CN110733493A - Power distribution method for hybrid electric vehicles - Google Patents
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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
- B60W10/04—Conjoint control of vehicle sub-units of different type or different function including control of propulsion units
- B60W10/06—Conjoint control of vehicle sub-units of different type or different function including control of propulsion units including control of combustion engines
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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
- B60W10/04—Conjoint control of vehicle sub-units of different type or different function including control of propulsion units
- B60W10/08—Conjoint control of vehicle sub-units of different type or different function including control of propulsion units including control of electric propulsion units, e.g. motors or generators
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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
- B60W20/00—Control systems specially adapted for hybrid vehicles
- B60W20/10—Controlling the power contribution of each of the prime movers to meet required power demand
- B60W20/15—Control strategies specially adapted for achieving a particular effect
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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
- 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
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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
- 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
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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
- 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
- B60W40/107—Longitudinal acceleration
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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
- B60W2510/00—Input parameters relating to a particular sub-units
- B60W2510/24—Energy storage means
- B60W2510/242—Energy storage means for electrical energy
- B60W2510/244—Charge state
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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
- B60W2520/00—Input parameters relating to overall vehicle dynamics
- B60W2520/10—Longitudinal speed
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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
- B60W2520/00—Input parameters relating to overall vehicle dynamics
- B60W2520/10—Longitudinal speed
- B60W2520/105—Longitudinal acceleration
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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
- B60W2710/00—Output or target parameters relating to a particular sub-units
- B60W2710/06—Combustion engines, Gas turbines
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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
- B60W2710/00—Output or target parameters relating to a particular sub-units
- B60W2710/08—Electric propulsion units
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- 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
- Y02T10/62—Hybrid vehicles
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Abstract
The invention discloses a power distribution method of hybrid electric vehicles, which comprises the steps of monitoring the driving speed v and the acceleration a of the hybrid electric vehicle and the SOCS of an energy storage battery after the vehicle is drivensocMonitoring the running environment information of the hybrid electric vehicle, calculating the environmental influence factor α in the running process of the vehicle, and calculating the running speed v and the acceleration a of the hybrid electric vehicle and the SOC value S of the energy storage batterysocAnd an environmental impact factor α, controlling the mixing actionThe working states of the engine and the motor of the electric automobile are changed. The method can control the working states of the engine and the motor of the hybrid electric vehicle according to the driving environment and the state of the vehicle, so that the total energy consumption is minimum.
Description
Technical Field
The invention relates to a power distribution method of hybrid electric vehicles, belonging to the field of vehicle dynamics.
Background
The rapid development of the automobile industry records the bright course of the civil leap development of human beings, however, the continuous increase of the automobile holding amount pushes the energy and environmental problems to an increasingly serious situation while the rapid development of the economy in the world and the convenience for people are kept.
The energy consumed by the traditional automobile almost completely depends on finished products of petroleum, the petroleum resources in the world can only be used by human beings for 40-50 years according to the estimated total storage amount of the petroleum in the world, and the effective service life of the actual petroleum resources is shorter in consideration of the gradual shortage of the petroleum resources, the gradual reduction of the yield and the gradual rise of the exploitation cost. Meanwhile, with the problem of petroleum consumption of automobiles, environmental problems are not ignored, and 42 percent of the air pollution is caused by transportation at present.
In order to solve the problems of energy and environment, governments of various countries around the world and large automobile companies develop novel clean energy-saving automobiles so as to relieve the problems of environment and energy.
A hybrid vehicle is a vehicle in which a vehicle drive system is combined from two or more individual drive systems that can be operated simultaneously, and the vehicle drive power is provided by the individual drive systems individually or together depending on the actual vehicle driving state.
The hybrid vehicle is generally a gasoline-electric hybrid vehicle, i.e. a traditional internal combustion engine and an electric motor are used as power sources, and other alternative fuels are used by some engines after being modified.
Disclosure of Invention
The invention designs and develops power distribution methods of hybrid electric vehicles, which can control the working states of an engine and a motor of the hybrid electric vehicle according to the driving environment and the state of the vehicle, so that the total energy consumption is minimum.
The technical scheme provided by the invention is as follows:
A power distribution method for a hybrid electric vehicle comprises the following steps:
after the vehicle runs, the running speed v and the acceleration a of the hybrid electric vehicle and the SOCS of the energy storage battery are monitoredsoc;
Monitoring running environment information of the hybrid electric vehicle, and calculating an environment influence factor α in the running process of the vehicle;
according to the running speed v and the acceleration a of the hybrid electric vehicle and the SOC value S of the energy storage batterysocAnd an environmental impact factor α, controlling the operating conditions of the engine and the motor of the hybrid electric vehicle.
