CN116118709A - Energy management method and system for hybrid electric vehicle - 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
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- B60W20/10—Controlling the power contribution of each of the prime movers to meet required power demand
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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
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- B60W20/10—Controlling the power contribution of each of the prime movers to meet required power demand
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- B—PERFORMING OPERATIONS; TRANSPORTING
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- 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
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Abstract
The application provides an energy management method and system for a hybrid electric vehicle. The energy management method comprises the step of controlling the engine demand power of the automobile according to the solved equivalent fuel factor, wherein the equivalent fuel factor is solved according to traffic information and engineering experience information. The energy management method and the system for the hybrid electric vehicle can obtain the optimal engine-battery power distribution scheme on the premise of not depending on a complex algorithm, and further reduce the energy consumption of the whole vehicle while reducing the calculation power and the memory cost.
Description
Technical Field
The application relates generally to the field of energy management strategies of hybrid electric vehicles, and in particular to an energy management method and system of a hybrid electric vehicle.
Background
Along with the fact that China puts forward to realize the carbon peak before 2030 and realize the aim of carbon neutralization before 2060, the automobile industry as a large-carbon emission household in the traffic field faces huge pressure, and is particularly good for the traditional fuel oil vehicle enterprises. For a vehicle enterprise, developing new energy automobiles is an important way for realizing low-carbon development. The plug-in hybrid electric vehicle (including extended range electric vehicle) has the advantages of low fuel consumption and low emission due to the economical efficiency and the endurance mileage, and is accepted by consumers, and the market attention and the conservation amount of the plug-in hybrid electric vehicle are gradually increased.
For a plug-in hybrid electric vehicle/extended range electric vehicle (hereinafter collectively referred to as a hybrid electric vehicle), since the vehicle is provided with two power sources, namely a power battery and an engine, an energy management strategy between the two power sources is a key for determining a final energy consumption level of the vehicle, a conventional energy management strategy is usually a power consumption-power retention strategy, and the power is consumed until a State of Charge (SOC) of the battery drops to a user-defined target value, while the engine does not operate; the engine starts operating after the SOC falls below the target value to maintain the battery SOC near the target value. The strategy is mature and widely applied to various hybrid electric vehicle types. However, this strategy also has the obvious disadvantage that the operating point of the engine cannot be optimized without taking into account the actual energy/power requirements, and the energy consumption level is high. Therefore, there is also a great potential for optimizing the energy management strategy of hybrid vehicles.
The current mainstream predictive energy management strategy generally depends on some complex optimization algorithms, such as predictive control (Model Predicted Control, MPC) based on models, dynamic planning (Dynamic Programming, DP) based on global information, and the like, and the optimization algorithm can play a certain role, but has higher requirements on the memory and computational power of the controller, needs to be deployed in a cloud or high-performance controller, and limits popularization and use of the controller. Accordingly, there is a need in the art for an energy management strategy that requires less power and can be deployed for direct use in existing vehicle controller platforms.
Disclosure of Invention
The technical problem to be solved by the application is to provide an energy management method and system for a hybrid electric vehicle, which can obtain an optimal engine-battery power distribution scheme on the premise of not depending on a complex algorithm, and further reduce the energy consumption of the whole vehicle while reducing the calculation power and the memory cost.
In order to solve the technical problems, the application provides an energy management method of a hybrid electric vehicle, which comprises the step of controlling the engine demand power of the vehicle according to the solved equivalent fuel factor, wherein the equivalent fuel factor is solved according to traffic information and engineering experience information.
Optionally, the energy management method further comprises determining an optimal engine demand power according to a cost function after solving the equivalent fuel factor, wherein the optimal engine demand power makes the total energy consumption cost of the automobile lowest.
Optionally, the cost function is:
wherein J is the total cost of the energy consumption,
for the real-time fuel consumption rate->
And (3) for the battery SOC change rate of the automobile, eqFac (t) is the solved equivalent fuel factor.
Optionally, the energy management method further includes listing total energy consumption costs corresponding to a plurality of engine demand powers according to the cost function by a limited exhaustion method to confirm the optimal engine demand power that minimizes the total energy consumption costs of the vehicle.
