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

Abstract

The application provides an energy management method, device and equipment of 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 it is detected that a target vehicle starts a vehicle-mounted air conditioner heating, vehicle running information and traffic information are acquired; the vehicle running information and the traffic information are input into an energy consumption prediction model and an oil consumption prediction model to obtain predicted energy consumption of the target vehicle in a driving process of heating the vehicle-mounted air conditioner to a target temperature in a predicted energy management function opening state and predicted oil consumption of the target vehicle in the driving process of heating the vehicle-mounted air conditioner to the target temperature in a predicted energy management function closing state; and the predicted energy management function is controlled to be opened or closed in the driving process of heating the vehicle-mounted air conditioner to the target temperature based on a comparison result of the predicted energy consumption and the predicted oil consumption. The method of the application can select a more energy-saving energy management mode when the vehicle cabin is heated, and the technical effect of vehicle energy saving and emission reduction is achieved.

Description

Technical Field

[0001] This application relates to the field of hybrid electric vehicles, and more particularly to an energy management method, apparatus, device, and storage medium for hybrid electric vehicles. Background Technology

[0002] Existing hybrid vehicles are generally equipped with predictive energy management functions. These functions can collect traffic information along the route and use predictive energy management technology and algorithms to optimize the best battery power consumption curve during the journey to the destination. This optimizes the engine to operate more in its high-efficiency range, allowing the vehicle to generate more electricity in the high-efficiency range and use pure electric power more in the low-efficiency range, thus achieving fuel saving and emission reduction.

[0003] However, in the harsh winter, vehicles often run their air conditioning to heat the cabin. When using predictive energy management, the vehicle will operate in pure electric mode during the engine's inefficient range. Since the engine is not running in pure electric mode, the air conditioning heating requires power from the PTC (Positive Temperature Coefficient) thermistor. In this mode, compared to a vehicle without predictive energy management and using parallel direct drive, it actually consumes more energy, negatively impacting the energy-saving effect of predictive energy management.

[0004] Therefore, to address the aforementioned issues, existing technologies generally employ a lookup table method. This involves consulting pre-calibrated ambient temperatures, engine coolant temperatures, and remaining battery charge to determine energy consumption under different energy management modes. Based on this energy consumption, the predictive energy management function is then activated or deactivated when the vehicle's air conditioning is heating. However, in actual vehicle operation, energy consumption is dynamically influenced by various factors. The lookup table method cannot accurately determine vehicle energy consumption, thus hindering the selection of the correct energy management mode and leading to increased vehicle energy consumption. Summary of the Invention

[0005] This application provides an energy management method, apparatus, device, and storage medium for hybrid electric vehicles, to solve the problem that hybrid electric vehicles cannot accurately determine the vehicle energy management mode during the vehicle cabin heating process, resulting in increased vehicle energy consumption.

[0006] According to a first aspect disclosed in this application, an energy management method for a hybrid electric vehicle is provided, comprising:

[0007] If the target vehicle is detected to have its air conditioning on for heating, the vehicle's operating information is obtained, and traffic information is obtained based on the target vehicle's navigation information.

[0008] The vehicle operation information and traffic information are input into the energy consumption prediction model to obtain the predicted energy consumption of the target vehicle during the driving process when the vehicle air conditioner heats up to the target temperature while the predicted energy management function is turned on; wherein, the predicted energy consumption includes the heating power consumption of the vehicle air conditioner and the driving fuel consumption of the target vehicle.

[0009] The vehicle operation information and the traffic information are input into the fuel consumption prediction model to obtain the predicted fuel consumption of the target vehicle when it is driving in parallel direct drive mode with the predicted energy management function off and the vehicle air conditioning is heating to the target temperature.

[0010] The predicted energy consumption and the predicted fuel consumption are compared, and based on the comparison results, the predicted energy management function is controlled to be turned on or off during the driving process when the vehicle's air conditioning is heating to the target temperature.

[0011] The predictive energy management function includes: based on the traffic information, predicting the engine inefficiency range and engine efficiency range of the target vehicle on the driving route, controlling the target vehicle to adopt a pure electric drive mode in the engine inefficiency range, and controlling the target vehicle to adopt a parallel charging mode in the engine efficiency range.

