CN108515963B - Energy management method of plug-in hybrid electric vehicle based on ITS system - Google Patents

Abstract

The invention relates to a plug-in hybrid electric vehicle energy management method based on an ITS system, which is characterized in that information of a starting point position and an end point position of an automobile is sent to the ITS system, so that a travel path is planned and working condition characteristic parameters are calculated, an HCU calculates and draws a relation curve chart of speed and time for predicting a working condition according to the travel path and the working condition characteristic parameters, a more reasonable SOC using condition in the whole travel process can be further planned according to the above to obtain a reference SOC, △ SOC is obtained according to the real-time SOC and the reference SOC, and an equivalent coefficient is established by utilizing an ECMS equivalent fuel consumption method

The invention has better real-time performance and smaller calculated amount, the calculated reference SOC can well reflect the power demand condition of the road section, the distribution of the SOC is more accurate, and the ECMS equivalent fuel consumption method is utilized to determine the parameter relationship, thereby forming a more accurate energy management method and improving the fuel economy of the vehicle.

Description

Energy management method of plug-in hybrid electric vehicle based on ITS system

Technical Field

The invention belongs to the field of new energy automobile control, and particularly relates to an energy management method of a plug-in hybrid electric vehicle based on an Intelligent Transportation System (ITS).

Background

In recent years, due to the use of fossil resources in large quantities, the energy crisis is increased and the environmental pollution problem is highlighted. In order to reduce the dependence of the automobile industry on fossil resources and protect the environment, the development of energy-saving automobiles and new energy automobiles is becoming more popular at home and abroad. The energy management control method is the key of the plug-in hybrid electric vehicle, and the reasonable control method can effectively reduce fuel consumption and improve the fuel economy of the vehicle.

The plug-in hybrid electric vehicle (PHEV) has the dual advantages of an internal combustion engine vehicle and an electric vehicle, a battery with large capacity can be charged by an external power grid, so that the driving mileage of the pure electric vehicle is greatly increased, and meanwhile, the internal combustion engine can ensure the long-distance driving of the vehicle, so that the PHEV becomes a hotspot of the development of novel vehicles. With the slow popularization of the ITS system in China, the driving road condition can be predicted and navigated by using the ITS system, and an efficient energy management control method can be formed by combining the provided road condition information with a PHEV energy management control method.

Disclosure of Invention

The invention aims to provide a plug-in hybrid electric vehicle energy management method based on an ITS system, so that the energy management control precision is improved, and the fuel economy of a vehicle is improved.

In order to achieve the purpose, the technical scheme of the invention is as follows: an ITS system-based plug-in hybrid electric vehicle energy management method comprises the following steps: step S1: sending the information of the starting position and the end position of the automobile to an ITS system; step S2: the ITS system plans a driving path according to the starting position and the end position of the automobile and calculates the characteristic parameters of the working conditions of the automobile; step S3: the driving path and the working condition characteristic parameters obtained in the step S2 are sent to the HCU; step S4: the HCU calculates and draws a relation curve graph of the speed and the time of the predicted working condition according to the driving path and the working condition characteristic parameters; step S5: planning a reasonable SOC use condition in the whole driving process according to the working condition characteristic parameters obtained in the step S2 and the speed-time relation graph calculated in the step S4, namely obtaining a reference SOC (SOC)_ref) (ii) a Step S6: from the obtained SOC_ref△ SOC is solved, the ECMS equivalent fuel consumption method is used to establish the relation between the equivalent coefficient lambda (t) and △ SOC, step S7, the output power P of the battery is solved by establishing Hamilton function according to the ECMS equivalent fuel consumption method_batCalculating the output power P of the engine according to the current power demand of the whole vehicle_eng。

In an embodiment of the present invention, in step S2, the characteristic parameters of the operating condition include: total stroke S and each small segment stroke S_i(ii) a Highest car of each small road sectionVelocity v_{i_max}Average velocity v_{i_ave}Average acceleration a_{i_acc}Average deceleration a_{i_dec}Road surface gradient i and average waiting time t of traffic signal lamp_ave(ii) a Wherein the small section of travel S_iTwo traffic signal lamps are a small road section, and i is more than or equal to 1.