Optionally, the traffic information includes vehicle speed information and gradient information, the information can be from map data provided by a navigation system, and can also be obtained according to historical vehicle operation data, the engineering experience information includes a reference equivalent fuel factor obtained according to engineering experience and an engineering experience weight coefficient, and the solved equivalent fuel factor is solved by the following formula:
wherein ,vt The vehicle speed at the current moment or the average vehicle speed in a preset time period; v avrg The whole-course average speed of the automobile in the running process is set;
the current road gradient after normalization processing; />
The whole-course average gradient after normalization treatment; eqFac base The method comprises the steps that the standard equivalent fuel factor is a preset fixed value, and the preset fixed value is obtained according to the engine characteristics of the automobile model of the automobile and combining engineering experience; SOC (State of Charge) t For the actual SOC of the car at the current time t, and (2)>
The target SOC of the automobile at the time t is obtained; and a, b, c are engineering experience weight coefficients.
Optionally, the said
The solution is obtained by the following formula:
wherein ,
for the target SOC of the automobile at the time t, SOC ini For the SOC of the automobile at the start of the journey, SOC end For the target SOC of the vehicle at the end of the journey, d t D is the distance from the current position of the automobile to the starting point trip For the length of the full journey of the vehicle during its travel.
Optionally, the energy management method further comprises controlling the battery demand power while controlling the engine demand power according to the following formula:
P m (t)＝P ICE (t)+P batt (t)
wherein ,Pm (t) is the current actual power demand of the automobile, P ICE (t) is the engine demand power, P batt And (t) is the battery demand power.
In order to solve the technical problems, the application provides an energy management system of a hybrid electric vehicle, which comprises a vehicle-mounted power assembly controller, a control system and a control system, wherein the vehicle-mounted power assembly controller is configured to control the engine demand power of the vehicle according to an equivalent fuel factor after solving, and the equivalent fuel factor is solved according to traffic information and engineering experience information; the man-machine interaction interface is configured to respond to instruction information of pressure, touch or sound, display travel information of the automobile and provide traffic information in the travel information for the vehicle-mounted power assembly controller; and the traffic information server is configured to provide the journey information for the man-machine interaction interface.
Optionally, the traffic information includes vehicle speed information and gradient information, the information can be from map data provided by a navigation system, and can also be obtained according to historical vehicle operation data, the engineering experience information includes a reference equivalent fuel factor obtained according to engineering experience and an engineering experience weight coefficient, and the solved equivalent fuel factor is solved by the following formula:
wherein ,vt The vehicle speed at the current moment or the average vehicle speed in a preset time period; v avrg The whole-course average speed of the automobile in the running process is set;
the current road gradient after normalization processing; />
The whole-course average gradient after normalization treatment; eqFac base The method comprises the steps that the standard equivalent fuel factor is a preset fixed value, and the preset fixed value is obtained according to the engine characteristics of the automobile model of the automobile and combining engineering experience; SOC (State of Charge) t For the actual SOC of the car at the current time t, and (2)>
The target SOC of the automobile at the time t is obtained; and a, b, c are engineering experience weight coefficients.
To solve the above technical problem, the present application provides a computer readable medium storing computer program code which, when executed by a processor, implements the energy management method as described above.
Compared with the prior art, the application provides an energy management method of a hybrid electric vehicle, which is a set of hybrid electric vehicle predictive energy management control strategy based on navigation data. Compared with the traditional power consumption-power retention energy management strategy, the method and the device have the advantages that the optimal engine-battery power distribution scheme is calculated through the equivalent minimum energy consumption method by dynamically adjusting the equivalent fuel oil coefficient, and the engine oil consumption can be further reduced. Compared with other predictive energy management strategies, the method is independent of complex algorithms through formulated equivalent fuel factor optimization rules, has low calculation force and memory requirements, and can be directly deployed on the existing vehicle controller.
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The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiments of the application and together with the description serve to explain the principles of the application. In the accompanying drawings:
fig. 1 is a schematic flow chart of an energy management method of a hybrid vehicle according to an embodiment of the present application.
Fig. 2 is an architecture diagram of an energy management system of a hybrid vehicle according to an embodiment of the present application.
Fig. 3 is a schematic logic diagram of an energy management method of a hybrid vehicle according to an embodiment of the present application.
