CN114572053A - Electric automobile energy management method and system based on working condition identification - Google Patents
Electric automobile energy management method and system based on working condition identification Download PDFInfo
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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
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- B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
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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
- B60L3/00—Electric devices on electrically-propelled vehicles for safety purposes; Monitoring operating variables, e.g. speed, deceleration or energy consumption
- B60L3/12—Recording operating variables ; Monitoring of operating variables
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- B—PERFORMING OPERATIONS; TRANSPORTING
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- B60L50/00—Electric propulsion with power supplied within the vehicle
- B60L50/40—Electric propulsion with power supplied within the vehicle using propulsion power supplied by capacitors
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- 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
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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]
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Abstract
The invention discloses an electric automobile energy management method and system based on working condition identification, wherein the method comprises the following steps: constructing an energy management model based on a neural network under three working condition modes; collecting real-time running condition speed data, extracting the characteristics of a working condition section through a sliding window, and performing principal component analysis; inputting the characteristic parameters into fuzzy logic to obtain a working condition identification result; selecting an energy management model based on a neural network corresponding to the classification result according to the working condition identification result; and inputting the current and voltage and the speed information characteristics of the super capacitor and the lithium battery into the trained neural network model to obtain the reference current of the super capacitor, thereby realizing real-time energy management. The invention adjusts the energy management strategy in real time according to the working condition, fully utilizes the advantages of the super capacitor and effectively prolongs the service life of the lithium battery.
Description
Technical Field
The invention belongs to the technical field of electric automobiles, and particularly relates to an electric automobile energy management method and system based on working condition identification.
Background
With the continuous improvement of living standard, the automobile has become one of the indispensable vehicles for people to go out, but the problem that the great consumption of automobile exhaust and petroleum is difficult to solve is always generated. Therefore, the development of green and environmentally friendly electric vehicles has become a common choice for governments and vehicle manufacturers worldwide. In the current market, lithium batteries are widely applied to electric vehicles by virtue of the advantages of high energy density, small volume, wide temperature application range and the like. However, the main problem of how to prolong the service life of the battery as long as possible under the premise of providing higher power density is limited by the fact that the current technology cannot be well solved. As a novel energy source, the super capacitor has the advantages of high power density, long cycle life, high charge-discharge efficiency, short response time and the like, is complementary with the characteristics of a lithium battery, and forms a hybrid energy storage system. The hybrid energy storage system is beneficial to prolonging the service life of the lithium battery and improving the economic performance of the whole vehicle.
The existing energy management strategies of the electric automobile are mainly divided into two types, namely a rule-based energy management strategy and an optimization-based energy management strategy. The rule-based energy management strategy sets the rules by more using expert experience, the algorithm is relatively simple, the execution efficiency is high, the real-time performance is strong, but the setting of the rules is too dependent on the experience and is not an optimal scheme. The energy management strategy based on optimization usually selects corresponding parameters to set an optimization target, and obtains an optimal scheme by solving under the condition of satisfying constraint conditions, but needs prior knowledge, and has higher calculation cost compared with the strategy based on rules.
In addition, in order to better prolong the service life of the lithium battery and increase the economy of the electric automobile, the influence of the driving condition on the energy management strategy needs to be comprehensively considered. In a congested city, the electric automobile is slow in speed and is frequently started and stopped; on the expressway, the speed is higher and more stable; the suburb is moderate. Therefore, energy management strategies under different working conditions should be designed by comprehensively considering different characteristics of the working conditions.
In recent years, there have been many patents for inventions that have been developed. Rogin et al provide a method for identifying and controlling the working conditions of a pure electric vehicle, wherein a neural network controller identifies characteristic parameters of historical driving data to obtain the working condition type of the historical driving data, and the result is used for calling corresponding fuzzy controller parameters to realize energy management. However, the rule setting of fuzzy logic is dependent on expert experience and is far from optimal for energy management. On the other hand, for the uncertain fuzzy concept of the working condition type, the fuzzy logic is more suitable for working condition identification. Holeman et al disclose an electric vehicle and a method and apparatus for identifying operating conditions thereof, wherein a torque requested by a motor of the electric vehicle is required to be obtained, a current vehicle speed and a steering wheel angle are used as known conditions to identify the operating conditions, and no energy management strategy is designed.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides the electric automobile energy management method based on the working condition identification, so that the service life of the lithium battery is effectively prolonged, and the economy of the whole automobile is improved.