In an embodiment of the present invention, in the step S4, the step of calculating the speed of the predicted operating condition versus time includes: step S41: calculating the passing time t of each road section_iThe trapezoidal mode working condition is established in each road section and consists of an acceleration section (0-t)₁) And a constant speed section (t)₁One t₂) Speed reduction section (t)₂One t₃) Idle speed section (t)₃One t₄) Composition, calculating t₁、t₂、t₃、t₄Value of (2), next connecting (0,0), (t)₁，v_{i_max})、(t₂，v_{i_max})、(t₃，0)、t₄0), interpolating at certain time intervals to obtain the relation between the speed and the time of the working condition section, integrating the relation to obtain the initially selected driving mileage, and comparing the difference value of the initially selected driving mileage with the actual mileage until the error is within a certain range; step S42: obtaining a relation curve of the speed and the time of the road section, and repeating the steps to obtain the relation of the speed and the time of all the road sections; step S43: and integrating the obtained vehicle speed-time relation curve to obtain a distance-time relation curve, and converting the road slope-distance curve into a road slope-time curve by using a road slope-distance curve obtained by an ITS system.

In an embodiment of the present invention, in step S5, the step of generating the reference SOC includes: step S51: calculating the power required by the vehicle running on each road section; step S52: calculating the proportional coefficient k of the required power of each path section_{i_p}And the proportional coefficient k of the running distance of each road section_{i_s}So as to obtain the variation of the reference SOC of each road section, and further obtain the initial SOC and the final SOC of each road section;

step S53: and connecting the starting SOC and the ending SOC of each road section by taking the driving mileage as an abscissa and the SOC as an ordinate, wherein the obtained curve is a reference SOC curve.

In an embodiment of the present invention, in the step S6, Δ SOC is obtained according to a known reference SOC:

△SOC＝SOC_ref-SOC_real(ii) a Wherein the SOC_realAnd then the relation between the equivalent coefficient and △ SOC can be obtained according to the ECMS equivalent fuel consumption method as follows:

wherein k is_pIs a scale factor, k_lFor the integration factor, △ SOC is the difference between the reference SOC and the actual SOC.

In an embodiment of the present invention, in step S7, a hamiltonian is established according to the ECMS equivalent fuel consumption method, and the obtained equivalent coefficient λ (t) is utilized to convert the optimization problem into the battery output power u^*(t)：

u^*(t)＝argminH(u,SOC_ref,λ,P_req)|_λ(t)

Wherein P is_reqIs the power required by the whole vehicle, H (u, SOC)_ref,λ,P_req) Is a Hamiltonian; when λ is the obtained equivalent coefficient λ (t), u^*(t) is the battery output power P_bat(ii) a From the determined battery output power P_batAnd then according to the required power P of the whole vehicle_reqI.e. the output power P of the engine can be calculated_eng：P_eng＝P_req-P_bat(ii) a And calculating the minimum fuel consumption at the moment according to the output power of the battery and the output power of the engine.

Compared with the prior art, the invention provides a method for synthesizing the trapezoidal modal working condition by providing the road condition information by using the intelligent traffic technology, and the method has the advantages of better real-time performance and smaller calculated amount. The calculated reference SOC can well reflect the power demand condition of the road section, so that the distribution of the SOC is more accurate. And determining the parameter relation by using an ECMS equivalent fuel consumption method, constructing a Hamiltonian, and calculating the output power of the battery and the engine, so as to form a more accurate energy management method and improve the vehicle fuel economy.

Drawings

FIG. 1 is a hardware block diagram of a plug-in hybrid power system based on an ITS system.

FIG. 2 is a flowchart of an ITS system-based energy management control algorithm for a plug-in hybrid electric vehicle.