Detailed Description
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are used in the description of the embodiments will be briefly described below. It is apparent that the drawings in the following description are only some examples or embodiments of the present application, and it is obvious to those skilled in the art that the present application may be applied to other similar situations according to the drawings without inventive effort. Unless otherwise apparent from the context of the language or otherwise specified, like reference numerals in the figures refer to like structures or operations.
As used in this application and in the claims, the terms "a," "an," "the," and/or "the" are not specific to the singular, but may include the plural, unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that the steps and elements are explicitly identified, and they do not constitute an exclusive list, as other steps or elements may be included in a method or apparatus.
The relative arrangement of the components and steps, numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present application unless it is specifically stated otherwise. Meanwhile, it should be understood that the sizes of the respective parts shown in the drawings are not drawn in actual scale for convenience of description. Techniques, methods, and apparatus known to one of ordinary skill in the relevant art may not be discussed in detail, but should be considered part of the specification where appropriate. In all examples shown and discussed herein, any specific values should be construed as merely illustrative, and not a limitation. Thus, other examples of the exemplary embodiments may have different values. It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further discussion thereof is necessary in subsequent figures.
In the description of the present application, it should be understood that, where azimuth terms such as "front, rear, upper, lower, left, right", "transverse, vertical, horizontal", and "top, bottom", etc., indicate azimuth or positional relationships generally based on those shown in the drawings, only for convenience of description and simplification of the description, these azimuth terms do not indicate and imply that the apparatus or elements referred to must have a specific azimuth or be constructed and operated in a specific azimuth, and thus should not be construed as limiting the scope of protection of the present application; the orientation word "inner and outer" refers to inner and outer relative to the contour of the respective component itself.
Spatially relative terms, such as "above … …," "above … …," "upper surface at … …," "above," and the like, may be used herein for ease of description to describe one device or feature's spatial location relative to another device or feature as illustrated in the figures. It will be understood that the spatially relative terms are intended to encompass different orientations in use or operation in addition to the orientation depicted in the figures. For example, if the device in the figures is turned over, elements described as "above" or "over" other devices or structures would then be oriented "below" or "beneath" the other devices or structures. Thus, the exemplary term "above … …" may include both orientations of "above … …" and "below … …". The device may also be positioned in other different ways (rotated 90 degrees or at other orientations) and the spatially relative descriptors used herein interpreted accordingly.
In addition, the terms "first", "second", etc. are used to define the components, and are merely for convenience of distinguishing the corresponding components, and unless otherwise stated, the terms have no special meaning, and thus should not be construed as limiting the scope of the present application. Furthermore, although terms used in the present application are selected from publicly known and commonly used terms, some terms mentioned in the specification of the present application may be selected by the applicant at his or her discretion, the detailed meanings of which are described in relevant parts of the description herein. Furthermore, it is required that the present application be understood, not simply by the actual terms used but by the meaning of each term lying within.
It will be understood that when an element is referred to as being "on," "connected to," "coupled to," or "contacting" another element, it can be directly on, connected or coupled to, or contacting the other element or intervening elements may be present. In contrast, when an element is referred to as being "directly on," "directly connected to," "directly coupled to," or "directly contacting" another element, there are no intervening elements present. Likewise, when a first element is referred to as being "electrically contacted" or "electrically coupled" to a second element, there are electrical paths between the first element and the second element that allow current to flow. The electrical path may include a capacitor, a coupled inductor, and/or other components that allow current to flow even without direct contact between conductive components.
The present application proposes an energy management method 10 (hereinafter referred to as "energy management method 10") for a hybrid vehicle with reference to fig. 1. Exemplary hybrid electric vehicles to which the technical scheme of the present invention is applicable include plug-in hybrid electric vehicle PHEV, extended range electric vehicle REV, and the like. The energy management method 10 can obtain an optimal engine-battery power distribution scheme on the premise of not depending on a complex algorithm, and further reduce engine oil consumption while reducing calculation power and memory cost.
Fig. 1 of the present application uses a flowchart to illustrate operations performed by a system according to embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in order precisely. Rather, the various steps may be processed in reverse order or simultaneously. At the same time, other operations are added to or removed from these processes.
According to fig. 1, the energy management method 10 includes the following steps.