Preferably, the environment information includes: ambient temperature T, ambient humidity RH, road slope δ, wind speed κ.
Preferably, the controlling of the operating states of the engine and the motor of the hybrid electric vehicle includes:
step , acquiring the running speed v and the acceleration a of the hybrid electric vehicle and the SOC value S of the energy storage battery according to the sampling periodsocAnd an environmental impact factor α;
step two, the obtained parameters are classified into in sequence, and the input layer vector of the three-layer BP neural network is determined to be x ═ x1,x2,x3,x4,x5}; wherein x is1Is the running speed coefficient, x, of the motor vehicle2Is the acceleration coefficient, x, of the vehicle3For the SOC value coefficient, x, of the energy storage battery4Is an environmental impact factor coefficient;
step three, the input layer vector is mapped to a middle layer, and the middle layer vector y is { y ═ y1,y2,…,ym}; m is the number of intermediate layer nodes;
step four, obtaining an output layer vector o ═ o1,o2},o1For engine adjustment factor, o2Adjusting the coefficient for the motor;
and step five, inputting the output layer vector into the fuzzy controller to obtain an output vector group representing the adjustment type, and outputting the output vector group as an adjustment answer.
Preferably, the operation process of the fuzzy controller comprises:
comparing the engine regulating coefficient with a preset engine regulating coefficient to obtain an engine regulating deviation signal, and comparing the motor regulating coefficient with a preset motor regulating coefficient to obtain a motor regulating deviation signal;
carrying out differential calculation on the engine regulation deviation signal to obtain an engine regulation change rate signal, and carrying out differential calculation on the motor regulation deviation signal to obtain a motor regulation change rate signal;
the engine regulation change rate signal and the motor regulation change rate signal are amplified and then input into a fuzzy controller, and the regulation grade is output.
Preferably, the empirical formula of the battery compartment inlet air flow of the energy storage battery satisfies:
wherein q is0Is a reference value, k, of the set battery compartment intake air flowcIs the coefficient of contraction, k1Is the coefficient of resistance, V, inside the battery compartment1Is the cell volume, VCIs the total volume of the battery compartment, PiIs the working pressure in the battery compartment, P0Is the initial pressure within the battery compartment.
Preferably, the empirical formula of the environmental impact factor satisfies:
wherein T is the ambient temperature, T0RH is the environmental humidity for the set environmental temperature reference value,for a set reference value of ambient temperature, δ is the road gradient, δ0For a set road slope reference value, κ is the wind speed, κ0Is the set wind speed reference value.
Preferably, the formula classified into in the second step is as follows:
wherein x isjFor parameters in the input layer vector, XjRespectively as measurement parameters v, a and SsocAnd α, j ═ 1,2,3,4, XjmaxAnd XjminRespectively, a maximum value and a minimum value in the corresponding measured parameter.
Preferably, the number m of the intermediate layer nodes satisfies:wherein n is the number of nodes of the input layer, and p is the number of nodes of the output layer.
The invention has the following beneficial effects: the invention can monitor the hybrid electric vehicle in real time during the driving process, control the working states of the motor and the engine of the hybrid electric vehicle according to the driving condition of the hybrid electric vehicle, realize the power distribution and the energy recovery of the hybrid electric vehicle and improve the energy utilization rate.
In the running process of the hybrid electric vehicle, the power mode of the hybrid electric vehicle is controlled by controlling the air inlet flow of the battery compartment of the energy storage battery, so that the damage to the energy storage battery is reduced, and the energy distribution under different modes is realized.
The working states of a motor and an engine of the hybrid electric vehicle are controlled and adjusted through the BP neural network and the fuzzy control, so that the energy recovery of the hybrid electric vehicle is realized, and the energy utilization rate of the hybrid electric vehicle is improved.
Detailed Description
The present invention is described in further detail at step to enable those skilled in the art to practice the invention in light of the description herein.
The invention provides power distribution methods of a hybrid electric vehicle, which can monitor the hybrid electric vehicle in real time during the driving process, control the working states of a motor and an engine of the hybrid electric vehicle according to the driving condition of the hybrid electric vehicle, realize the power distribution and the energy recovery of the hybrid electric vehicle, and improve the energy utilization rate.