[0012] In one feasible implementation, the predicted energy consumption and the predicted fuel consumption are compared, and based on the comparison result, the predicted energy management function is controlled to be turned on or off during the driving process when the vehicle's air conditioning is heating to the target temperature, including:

[0013] The heating power consumption is calculated by oil-to-electricity conversion to obtain the equivalent oil consumption;

[0014] The equivalent fuel consumption is obtained based on the sum of the equivalent fuel consumption and the driving fuel consumption.

[0015] If the equivalent fuel consumption is not greater than the predicted fuel consumption, then during the driving process when the vehicle air conditioning is heating to the target temperature, the predicted energy management function is activated.

[0016] If the equivalent fuel consumption is greater than the predicted fuel consumption, then during the driving process when the vehicle's air conditioning is heating to the target temperature, the predicted energy management function is turned off and the target vehicle is controlled to adopt a parallel direct drive mode.

[0017] In one feasible implementation, the heating power consumption is calculated using an oil-to-electricity conversion method to obtain the equivalent oil consumption, including:

[0018] Obtain the thermal efficiency of the engine and the calorific value of the fuel in the target vehicle;

[0019] The equivalent fuel consumption for heating is obtained by dividing the heating power consumption by the product of the power generation thermal efficiency and the fuel calorific value.

[0020] In one feasible implementation, the method further includes:

[0021] Multiply the equivalent heating fuel consumption by the heating energy consumption correction factor to obtain the comprehensive heating fuel consumption as the equivalent fuel consumption.

[0022] In one feasible implementation, the method further includes:

[0023] With the energy prediction management function enabled, the heating temperature rise rate during the process of the vehicle air conditioner heating to the target temperature is obtained.

[0024] The heating temperature rise rate is normalized to obtain the heating energy consumption correction coefficient; wherein the heating energy consumption correction coefficient is inversely proportional to the heating temperature rise rate.

[0025] In one feasible implementation, the method further includes:

[0026] If the remaining battery power of the target vehicle is less than the heating power consumption, then during the driving process when the vehicle air conditioner heats up to the target temperature, the predictive energy management function is turned off and the target vehicle is controlled to adopt parallel direct drive mode.

[0027] In one feasible implementation, the method further includes:

[0028] Obtain the vehicle attribute parameters of the target vehicle, and perform simulation based on the vehicle attribute parameters to obtain the energy consumption prediction model; or,

[0029] The target vehicle's driving data is obtained when the vehicle's air conditioning is heating and the predictive energy management function is turned on. The driving data under the predicted energy management function's on state is then used for machine learning training to obtain the energy consumption prediction model.

[0030] In one feasible implementation, the method further includes:

[0031] Obtain the vehicle attribute parameters of the target vehicle, and perform simulation based on the vehicle attribute parameters to obtain the fuel consumption prediction model; or,

[0032] The target vehicle's driving data in parallel direct drive mode with the onboard air conditioning in heating mode turned off and the predictive energy management function turned off is obtained, and the driving data in parallel direct drive mode with the predictive energy management function turned off is used for machine learning training to obtain the fuel consumption prediction model.

[0033] In one feasible implementation, the method further includes:

[0034] When the vehicle air conditioner detects that it has reached the target temperature, the predictive energy management function is activated.

[0035] According to a second aspect disclosed in this application, an energy management device for a hybrid electric vehicle is provided, comprising:

[0036] The information acquisition module is used to acquire vehicle operation information and traffic information based on the navigation information of the target vehicle if the target vehicle is detected to have its air conditioning on for heating.

[0037] The energy consumption prediction module is used to input the vehicle operation information and the traffic information into the energy consumption prediction model to obtain the predicted energy consumption of the target vehicle during the driving process when the vehicle air conditioner heats up to the target temperature while the predicted energy management function is turned on; wherein, the predicted energy consumption includes the heating power consumption of the vehicle air conditioner and the driving fuel consumption of the target vehicle.