And step 11, controlling the engine demand power of the automobile according to the solved equivalent fuel factor, wherein the equivalent fuel factor is solved according to traffic information and engineering experience information.
For example, in some embodiments of the present invention, after solving the equivalent fuel factor after solving, the optimal engine demand power may be determined by calculating a cost function, where the optimal engine demand power minimizes the total cost of energy consumption of the vehicle.
Specifically, in some embodiments of the present application, an optimal engine demand power is determined based on an equivalent minimum fuel consumption strategy (Equivalent Consumption Minimization Strategy, ECMS) based on a cost function of the ECMS strategy. The cost function of an ECMS policy can be expressed by the following formula:
further, in the above formula (1), J is the total cost (energy consumption).
For the real-time fuel consumption rate, namely the cost of the engine using the fuel as an energy source, the specific numerical value can be obtained through the fuel consumption characteristic curve of the engine according to the engine power lookup table. />
The change rate of the battery SOC can be equivalently converted into electric power based on the ampere-hour integration principle, and EqFac is an equivalent fuel factor for converting electricity consumption into oil consumption. The total cost J obtained by the above formula (1) is a result of uniformly converting the energy consumption of the engine and the battery into the fuel consumption, so that determining the equivalent fuel factor EqFac in the above formula (1) is a key point of the method, and therefore, the above formula (1) can be rewritten as:
in equation (2), eqFac (t) is the solved equivalent fuel factor.
In the prior art, eqFac (t) in equation (2) above can be generally derived from equation (3) as follows:
in the above formula (3), SOC (t) is the actual SOC at the current time t,
for the target SOC at time t, P m And (t) is the system demand power at time t. Although the optimization algorithm can have a certain effect, the optimization algorithm has higher requirements on the memory and the computational power of the controller, needs to be deployed in a cloud or high-performance controller, and limits the popularization and the use of the optimization algorithm. In some embodiments of the present application, by way of example, the optimized formula of the present invention may be combined and the total energy consumption costs corresponding to the plurality of engine demand powers may be listed according to the cost function through a limited exhaustion method, so as to identify the total energy consumption costs corresponding to the optimal engine demand power.
More specifically, a plurality of preset engine powers P can be obtained according to the characteristics of the power system ICE And battery pack power P Batt The specific number of the power distribution points of the engine power and the battery pack power is determined according to the actual characteristics of the system, and it should be noted that the more the number of the power distribution points is, the more power distribution costs are required to be calculated, the calculation efficiency is also affected, and the compromise is required to be considered in the practical application. For each distribution point of the engine power and the battery pack power, the corresponding total energy consumption cost can be calculated based on the above formula (2), and it should be noted that the specific coverage area of the power distribution point needs to be based on the system required power P at the current time t m (t). Thereby obtaining the power division corresponding to the total cost of the lowest energy consumption and the total cost of the two lowest energy consumption in the range of the selected power distribution pointsThe matching scheme is the optimal control scheme.
The existing EqFac (t) solving method generally adopts a complex model based on an optimal control theory or an AI algorithm, and has high requirements on the memory and calculation force of a controller. The source of traffic information may be navigation information provided by a network system, or historical common route condition information obtained based on historical driving behavior. The equivalent fuel factor after solving is solved by the following formula (4):
further, in the above formula (4), v t The vehicle speed at the current moment or the average vehicle speed in a preset time period; v avrg The whole-course average speed of the automobile in the running process can be provided by a navigation system;
the current road gradient after normalization processing can be provided by a navigation system; />
The whole-course average gradient after normalization processing can be provided by a navigation system as well; eqFac base The reference equivalent fuel factor is a preset fixed value, and the preset fixed value can be obtained according to the engine characteristics of the automobile model of the automobile and combining engineering experience; SOC (State of Charge) t For the actual SOC of the car at the current time t, and (2)>
The target SOC of the automobile at the time t is obtained; to be used forAnd a, b and c are engineering experience weight coefficients, and are given by an expert in combination with engineering experience.
It should be noted that, on the basis of the above formula (4), the calculation method based on traffic information and experience of engineers provided in the present invention can be further extended to introduce other parameters for correction, including, but not limited to, rainy and snowy weather, air temperature, driver style, etc., for dynamic calculation of equivalent fuel factors in the predictive energy management algorithm. The above parameters can be added as additional correction terms to equation 4, and combined with expert engineering experience, for EqFac base Additional corrections are made. The dynamic calculation method of the equivalent fuel factor adopting the similar scheme is within the protection scope of the invention.