Fig. 3 is a schematic diagram of a trapezoidal modal condition of a certain road section, for example, the X axis of the diagram is time, and the Y axis is the speed of the vehicle.

FIG. 4 is a diagram of an energy management control algorithm based on the ECMS equivalent fuel consumption method.

Detailed Description

The technical solution of the present invention is further explained with reference to the accompanying drawings and embodiments.

The invention provides a plug-in hybrid electric vehicle energy management method based on an ITS system, which is characterized in that driving condition information is obtained by using the ITS system, a future working condition is predicted by a prediction algorithm, so that a relation curve graph of speed and time of the predicted working condition is calculated, a reference SOC is generated by referring to an SOC algorithm, an equivalent coefficient lambda (t) and delta SOC relation expression and a constructed Hamilton function are solved by using an ECMS equivalent fuel consumption method, and output power of a battery and an engine is obtained according to the required power of a whole vehicle, so that the whole vehicle control of a PHEV is realized.

Further, in the present embodiment, as shown in fig. 1, which is a hardware diagram of a plug-in hybrid system based on the ITS system according to the present invention, the vehicle controller exchanges information and communicates with a Battery Management Unit (BMU), an automatic Transmission Controller (TCU), a Motor Controller (MCU), and an Engine Controller (ECU) through a CAN bus. The PHEV system hardware also comprises a GPS positioning system, a remote communication module, a driving pedal, a brake pedal and an ITS system (navigation, traffic condition and geographic information). The working process of the intelligent control system is that the ITS obtains a starting point and an end point of vehicle running, a running path is planned through an internal system, and road condition information of the path is sent to the vehicle control unit through a remote control module, so that the vehicle control unit makes a corresponding control command.

Further, in the present embodiment, as shown in fig. 2, it is a flowchart of an ITS system-based plug-in hybrid electric vehicle energy management control algorithm provided by the present invention.

The method specifically comprises the following steps:

step S1: referring to fig. 1, the vehicle obtains the starting position by GPS positioning, and the driver obtains the destination position by setting the destination position on the navigation system, and transmits the destination position to the ITS system through the remote control module.

Step S2: the ITS system plans the driving path according to the starting position and the end position of the automobile, and calculates the working condition characteristic parameters according to the electronic map and the speed measuring point data, and mainly comprises the following steps:

1) and (4) total stroke S: the calculation can be performed according to the electronic map and the known starting point and end point position information.

Each section of travel S_i: dividing the total route into several small sections according to the traffic signal lamps, two traffic signal lamps are one small section, calculating its route S_i。

2) Maximum vehicle speed v of each small road section_{i_max}: and measuring the highest vehicle speed through the speed measurement point data.

Average vehicle speed v of each small road section_{i_ave}: and calculating the average vehicle speed through the speed measurement point data.

3) Average acceleration a of each small section_{i_acc}And average deceleration a_{i_dec}: and (4) measuring the data through the speed measuring point.

4) Road surface gradient i: and acquiring road surface gradient information according to the electronic map information to obtain a road surface gradient-distance curve.

5) Traffic light information: signal lamp position and average waiting time t of traffic signal lamp_ave。

Step S3: transmitting the driving path and the working condition characteristic parameters obtained in the step S2 to a vehicle control unit (HCU);

step S4: the HCU calculates the relationship between the speed and time of the predicted operating condition according to the driving route and the operating condition characteristic parameters, and is described with reference to fig. 3:

1) calculating the passing time of each road section:

2) calculating t₁、t₂、t₃、t₄

3) Connection points (0,0), (t)₁，v_{i_max})、(t₂，v_{i_max})、(t₃，0)、(t₄And 0), interpolating at intervals of 1S to obtain the relation between the vehicle speed and the time of the working condition section, integrating the relation to obtain the initially selected driving mileage, and comparing the initially selected driving mileage with the actual mileage until the error is within a certain range.

