，x6}; wherein x is1Is the amount of fluctuation of the rotational speed of the motor, x2Is the residual capacity coefficient, x, of the first battery pack3Is the residual capacity coefficient, x, of the second battery pack4Is the vibration intensity of the automobile, x5As the speed of travel of the vehicle, x6Is the road surface gradient;
mapping the input layer vector to a middle layer, wherein the number of the neurons in the middle layer is m;
step four, obtaining an output layer neuron vector o ═ o1，o2}; wherein o is1Is the operating state of the first battery pack, o2The neuron value of the output layer is the working state of the second battery pack
Figure BDA0002416820410000031
k is output layer neuron sequence number, k is {1, 2}, when okWhen 1, it is in working state, when okWhen 0, it indicates the off state.
It is preferable that the first and second liquid crystal layers are formed of,
when o is1＝1，o2When the battery pack is equal to 1, the first battery pack works, and the second battery pack does not work;
when o is1＝1，o2When the battery pack is equal to 1, the first battery pack does not work, and the second battery pack works;
when o is1＝1，o2When the power is 1, the first battery pack and the second battery pack are both operated, and the operating power P of the first battery pack1And operating power P of the second battery pack2Respectively satisfy:
P1＝Pn-P2；
Figure BDA0002416820410000032
wherein, P1Is the operating power of the first battery pack, PnFor the power demand of the vehicle, P2Is the operating power of the second battery, m is the weight of the vehicle, frIs the rolling resistance coefficient of the automobile wheel, Vs，0Is the ideal vibration intensity of the automobile, VsIs the real-time vibration intensity of the automobile, rhoaIs the density of air, CdIs the coefficient of air resistance, AfIs the windward area of the automobile, v is the running speed of the automobile, α is the included angle between the running road surface of the automobile and the horizontal plane, T is the ambient temperature, T is the temperature0Is the standard temperature, e is the base of the natural logarithm, and g is the acceleration of gravity.
Preferably, the automobile vibration intensity is as follows:
Figure BDA0002416820410000033
wherein, VsIs the vibration intensity of the automobile, ViFor the measured vibration velocity value, M is the measured vibration signal sample length.
Preferably, the rotation speed fluctuation amount of the motor is:
Figure BDA0002416820410000034
wherein n isΔThe rotation speed fluctuation quantity of the motor is represented by n, n is the instantaneous rotation speed waveform representing times in one working cycle of the motorimaxMaximum value of each motor fluctuation, niminIs the minimum value of each fluctuation of the motor.
It is preferable that the first and second liquid crystal layers are formed of,
the remaining capacity coefficient of the first battery pack is as follows:
Figure BDA0002416820410000041
the remaining capacity coefficient of the second battery pack is as follows:
Figure BDA0002416820410000042
wherein S is1Is the remaining capacity coefficient of the first battery pack, EC1Is the real-time remaining capacity of the first battery pack, ET1The first battery pack is fully charged ξ1Is the attenuation ratio of the first battery set, S2Is the remaining capacity coefficient of the second battery pack, EC2Is the real-time remaining capacity of the second battery pack, ET2For the second battery pack to be fully charged, ξ2Is the decay rate of the second battery pack.
Preferably, the gradient of the traveling road surface of the automobile is:
Figure BDA0002416820410000043
wherein tau is the gradient of the automobile running road surface, and α is the included angle between the automobile running road surface and the horizontal plane.
Preferably, the excitation functions of the intermediate layer and the output layer both adopt S-shaped functions fi(x)＝1/(1+e-x)。
The invention has the following beneficial effects:
(1) the battery management system for the electric automobile, which is designed and developed by the invention, is provided with the double battery packs, and the double battery packs are matched for use, so that the battery management system is long in endurance time and sufficient in power.
(2) According to the electric vehicle battery management method designed and developed by the invention, when the electric vehicle runs, the working states of the first battery pack and the second battery pack are determined based on the BP neural network. The invention can also accurately control the working power of the first battery pack and the second battery pack, and has long endurance time and enough power by matching the double battery packs.