For a hybrid vehicle, for example, the optimum SOC variation trend is such that the SOC exhibits a linear decrease between the start point and the end point, and therefore,
calculated by the following formula (5):
further, SOC ini SOC, SOC being the trip origin end Target SOC, d, for trip end point t D is the distance from the current position of the vehicle to the starting point trip Is the length of the full journey.
The energy management of a hybrid vehicle aims to distribute power among different power sources, namely an engine and a battery (or called a power battery) based on the current actual power required by the vehicle, so as to maximize the system efficiency on the premise of meeting the power requirement of a driver and the safety requirement of the system. The actual power demand of the vehicle is determined by the driver and depends on the sum of the engine power demand and the battery power demand. The sum of the engine demand power and the battery demand power can be found by the following formula (6):
P m (t)＝P ICE (t)+P batt (t) (6)
wherein ,Pm (t) is the current actual power demand of the automobile, P ICE (t) is the engine demand power, P batt And (t) is the battery demand power. The invention uses the engine demand power P ICE (t) is a control target. Further specifically, for a vehicle in a non-autonomous driving mode, P m (t) may be determined by the vehicle accelerator pedal, while for an autonomous mode vehicle, P m (t) determining from the target acceleration/deceleration and the vehicle mass, which can be calculated by the autopilot system
A specific embodiment is listed below in conjunction with the different features described above. In this embodiment, a limited exhaustion approach is used to find the optimal engine demand power. The combinations of parameters listed using the limited exhaustion method are shown in Table 1 below, where P ICE1 To P ICE5 ，P Batt1 To P Batt5 The specific values of the preset engine demand power and battery demand power are obtained according to the characteristics of the power system, and the number of the engine power and the number of battery pack power points listed in the current table 1 are both schematic, and the specific number needs to be determined according to the actual characteristics of the system, which is not a limitation of the present invention. As shown in the following table, each engine demand power/battery demand power distribution point can be calculated based on the formula (2) to obtain the corresponding total energy consumption Cost mn . It should be noted that the specific power distribution point coverage needs to be based on the current actual power requirement P m And (t) is calculated by a formula (6).
| P ICE1 | P ICE2 | P ICE3 | P ICE4 | P ICE5 | |
| P batt1 | Cost 11 | Cost 12 | Cost 13 | Cost 14 | Cost 15 |
| P batt2 | Cost 21 | Cost 22 | Cost 23 | Cost 24 | Cost 25 |
| P batt3 | Cost 31 | Cost 32 | Cost 33 | Cost 34 | Cost35 35 |
| P batt4 | Cost 41 | Cost 42 | Cost 43 | Cost 44 | Cost 45 |
| P batt5 | Cost 51 | Cost 52 | Cost 53 | Cost 54 | Cost 55 |
TABLE 1 schematic table of calculating total energy consumption costs for power split points using limited exhaustion method
The optimized formula is combined with a limited exhaustion method, so that the optimal engine-battery power distribution scheme can be obtained on the premise of not depending on a complex algorithm, and traffic information and engineering experience information are comprehensively considered by the optimized formula, so that the reliability and universality of the optimal engine required power are further improved.
Another aspect of the present invention also proposes an energy management system 20 (hereinafter referred to as "energy management system 20") of a hybrid vehicle, referring to fig. 2, which is a structural diagram of the energy management system 20, as shown in fig. 2. According to FIG. 2, the energy management system 20 includes an onboard powertrain controller 21, a human-machine interaction interface 22, and a traffic information server 23.
Specifically, the vehicle-mounted powertrain controller 21 is configured to control the engine demand power of the vehicle according to the solved equivalent fuel factor. The human-machine interaction interface 22 is configured to display travel information of the automobile in response to instruction information of pressure, touch or sound, and to provide traffic information in the travel information to the in-vehicle powertrain controller. The traffic information server 23 is configured to provide the trip information to the man-machine interaction interface.