The technical scheme adopted by the invention for solving the technical problems is as follows:
an electric automobile energy management method based on working condition identification comprises the following steps:
step 1, dividing various public working condition data sets into working condition blocks, extracting characteristic parameters of the working condition blocks, reducing characteristic redundancy through principal component analysis, recombining working conditions with high characteristic similarity, and constructing a typical working condition data set;
step 2, constructing a multi-objective optimization problem of energy management, and performing dynamic programming off-line solving according to the load power demand to obtain an off-line optimal reference value;
step 3, taking key variables required by dynamic programming solution and an offline optimal solution obtained by solution as input and output, and constructing an optimal energy management strategy data set under three working condition types; using the typical working condition data set constructed in the step 1 for neural network training, and constructing an energy management model based on the neural network under three working condition modes;
step 4, collecting real-time running condition speed data, extracting the characteristics of the working condition section through a sliding window, and analyzing the principal components;
step 5, inputting the characteristic parameters into a fuzzy logic working condition recognizer to obtain a working condition recognition result;
step 6, selecting an energy management model based on a neural network corresponding to the classification result according to the working condition identification result;
step 7, inputting current, voltage and speed information characteristics of the super capacitor and the lithium battery into the trained neural network model to obtain reference current of the super capacitor, so as to realize real-time energy management;
an electric vehicle energy management system based on operating condition identification comprises:
the acquisition module is used for acquiring the current and the voltage of the lithium battery and the super capacitor in the hybrid energy storage module and the speed information of the electric automobile in real time, transmitting the current and the voltage of the lithium battery and the super capacitor to the energy management control module, and transmitting the speed information to the working condition type identification module;
the driving module is used for receiving the signal of the control module and outputting a PWM signal to control the switching element to be switched on and off so that the current of the super capacitor follows the reference signal given by the control module, the input end of the driving module is electrically connected with the control module, and the output end of the driving module is electrically connected with the hybrid energy storage module;
the working condition type identification module is used for classifying in real time through a fuzzy logic classifier preset according to a typical working condition data set according to the speed data acquired in real time, outputting the type of the current working condition, and is electrically connected with the control module;
and the control module is used for selecting a corresponding neural network model according to the working condition type of the working condition type identification module result, for example: the urban working condition corresponds to a neural network model 1, the suburban working condition corresponds to a neural network model 2, and the high-speed working condition corresponds to a neural network model 3; inputting the signals acquired by the acquisition module and the characteristics extracted by the speed information into the corresponding neural network model, and calculating to obtain control signals sent to the hybrid energy storage module;
and the hybrid energy storage module is used for storing or releasing electric energy according to the signal of the driving module.
The invention has the beneficial effects that: the invention identifies the working condition type to adaptively adjust the energy management strategy, extracts the current and voltage characteristics, speed, acceleration and other information of the lithium battery, the super capacitor and the load as the input of the trained neural network, and outputs the reference current by the neural network, thereby effectively prolonging the service life of the lithium battery, reducing the system loss and the replacement cost of the battery pack and improving the economy of the whole vehicle. Compared with the prior energy management strategy, the circuit design is simple, the optimization effect is obvious, and the real-time performance is good.
Drawings
FIG. 1 is a block diagram of an energy management device based on condition identification provided by the present invention;
FIG. 2 is a flow chart of a method for energy management based on condition identification according to the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
As shown in fig. 1, fig. 1 is a schematic structural diagram of an electric vehicle hybrid energy storage system based on operating condition identification according to the present invention, which includes:
the acquisition module 100: the system comprises an energy management control module, a working condition type identification module, a lithium battery and a super capacitor, wherein the energy management control module is used for acquiring the current and the voltage of the lithium battery and the super capacitor in the hybrid energy storage module and the speed information of the electric automobile in real time, transmitting the current and the voltage of the lithium battery and the super capacitor to the energy management control module, and transmitting the speed information to the working condition type identification module;
energy management control module 200: the neural network model selection method is used for selecting a corresponding neural network model according to the working condition type of the working condition type identification module result, for example: the urban working condition corresponds to a neural network model 1, the suburban working condition corresponds to a neural network model 2, and the high-speed working condition corresponds to a neural network model 3; inputting the signals acquired by the acquisition module and the characteristics extracted by the speed information into the corresponding neural network model, and calculating to obtain control signals sent to the hybrid energy storage module;
the driving module 300: the input end of the super capacitor current following reference signal given by the control module is electrically connected with the control module, and the output end of the super capacitor current following reference signal is electrically connected with the hybrid energy storage module;
hybrid energy storage module 400: for storing or releasing electrical energy in accordance with the drive module signal.