Drawings
Fig. 1 is a schematic diagram of a battery management system of an electric vehicle 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 battery management system for an electric vehicle, comprising: and the motor 110 is connected with the automobile hub and used for driving the electric automobile to run. And the first battery pack 120 and the second battery pack 130 are respectively connected with the motor and used for supplying energy to the motor so as to drive the electric automobile to run. And a motor monitoring module 140 for collecting motor vibration data and instantaneous rotational speed. And the whole vehicle monitoring module 150 is used for acquiring the running speed of the vehicle, the road gradient, the ambient temperature and the residual electric quantity of the first battery pack and the second battery pack. And the main control computer 160 is used for receiving the detection data of the motor monitoring module 140 and the vehicle monitoring module 150 and controlling the first battery pack 120 and the second battery pack 130 to work. First battery stack 120 is a high capacity battery and second battery stack 130 is a high power battery.
The motor monitoring module 140 includes a rotation speed sensor 141 for measuring the instantaneous rotation speed of the motor and a vibration acceleration sensor 142 for measuring the vibration intensity of the motor.
The vehicle monitoring module 150 includes a temperature sensor 151 for measuring an ambient temperature, a speed sensor 152 for measuring a traveling speed of the vehicle, a gradient sensor 153 for measuring a gradient of a traveling road surface of the vehicle, and a remaining capacity sensor 154 for detecting remaining capacities of the first battery pack 120 and the second battery pack 130.
The battery management system for the electric automobile, which is designed and developed by the invention, is provided with the double battery packs, and the double battery packs are matched for use, so that the battery management system is long in endurance time and sufficient in power.
The invention also provides a battery management method of the electric automobile, which is used for determining the working states of the first battery pack and the second battery pack based on the BP neural network when the electric 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 BDA0002416820410000051
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 driving state of the automobile are correspondingly provided, and the signal parameters are given by a data preprocessing module; the second layer is an intermediate layer, and the intermediate layer comprises 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-2, and the number of nodes of a hidden layer is m-5.
The input layer 6 parameters are respectively expressed as: x is the number of1Is the amount of fluctuation of the rotational speed of the motor, x2Is the residual capacity coefficient, x, of the first battery pack3Is the residual capacity coefficient, x, of the second battery pack4Is the vibration intensity of the automobile, x5As the speed of travel of the vehicle, x6Is the road surface gradient;
wherein, road surface slope does:
Figure BDA0002416820410000061
wherein tau is the gradient of the road surface, α is the included angle between the running road surface of the automobile and the horizontal plane, when α is positive, the running road surface is an uphill slope, and when α is negative, the running road surface is a downhill slope.
The vibration intensity of the automobile is as follows:
Figure BDA0002416820410000062
in the formula, VsIs the vibration intensity of the automobile, ViFor the measured vibration velocity value of the car, M is the measured vibration signal sample length.
The rotation speed fluctuation amount of the motor is as follows:
Figure BDA0002416820410000063
wherein n isΔThe rotation speed fluctuation quantity of the motor is represented by n, n is the instantaneous rotation speed waveform representing times in one working cycle of the motorimaxMaximum value of each motor fluctuation, niminIs the minimum value of each fluctuation of the motor.
The remaining capacity coefficient of the first battery pack is:
Figure BDA0002416820410000071
the remaining capacity coefficient of the second battery pack is:
Figure BDA0002416820410000072
wherein S is1Is the remaining capacity coefficient of the first battery pack, EC1Is the real-time remaining capacity of the first battery pack, ET1The first battery pack is fully charged ξ1Is the attenuation ratio of the first battery set, S2Is the remaining capacity coefficient of the second battery pack, EC2Is the real-time remaining capacity of the second battery pack, ET2Is a secondFull charge of the battery ξ2Is the decay rate of the second battery pack.
It should be noted here that when the remaining capacity coefficient S of the battery is less than or equal to 10%, the battery enters a low-capacity state, and the battery does not work any more until the battery is charged to make the remaining capacity coefficient S > 10%, and the battery can work normally.
The output layer 2 parameters are respectively expressed as: o1Is the operating state of the first battery pack, o2The neuron value of the output layer is the working state of the second battery pack
Figure BDA0002416820410000073
k is output layer neuron sequence number, k is {1, 2}, when okWhen 1, it is in working state, when okWhen 0, it indicates the off state.