For a better illustration of the energy management system 20, the present application refers to FIG. 3, which illustrates logic of one embodimentSchematic diagram. According to fig. 3, the energy management method and the system control strategy of the hybrid electric vehicle provided by the invention are deployed in the vehicle-mounted power assembly controller 21, a user firstly sets a destination of the current journey through the vehicle-mounted large screen (i.e. the man-machine interaction interface 22 shown in fig. 2), determines a navigation path, and a map provider (i.e. the traffic information server 23 shown in fig. 2) provides real-time navigation information along the journey according to the navigation path, wherein the real-time navigation information comprises the position of the current vehicle, mileage of a distance end point, traffic flow speed along the journey, road gradient and the like. These information can be used as traffic information in the above-mentioned energy management method of the present invention. The navigation information is transmitted to the in-vehicle powertrain controller 21 through CAN or ethernet. By combining the global optimization method of the equivalent fuel factor, the self-adaptive global optimization of the equivalent fuel factor is realized. Finally, according to the current state of the vehicle power system, combining the equivalent fuel factor with the optimal current road section/period, obtaining the distribution result of the expected power of the engine with the lowest equivalent total energy consumption based on the equivalent minimum fuel consumption (Equivalent Consumption Minimum Strategy, ECMS) algorithm, namely obtaining the optimal engine required power P by solving ICE (t)。
On the basis of this, another aspect of the invention also proposes a computer readable medium storing computer program code which, when executed by a processor, implements the energy management method of a hybrid vehicle described above.
While the basic concepts have been described above, it will be apparent to those skilled in the art that the foregoing application disclosure is by way of example only and is not intended to be limiting. Although not explicitly described herein, various modifications, improvements, and adaptations of the present application may occur to one skilled in the art. Such modifications, improvements, and modifications are intended to be suggested within this application, and are therefore within the spirit and scope of the exemplary embodiments of this application.
Meanwhile, the present application uses specific words to describe embodiments of the present application. Reference to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic is associated with at least one embodiment of the present application. Thus, it should be emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various positions in this specification are not necessarily referring to the same embodiment. Furthermore, certain features, structures, or characteristics of one or more embodiments of the present application may be combined as suitable.
Some aspects of the present application may be performed entirely by hardware, entirely by software (including firmware, resident software, micro-code, etc.) or by a combination of hardware and software. The above hardware or software may be referred to as a "data block," module, "" engine, "" unit, "" component, "or" system. The processor may be one or more Application Specific Integrated Circuits (ASICs), digital Signal Processors (DSPs), digital signal processing devices (DAPDs), programmable Logic Devices (PLDs), field Programmable Gate Arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, or a combination thereof. Furthermore, aspects of the present application may take the form of a computer product, comprising computer-readable program code, embodied in one or more computer-readable media. For example, computer-readable media can include, but are not limited to, magnetic storage devices (e.g., hard disk, floppy disk, tape … …), optical disk (e.g., compact disk CD, digital versatile disk DVD … …), smart card, and flash memory devices (e.g., card, stick, key drive … …).
The computer readable medium may comprise a propagated data signal with the computer program code embodied therein, for example, on a baseband or as part of a carrier wave. The propagated signal may take on a variety of forms, including electro-magnetic, optical, etc., or any suitable combination thereof. A computer readable medium can be any computer readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code located on a computer readable medium may be propagated through any suitable medium, including radio, cable, fiber optic cable, radio frequency signals, or the like, or a combination of any of the foregoing.
Likewise, it should be noted that in order to simplify the presentation disclosed herein and thereby aid in understanding one or more application embodiments, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure. This method of disclosure, however, is not intended to imply that more features than are presented in the claims are required for the subject application. Indeed, less than all of the features of a single embodiment disclosed above.
In some embodiments, numbers describing the components, number of attributes are used, it being understood that such numbers being used in the description of embodiments are modified in some examples by the modifier "about," approximately, "or" substantially. Unless otherwise indicated, "about," "approximately," or "substantially" indicate that the number allows for a 20% variation. Accordingly, in some embodiments, numerical parameters set forth in the specification and claims are approximations that may vary depending upon the desired properties sought to be obtained by the individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and employ a method for preserving the general number of digits. Although the numerical ranges and parameters set forth herein are approximations that may be employed in some embodiments to confirm the breadth of the range, in particular embodiments, the setting of such numerical values is as precise as possible.
