CN116639110A

CN116639110A - Energy management method, device, equipment and storage medium for hybrid electric vehicle

Info

Publication number: CN116639110A
Application number: CN202310786883.2A
Authority: CN
Inventors: 吴宁
Original assignee: Zhejiang Geely Holding Group Co Ltd; Geely Automobile Research Institute Ningbo Co Ltd
Current assignee: Zhejiang Geely Holding Group Co Ltd; Geely Automobile Research Institute Ningbo Co Ltd
Priority date: 2023-06-29
Filing date: 2023-06-29
Publication date: 2023-08-25

Abstract

The application provides an energy management method, device and equipment for a hybrid electric vehicle and a storage medium, which can be used in the field of hybrid electric vehicles. The method comprises the following steps: if the target vehicle is detected to start the vehicle-mounted air conditioner to heat, acquiring vehicle running information and traffic information; inputting vehicle running information and traffic information into an energy consumption prediction model and a fuel consumption prediction model to obtain the predicted energy consumption of a target vehicle in the running process of the vehicle-mounted air conditioner heating to the target temperature under the state of starting the predicted energy management function and the predicted fuel consumption of the target vehicle in the running process of the vehicle-mounted air conditioner heating to the target temperature under the state of closing the predicted energy management function; and controlling the opening or closing of the predicted energy management function in the driving process of the vehicle-mounted air conditioner heating to the target temperature based on the comparison result of the predicted energy consumption and the predicted oil consumption. The method can select a more energy-saving energy management mode when the vehicle cabin heats, and achieves the technical effects of energy saving and emission reduction of the vehicle.

Description

Energy management method, device, equipment and storage medium for hybrid electric vehicle

Technical Field

The present application relates to the field of hybrid vehicles, and in particular, to an energy management method, apparatus, device, and storage medium for a hybrid vehicle.

Background

The existing hybrid electric vehicle is generally provided with a predictive energy management function, traffic information on a route can be collected by the predictive energy management function, and an optimal battery electric quantity consumption curve in the process of reaching a destination is optimized by using a predictive energy management technology and an algorithm, so that more engines are optimized to run in an engine efficient section, the vehicle can generate electricity in multiple ways in the engine efficient section, and the engine low-efficiency section is multipurpose and pure electric, so that the effects of saving oil and reducing emission are achieved.

However, in severe winter, the vehicle can start the vehicle-mounted air conditioner for a long time to heat the cabin. After the prediction energy management function is used, the vehicle can run in a pure electric driving mode in an engine low-efficiency section, and because the engine does not work in the pure electric driving mode, the vehicle-mounted air conditioner heat modulation needs to be carried out by PTC (Positive Temperature Coefficient, positive temperature coefficient thermistor) consumed electric quantity, and at the moment, compared with the vehicle without starting the prediction energy management function, the vehicle adopting the parallel direct driving mode consumes more energy, and has a negative effect on energy conservation of the prediction energy management function.

Therefore, in order to solve the above-mentioned problems, in the prior art, a table lookup method is generally used, that is, conditions such as an environmental temperature, an engine water temperature, and a battery remaining power, which are calibrated in advance through a test are referred to, energy consumption in different energy management modes under related conditions is determined, and then a predictive energy management function is selected to be turned on or off when the vehicle-mounted air conditioner is heated according to the energy consumption. However, in the actual vehicle driving process, the vehicle energy consumption is dynamically affected by various factors, and the energy consumption of the vehicle cannot be accurately obtained by adopting a table look-up method, so that a correct energy management mode is selected, and the problem of increasing the vehicle energy consumption is caused.

Disclosure of Invention

The application provides an energy management method, device and equipment of a hybrid electric vehicle and a storage medium, which are used for solving the problem that the energy consumption of the vehicle is increased because the hybrid electric vehicle cannot accurately determine the energy management mode of the vehicle in the heating process of a cabin of the vehicle.

According to a first aspect of the present disclosure, there is provided an energy management method of a hybrid vehicle, including:

if the fact that the target vehicle starts the vehicle-mounted air conditioner to heat is detected, vehicle running information is obtained, and traffic information is obtained based on navigation information of the target vehicle;

Inputting the vehicle running information and the traffic information into an energy consumption prediction model to obtain the predicted energy consumption of the target vehicle in the running process from the vehicle-mounted air conditioner heating to the target temperature under the state that the predicted energy management function is started; the predicted energy consumption comprises heating power consumption of the vehicle-mounted air conditioner and driving oil consumption of the target vehicle;

inputting the vehicle running information and the traffic information into a fuel consumption prediction model to obtain the predicted fuel consumption of the target vehicle in the running process of the vehicle-mounted air conditioner heating to the target temperature by adopting a parallel direct drive mode under the state that the predicted energy management function is closed;

comparing the predicted energy consumption with the predicted oil consumption, and controlling the predicted energy management function to be started or stopped in the driving process of the vehicle-mounted air conditioner heating to the target temperature based on a comparison result;

wherein the predictive energy management function includes: and predicting an engine low-efficiency section and an engine high-efficiency section of the target vehicle on a driving route based on the traffic information, controlling the target vehicle to adopt a pure electric driving mode in the engine low-efficiency section, and controlling the target vehicle to adopt a parallel charging mode in the engine high-efficiency section.

In a possible implementation manner, the predicted energy consumption and the predicted oil consumption are compared, and based on a comparison result, the predicted energy management function is controlled to be turned on or off during the driving process of the vehicle-mounted air conditioner heating to the target temperature, including:

performing oil-electricity conversion calculation on the heating power consumption to obtain equivalent fuel consumption;

obtaining equivalent oil consumption based on the sum of the equivalent oil consumption and the driving oil consumption;

if the equivalent oil consumption is not greater than the predicted oil consumption, controlling the predicted energy management function to be started in the driving process of the vehicle-mounted air conditioner heating to the target temperature;

and if the equivalent oil consumption is larger than the predicted oil consumption, controlling the predicted energy management function to be closed and controlling the target vehicle to adopt a parallel direct-drive mode in the running process of the vehicle-mounted air conditioner heating to the target temperature.

In a possible implementation manner, the calculation of the oil-electricity conversion of the heating power consumption to obtain the equivalent oil consumption includes:

acquiring the power generation thermal efficiency and the fuel heating value of an engine in the target vehicle;

and dividing the heating power consumption by the product of the power generation heat efficiency and the fuel oil heat value to obtain the equivalent heating fuel consumption as the equivalent fuel consumption.

In a possible embodiment, the method further comprises:

and multiplying the equivalent heating oil consumption by a heating energy consumption correction coefficient to obtain heating comprehensive evaluation oil consumption as the equivalent oil consumption.

In a possible embodiment, the method further comprises:

acquiring a heating temperature rise rate of the vehicle-mounted air conditioner in the process of heating to a target temperature under the state that the energy prediction management function is started;

normalizing the heating temperature rise rate to obtain the heating energy consumption correction coefficient; wherein the heating energy consumption correction coefficient is inversely proportional to the heating temperature rise rate.