4) And integrating the obtained vehicle speed-time relation curve to obtain a distance-time relation curve, and converting the gradient-distance curve into a gradient-time curve by using a road gradient-distance curve obtained by ITS.

Step S5: according to the working condition characteristic parameters and the speed-time relation curve chart, the SOC using condition is planned, and the reference SOC is calculated, wherein the steps are as follows:

1) calculating the power required by the vehicle running on each road section

Wherein, P_eiPower required for i-th vehicle travel, v_iFor predicting the speed of the vehicle under the working condition, m is the mass of the whole vehicle, i_sIs road grade, η_tFor mechanical efficiency, C_DIs the wind resistance coefficient, A is the windward area, is the rotating mass conversion coefficient,

is the vehicle acceleration.

2) Calculating the proportional coefficient K of the required power of each road section_piAnd a travel distance proportionality coefficient K_si

Wherein N is the total number of road sections.

3) Calculating reference SOC variation △ SOC of each path section_i

△SOC_i＝(SOC₀-SOC_t)×K_si×K_pi

Wherein the SOC₀For the initial SOC, SOC of the road section_tThe SOC is terminated for the road segment.

4) Calculating initial SOC of each road section₀Terminate SOC_t。

5) Connecting the initial SOC and the final SOC of each road section by taking the driving mileage as an abscissa and the SOC as an ordinate, wherein the obtained curve is a reference SOC curve, namely the SOC_ref。

Further, in this embodiment, the following description is made in conjunction with the schematic diagram of the energy management control algorithm based on the ECMS equivalent fuel consumption method in fig. 4:

step S6: from the known reference SOC, Δ SOC can be determined:

△SOC＝SOC_ref-SOC_real

wherein the SOC_realIs the actual SOC value of the vehicle.

Then, the relation between the equivalent coefficient and the delta SOC can be obtained according to an ECMS equivalent fuel consumption method as follows:

wherein k is_pIs a scale factor, k_lIs an integration factor.

Step S7: according to the ECMS equivalent fuel consumption method, a Hamiltonian is established by using the vehicle required power, the reference SOC and the equivalent coefficient lambda (t), and the obtained equivalent coefficient lambda (t) is utilized to convert the optimization problem into the optimization problemFor obtaining battery output power u^*(t)。

u^*(t)＝argminH(u,SOC_ref,λ,P_req)|_λ(t)

Wherein P is_reqIs the power required by the whole vehicle, H (u, SOC)_ref,λ,P_req) Is a hamiltonian. When λ is the obtained equivalent coefficient λ (t), u^*(t) is the battery output power P_bat。

From the determined battery output power P_batAnd then according to the required power P of the whole vehicle_reqI.e. the output power P of the engine can be calculated_eng：

P_eng＝P_req-P_bat

According to the output power of the battery and the output power of the engine, the minimum fuel consumption at the moment can be obtained.

The above are preferred embodiments of the present invention, and all changes made according to the technical scheme of the present invention that produce functional effects do not exceed the scope of the technical scheme of the present invention belong to the protection scope of the present invention.

Claims (4)

1. An ITS system-based plug-in hybrid electric vehicle energy management method is characterized in that: the method comprises the following steps:

step S1: sending the information of the starting position and the end position of the automobile to an ITS system;

step S2: the ITS system plans a driving path according to the starting position and the end position of the automobile and calculates the characteristic parameters of the working conditions of the automobile;

step S3: the driving path and the working condition characteristic parameters obtained in the step S2 are sent to the HCU;

step S4: the HCU calculates and draws a relation curve graph of the speed and the time of the predicted working condition according to the driving path and the working condition characteristic parameters;

step S5: planning a reasonable SOC use condition in the whole driving process according to the working condition characteristic parameters obtained in the step S2 and the speed-time relation graph calculated in the step S4, namely obtaining a reference SOC which is marked as the SOC_ref；