While the present application has been described with reference to the present specific embodiments, those of ordinary skill in the art will recognize that the above embodiments are for illustrative purposes only, and that various equivalent changes or substitutions can be made without departing from the spirit of the present application, and therefore, all changes and modifications to the embodiments described above are intended to be within the scope of the claims of the present application.
Claims (10)
1. The energy management method of the hybrid electric vehicle is characterized by comprising the step of controlling the engine demand power of the vehicle according to the solved equivalent fuel factor, wherein the equivalent fuel factor is solved according to traffic information and engineering experience information.
2. The method of claim 1, further comprising determining an optimal engine demand power as a function of a cost function after solving the equivalent fuel factor, wherein the optimal engine demand power minimizes a total cost of energy consumption of the vehicle.
3. The method of claim 2, wherein the cost function is:
wherein J is the total cost of the energy consumption,
for the real-time fuel consumption rate->
And (3) for the battery SOC change rate of the automobile, eqFac (t) is the solved equivalent fuel factor.
4. A method according to claim 2 or 3, further comprising listing the total cost of energy consumption corresponding to a plurality of engine demand powers according to the cost function by a limited exhaustion method to identify the optimal engine demand power that minimizes the total cost of energy consumption of the vehicle.
5. A method according to any one of claims 1 to 3, wherein the traffic information includes vehicle speed information and gradient information, the engineering experience information includes a reference equivalent fuel factor obtained from engineering experience and an engineering experience weight coefficient, and the solved equivalent fuel factor is solved by the following formula:
wherein ,vt The vehicle speed at the current moment or the average vehicle speed in a preset time period; v avrg The whole-course average speed of the automobile in the running process is set;
the current road gradient after normalization processing; />
The whole-course average gradient after normalization treatment; eqFac base The method comprises the steps that the standard equivalent fuel factor is a preset fixed value, and the preset fixed value is obtained according to the engine characteristics of the automobile model of the automobile and combining engineering experience; SOC (State of Charge) t For the actual SOC of the car at the current time t, and (2)>
The target SOC of the automobile at the time t is obtained; and a, b, c are engineering experience weight coefficients.
6. The method of claim 5, wherein the
The solution is obtained by the following formula:
wherein ,
for the target SOC of the automobile at the time t, SOC ini For the SOC of the automobile at the start of the journey, SOC end For the car travelingTarget SOC, d at end of range t D is the distance from the current position of the automobile to the starting point trip For the length of the full journey of the vehicle during its travel.
7. The method of claim 1, further comprising controlling battery demand power while controlling the engine demand power according to the following equation:
P m (t)＝P ICE (t)+P batt (t)
wherein ,Pm (t) is the current actual power demand of the automobile, P ICE (t) is the engine demand power, P batt And (t) is the battery demand power.
8. An energy management system for a hybrid vehicle, comprising:
the vehicle-mounted power assembly controller is configured to control the engine demand power of the automobile according to the solved equivalent fuel factor, wherein the equivalent fuel factor is solved according to traffic information and engineering experience information;
the man-machine interaction interface is configured to respond to instruction information of pressure, touch or sound, display travel information of the automobile and provide traffic information in the travel information for the vehicle-mounted power assembly controller; and
and the traffic information server is configured to provide the journey information for the man-machine interaction interface.
9. The energy management system of claim 8, wherein the traffic information includes vehicle speed information and grade information, the engineering experience information includes a reference equivalent fuel factor obtained from engineering experience and an engineering experience weight coefficient, and the solved equivalent fuel factor is solved by the following formula:
wherein ,vt The vehicle speed at the current moment or the average vehicle speed in a preset time period; v avrg The whole-course average speed of the automobile in the running process is set;
the current road gradient after normalization processing; />
The whole-course average gradient after normalization treatment; eqFac base The method comprises the steps that the standard equivalent fuel factor is a preset fixed value, and the preset fixed value is obtained according to the engine characteristics of the automobile model of the automobile and combining engineering experience; SOC (State of Charge) t For the actual SOC of the car at the current time t, and (2)>
The target SOC of the automobile at the time t is obtained; and a, b, c are engineering experience weight coefficients.
10. A computer readable medium storing computer program code which, when executed by a processor, implements the method of any of claims 1-7.
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