In a possible embodiment, the method further comprises:

and if the residual electric quantity of the target vehicle is smaller than the heating electric quantity, controlling the predicted energy management function to be closed and controlling the target vehicle to adopt a parallel direct-drive mode in the running process of the vehicle-mounted air conditioner heating to the target temperature.

In a possible embodiment, the method further comprises:

acquiring vehicle attribute parameters of the target vehicle, and performing simulation based on the vehicle attribute parameters to obtain the energy consumption prediction model; or,

And when the target vehicle is heated by the vehicle-mounted air conditioner, starting the running data of the predicted energy management function, and performing machine learning training on the running data of the predicted energy management function in the starting state to obtain the energy consumption prediction model.

In a possible embodiment, the method further comprises:

acquiring vehicle attribute parameters of the target vehicle, and performing simulation based on the vehicle attribute parameters to obtain the fuel consumption prediction model; or,

and when the target vehicle is heated by the vehicle-mounted air conditioner, closing the running data of the predictive energy management function in a parallel direct drive mode, and performing machine learning training on the running data of the predictive energy management function in the parallel direct drive mode in the closed state to obtain the fuel consumption prediction model.

In a possible embodiment, the method further comprises:

and when detecting that the vehicle-mounted air conditioner is heated to the target temperature, controlling the predictive energy management function to be started.

According to a second aspect of the present disclosure, there is provided an energy management device of a hybrid vehicle, including:

the information acquisition module is used for acquiring vehicle running information if the target vehicle is detected to start the vehicle-mounted air conditioner heat, and acquiring traffic information based on navigation information of the target vehicle;

The energy consumption prediction module is used for inputting the vehicle running information and the traffic information into an energy consumption prediction model to obtain the predicted energy consumption of the target vehicle in the running process of the vehicle-mounted air conditioner heating to the target temperature under the state that the predicted energy management function is started; the predicted energy consumption comprises heating power consumption of the vehicle-mounted air conditioner and driving oil consumption of the target vehicle;

the fuel consumption prediction module is used for inputting the vehicle running information and the traffic information into a fuel consumption prediction model to obtain the predicted fuel consumption of the target vehicle in the running process of the vehicle-mounted air conditioner heating to the target temperature under the state that the predicted energy management function is closed and the parallel direct drive mode is adopted for running;

the energy management module is used for comparing the predicted energy consumption with the predicted oil consumption and controlling the predicted energy management function to be started or stopped in the driving process of the vehicle-mounted air conditioner heating to the target temperature based on a comparison result;

According to a third aspect of the present disclosure, there is provided an electronic device comprising a processor, and a memory communicatively coupled to the processor;

the memory stores computer-executable instructions;

the processor executes computer-executable instructions stored in the memory to implement the method of any one of the first aspects.

According to a fourth aspect of the present disclosure, there is provided a computer-readable storage medium having stored therein computer-executable instructions for implementing the method of any one of the first aspects when executed by a processor.

According to a fifth aspect of the present disclosure, there is provided a computer program product comprising a computer program for implementing the method of any one of the first aspects when the computer program is executed by a processor.

Compared with the prior art, the application has the following beneficial effects:

according to the energy management method, the device, the equipment and the storage medium of the hybrid electric vehicle, the energy consumption of the vehicle is predicted in the process of heating the vehicle-mounted air conditioner to the target temperature under different energy management modes, the lower energy consumption under which energy management mode is confirmed in a mode of energy consumption comparison, and accordingly the starting or closing of the predicted energy management function of the target vehicle is controlled, and the proper energy management mode is selected correspondingly. Therefore, in a low-temperature environment, a more energy-saving energy management mode is accurately selected, and the technical effects of energy saving and emission reduction of the vehicle are achieved.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application. It is evident that the drawings in the following description are only some embodiments of the application and that other drawings may be obtained from these drawings without inventive effort for a person skilled in the art. Wherein:

fig. 1 is a schematic flow chart of an energy management method of a hybrid electric vehicle according to an embodiment of the present application;

fig. 2 is a schematic flow chart of another energy management method for a hybrid electric vehicle according to an embodiment of the present application;

FIG. 3 is a schematic flow chart of converting heating power consumption into equivalent fuel consumption according to an embodiment of the present application;

FIG. 4 is a schematic diagram of another process for converting heating power consumption into equivalent fuel consumption according to an embodiment of the present application;

fig. 5 is a schematic structural diagram of an energy management device of a hybrid vehicle according to an embodiment of the present application;

fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present application.

Specific embodiments of the present application have been shown by way of the above drawings and will be described in more detail below. The drawings and the written description are not intended to limit the scope of the inventive concepts in any way, but rather to illustrate the inventive concepts to those skilled in the art by reference to the specific embodiments.

Detailed Description

Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples do not represent all implementations consistent with the application. Rather, they are merely examples of apparatus and methods consistent with aspects of the application as detailed in the accompanying claims.

The existing hybrid electric vehicle is generally provided with a prediction energy management function, the prediction energy management function can collect traffic information on a route based on navigation information, an engine high-efficiency section and an engine low-efficiency section in the whole route are predicted by using a prediction energy management technology and an algorithm, the vehicle is controlled to be driven by adopting a parallel charging mode (the parallel charging mode is adopted for controlling the output power of the engine to be larger than the current required power of the whole vehicle and charging a power battery by adopting excessive power) in the engine high-efficiency section through switching of a driving mode, the vehicle is driven to run by utilizing the engine to charge in advance, and the vehicle is driven by adopting a pure electric driving mode in the engine low-efficiency section to use battery power prepared in advance, so that the total fuel efficiency of the whole route is improved, and the purposes of energy conservation and emission reduction are achieved.

In general, the on-state prediction energy management function is more energy-saving than the off-state prediction energy management function which adopts a parallel direct drive mode (i.e., the power battery is not in operation, the vehicle is directly driven by the engine, and the output power of the engine is equivalent to the current required power of the whole vehicle). However, in severe cold winter, since the vehicle needs to start the vehicle-mounted air conditioner for a long time, when the vehicle with the predictive energy management function started runs in an engine low-efficiency zone, and the predictive energy management function controls the vehicle to adopt a pure electric mode, the vehicle-mounted air conditioner also needs to be heated by PTC (Positive Temperature Coefficient, positive temperature coefficient thermistor) consumed electric quantity, and extra energy consumption is generated. And the parallel direct drive mode is adopted, and the mode of heating by using the waste heat of the engine is adopted, so that no extra energy consumption is caused. Therefore, in the vehicle-mounted air conditioning heating process, the predicted energy management function is started to drive the vehicle, and more energy may be consumed than the vehicle is driven by the parallel direct drive mode to drive the vehicle, so that the energy saving of the predicted energy management function is negatively affected.