The power distribution method of the hybrid electric vehicle is used in a parallel hybrid electric vehicle, and a power system of the hybrid electric vehicle consists of an engine (an internal combustion engine) and a motor (the power is supplied by an energy storage battery). The measurement parameters of the invention are obtained through a CAN bus, and the method specifically comprises the following steps:
after the vehicle runs, the running speed v and the acceleration a of the hybrid electric vehicle and the SOCS of the energy storage battery are monitoredsoc;
Monitoring running environment information of the hybrid electric vehicle, and calculating an environment influence factor α in the running process of the vehicle;
wherein the empirical formula of the environmental impact factor satisfies:
wherein T is the ambient temperature in degrees Celsius0The standard value of the set environmental temperature is expressed in the unit of DEG C, RH is the environmental humidity and is expressed in the unit of percent,is a set reference value of the environmental temperature, wherein the unit is percent and delta is the road surface gradient and the unit is percent and delta0Is a set road surface gradient reference value with the unit of percent kappa is the wind speed with the unit of m/s and kappa0The unit is m/s for the set wind speed reference value.
According to the running speed v and the acceleration a of the hybrid electric vehicle and the SOC value S of the energy storage batterysocAnd an environmental impact factor α, controlling the operating conditions of the engine and the motor of the hybrid electric vehicle.
The empirical formula of the air inlet flow of the battery compartment of the energy storage battery meets the following requirements:
in the formula, q0Is a set reference value of the air inlet flow of the battery compartment and has the unit of m3/h,kcIs the coefficient of contraction, k1Is the coefficient of resistance, V, inside the battery compartment1Is the cell volume, and has the unit of m3,VCIs the total volume of the battery compartment, and has a unit of m3,PiIs the working pressure in the battery compartment,units are Pa, P0Is the initial pressure in Pa inside the battery compartment.
The method for controlling the working states of the engine and the motor of the hybrid electric vehicle through the BP neural network comprises the following steps:
and step , establishing a BP neural network model.
The BP network system structure adopted by the invention comprises three layers, wherein the th layer is an input layer and n nodes in total, and corresponds to n monitoring signals representing the working state of equipment, the signal parameters are given by a data preprocessing module, the second layer is a hidden layer and m nodes in total, and are determined by the training process of the network in a self-adaptive mode, and the third layer is an output layer and p nodes in total, and is determined by the response actually required to be output 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 ═ O1,o2,...,op)T
In the invention, the number of nodes of an input layer is n-4, and the number of nodes of an output layer is p-2. The number m of hidden layer nodes is estimated by the following formula:
the 4 parameters of the input signal are respectively expressed as: x is the number of1Is the running speed coefficient, x, of the motor vehicle2Is the acceleration coefficient, x, of the vehicle3For the SOC value coefficient, x, of the energy storage battery4Is an environmental impact factor coefficient;
the running speed v and the acceleration a of the electric automobile and the SOC value S of the energy storage battery are calculatedsocAnd the environmental influence factor α is processed into formula
Wherein,xjFor parameters in the input layer vector, XjRespectively as measurement parameters v, a and Ssoc、α,j=1,2,3,4;XjmaxAnd XjminFor maximum and minimum values, respectively, of the corresponding measured parameter, using an S-shaped function, fj(x)=1/(1+e-x)。
Two parameters of the output signal are respectively expressed as: o ═ o1,o2},o1For engine adjustment factor, o2The coefficients are adjusted for the motor.
Step two, carrying out BP neural network training
After the BP neural network node model is established, the training of the BP neural network can be carried out. Obtaining training samples according to historical experience data, and giving a connection weight W between an input node i and a hidden layer node jijConnection weight W between hidden layer node j and output layer node kjkThreshold value theta of hidden layer node jjThreshold value theta of output layer node kk、Wij、Wjk、θj、θkAre all random numbers between-1 and 1.
During the training process, continuously correcting Wij、WjkUntil the system error is less than or equal to the expected error, the training process of the neural network is completed.
Training method
During training, training samples are provided, each sample is composed of input sample and ideal output pair, when all the actual outputs of the network are consistent with the ideal output , the training is finished, otherwise, the ideal outputs of the network are consistent with the actual outputs by correcting the weight, and the input samples during the training of each subnet are as shown in table 1:
TABLE 1
In the case of specifying the learning samples and the number, the system can perform self-learning to continuously improve the network performance, and the output samples after each subnet training are shown in table 2:
TABLE 2
And step three, collecting and transmitting the operation parameters of each unit to input the operation parameters into a neural network to obtain the output power regulation of the motor and the output signal of the power converter.