The operating condition type identification module 500: the system comprises a control module, a fuzzy logic classifier and a data processing module, wherein the fuzzy logic classifier is used for classifying in real time according to speed data acquired in real time and preset according to a typical working condition data set, outputting the type of the current working condition, and is electrically connected with the control module;
as shown in FIG. 2, FIG. 2 is a flow chart of an energy management method based on condition identification according to the present invention. The method comprises the following steps:
step 1, dividing working condition blocks of various public working condition data sets, extracting characteristic parameters of the working condition blocks, reducing characteristic redundancy through principal component analysis, and carrying out working condition recombination on high characteristic similarity to construct a typical working condition data set;
the typical working condition data set is constructed by dividing working condition blocks according to various public working condition data sets, extracting characteristic parameters of the working condition blocks and recombining typical working conditions with high characteristic similarity. Wherein the condition block is divided by the continuous driving time from one idle segment to the next idle segment. For the high-speed road condition, the working conditions are divided by adopting a composite equal division method, the characteristic parameters of the working condition blocks are provided, and the number of samples is further increased.
Step 2, constructing a multi-objective optimization problem of energy management, and performing dynamic programming off-line solving according to the load power demand to obtain an off-line optimal reference value; under the condition of simultaneously considering the service life of the battery and the power loss of the hybrid energy storage system, constructing a multi-objective optimization problem, wherein the optimization objective is as follows: (1) reducing power loss of the hybrid energy storage system; (2) prolonging battery life by reducing battery current and reducing current variation, the objective functions are respectively set as f1And f2As follows:
wherein, Ploss(k) Is the power loss of the hybrid energy storage system at the k-th time, Ib(k) Is the current of the lithium battery at the kth time, Ib(k)-Ib(k-1) represents the change in lithium battery current at the k-th time, and the maximum value P of power loss is normalizedloss,maxIs set to 8000W, the maximum value of variation of the lithium battery is Delta Ib,maxAt 40A, weighting coefficients are given to two objective functions in the multi-objective optimization problem, wherein the weighting coefficients are respectively 0.5 and 0.5, and the sum of products of the weighting coefficients is used as a new objective function;
in order to establish a power loss model, the lithium battery and the super capacitor are equivalent to a simplified model of the voltage source and the internal resistance. Wherein, lithiumThe battery is supplied by a voltage source Vb,ocAnd internal resistance RbComposition, voltage is represented as Vb(ii) a The super capacitor is driven by a voltage source Vuc,ocAnd internal resistance RucComposition, voltage is represented as Vuc. The inductor is equivalent to inductance L and internal resistance RL(ii) a When the MOS tube is conducted, the equivalent resistance is Rsw(ii) a The body diode is equivalent to a voltage source VDAnd a resistor RDRepresents the voltage drop of the forward biased diode in the on state.