In order to solve the problem, in the prior art, a table lookup method is generally used, that is, conditions such as environmental temperature, engine water temperature, battery residual capacity and the like calibrated by a pre-test are referred to, energy consumption under different energy management modes under related conditions is determined, and then a predictive energy management function is selected to be started or stopped when the vehicle-mounted air conditioner is heated according to the energy consumption. However, in the actual vehicle driving process, the vehicle energy consumption is dynamically affected by various factors, and the energy consumption of the vehicle cannot be accurately obtained by adopting a table look-up method, so that a correct energy management mode is selected. Therefore, in the conventional hybrid vehicle, the conventional energy management method cannot accurately determine what energy management mode the vehicle adopts under the vehicle-mounted air conditioning heating condition, and thus the energy can be saved more.

In order to solve the problems, the application provides an energy management method of a hybrid electric vehicle, which accurately predicts the energy consumption of different energy management modes in the heating process of a vehicle cabin by utilizing vehicle operation information and traffic information, so as to accurately select the most energy-saving energy management mode according to the predicted energy consumption, thereby achieving the effects of energy conservation and emission reduction of the vehicle.

The following describes in detail the technical scheme of the energy management method of the hybrid electric vehicle provided by the application through a specific embodiment. It should be noted that the following embodiments may exist alone or in combination with each other, and for the same or similar content, the description may not be repeated in different embodiments.

It should be noted that, the execution main body of the energy management method of the hybrid electric vehicle provided by the embodiment of the application may be a vehicle machine system or a cloud server, and when the execution main body is the cloud server, the cloud server is in communication connection with the target vehicle through the internet of vehicles and the like.

Compared with the vehicle system serving as an execution main body, the cloud server serving as the execution main body has higher execution speed, lower requirements on vehicle hardware and no additional increase of vehicle cost.

The cloud server may store related data of a plurality of vehicles, and may store data of the corresponding vehicles by using VIN codes (Vehicle Identification Number, vehicle identification codes) of the vehicles as identification indexes.

Fig. 1 is a schematic flow chart of an energy management method of a hybrid electric vehicle according to an embodiment of the present application, referring to fig. 1, in some embodiments, the flow chart of the energy management method of the hybrid electric vehicle includes the following steps:

s101, if the target vehicle is detected to start the vehicle-mounted air conditioner to heat, vehicle running information is acquired, and traffic information is acquired based on navigation information of the target vehicle.

Among them, since the energy consumption of the target vehicle is affected by various factors, two types are the most important, one type is vehicle operation information reflecting the state of the target vehicle, and the other type is traffic information reflecting the traffic condition.

Specifically, the vehicle operation information includes: vehicle-mounted air conditioner information such as a real-time heating power request value of a vehicle-mounted air conditioner, temperature and pressure signals (evaporator and condenser) of a vehicle-mounted air conditioner pipeline, cabin temperature, air volume information and the like; battery information such as battery temperature, remaining capacity, battery voltage, battery current limit, battery SOH (State Of Health), and the like; engine information such as engine water temperature, engine speed, etc.; PTC information such as PTC voltage, PTC current, etc.; driving information such as brake size, throttle information, gear information, driving style information, driving mode information, and the like; environmental information, such as weather information, ambient temperature information, and the like.

Specifically, the traffic information includes vehicle speed information, ramp information, traffic light information, preceding vehicle distance information, and the like. The vehicle speed information is a navigation vehicle speed predicted according to the navigation information, and the vehicle speed of the target vehicle in the engine inefficiency zone is predicted more accurately by adopting the navigation vehicle speed than the current actual vehicle speed.

Preferably, the target temperature is a vehicle-mounted air conditioner set temperature or a temperature value having a preset temperature difference from the vehicle-mounted air conditioner set temperature.

The target temperature may be an air conditioner set temperature or a temperature value near the vehicle-mounted air conditioner set temperature, because according to the heating characteristics of the air conditioner, when the air conditioner heats up in a certain range of the air conditioner set temperature, that is, when the temperature of the air conditioner heats up to be close to the tail sound, a stage of heating up by the air conditioner consumes a great deal of energy has elapsed, and a constant temperature maintaining stage is followed. In the subsequent constant temperature maintaining process, compared with the opening prediction energy management function and the closing prediction energy management function, the heating energy consumption between the opening prediction energy management function and the closing prediction energy management function is not great, and the heating mode can be compared in the subsequent stage to save more energy. Therefore, the target temperature may be a temperature value having a preset temperature difference from the vehicle-mounted air conditioner set temperature.

Specifically, the preset temperature difference is a calibratable value, the value range is generally 3-5 ℃, and the value can be changed in a floating manner according to parameters such as ambient temperature, vehicle speed, sunlight and the like.

S102, inputting vehicle running information and traffic information into an energy consumption prediction model to obtain the predicted energy consumption of a target vehicle in the running process of the vehicle-mounted air conditioner heating to the target temperature under the state that a predicted energy management function is started; the predicted energy consumption includes heating power consumption of the vehicle-mounted air conditioner and driving fuel consumption of the target vehicle.

The energy consumption prediction model predicts the energy consumption of the target vehicle in the running process of the vehicle-mounted air conditioner heating to the target temperature under the state of starting the predicted energy management function and is used for subsequently judging the energy consumption of the target vehicle in the running process of the vehicle-mounted air conditioner heating to the target temperature under different energy management modes.

Specifically, because the target vehicle comprises an engine high-efficiency interval and an engine low-efficiency interval in the running process of the vehicle-mounted air conditioner heating to the target temperature, and because the target vehicle runs in a parallel charging mode in the engine high-efficiency interval when the prediction energy management function is started, the vehicle engine can be driven to charge at the moment, the vehicle-mounted air conditioner can heat by using the engine waste heat, and no extra energy is consumed; when the vehicle runs in a pure electric driving mode in a target vehicle in an engine low-efficiency section, the electric quantity additionally charged when the power consumption of the vehicle driving is in a parallel charging mode, and the vehicle-mounted air conditioner needs additional power consumption to heat. Therefore, when the predictive energy management function is turned on, the predicted energy consumption during the running of the vehicle-mounted air conditioner to the target temperature includes the power consumption of the vehicle-mounted air conditioner for pure electric heating in the engine low-efficiency section and the fuel consumption of the engine in the parallel charging mode in the engine high-efficiency section. Accordingly, the predicted energy consumption includes both the heating power consumption of the vehicle-mounted air conditioner and the driving fuel consumption of the target vehicle.

Preferably, acquiring vehicle attribute parameters of a target vehicle, and performing simulation based on the vehicle attribute parameters to obtain an energy consumption prediction model; or when the vehicle-mounted air conditioner is used for heating, starting the running data of the predicted energy management function, and performing machine learning training on the running data of the predicted energy management function in the starting state to obtain an energy consumption prediction model.