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 middle 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 BDA0002416820410000074
Figure BDA0002416820410000081
(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 BDA0002416820410000082
In the formula (I), the compound is shown in the specification,
Figure BDA0002416820410000083
for the weighted sum of the j unit information of the l layer at the nth calculation,
Figure BDA0002416820410000084
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 BDA0002416820410000085
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 BDA0002416820410000086
Figure BDA0002416820410000087
Figure BDA0002416820410000088
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 BDA0002416820410000089
And is
Figure BDA00024168204100000810
If the neuron j belongs to the first intermediate layer (l ═ 1), then there are
Figure BDA00024168204100000811
If neuron j belongs to the output layer (L ═ L), then there are
Figure BDA00024168204100000812
And ej(n)＝xj(n)-oj(n)；
(b) And (3) calculating the error reversely:
for output unit
Figure BDA00024168204100000813
Pair hidden unit
Figure BDA0002416820410000091
(c) Correcting the weight value:
Figure BDA0002416820410000092
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 is1＝1，o2When the power is equal to 0, the first battery pack works, the second battery pack does not work, namely the first battery pack supplies power according to the required power of the running of the automobile;
when o is1＝0，o2When the power is equal to 1, the first battery pack does not work, and the second battery pack works, namely the second battery pack supplies power according to the required power of the running of the automobile;
when o is1＝1，o2When the power is 1, the first battery pack and the second battery pack are both operated, and the operating power P of the first battery pack1And operating power P of the second battery pack2Respectively satisfy:
P1＝Pn-P2；
Figure BDA0002416820410000093
wherein, P1Is the operating power of the first battery pack, PnFor the power demand of the vehicle, P2Is the operating power of the second battery, m is the weight of the vehicle, frIs the rolling resistance coefficient of the automobile wheel, Vs，0Is the ideal vibration intensity of the automobile, VsIs the real-time vibration intensity of the automobile, rhoaIs the density of air, CdIs the coefficient of air resistance, AfIs the windward area of the automobile, v is the running speed of the automobile, α is the included angle between the running road surface of the automobile and the horizontal plane, T is the ambient temperature, T is the temperature0Is standard temperature, e is base of natural logarithmAnd g is the acceleration of gravity.
The following describes the battery management method of an electric vehicle according to the present invention with reference to specific embodiments.
A first battery pack with the capacity of 50kwh and a second battery pack with the power of 120kW are selected to simulate the running of an automobile under different slopes and simulate different running conditions and automobile conditions. And selecting fuel automobiles (full oil) with the same type, and testing under the same condition to compare the dynamic property of the automobiles, wherein the specific simulation data is shown in table 2.
Table 2 simulation test data
Figure BDA0002416820410000101
The method of the invention is adopted to determine the working state of the first battery pack, the working state of the second battery pack and the corresponding working power, and determine the time difference (the running time of the electric automobile-the running time of the fuel automobile) of running the same distance (5km) under the same test condition. Specific results are shown in table 3.
TABLE 3 test results
Figure BDA0002416820410000111
As can be seen from table 3, the battery management method for the electric vehicle controls the working power of the first battery pack and the second battery pack, and the power difference is smaller than that of a fuel vehicle due to the combined use of the two battery packs, so that the power performance of the electric vehicle is ensured, and the cruising time is also improved.
According to the electric vehicle battery management method designed and developed by the invention, when the electric vehicle runs, the working states of the first battery pack and the second battery pack are determined based on the BP neural network. The invention can also accurately control the working power of the first battery pack and the second battery pack, and has long endurance time and enough power by matching the double battery packs.
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. An electric vehicle battery management system, comprising:
the motor is used for driving the electric automobile to run; and
the first battery pack and the second battery pack are respectively connected with the motor and used for supplying energy to the motor;
the motor monitoring module is used for acquiring motor vibration data and instantaneous rotating speed;
the whole vehicle monitoring module is used for acquiring the running speed of a vehicle, the gradient of a road surface, the ambient temperature and the residual electric quantity of the first battery pack and the second battery pack;
the main control machine is used for receiving the detection data of the motor monitoring module and the whole vehicle monitoring module and controlling the first battery pack and the second battery pack to work;
the first battery pack is a high-capacity battery, and the second battery pack is a high-power battery.
2. The electric vehicle battery management system of claim 1, 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.
3. The battery management system of an electric vehicle of claim 2, wherein the vehicle monitoring module comprises:
a temperature sensor for measuring an ambient temperature;
a speed sensor for measuring a vehicle running speed;
a gradient sensor for measuring a gradient of a road surface on which the vehicle travels;
and a remaining power sensor for detecting remaining power of the first battery pack and the second battery pack.