Further, the calculation formula of the state of charge SoC of the super capacitor is as follows:
wherein, Vuc(t) represents the voltage, V, of the supercapacitor collected at time tuc,normIs the nominal voltage of the supercapacitor;
the bi-directional DC/DC converter has two different modes of operation, i.e., boost and buck modes. In boost mode, the duty cycle of DC/DC is expressed as:
θ1＝(Vb,oc+VD-IdmdRb+IL(RD-Rsw+2Rb))2-4ILRb(IL(RD+RL+Ruc+Rb)-Vuc,oc+Vb,oc-IdmdRb+VD)
in buck mode, the duty cycle of DC/DC is expressed as:
θ2＝(Vb,oc+VD-IdmdRb+IL(Rsw-RD))2-4ILRb(IL(RD+RL+Ruc)-Vuc,oc-VD)
average output capacitor voltage VcComprises the following steps:
power loss P of hybrid energy storage systemlossComprising conduction losses P of a DC/DC converter in both boost and buck modesdc,lossSwitching loss Psw,lossAnd power loss in the battery super capacitor, conduction loss P of the DC/DC converterdc,lossIs composed of
DC/DC, switching loss P of convertersw,lossIs composed of
Switching frequency fsIs 50khz, trAnd tfRepresenting the rising time and the falling time of the MOS tube during the switching period, respectively 13ns and 12ns, CossIs an output capacitor of MOS transistor, and has 1860pF, QtIs the gate charge due to the gate capacitance charged by the gate voltage, 490n, QrrIs a reverse recovery charge capacity of 2 μ C, a gate voltage of 30V, and DC/DC converter efficiencies of a step-up mode and a step-down mode considering both conduction loss and switching loss
The total power loss in the hybrid energy storage system is the sum of the power losses in the bi-directional DC/DC converter and the battery super-capacitor, so that the total power loss P can be obtainedloss
In this optimization problem, the output current I of the DC/DC converter is selectedconv(k) As a control variable, obtaining the current I of the lithium battery according to a load demand current conservation equationbComprises the following steps:
Ib(k)＝Idmd(k)-Iconv(k)
further, PdmdThe load demand power calculation formula is as follows:
wherein M is the vehicle mass, a is the acceleration of the electric vehicle, g is the acceleration of gravity, v is the speed of the electric vehicle, CrIs the rolling resistance coefficient of the electric automobile, rho is the air density, AfIs the front area of the electric vehicle, CdIs the aerodynamic drag coefficient, eta, of the electric vehicle1And η2Respectively representing the electric energy conversion efficiency of the electric automobile and the feedback efficiency during braking; m, C thereinr、Af、Cd、η1And η2All are intrinsic parameters of the electric automobile, and the parameters are 1460kg of vehicle mass, 0.016 of rolling resistance coefficient, 0.28 of pneumatic resistance coefficient and 2.2m of frontal area2The electric energy conversion efficiency is 0.92, and the kinetic energy feedback efficiency is 0.8. The load power can be obtained by calculation, and the required current I is obtained from P ═ UIdmdWhere U is the bus voltage.
Based on a state space average model of the bidirectional DC/DC converter, super-capacitor current is obtained, namely the inductance current is as follows:
and calculating to obtain the load required power according to the given working condition data and the vehicle dynamic model, and solving off-line by using dynamic programming to obtain an off-line optimal solution.
And 3, taking key variables required by dynamic programming solution and an offline optimal solution obtained by solution as input and output, and constructing an optimal energy management strategy data set under three working condition types. The data set is used for neural network training, and an energy management model based on the neural network under three working condition modes is constructed;
the optimal energy management strategy data set is constructed by taking the characteristics of speed, acceleration, lithium battery current at the previous moment, load demand, super capacitor SoC state and the like as input and taking the optimal offline reference obtained by dynamic programming solution as output. And solving the constructed three typical working condition data sets of city, suburb and high speed by using dynamic programming to obtain energy management strategy data sets required by training the neural network under the three types. The data set is used for neural network training, and the trained neural network can realize a quasi-optimal strategy according to the characteristics of different working condition types.
The neural network model adopts a back propagation neural network and consists of an input layer, two hidden layers and an output layer; the number of nodes of the input layer is 5, the number of nodes of the output layer is 1, and the number of nodes of the hidden layer under the three working condition modes is different; during training, the iteration number is set to be 200, a Levenberg Marquardt algorithm is used for solving, a tan sig transfer function is adopted by a hidden layer, and a purelin transfer function is adopted by neurons of an output layer.
Step 4, collecting real-time running condition speed data, extracting the characteristics of the working condition section through a sliding window, and analyzing the principal components;
and acquiring real-time speed information, and extracting working condition section characteristics including acceleration time, deceleration time, idle speed time, cruise time, maximum speed, average speed without idle speed, speed standard deviation, average acceleration, average deceleration, acceleration standard deviation, acceleration time ratio and deceleration time ratio according to a sliding window. And then, carrying out principal component analysis on the characteristics, and selecting parameters with the accumulative contribution rate of more than 80% as characteristic parameters for identifying the working conditions.