The energy consumption prediction model can be obtained in two modes, and one is obtained according to simulation of vehicle attribute parameters of the target vehicle, and the vehicle attribute parameters comprise structural parameters and physical characteristic parameters of the target vehicle. After the simulation of the energy consumption prediction model is completed, the energy consumption prediction model can simulate the heating and driving processes of the target vehicle in the predicted energy management starting state after the collected vehicle running information and traffic information are input, and the predicted energy consumption after the simulated driving is obtained.

Specifically, the energy consumption prediction model obtained by simulation is essentially a whole vehicle simulation model of the target vehicle, so that the energy consumption of the target vehicle in the heating and driving processes can be simulated. The whole vehicle simulation model generally comprises an air conditioner simulation model, an engine simulation model, a motor simulation model, a battery simulation model, a controller strategy simulation model, a whole vehicle dynamics simulation model and other simulation sub-models. The vehicle-mounted air conditioner can obtain the heating power consumption amount of PTC heating consumption when the vehicle-mounted air conditioner is in a pure electric driving mode in an engine low-efficiency interval in the process of heating the vehicle-mounted air conditioner to a target temperature under the state of starting the predicted energy management function through the air conditioner simulation model.

And the other is to train the machine learning model by taking the driving data of the target vehicle when the vehicle-mounted air conditioner is heated in the state of starting the predictive energy management function as a training set, and obtain an energy consumption predictive model taking vehicle operation information and traffic information as inputs and predictive energy consumption as output after the machine learning training is finished. Specifically, the machine learning model includes a deep learning model, an artificial neural network model, and the like.

And S103, inputting vehicle running information and traffic information into a fuel consumption prediction model to obtain the predicted fuel consumption of the target vehicle in the running process of the vehicle-mounted air conditioner heating to the target temperature by adopting a parallel direct drive mode under the state that the predicted energy management function is closed.

The fuel consumption prediction model predicts the predicted fuel consumption of the target vehicle in the running process of the vehicle-mounted air conditioner heating to the target temperature when the vehicle adopts a parallel direct drive mode in the state of closing the predicted energy management function, and is used for subsequently judging the energy consumption of the target vehicle in the running process of the vehicle-mounted air conditioner heating to the target temperature in different energy management modes.

Specifically, in the parallel direct-drive mode, the vehicle-mounted air conditioner always heats by using the waste heat of the engine without additional power consumption, so that in the parallel direct-drive mode adopted by the shutdown prediction energy management function, the energy consumption in the running process of the vehicle-mounted air conditioner heating to the target temperature is the predicted fuel consumption of the engine in the parallel direct-drive mode.

Preferably, acquiring vehicle attribute parameters of a target vehicle, and performing simulation based on the vehicle attribute parameters to obtain a fuel consumption prediction model; or when the vehicle-mounted air conditioner is used for heating, the running data of the parallel direct-drive mode is adopted by the closed prediction energy management function, and the running data of the parallel direct-drive mode in the state that the prediction energy management function is closed is subjected to machine learning training to obtain the fuel consumption prediction model.

The fuel consumption prediction model can be obtained in two modes, and one is obtained according to simulation of vehicle attribute parameters of the target vehicle, and specifically, the vehicle attribute parameters comprise structural parameters and physical characteristic parameters of the target vehicle. After the simulation of the fuel consumption prediction model is completed, the fuel consumption prediction model can simulate the heating and running process of the target vehicle in the parallel direct-drive mode adopted by the shutdown prediction energy management function after the collected vehicle running information and traffic information are input, and the predicted fuel consumption after the simulated running is obtained.

Specifically, the fuel consumption prediction model obtained by simulation is essentially a complete vehicle simulation model of the target vehicle, so that the energy consumption of the target vehicle in the heating and driving processes can be simulated. The whole vehicle simulation model generally comprises an air conditioner simulation model, an engine simulation model, a motor simulation model, a battery simulation model, a controller strategy simulation model, a whole vehicle dynamics simulation model and other simulation sub-models.

And the other is that under the state that the predicted energy management function is closed, the target vehicle adopts a parallel direct-drive mode, the running data of the vehicle-mounted air conditioner during heating is used as a training set to train the machine learning model, and after the machine learning training is finished, the fuel consumption prediction model taking the vehicle running information and the traffic information as inputs and the predicted fuel consumption as output is obtained. Specifically, the machine learning model includes a deep learning model, an artificial neural network model, and the like.

S104, comparing the predicted energy consumption with the predicted oil consumption, and controlling the predicted energy management function to be started or stopped in the driving process of the vehicle-mounted air conditioner heating to the target temperature based on the comparison result.

The stage of maximum energy consumption of the vehicle-mounted air conditioner is a heating stage of heating the vehicle-mounted air conditioner from the current temperature to the target temperature. Because when the temperature of the cabin of the vehicle is low, the electric elements of the vehicle-mounted air conditioner are started and the compressor is operated in the heating stage of the vehicle-mounted air conditioner, which consumes high peak power, and more energy is consumed in the heating stage. When the vehicle-mounted air conditioner heats to the target temperature, the vehicle-mounted air conditioner enters a low power consumption mode in a constant temperature maintenance stage, the energy consumption of the vehicle-mounted air conditioner is obviously reduced, and at the moment, no obvious difference exists between the vehicle-mounted air conditioner heating energy consumption for starting the predictive energy management function and the vehicle-mounted air conditioner heating energy consumption for closing the predictive energy management function.

Therefore, under the vehicle-mounted air conditioner heating condition, the driving phase in which the energy consumption of the on-state prediction energy management function is possibly larger than the energy consumption of the off-state prediction energy management function is generally the driving phase in which the vehicle-mounted air conditioner heating is at the target temperature.

Therefore, the predicted energy consumption in the on-state predicted energy management function state is compared with the predicted fuel consumption in the off-state predicted energy management function state during the travel of the vehicle-mounted air conditioner heating to the target temperature, and after determining what energy management mode is more energy-efficient, a more energy-efficient energy management mode is selected during the travel of the vehicle-mounted air conditioner heating to the target temperature.

In addition, for the hybrid electric vehicle with the predictive energy management function, the energy management mode can be selected by opening or closing the predictive energy management function, so that the predictive energy management function is controlled to be opened or closed in the running process of the vehicle-mounted air conditioner heating to the target temperature, the energy management mode with more energy conservation can be selected, and the technical effect of vehicle energy conservation is achieved.

Wherein the predictive energy management function includes: based on traffic information, an engine low-efficiency section and an engine high-efficiency section of the target vehicle on a running route are predicted, the target vehicle is controlled to adopt a pure electric driving mode in the engine low-efficiency section, and the target vehicle is controlled to adopt a parallel charging mode in the engine high-efficiency section.

