CN110733493A - Power distribution method for hybrid electric vehicles - Google Patents

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 driven_socMonitoring 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 battery_socAnd 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

Power distribution method for hybrid electric vehicles

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 monitored_soc；

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 battery_socAnd 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 period_socAnd 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,…,y_m}; 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 flow_cIs the coefficient of contraction, k₁Is the coefficient of resistance, V, inside the battery compartment₁Is the cell volume, V_CIs the total volume of the battery compartment, P_iIs 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 is_jFor parameters in the input layer vector, X_jRespectively as measurement parameters v, a and S_socAnd α, j ═ 1,2,3,4, X_jmaxAnd X_jminRespectively, 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 monitored_soc；

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 battery_socAnd 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，k_cIs the coefficient of contraction, k₁Is the coefficient of resistance, V, inside the battery compartment₁Is the cell volume, and has the unit of m³，V_CIs the total volume of the battery compartment, and has a unit of m³，P_iIs 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,...,x_n)^T

Intermediate layer vector: y ═ y₁,y₂,...,y_m)^T

Outputting a vector: o ═ O₁,o₂,...,o_p)^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 calculated_socAnd the environmental influence factor α is processed into formula

Wherein，x_jFor parameters in the input layer vector, X_jRespectively as measurement parameters v, a and S_soc、α，j＝1,2,3,4；X_jmaxAnd X_jminFor maximum and minimum values, respectively, of the corresponding measured parameter, using an S-shaped function, f_j(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 j_ijConnection weight W between hidden layer node j and output layer node k_jkThreshold value theta of hidden layer node j_jThreshold value theta of output layer node k_k、W_ij、W_jk、θ_j、θ_kAre all random numbers between-1 and 1.

During the training process, continuously correcting W_ij、W_jkUntil 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 parameters

Obtaining 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 coefficients

Comparing to obtain the engine regulation deviation signal and regulating the motor regulation coefficient o₂With preset motor regulation factor

Comparing 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 monitored_soc；

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 battery_socAnd 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 period_socAnd 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,…,y_m}; 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 flow_cIs the coefficient of contraction, k₁Is the coefficient of resistance, V, inside the battery compartment₁Is the cell volume, V_CIs the total volume of the battery compartment, P_iIs 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:

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 referenceValue, κ wind speed, κ₀Is the set wind speed reference value.

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 is_jFor parameters in the input layer vector, X_jRespectively as measurement parameters v, a and S_socAnd α, j ═ 1,2,3,4, X_jmaxAnd X_jminRespectively, a maximum value and a minimum value in the corresponding measured parameter.

8. The power distribution method of a hybrid electric vehicle according to claim 7, wherein the number m of intermediate nodes satisfies:

wherein n is the number of nodes of the input layer, and p is the number of nodes of the output layer.