[0038] The fuel consumption prediction module is used to input the vehicle operation information and the traffic information into the fuel consumption prediction model to obtain the predicted fuel consumption of the target vehicle when it is driving in parallel direct drive mode with the on-board air conditioning heating to the target temperature under the predicted energy management function off state.

[0039] An energy management module is used to compare the predicted energy consumption and the predicted fuel consumption, and based on the comparison results, control the predicted energy management function to be turned on or off during the driving process when the vehicle's air conditioning is heating to the target temperature;

[0040] The predictive energy management function includes: based on the traffic information, predicting the engine inefficiency range and engine efficiency range of the target vehicle on the driving route, controlling the target vehicle to adopt a pure electric drive mode in the engine inefficiency range, and controlling the target vehicle to adopt a parallel charging mode in the engine efficiency range.

[0041] According to a third aspect disclosed in this application, an electronic device is provided, including a processor and a memory communicatively connected to the processor;

[0042] The memory stores computer-executed instructions;

[0043] The processor executes computer execution instructions stored in the memory to implement the method described in any one of the first aspects.

[0044] According to a fourth aspect disclosed in this application, a computer-readable storage medium is provided, wherein computer-executable instructions are stored therein, which, when executed by a processor, are used to implement the method described in any one of the first aspects.

[0045] According to the fifth aspect disclosed in this application, a computer program product is provided, comprising a computer program that, when executed by a processor, is used to implement the method described in any one of the first aspects.

[0046] Compared with the prior art, this application has the following beneficial effects:

[0047] The energy management method, device, equipment, and storage medium for hybrid electric vehicles provided in this application predict the vehicle's energy consumption during the heating process from the onboard air conditioner to the target temperature under different energy management modes. By comparing energy consumption, it identifies which energy management mode has lower energy consumption and controls the activation or deactivation of the predictive energy management function of the target vehicle accordingly, selecting the appropriate energy management mode. This allows for accurate selection of a more energy-efficient energy management mode in low-temperature environments, achieving the technical effect of energy saving and emission reduction in vehicles. Attached Figure Description

[0048] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without inventive effort. Wherein:

[0049] Figure 1 A flowchart illustrating an energy management method for a hybrid electric vehicle provided in this application embodiment;

[0050] Figure 2 A flowchart illustrating another energy management method for a hybrid electric vehicle provided in this application embodiment;

[0051] Figure 3 This application provides a schematic diagram of a process for converting heating power consumption into equivalent fuel consumption in an embodiment of the present application.

[0052] Figure 4 This application provides another schematic diagram of a process for converting heating power consumption into equivalent fuel consumption in an embodiment of the present application.

[0053] Figure 5 This is a schematic diagram of the structure of an energy management device for a hybrid electric vehicle provided in an embodiment of this application;

[0054] Figure 6This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0055] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0056] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0057] Existing hybrid electric vehicles are generally equipped with predictive energy management functions. These functions can collect traffic information along the route based on navigation information and use predictive energy management technology and algorithms to predict the engine's high-efficiency and low-efficiency ranges throughout the entire journey. By switching the driving mode, in the high-efficiency range, the vehicle is controlled to use a parallel charging mode (in parallel charging mode, the engine's output power is greater than the vehicle's current power demand, and the excess power is used to charge the power battery) to drive the vehicle. While driving the vehicle, the engine charges in advance. In the low-efficiency range, the vehicle switches to pure electric drive mode using the pre-prepared battery power to drive purely on electricity, thereby improving the overall fuel efficiency of the entire journey and achieving the goal of energy saving and emission reduction.

[0058] Generally, enabling predictive energy management is more energy-efficient than disabling it and operating in parallel direct drive mode (where the battery is not engaged and the engine directly drives the vehicle, with the engine's output power matching the vehicle's current power requirements). However, in severe winter conditions, when vehicles need to run their air conditioning for heating for extended periods, if predictive energy management is enabled and the engine is operating in its inefficient range, the air conditioning system still consumes electricity from the PTC (Positive Temperature Coefficient) thermistor for heating, resulting in additional energy consumption. Parallel direct drive mode, which utilizes engine waste heat for heating, does not consume additional energy. Therefore, in this situation, using predictive energy management while the air conditioning is heating may consume more energy than using parallel direct drive mode with predictive energy management disabled, negatively impacting the energy-saving effect of predictive energy management.