In this embodiment, the energy consumption of the vehicle is predicted in the process of heating the vehicle-mounted air conditioner to the target temperature in different energy management modes, and in a mode of comparing the energy consumption, it is confirmed which energy management mode has lower energy consumption, and accordingly the target vehicle is controlled to predict whether the energy management function is on or off, and a proper energy management mode is selected accordingly. Therefore, in a low-temperature environment, a more energy-saving energy management mode is accurately selected, and the technical effects of energy saving and emission reduction of the vehicle are achieved.

On the basis of the embodiment shown in fig. 1, the technical scheme of the energy management method of the hybrid electric vehicle is further described below with reference to fig. 2.

Fig. 2 is a schematic flow chart of another method for energy management of a hybrid vehicle according to an embodiment of the present application, referring to fig. 2, in some embodiments, the flow chart of the method for energy management of a hybrid vehicle includes the following steps:

s201, if the environmental temperature of the target vehicle is smaller than the preset temperature and the difference between the current temperature of the cabin of the target vehicle and the target temperature is larger than the preset temperature difference, executing the steps of acquiring vehicle running information if the target vehicle is detected to start the vehicle-mounted air conditioner, and acquiring traffic information based on the navigation information of the target vehicle.

It should be noted that, in step S201, the execution process of "if the target vehicle is detected to turn on the vehicle-mounted air conditioner, the vehicle running information is obtained, and the traffic information is obtained based on the navigation information of the target vehicle" is the same as that in step S101, and will not be described herein.

In step S201, two determination conditions are added, namely, the "the environmental temperature of the target vehicle is less than the preset temperature" and the "the difference between the current temperature of the cabin of the target vehicle and the target temperature is greater than the preset temperature difference". The aim is that:

the determination condition of whether the ambient temperature is less than the preset temperature threshold value is that the vehicle has a heating request only under a certain low-temperature condition, for example, the vehicle does not have a heating request in summer, and unnecessary data processing calculation is avoided under other conditions such as summer through the determination of the ambient temperature. Specifically, for example, the preset temperature threshold may be set at-10 degrees celsius.

The purpose of the method is to judge whether the difference between the current temperature and the target temperature of the cabin of the target vehicle is larger than the preset temperature difference judging condition, and in the state of judging that the predicted energy management function is started, the PTC heating power requirement of the vehicle-mounted air conditioner cannot be too large, if the difference between the current temperature and the target temperature is smaller, the fact that the vehicle-mounted air conditioner basically enters a heating temperature rising tail sound stage or a constant temperature maintaining stage is indicated, the heating power requirement of PTC heating is not too large, and the situation that the predicted energy management function is started to consume a large amount of electricity to heat in a pure electric driving mode does not occur. In this case, the PTC heating does not have a significant difference in energy consumption compared to the engine waste heat heating, and it is not necessary to predict the energy consumption in both energy management modes.

S202, inputting vehicle running information and traffic information into an energy consumption prediction model to obtain predicted energy consumption of a target vehicle in a running process of a vehicle-mounted air conditioner heating to a target temperature under a state that a predicted energy management function is started; the predicted energy consumption includes heating power consumption of the vehicle-mounted air conditioner and driving fuel consumption of the target vehicle.

It should be noted that the execution process of step S202 is the same as the execution process of step S102, and will not be described here again.

And S203, inputting vehicle running information and traffic information into a fuel consumption prediction model to obtain the predicted fuel consumption of the target vehicle in the running process of the vehicle-mounted air conditioner heating to the target temperature by adopting a parallel direct drive mode under the state that the predicted energy management function is closed.

It should be noted that the execution process of step S203 is the same as the execution process of step S103, and will not be described here again.

S204, judging whether the residual electric quantity of the target vehicle is smaller than the heating power consumption.

And S205, if the residual electric quantity of the target vehicle is smaller than the heating electric quantity, controlling the prediction energy management function to be closed and controlling the target vehicle to adopt a parallel direct drive mode in the running process of the vehicle-mounted air conditioner heating to the target temperature.

Because the parallel direct drive mode is switched to the pure electric drive mode in the low-efficiency engine section according to the characteristic of the predictive energy management function, the low-efficiency engine section may be included in the stage of heating the vehicle-mounted air conditioner to the target temperature, and the vehicle-mounted air conditioner needs to consume electric quantity for heating at the moment. Because the extra charge quantity of the parallel charge mode adopted by the high-efficiency section of the engine can be used when the pure electric drive mode is adopted by the low-efficiency section of the engine in the state that the predictive energy management function is started, the electric energy of the heat consumption of the vehicle-mounted air conditioner in the low-efficiency section of the engine can be provided by the residual electric quantity of the power battery. The determination condition that whether the heating power consumption is smaller than the residual power is added, if the heating power consumption is larger than the residual power, the residual power is indicated to not support the power consumed by the vehicle-mounted air conditioner when the target vehicle is in the state of starting the predictive energy management function.

At this time, the predictive energy management function can be turned off, so that the target vehicle can run in a parallel direct drive mode at this stage, and the vehicle-mounted air conditioner can be ensured to heat normally.

And S206, if the residual electric quantity of the target vehicle is not less than the heating electric quantity, performing oil-electricity conversion calculation on the heating electric quantity to obtain equivalent oil consumption.

In order to compare the predicted energy consumption with the predicted fuel consumption, the heating power consumption is converted into the corresponding equivalent fuel consumption, so that the predicted energy consumption is converted into a unit of measurement unified with the predicted fuel consumption, the predicted energy consumption and the predicted fuel consumption can be compared, and the opening and closing of the predicted energy management function can be controlled based on the comparison result.

S207, obtaining the equivalent fuel consumption based on the sum of the equivalent fuel consumption and the driving fuel consumption.

And adding the equivalent fuel consumption and the predicted fuel consumption to obtain the total fuel consumption of the vehicle-mounted air conditioner heated to the target temperature under the state of starting the predicted energy management function.

S208, judging whether the equivalent fuel consumption is larger than the predicted fuel consumption.

And S209, if the equivalent fuel consumption is not greater than the predicted fuel consumption, controlling the predicted energy management function to be started in the driving process of the vehicle-mounted air conditioner heating to the target temperature.

When the equivalent oil consumption is not greater than the predicted oil consumption, the predicted energy management function is started and is not more energy-consuming than the predicted energy management function is closed in a driving stage of heating the air conditioner to a target temperature, and the predicted energy management function is maintained to be started, at the moment, the predicted energy management function controls the target vehicle to adopt a pure electric driving mode in an engine low-efficiency zone and adopts a parallel direct driving mode in an engine high-efficiency zone according to the functional characteristics of the target vehicle in the engine low-efficiency zone.

And S210, if the equivalent fuel consumption is larger than the predicted fuel consumption, controlling the predicted energy management function to be closed and controlling the target vehicle to adopt a parallel direct-drive mode in the running process of the vehicle-mounted air conditioner heating to the target temperature.

When the equivalent oil consumption is larger than the predicted oil consumption, the method indicates that the energy consumption of the on predicted energy management function is more than that of the off predicted energy management function in the driving stage of heating the air conditioner to the target temperature, and at the moment, the off predicted energy management function is selected, and the parallel direct-drive mode is adopted for driving.