Step S6: from the obtained SOC_ref△ SOC is solved, and the relation between the equivalent coefficient lambda (t) and △ SOC is established by using an ECMS equivalent fuel consumption method;

step S7: according to the ECMS equivalent fuel consumption method, the output power P of the battery is solved by establishing a Hamiltonian_batCalculating the output power P of the engine according to the current power demand of the whole vehicle_eng；

In step S6, Δ SOC is obtained from the known reference SOC,

ΔSOC＝SOC_ref-SOC_real；

wherein the SOC_realThe actual SOC value of the vehicle;

then, the relation between the equivalent coefficient and the delta SOC can be obtained according to the ECMS equivalent fuel consumption method as follows:

wherein k is_pIs a scale factor, k_lFor the integration factor, △ SOC is the difference between the reference SOC and the actual SOC;

in the step S7, a hamiltonian is established according to the ECMS equivalent fuel consumption method, and the obtained equivalent coefficient λ (t) is used to convert the optimization problem into the battery output power u^*(t)：

u^*(t)＝arg min H(u,SOC_ref,λ,P_req)|_λ(t)

Wherein P is_reqIs the power required by the whole vehicle, H (u, SOC)_ref,λ,P_req) Is a Hamiltonian; when λ is the obtained equivalent coefficient λ (t), u^*(t) is the battery output power P_bat；

From the determined battery output power P_batAnd then according to the required power P of the whole vehicle_reqI.e. the output power P of the engine can be calculated_eng：

P_eng＝P_req-P_bat

And calculating the minimum fuel consumption at the moment according to the output power of the battery and the output power of the engine.

2. The ITS-system-based plug-in hybrid vehicle energy management method of claim 1, wherein: in step S2, the operating condition characteristic parameters include: total stroke S and each small segment stroke S_i(ii) a Maximum vehicle speed v of each small road section_{i_max}Average velocity v_{i_ave}Average acceleration a_{i_acc}Average deceleration a_{i_dec}Road surface gradient i and average waiting time t of traffic signal lamp_ave(ii) a Wherein the small section of travel S_iTwo traffic signal lamps are a small road section, and i is more than or equal to 1.

3. The ITS-system-based plug-in hybrid vehicle energy management method of claim 1, wherein: in step S4, the step of calculating the speed versus time relationship of the predicted operating condition is as follows:

step S41: calculating the passing time t of each road section_iThe trapezoidal mode working condition is established in each road section and consists of an acceleration section (0-t)₁) And a constant speed section (t)₁- t₂) Speed reduction section (t)₂- t₃) Idle speed section (t)₃- t₄) Composition, calculating t₁、t₂、t₃、t₄Value of (2), next connecting (0,0), (t)₁，v_{i_max})、(t₂，v_{i_max})、(t₃，0)、(t₄0), interpolating at certain time intervals to obtain the relation between the speed and the time of the working condition section, integrating the relation to obtain the initially selected driving mileage, and comparing the difference value of the initially selected driving mileage with the actual mileage until the error is within a certain range;

step S42: obtaining a relation curve of the speed and the time of the road section, and repeating the steps to obtain the relation of the speed and the time of all the road sections;

step S43: and integrating the obtained vehicle speed-time relation curve to obtain a distance-time relation curve, and converting the road slope-distance curve into a road slope-time curve by using a road slope-distance curve obtained by an ITS system.

4. The ITS-system-based plug-in hybrid vehicle energy management method of claim 1, wherein: in step S5, the step of generating the reference SOC is as follows:

step S51: calculating the power required by the vehicle running on each road section;

step S52: calculating the proportional coefficient k of the required power of each path section_{i_p}And the proportional coefficient k of the running distance of each road section_{i_s}So as to obtain the variation of the reference SOC of each road section, and further obtain the initial SOC and the final SOC of each road section;

step S53: and connecting the starting SOC and the ending SOC of each road section by taking the driving mileage as an abscissa and the SOC as an ordinate, wherein the obtained curve is a reference SOC curve.

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