The trained artificial neural network is solidified in the chip, so that the hardware circuit has the functions of prediction and intelligent decision making, and intelligent hardware is formed.
Meanwhile, parameters acquired by a sensor are used, and the initial input vector of the BP neural network is obtained by normalizing the parametersObtaining an initial output vector through operation of a BP neural network
Judging the working states of the motor and the power converter in the (i + 1) th cycle according to the environmental influence factor in the (i) th cycle, the SOC lower limit value of the energy storage battery and the sampling signal of the output power of the energy storage battery, and inputting the obtained output layer vector into the fuzzy controller;
adjusting the engine by a factor o1With preset engine adjustment coefficientsComparing to obtain the engine regulation deviation signal and regulating the motor regulation coefficient o2With preset motor regulation factorComparing to obtain a motor regulation deviation signal;
The engine regulation deviation signal is subjected to differential calculation to obtain an engine regulation change rate signal e1The motor regulation deviation signal is differentiated to obtain a motor regulation change rate signal e2;
Adjusting the engine by a rate of change signal e1Motor regulation rate of change signal e2Amplifying the signals, inputting the amplified signals into a fuzzy controller, and outputting an adjustment level I ═ I0,I1,I2,I3In which I0For normal operation, I1For regulation at level , I2For the second-order regulation, I3Is an alarm signal.
Wherein e is1、e2Respectively has a practical variation range of [ -1,1 [ -1 [ ]],[-1,1](ii) a The discrete domains of discourse are { -6, -5, -4, -3, -2, -1, 0,1,2,3, 4, 5, 6}, the discrete domains of discourse of I are {0,1,2,3}, the quantization factor is, and k is1=6/1,k2=6/1;
Defining fuzzy subsets and membership functions:
the engine regulation rate of change signal is divided into 7 fuzzy states: PB (positive big), PM (positive middle), PS (positive small), ZR (zero), NS (negative small), NM (negative middle) and NB (negative big), and combining experience to obtain the air conditioner regulation change rate signal e1As shown in table 3:
TABLE 3
e1 | -6 | -5 | -4 | -3 | -2 | -1 | 0 | +1 | +2 | +3 | +4 | +5 | +6 |
PB | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.4 | 0.8 | 1.0 |
PM | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.2 | 0.7 | 1.0 | 0.5 | 0.1 |
PS | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.4 | 1.0 | 0.8 | 0.7 | 0 | 0 |
ZR | 0 | 0 | 0 | 0 | 0.2 | 0.7 | 1.0 | 0 | 0 | 0 | 0 | 0 | 0 |
NB | 0 | 0 | 0.3 | 0.6 | 1.0 | 0.8 | 0.5 | 0 | 0 | 0 | 0 | 0 | 0 |
NM | 0.2 | 0.4 | 1.0 | 0.6 | 0.1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
NS | 1.0 | 0.6 | 0.4 | 0.2 | 0 | 0 | 0 | 0.2 | 0 | 0 | 0 | 0 | 0 |
The engine regulation rate of change signal is divided into 7 fuzzy states: PB (positive big), PM (positive middle), PS (positive small), ZR (zero), NS (negative small), NM (negative middle) and NB (negative big), and combining experience to obtain the air conditioner regulation change rate signal e1As shown in table 4:
TABLE 4
e2 | -6 | -5 | -4 | -3 | -2 | -1 | 0 | +1 | +2 | +3 | +4 | +5 | +6 |
PB | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.4 | 0.8 | 1.0 |
PM | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.2 | 0.7 | 1.0 | 0.5 | 0.1 |
PS | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.4 | 1.0 | 0.8 | 0.7 | 0 | 0 |
ZR | 0 | 0 | 0 | 0 | 0.2 | 0.7 | 1.0 | 0 | 0 | 0 | 0 | 0 | 0 |
NB | 0 | 0 | 0.3 | 0.6 | 1.0 | 0.8 | 0.5 | 0 | 0 | 0 | 0 | 0 | 0 |
NM | 0.2 | 0.4 | 1.0 | 0.6 | 0.1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
NS | 1.0 | 0.6 | 0.4 | 0.2 | 0 | 0 | 0 | 0.2 | 0 | 0 | 0 | 0 | 0 |
The fuzzy reasoning process has to execute complex matrix operation, the calculated amount is very large, the on-line reasoning is difficult to meet the real-time requirement of a control system, the fuzzy reasoning method is adopted to carry out the fuzzy reasoning operation, the fuzzy reasoning decision adopts a three-input single-output mode to summarize the preliminary control rule of the fuzzy controller through experience, the fuzzy controller carries out defuzzification on the output signal according to the obtained fuzzy value to obtain the fault level I and obtain a fuzzy control query table, and as the domain of discourse is discrete, the fuzzy control rule can be expressed as fuzzy matrices, and the single-point fuzzification is adopted to obtain the control rule I shown in the table 5.