S211, when the vehicle-mounted air conditioner is detected to be heated to the target temperature, the predictive energy management function is controlled to be started.

When the vehicle-mounted air conditioner heats to the target temperature, the vehicle-mounted air conditioner enters a constant temperature maintenance stage, and the energy consumption of pure electric heating or engine waste heat heating of the vehicle-mounted air conditioner is not greatly different, so that after the vehicle-mounted air conditioner heats to the target temperature, the predicted energy management function is recovered to be normally started.

Specifically, two situations exist in which the predictive energy management function is controlled to be started, one is that the predictive energy management function is started before, and the starting of the predictive energy management function is continuously maintained; the other is that the predictive energy management function is turned off before, and then the predictive energy management function is turned back on.

In this embodiment, after the predicted power consumption is converted into the equivalent fuel consumption, the predicted power consumption and the predicted fuel consumption may be compared in the same measurement unit, so as to accurately determine whose power consumption is lower, and thus, according to the comparison result, a more energy-saving energy management mode is selected.

On the basis of the embodiment shown in fig. 2, the heating oil consumption needs to be converted into the equivalent oil consumption, and in the technical scheme of the energy management method of the hybrid electric vehicle, the content of converting the heating electricity consumption into the equivalent oil consumption is further described below with reference to fig. 3.

Fig. 3 is a schematic flow chart of converting heating power consumption into equivalent fuel consumption according to an embodiment of the present application, referring to fig. 3, in some embodiments, the flow chart of converting heating power consumption into equivalent fuel consumption includes the following steps:

s301, acquiring the power generation thermal efficiency and the fuel heating value of an engine in a target vehicle.

The power generation thermal efficiency refers to the conversion efficiency of converting heat energy generated by burning fuel oil into electric energy by an engine, is a dimensionless index and is generally expressed in percentage.

Wherein the heating value of the fuel is the heat generated after each kilogram of fuel is completely combusted.

S302, dividing the heating power consumption by the product of the power generation heat efficiency and the fuel oil heat value to obtain the equivalent heating power consumption as the equivalent fuel consumption.

And dividing the heating power consumption by the product of the power generation heat efficiency and the fuel heat value according to the calculation relation among the power generation heat efficiency, the fuel heat value and the fuel consumption of the engine, and obtaining the equivalent heating fuel consumption as the equivalent fuel consumption.

In this embodiment, by converting the heating power consumption into the equivalent fuel consumption, the unit of measurement of the predicted energy consumption and the predicted fuel consumption can be unified, and comparison is facilitated.

On the basis of the embodiment shown in fig. 2, the heating oil consumption needs to be converted into the equivalent oil consumption, and in the technical scheme of the energy management method of the hybrid electric vehicle, the content of converting the heating electricity consumption into the equivalent oil consumption is further described below with reference to fig. 4.

Fig. 4 is a schematic diagram of another flow chart for converting heating power consumption into equivalent fuel consumption according to an embodiment of the present application, referring to fig. 4, in some embodiments, the flow chart for converting heating power consumption into equivalent fuel consumption includes the following steps:

s401, acquiring the power generation thermal efficiency and the fuel heating value of an engine in a target vehicle.

S402, dividing the heating power consumption by the product of the power generation heat efficiency and the fuel oil heat value to obtain the equivalent heating fuel consumption.

It should be noted that the execution process of steps S401 to S402 is the same as the execution process of steps S301 to S302, and will not be described here again.

S403, multiplying the equivalent heating oil consumption by a heating energy consumption correction coefficient to obtain heating comprehensive evaluation oil consumption as the equivalent oil consumption.

The power consumption of the vehicle-mounted air conditioner is related to the heating effect of the vehicle-mounted air conditioner when a pure electric driving mode is adopted in an engine low-efficiency interval, so that the power consumption of the vehicle-mounted air conditioner is calculated by combining the power consumption of the vehicle-mounted air conditioner with the heating energy consumption coefficient, and the heating energy consumption of the vehicle-mounted air conditioner with the same heating effect is obtained when the prediction energy management function is in a closed state. Specifically, the higher the heating power consumption of the vehicle-mounted air conditioner to the target temperature, the higher the heating temperature rise rate of the vehicle-mounted air conditioner is, the better the heating effect is, and the shorter the heating time to the target temperature is.

Specifically, by introducing the heating energy consumption correction coefficient, the heating energy consumption correction coefficient is used as a discount coefficient of pure electric heating, and the increased energy consumption cost of customers caused by the heating effect in the pure electric heating process is reflected. The method aims to unify the heating effect of the on-prediction energy management function and the heating effect of the off-prediction energy management function so as to obtain energy consumption in different energy management modes under the same heating effect. For example, when the heating energy consumption correction coefficient is 1, it is indicated that the heating effect of pure electric heating is identical to that of engine waste heat heating. However, when the heating energy consumption correction coefficient is 0.5, the pure electric heating effect is better, the heating rate is faster, and when the heating effect of pure electric heating is unified with the heating effect of the waste heat of the engine, the energy consumption consumed by the pure electric heating for pursuing the heating effect is reduced by the heating energy consumption correction coefficient, and then the energy consumption in the two modes is compared.

In the pure electric mode, the vehicle-mounted air conditioner mainly heats through the PTC, the heating effect of the PTC is controllable, for example, the heating power of the PTC is increased, the heating effect of the PTC can be controlled, and for the heating of the engine waste heat, the heating effect is stable and cannot be adjusted because the vehicle-mounted air conditioner heats through the engine cooling water waste heat. Therefore, the introduced heating energy consumption correction coefficient is mainly used for correcting the equivalent heating oil consumption in the pure electric driving mode, and the heating comprehensive evaluation oil consumption obtained after correction is used as the equivalent heating oil consumption of the vehicle-mounted air conditioner in the state of starting the predictive energy management function, so that the comparison of the energy consumption is carried out later, and the opening and closing of the predictive energy management function are controlled in the stage of the vehicle-mounted air conditioner heating to the target temperature based on the comparison result.

Specifically, when the equivalent fuel consumption is not greater than the predicted fuel consumption, it indicates that in a driving stage from heating of the air conditioner to a target temperature, under the condition of the same heating effect, the predicted energy management function is started and is not more energy-consuming than the predicted energy management function is closed, and the predicted energy management function is maintained to be started.

Specifically, when the comprehensive evaluation fuel consumption is greater than the predicted fuel consumption, it indicates that in the driving stage from the heating of the air conditioner to the target temperature, under the condition of the same heating effect, the on-prediction energy management function consumes more energy than the off-prediction energy management function, and at this time, the off-prediction energy management function is selected and the parallel direct driving mode is adopted for driving.

In this embodiment, by introducing the heating energy consumption correction parameter, the equivalent fuel consumption and the predicted fuel consumption can be compared under the same heating effect and the same measurement unit, so as to accurately determine whose energy consumption is lower, and select a more energy-saving energy management mode according to the comparison result.