Step 5, inputting the main characteristic parameters into a fuzzy logic working condition recognizer to obtain a working condition recognition result;
the fuzzy logic working condition recognizer comprises three parts, namely fuzzification, fuzzy reasoning and defuzzification. Fuzzification is carried out on the characteristic data serving as a logic input value, the characteristic data are converted into membership degrees of three types of city, suburb and high speed, and after fuzzy reasoning, defuzzification is carried out on results to obtain recognition results of three working conditions of city, suburb and high speed.
Step 6, selecting an energy management model based on the neural network corresponding to the classification result according to the working condition identification result;
and selecting the corresponding energy management strategy based on the neural network according to the classification result. If the classification result is the urban working condition, the neural network model 1 corresponds to the classification result, the number of nodes of the input layer is 5, the number of nodes of the hidden layer is 40 and 22 respectively, and the number of nodes of the output layer is 1; the classification result is suburb working condition, corresponding to a neural network model 2, the number of nodes of an input layer is 5, the number of nodes of a hidden layer is 34 and 24 respectively, the number of nodes of an output layer is 1, the classification result is high-speed working condition, corresponding to a neural network model 3, the number of nodes of the input layer is 5, the number of nodes of the hidden layer is 30 and 21 respectively, and the number of nodes of the output layer is 1;
and 7, inputting the characteristics of the super capacitor, the current and voltage of the lithium battery, speed information and the like into the trained neural network model to obtain the reference current of the super capacitor, so as to realize real-time energy management.
Collecting the current and the voltage of the super capacitor and the lithium battery, calculating to obtain the required current according to the previously collected speed information, calculating the SoC state of the super capacitor according to the voltage of the super capacitor, and selecting the speed, the acceleration, the current of the lithium battery at the previous moment, the load requirement and the SoC state of the super capacitor to input into a trained neural network model to obtain the reference current of the super capacitor. And tracking the reference current of the super capacitor, and realizing real-time energy management through the PI controllers under two modes of the control module.
The invention adjusts the energy management strategy in real time according to the working condition, effectively prolongs the service life of the lithium battery, reduces the system loss and the replacement cost of the battery pack, and improves the economic performance of the whole vehicle. Compared with the prior energy management strategy, the circuit design is simple, the optimization effect is obvious, and the real-time performance is good.
Claims (8)
1. An electric automobile energy management method based on working condition identification is characterized by comprising the following steps:
step 1, dividing working condition blocks of various public working condition data sets, extracting characteristic parameters of the working condition blocks, reducing characteristic redundancy through principal component analysis, and carrying out working condition recombination on high characteristic similarity to construct a typical working condition data set;
step 2, constructing a multi-objective optimization problem of energy management, and performing dynamic programming off-line solving according to the load power demand to obtain an off-line optimal reference value;
step 3, taking key variables required by dynamic programming solution and optimal reference values obtained by solution as input and output, and constructing optimal energy management strategy data sets under three working condition types; using the typical working condition data set constructed in the step 1 for neural network training, and constructing an energy management model based on the neural network under three working condition modes;
step 4, collecting real-time running condition speed data, extracting the characteristics of the working condition section through a sliding window, and analyzing the principal components;
step 5, inputting the characteristic parameters into a fuzzy logic working condition recognizer to obtain a working condition recognition result;
step 6, selecting an energy management model based on the neural network corresponding to the classification result according to the working condition identification result;
and 7, inputting the current and voltage and speed information characteristics of the super capacitor and the lithium battery into the trained neural network model to obtain the reference current of the super capacitor, so as to realize real-time energy management.
2. The electric vehicle energy management method based on working condition identification as claimed in claim 1, wherein in the step 1, the typical working condition data set is constructed by dividing the working condition blocks of various public working condition data sets, extracting the characteristic parameters of the working condition blocks and recombining the typical working conditions with high characteristic similarity.