4. A battery management method for an electric vehicle is characterized in that when the electric vehicle runs, the working states of a first battery pack and a second battery pack are determined based on a BP neural network, and the method comprises the following steps:
step one, measuring the rotation speed fluctuation amount of a motor, the residual electric quantity coefficient of a first battery pack, the residual electric quantity coefficient of a second battery pack, the vibration intensity of an automobile, the driving speed of the automobile and the road surface gradient through a sensor according to a sampling period;
step two, determining an input layer neuron vector x ═ x of the three-layer BP neural network1，x2，x3，x4，x5，x6}; wherein x is1Is the amount of fluctuation of the rotational speed of the motor, x2Is the residual capacity coefficient, x, of the first battery pack3Is the residual capacity coefficient, x, of the second battery pack4Is the vibration intensity of the automobile, x5As the speed of travel of the vehicle, x6Is the road surface gradient;
mapping the input layer vector to a middle layer, wherein the number of the neurons in the middle layer is m;
step four, obtaining an output layer neuron vector o ═ o1，o2}; wherein o is1Is the operating state of the first battery pack, o2The neuron value of the output layer is the working state of the second battery pack
Figure FDA0002416820400000021
k is output layer neuron sequence number, k is {1, 2}, when okWhen 1, it is in working state, when okWhen 0, it indicates the off state.
5. The battery management method for electric vehicles according to claim 4,
when o is1＝1，o2When equal to 0, the firstOne battery pack works, and the second battery pack does not work;
when o is1＝0，o2When the battery pack is equal to 1, the first battery pack does not work, and the second battery pack works;
when o is1＝1，o2When the power is 1, the first battery pack and the second battery pack are both operated, and the operating power P of the first battery pack1And operating power P of the second battery pack2Respectively satisfy:
P1＝Pn-P2；
Figure FDA0002416820400000022
wherein, P1Is the operating power of the first battery pack, PnFor the power demand of the vehicle, P2Is the operating power of the second battery, m is the weight of the vehicle, frIs the rolling resistance coefficient of the automobile wheel, Vs，0Is the ideal vibration intensity of the automobile, VsIs the real-time vibration intensity of the automobile, rhoaIs the density of air, CdIs the coefficient of air resistance, AfIs the windward area of the automobile, v is the running speed of the automobile, α is the included angle between the running road surface of the automobile and the horizontal plane, T is the ambient temperature, T is the temperature0Is the standard temperature, e is the base of the natural logarithm, and g is the acceleration of gravity.
6. The battery management method for the electric vehicle according to claim 5, wherein the vehicle vibration intensity is as follows:
Figure FDA0002416820400000023
wherein, VsIs the vibration intensity of the automobile, ViFor the measured vibration velocity value, M is the measured vibration signal sample length.
7. The battery management method for the electric vehicle according to claim 6, wherein the rotation speed fluctuation amount of the motor is:
Figure FDA0002416820400000031
wherein n isΔThe rotation speed fluctuation quantity of the motor is represented by n, n is the instantaneous rotation speed waveform representing times in one working cycle of the motorimaxMaximum value of each motor fluctuation, niminIs the minimum value of each fluctuation of the motor.
8. The battery management method for electric vehicles according to claim 7,
the remaining capacity coefficient of the first battery pack is as follows:
Figure FDA0002416820400000032
the remaining capacity coefficient of the second battery pack is as follows:
Figure FDA0002416820400000033
wherein S is1Is the remaining capacity coefficient of the first battery pack, EC1Is the real-time remaining capacity of the first battery pack, ET1The first battery pack is fully charged ξ1Is the attenuation ratio of the first battery set, S2Is the remaining capacity coefficient of the second battery pack, EC2Is the real-time remaining capacity of the second battery pack, ET2For the second battery pack to be fully charged, ξ2Is the decay rate of the second battery pack.
9. The battery management method for the electric vehicle according to claim 8, wherein the gradient of the traveling road surface of the vehicle is:
Figure FDA0002416820400000034
wherein tau is the gradient of the automobile running road surface, and α is the included angle between the automobile running road surface and the horizontal plane.
10. The battery management method for electric vehicles according to claim 9, wherein the excitation functions of the intermediate layer and the output layer are both sigmoid functions fj(x)＝1/(1+e-x)。
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