Preferably, in the state that the energy prediction management function is started, the heating temperature rise rate of the vehicle-mounted air conditioner in the process of heating to the target temperature is obtained; normalizing the heating temperature rise rate to obtain a heating energy consumption correction coefficient; wherein the heating energy consumption correction coefficient is inversely proportional to the heating temperature rise rate.

The heating energy consumption correction coefficient is obtained after normalization processing of the heating temperature rise rate, and the higher the heating temperature rise rate is, the faster the heating rate is, the better the heating effect is, the better the perception of a user is, and the more energy the user is willing to expend. Because the vehicle is heated by the waste heat of the engine in the whole course when the predictive energy management function is closed, the heating effect is stable. When the predictive energy management function is started, the vehicle-mounted air conditioner can heat by using PTC (Positive temperature coefficient) to consume additional electric energy in an engine low-efficiency road section, and the heating effect of pure electric heating is different according to different user selections, for example, the user can increase the heating power of the PTC, and select more energy to improve the heating effect. Therefore, if the pure electric heating and the engine waste heat heating have the same heating effect, a discount coefficient of the pure electric heating, namely a heating energy consumption correction coefficient, can be obtained by normalizing the heating temperature rise rate in the starting state of the energy prediction management function, and the heating energy consumption correction coefficient reflects the increased energy consumption cost of users brought by the heating effect in the pure electric heating process.

Therefore, the higher the heating temperature rise rate, the better the heating effect, the more the energy consumption cost is increased by the user, the more the cost to be discounted is calculated, and the smaller the discount coefficient is. Therefore, in normalizing the heating temperature rise rate, the heating temperature rise rate is inversely proportional to the heating energy consumption correction coefficient.

Specifically, a data acquisition window may be selected for the heating temperature rise rate, such as an average temperature rise rate when the vehicle air conditioner is heated to 50% of the target temperature difference. By selecting the heating to the target temperature difference of 50% as the window for calculating the rate of rise, we will record the time and energy taken by the temperature to reach the target temperature 50% before it reaches during heating, and calculate the heating rate. This window may reflect the response speed and stability of the heating system and help predict the trend of the time and energy required by the system in future heating processes, thereby better controlling the heating process and determining optimal operating parameters. Of course, this window may be set to other values, for example, 70%, 80%, etc., as the set amount.

Specifically, the heating energy consumption correction coefficient has a value range of (0, 1).

Fig. 5 is a schematic structural diagram of an energy management device for a hybrid vehicle according to an embodiment of the present application, and referring to fig. 5, the energy management device for a hybrid vehicle includes functional modules for implementing the foregoing energy management method for a hybrid vehicle, where any functional module may be implemented by software and/or hardware.

In some embodiments, the energy management device 500 of the hybrid electric vehicle includes an information acquisition module 501, an energy consumption prediction module 502, a fuel consumption prediction module 503, and an energy management module 504; wherein:

the information acquisition module 501 is configured to acquire vehicle operation information if it is detected that the target vehicle starts the vehicle-mounted air conditioner, and acquire traffic information based on navigation information of the target vehicle;

the energy consumption prediction module 502 is configured to input vehicle operation information and traffic information into an energy consumption prediction model, so as to obtain predicted energy consumption of a target vehicle in a running process from a vehicle-mounted air conditioner heating to a target temperature in a state where a predicted energy management function is turned on; the predicted energy consumption comprises heating power consumption of the vehicle-mounted air conditioner and driving fuel consumption of the target vehicle;

the fuel consumption prediction module 503 is configured to input vehicle operation information and traffic information into a fuel consumption prediction model, so as to obtain predicted fuel consumption of a target vehicle in a process that the vehicle-mounted air conditioner heats to a target temperature in a parallel direct-drive mode;

the energy management module 504 is configured to compare the predicted energy consumption and the predicted fuel consumption, and control the predicted energy management function to be turned on or off during a driving process of the vehicle-mounted air conditioner heating to a target temperature based on a comparison result;

In some embodiments, the energy management module 504 is specifically configured to:

performing oil-electricity conversion calculation on the heating power consumption to obtain equivalent oil consumption;

obtaining the equivalent oil consumption based on the sum of the equivalent oil consumption and the driving oil consumption and based on the sum of the equivalent oil consumption and the driving oil consumption;

if the equivalent fuel consumption is larger than the predicted fuel consumption, the predicted energy management function is controlled to be closed and the target vehicle is controlled to adopt a parallel direct-drive mode in the running process of the vehicle-mounted air conditioner heating to the target temperature.

acquiring the power generation heat efficiency and the fuel heating value of an engine in a target vehicle;

acquiring a heating temperature rise rate of a vehicle-mounted air conditioner in the process of heating to a target temperature under the state that an energy prediction management function is started;

normalizing the heating temperature rise rate to obtain a heating energy consumption correction coefficient; wherein the heating energy consumption correction coefficient is inversely proportional to the heating temperature rise rate.

if the residual electric quantity of the target vehicle is smaller than the heating electric quantity, the predictive energy management function is controlled to be closed and the target vehicle is controlled to adopt a parallel direct drive mode in the running process of the vehicle-mounted air conditioner heating to the target temperature.

In some embodiments, the energy consumption prediction module 502 is specifically configured to:

acquiring vehicle attribute parameters of a target vehicle, and performing simulation based on the vehicle attribute parameters to obtain an energy consumption prediction model; or,

and when the vehicle-mounted air conditioner is used for heating, starting the running data of the predicted energy management function, and performing machine learning training on the running data of the target vehicle in the state that the predicted energy management function is started to obtain an energy consumption prediction model.

In some embodiments, the fuel consumption prediction module 503 is specifically configured to:

acquiring vehicle attribute parameters of a target vehicle, and performing simulation based on the vehicle attribute parameters to obtain a fuel consumption prediction model; or,

when the vehicle-mounted air conditioner is used for heating, the energy management function of the shut-down prediction adopts the driving data of the engine driving mode, and the driving data of the engine driving mode in the state that the energy management function of the prediction is shut-down is subjected to machine learning training to obtain the fuel consumption prediction model.

In some embodiments, the information acquisition module 501 is specifically configured to:

and if the environmental temperature of the target vehicle is smaller than the preset temperature and the difference between the current temperature of the cabin of the target vehicle and the target temperature is larger than the preset temperature difference, executing the steps of acquiring the vehicle running information and acquiring the traffic information based on the navigation information of the target vehicle.

when the vehicle-mounted air conditioner is detected to be heated to the target temperature, the predictive energy management function is controlled to be started.

The energy management device 500 for a hybrid vehicle according to the embodiment of the present application is configured to execute the technical scheme provided in the foregoing embodiment of the energy management method for a hybrid vehicle, and its implementation principle and technical effects are similar to those in the foregoing embodiment of the method, and are not described herein again.