3. The electric vehicle energy management method based on working condition identification as claimed in claim 1, wherein in the step 2, under the condition of simultaneously considering the battery life and the power loss of the hybrid energy storage system, a multi-objective optimization problem is constructed, and the optimization objective is as follows: (1) reducing power loss of the hybrid energy storage system; (2) the battery life is prolonged by reducing the battery current and reducing the current variation, and the objective functions are respectively set as f1And f2As follows:
wherein, Ploss(k) Is the power loss of the hybrid energy storage system at the k-th time, Ib(k) Is the current of the lithium battery at the k-th time, Ib(k)-Ib(k-1) represents the change in lithium battery current at the k-th time, and the maximum value P of power loss is normalizedloss,maxIs set to 8000W, the maximum value of variation of the lithium battery is Delta Ib,maxAt 40A, weighting coefficients are given to two objective functions in the multi-objective optimization problem, wherein the weighting coefficients are respectively 0.5 and 0.5, and the sum of products of the weighting coefficients is used as a new objective function;
in order to establish a power loss model, a lithium battery and a super capacitor are equivalent to form a simplified model of a voltage source and an internal resistance, wherein the lithium battery is composed of a voltage source Vb,ocAnd internal resistance RbComposition, voltage is represented as Vb(ii) a The super capacitor is driven by a voltage source Vuc,ocAnd internal resistance RucComposition, voltage is represented as Vuc. (ii) a The inductor is equivalent to inductance L and internal resistance RL(ii) a When the MOS tube is conducted, the equivalent resistance is Rsw(ii) a The transistor diode is equivalent to a voltage source VDAnd a resistor RDIs represented by a simplified modelA voltage drop of the forward biased diode in a conducting state;
the calculation formula of the state of charge (SoC) of the super capacitor is as follows:
wherein, Vuc(t) represents the voltage, V, of the supercapacitor collected at time tuc,normIs the nominal voltage of the supercapacitor;
the bidirectional DC/DC converter has two different working modes, namely a voltage boosting mode and a voltage reducing mode; in boost mode, the duty cycle of DC/DC is expressed as:
θ1＝(Vb,oc+VD-IdmdRb+IL(RD-Rsw+2Rb))2-4ILRb(IL(RD+RL+Ruc+Rb)-Vuc,oc+Vb,oc-IdmdRb+VD)
in buck mode, the duty cycle of DC/DC is expressed as:
θ2＝(Vb,oc+VD-IdmdRb+IL(Rsw-RD))2-4ILRb(IL(RD+RL+Ruc)-Vuc,oc-VD)
average output capacitor voltage VcComprises the following steps:
power loss P of hybrid energy storage systemlossComprising a conduction loss P of a DC/DC converter in a boost mode and a buck modedc,lossSwitching loss Psw,lossAnd power loss in the battery super capacitor, conduction loss P of the DC/DC converterdc,lossIs composed of
DC/DC, switching loss P of convertersw,lossIs composed of
Switching frequency fsIs 50khz, trAnd tfRepresenting the rising time and the falling time of the MOS tube during the switching period, respectively 13ns and 12ns, CossIs an output capacitor of MOS transistor, and has 1860pF, QtIs the gate charge due to the gate capacitance charged by the gate voltage, 490n, QrrThe reverse recovery charge capacity was 2 μ C, and the gate voltage was 30. The DC/DC converter efficiency in the boost and buck modes considering both conduction loss and switching loss is
The total power loss in the hybrid energy storage system is the sum of the power losses in the bidirectional DC/DC converter and the battery super capacitor, so that the total power loss P can be obtainedloss
In this optimization problem, the output current I of the DC/DC converter is selectedconv(k) As a control variable, obtaining the current I of the lithium battery according to a load demand current conservation equationbComprises the following steps:
Ib(k)＝Idmd(k)-Iconv(k)
further, PdmdThe load demand power calculation formula is as follows:
wherein M is the vehicle mass, a is the acceleration of the electric vehicle, g is the acceleration of gravity, v is the speed of the electric vehicle, CrIs the rolling resistance coefficient of the electric automobile, rho is the air density, AfIs the front area of the electric vehicle, CdIs the aerodynamic drag coefficient, eta, of the electric vehicle1And η2The electric energy conversion efficiency and the feedback efficiency during braking of the electric automobile are respectively obtained; m, C thereinr、Af、Cd、η1And η2All are intrinsic parameters of the electric automobile, and the parameters are 1460kg of vehicle mass, 0.016 of rolling resistance coefficient, 0.28 of pneumatic resistance coefficient and 2.2m of frontal area2The electric energy conversion efficiency is 0.92, and the kinetic energy feedback efficiency is 0.8; the load power can be obtained by calculation, and the required current I is obtained from P ═ UIdmdWherein U is the bus voltage;
based on a state space average model of the bidirectional DC/DC converter, super-capacitor current is obtained, namely the inductance current is as follows:
and calculating to obtain load required power according to the given working condition data and the vehicle dynamic model, and solving off-line by using dynamic programming to obtain an off-line optimal solution.