The working states of a motor and an engine of the hybrid electric vehicle are controlled and adjusted through the BP neural network and the fuzzy control, so that the energy recovery of the hybrid electric vehicle is realized, and the energy utilization rate of the hybrid electric vehicle is improved.
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 to various fields of endeavor for which the invention may be embodied with additional modifications as would be readily apparent to those skilled in the art, and it is therefore not intended to be limited to the details shown and described herein without departing from the -generic concept defined by the claims and their equivalents.
Claims (8)
1, A power distribution method for a hybrid electric vehicle, comprising:
after the vehicle runs, the running speed v and the acceleration a of the hybrid electric vehicle and the SOC value S of the energy storage battery are monitoredsoc;
Monitoring running environment information of the hybrid electric vehicle, and calculating an environment influence factor α in the running process of the vehicle;
according to the running speed v and the acceleration a of the hybrid electric vehicle and the SOC value S of the energy storage batterysocAnd environmental impact factor α, controlling the engine and motor of a hybrid electric vehicleAnd (5) working state.
2. The power distribution method of a hybrid electric vehicle according to claim 1, wherein the environmental information includes: ambient temperature T, ambient humidity RH, road slope δ, wind speed κ.
3. The power distribution method of a hybrid electric vehicle according to claim 2, wherein the controlling the operating states of the engine and the motor of the hybrid electric vehicle includes:
step , acquiring the running speed v and the acceleration a of the hybrid electric vehicle and the SOC value S of the energy storage battery according to the sampling periodsocAnd an environmental impact factor α;
step two, the obtained parameters are classified into in sequence, and the input layer vector of the three-layer BP neural network is determined to be x ═ x1,x2,x3,x4,x5}; wherein x is1Is the running speed coefficient, x, of the motor vehicle2Is the acceleration coefficient, x, of the vehicle3For the SOC value coefficient, x, of the energy storage battery4Is an environmental impact factor coefficient;
step three, the input layer vector is mapped to a middle layer, and the middle layer vector y is { y ═ y1,y2,…,ym}; m is the number of intermediate layer nodes;
step four, obtaining an output layer vector o ═ o1,o2},o1For engine adjustment factor, o2Adjusting the coefficient for the motor;
and step five, inputting the output layer vector into the fuzzy controller to obtain an output vector group representing the adjustment type, and outputting the output vector group as an adjustment answer.
4. The power distribution method of a hybrid electric vehicle according to claim 3, wherein the operation process of the fuzzy controller comprises:
comparing the engine regulating coefficient with a preset engine regulating coefficient to obtain an engine regulating deviation signal, and comparing the motor regulating coefficient with a preset motor regulating coefficient to obtain a motor regulating deviation signal;
carrying out differential calculation on the engine regulation deviation signal to obtain an engine regulation change rate signal, and carrying out differential calculation on the motor regulation deviation signal to obtain a motor regulation change rate signal;
the engine regulation change rate signal and the motor regulation change rate signal are amplified and then input into a fuzzy controller, and the regulation grade is output.
5. The power distribution method of a hybrid electric vehicle according to claim 4, wherein an empirical formula of a battery compartment intake air flow rate of the energy storage battery satisfies:
wherein q is0Is a reference value, k, of the set battery compartment intake air flowcIs the coefficient of contraction, k1Is the coefficient of resistance, V, inside the battery compartment1Is the cell volume, VCIs the total volume of the battery compartment, PiIs the working pressure in the battery compartment, P0Is the initial pressure within the battery compartment.
6. The power distribution method of a hybrid electric vehicle according to claim 5, wherein the empirical formula of the environmental impact factor satisfies:
7. The method of distributing power of a hybrid electric vehicle according to claim 6, wherein the formula classified into in the second step is:
wherein x isjFor parameters in the input layer vector, XjRespectively as measurement parameters v, a and SsocAnd α, j ═ 1,2,3,4, XjmaxAnd XjminRespectively, a maximum value and a minimum value in the corresponding measured parameter.
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