It should be noted that, it should be understood that the division of the modules of the above apparatus is merely a division of a logic function, and may be fully or partially integrated into a physical entity or may be physically separated. The modules can be realized in a form of calling the processing element through software, can be realized in a form of hardware, can be realized in a form of calling the processing element through part of the modules, and can be realized in a form of hardware. For example, the energy management module may be a processing element that is set up separately, may be implemented as integrated into a chip of the above-described apparatus, or may be stored in a memory of the above-described apparatus in the form of program codes, and the functions of the energy management module may be called and executed by a processing element of the above-described apparatus. The implementation of the other modules is similar. In addition, all or part of the modules can be integrated together or can be independently implemented. The processing element here may be an integrated circuit with signal processing capabilities. In implementation, each step of the above method or each module above may be implemented by an integrated logic circuit of hardware in a processor element or an instruction in a software form.

Fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present application, referring to fig. 6, the electronic device 600 includes: a processor 601 and a memory 602 communicatively coupled to the processor 601;

memory 602 stores computer-executable instructions;

the processor 601 executes computer-executable instructions stored in the memory 602 to implement the technical solution of the energy management method of the hybrid electric vehicle described above.

In the electronic device 600, the memory 602 and the processor 601 are directly or indirectly electrically connected to each other to realize data transmission or interaction. For example, the elements may be electrically connected to each other via one or more communication buses or signal lines, such as through a bus connection. The bus may be an industry standard architecture (Industry Standard Architecture, abbreviated as ISA) bus, an external device interconnect (Peripheral Component Interconnect, abbreviated as PCI) bus, or an extended industry standard architecture (Extended Industry Standard Architecture, abbreviated as EISA) bus, among others. Buses may be divided into address buses, data buses, control buses, etc., but do not represent only one bus or one type of bus. The memory 602 stores therein computer-executable instructions for implementing the aforementioned energy management method of the hybrid vehicle, including at least one software functional module that may be stored in the memory 602 in the form of software or firmware, and the processor 601 executes the software programs and modules stored in the memory 602 to thereby perform various functional applications and data processing.

The Memory 602 includes at least one type of readable storage medium, not limited to random access Memory (Random Access Memory, abbreviated as RAM), read Only Memory (abbreviated as ROM), programmable Read Only Memory (Programmable Read-Only Memory, abbreviated as PROM), erasable Read Only Memory (Erasable Programmable Read-Only Memory, abbreviated as EPROM), electrically erasable Read Only Memory (Electric Erasable Programmable Read-Only Memory, abbreviated as EEPROM), and the like. The memory 602 is used for storing a program, and the processor 601 executes the program after receiving an execution instruction. Further, the software programs and modules within the memory 602 may also include an operating system, which may include various software components and/or drivers for managing system tasks (e.g., memory management, storage device control, power management, etc.), and may communicate with various hardware or software components to provide an operating environment for other software components.

The processor 601 may be an integrated circuit chip with signal processing capabilities. The processor 601 may be a general-purpose processor, including a central processing unit (Central Processing Unit, abbreviated as CPU), a network processor (Network Processor, abbreviated as NP), a digital signal processor (Digital Signal Processor, abbreviated as DSP), an application specific integrated circuit (Application Specific Integrated Circuit, abbreviated as ASIC), and the like. The disclosed methods, steps, and logic blocks in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor, or the processor 601 may be any conventional processor or the like.

The electronic device 600 is configured to execute the technical scheme provided by the foregoing energy management method embodiment of the hybrid electric vehicle, and its implementation principle and technical effects are similar to those of the foregoing method embodiment, and are not repeated herein.

The embodiment of the application also provides a computer readable storage medium, wherein the computer readable storage medium stores computer execution instructions, and when the processor executes the computer execution instructions, the technical scheme of the energy management method of the hybrid electric vehicle is realized.

The computer readable storage medium described above may be implemented by any type or combination of volatile or nonvolatile memory devices such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disk. Such computer-readable storage media can be any available media that can be accessed by a general purpose or special purpose computer.

An exemplary readable storage medium is coupled to the processor such the processor can read information from, and write information to, the readable storage medium. In the alternative, the readable storage medium may be integral to the processor. The processor and the readable storage medium may reside in an application specific integrated circuit (Application Specific Integrated Circuits, ASIC for short). Of course, the processor and the readable storage medium may also be present as discrete components in a control device of an energy management device of a hybrid vehicle.

The embodiment of the application also provides a computer program product, which comprises a computer program, wherein the computer program is used for realizing the technical scheme of the energy management method of the hybrid electric vehicle when being executed by a processor.

In the above embodiments, those skilled in the art will appreciate that implementing the above method embodiments may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions in accordance with embodiments of the present application are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in or transmitted from one computer-readable storage medium to another, for example, by wired (e.g., coaxial cable, optical fiber, digital Subscriber Line (DSL)), or wireless (e.g., infrared, wireless network, microwave, etc.), from one website, computer, server, or data center. Computer readable storage media can be any available media that can be accessed by a computer or data storage devices, such as servers, data centers, etc., that contain an integration of one or more available media. The usable medium may be a magnetic medium (e.g., a floppy Disk, a hard Disk, a magnetic tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a Solid State Disk (SSD)), or the like.

Other embodiments of the application will be apparent to those skilled in the art from consideration of the specification and practice of the application disclosed herein. This application is intended to cover any variations, uses, or adaptations of the application following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the application pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.

It is to be understood that the application is not limited to the precise arrangements and instrumentalities shown in the drawings, which have been described above, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the application is limited only by the appended claims.

Claims

1. A method of energy management for a hybrid vehicle, comprising:

2. The method of claim 1, wherein comparing the predicted energy consumption and the predicted fuel consumption and controlling the predicted energy management function to be turned on or off during driving of the vehicle-mounted air conditioner to a target temperature based on a result of the comparison, comprises:

3. The method according to claim 2, wherein the performing the calculation of the oil-electricity conversion on the heating power consumption to obtain the equivalent fuel consumption includes:

4. A method according to claim 3, characterized in that the method further comprises:

5. The method according to claim 4, wherein the method further comprises:

6. The method according to any one of claims 1-5, further comprising:

7. The method according to any one of claims 1-5, further comprising:

8. The method according to any one of claims 1-5, further comprising:

9. The method of any one of claims 1-5, further comprising, prior to acquiring vehicle operating information and traffic information for the engine inefficiency interval:

10. The method according to any one of claims 1-5, further comprising:

11. An energy management device for a hybrid vehicle, comprising:

12. An electronic device comprising a processor and a memory communicatively coupled to the processor;

the memory stores computer-executable instructions;

the processor executes computer-executable instructions stored in the memory to implement the method of any one of claims 1 to 10.

13. A computer readable storage medium having stored therein computer executable instructions which when executed by a processor are adapted to carry out the method of any one of claims 1 to 10.