4. The electric vehicle energy management method based on the working condition identification as claimed in claim 1, wherein in the step 3, the speed, the acceleration, the current of the lithium battery at the previous moment, the load demand and the SoC state characteristic of the super capacitor are used as input, the off-line optimal solution obtained by the dynamic programming solution is used as output, and an energy management strategy data set required by the training of the neural network is constructed; solving the constructed data sets of three typical working conditions, namely urban, suburban and high-speed, by using dynamic programming to obtain energy management strategy data sets required by training neural networks under three types; and the trained neural network realizes a quasi-optimal strategy according to the characteristics of different working condition types.
5. The electric vehicle energy management method based on the working condition identification as claimed in claim 1, wherein the neural network model in the step 3 adopts a back propagation neural network, and is composed of an input layer, two hidden layers and an output layer; the number of nodes of an input layer of a neural network model 1 corresponding to a city is 5, the number of nodes of a hidden layer is 40 and 22 respectively, and the number of nodes of an output layer is 1; the node number of an input layer of the neural network model 2 corresponding to the suburb is 5, the node number of a hidden layer is 34 and 24 respectively, and the node number of an output layer is 1; the number of nodes of an input layer of the neural network model 3 corresponding to the high speed is 5, the number of nodes of a hidden layer is 30 and 21 respectively, and the number of nodes of an output layer is 1; during training, the iteration times of the three neural networks are set to be 200, a Levenberg Marquardt algorithm is used for solving, a tan sig transfer function is adopted by the hidden layer, and the transfer function of the neuron of the output layer is purelin.
6. The electric vehicle energy management method based on condition identification as claimed in claim 1, wherein in step 4, real-time driving condition speed data is collected, condition section characteristics are extracted by sliding window data with the length of 50 seconds, the extracted characteristic parameters comprise acceleration time, deceleration time, idle time, cruising time, maximum speed, average speed without idle speed, speed standard deviation, average acceleration, average deceleration, acceleration standard deviation, acceleration time ratio and deceleration time ratio, principal component analysis is performed, characteristic redundancy is reduced, and parameters with the accumulated contribution rate of more than 80% are selected.
7. The electric vehicle energy management method based on working condition identification as claimed in claim 1, wherein in the step 5, the fuzzy logic working condition identifier comprises three parts, namely fuzzification, fuzzy reasoning and defuzzification; fuzzification is carried out on the characteristic data serving as a logic input value, the characteristic data are converted into three types of membership degrees of city, suburb and high speed, and defuzzification is carried out on the result after fuzzy reasoning to obtain a working condition identification result.
8. An electric automobile energy management system based on operating mode discernment, its characterized in that includes:
the acquisition module is used for acquiring the current and the voltage of the lithium battery and the super capacitor in the hybrid energy storage module and the speed information of the electric automobile in real time, transmitting the current and the voltage of the lithium battery and the super capacitor to the energy management control module, and transmitting the speed information to the working condition type identification module;
the driving module is used for receiving the signal of the control module and outputting a PWM signal to control the switching element to be switched on and switched off so that the super capacitor current follows the reference signal given by the control module, the input end of the driving module is electrically connected with the control module, and the output end of the driving module is electrically connected with the hybrid energy storage module;
the working condition type identification module is used for classifying in real time through a fuzzy logic classifier preset according to a typical working condition data set according to the speed data acquired in real time, outputting the type of the current working condition, and is electrically connected with the control module;
the control module is used for selecting a corresponding neural network model according to the working condition type of the working condition type identification module result; inputting the signals acquired by the acquisition module and the characteristics extracted by the speed information into the corresponding neural network model, and calculating to obtain control signals sent to the hybrid energy storage module;
and the hybrid energy storage module is used for storing or releasing electric energy according to the signal of the driving module.
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