[0059] To address this issue, existing technologies typically employ a lookup table method. This involves consulting pre-calibrated ambient temperatures, engine coolant temperatures, and remaining battery charge to determine energy consumption under different energy management modes. Based on this energy consumption, the predictive energy management function is then activated or deactivated when the vehicle's air conditioning is in heating mode. However, in actual vehicle operation, energy consumption is dynamically influenced by various factors. The lookup table method cannot accurately determine the vehicle's energy consumption, thus hindering the selection of the correct energy management mode. Therefore, for existing hybrid vehicles, current energy management methods cannot accurately determine which energy management mode is most energy-efficient when the vehicle's air conditioning is in heating mode.

[0060] To address the aforementioned issues, this application proposes an energy management method for hybrid electric vehicles. By utilizing vehicle operation and traffic information, the energy consumption during the vehicle cabin heating process is accurately predicted using different energy management modes. Based on the predicted energy consumption, the most energy-efficient energy management mode is accurately selected, thereby achieving energy conservation and emission reduction for the vehicle.

[0061] The technical solution of the energy management method for hybrid electric vehicles provided in this application will be described in detail below through specific embodiments. It should be noted that the following embodiments may exist alone or in combination with each other, and the same or similar content may not be described again in different embodiments.

[0062] It should be noted that the execution subject of the energy management method for hybrid electric vehicles provided in this application embodiment can be a vehicle system or a cloud server. When the execution subject is a cloud server, the cloud server and the target vehicle are connected through communication such as the Internet of Vehicles.

[0063] Compared to using the vehicle's infotainment system as the execution entity, using a cloud server as the execution entity results in faster execution speed, lower requirements for vehicle hardware, and no additional vehicle cost.

[0064] The cloud server may store data related to multiple vehicles, and the VIN (Vehicle Identification Number) can be used as an identification index to store the data of the corresponding vehicle.

[0065] Figure 1 A flowchart illustrating the energy management method for a hybrid electric vehicle provided in this application is shown below. Figure 1 In some embodiments, the energy management method for this hybrid vehicle includes the following steps:

[0066] S101, if the target vehicle is detected to have its air conditioning on for heating, the vehicle operation information is obtained, and traffic information is obtained based on the target vehicle's navigation information.

[0067] Among them, the energy consumption of the target vehicle is affected by various factors, the most important of which are two types: one is vehicle operation information reflecting the status of the target vehicle, and the other is traffic information reflecting traffic conditions.

[0068] Specifically, vehicle operating information includes: vehicle air conditioning information, such as the real-time heating power request value of the vehicle air conditioning, temperature and pressure signals of the vehicle air conditioning pipes (evaporator, condenser), cabin temperature, airflow information, etc.; battery information, such as battery temperature, remaining charge, battery voltage, battery current, battery current limit, battery SOH (State of Health); engine information, such as engine coolant temperature, engine speed, etc.; PTC information, such as PTC voltage, PTC current, etc.; driving information, such as brake pressure, throttle information, gear information, driving style information, driving mode information, etc.; and environmental information, such as weather information, ambient temperature information, etc.

[0069] Specifically, traffic information includes vehicle speed, slope information, traffic light information, and distance to vehicles ahead. Among these, the vehicle speed information is a navigation speed predicted based on navigation information. Using navigation speed to predict the target vehicle's speed in the engine's inefficient range is more accurate than predicting the current actual speed.

[0070] Preferably, the target temperature is the set temperature of the vehicle air conditioner, or a temperature value with a preset temperature difference from the set temperature of the vehicle air conditioner.

[0071] The target temperature can be the air conditioning set temperature or a temperature value close to the vehicle's air conditioning set temperature. This is because, according to the heating characteristics of air conditioning, when the air conditioning reaches a certain range from the set temperature, meaning the heating process is nearing its end, the stage of high energy consumption for heating has passed, and the subsequent phase is one of maintaining a constant temperature. During this subsequent temperature maintenance, the energy consumption difference between enabling and disabling predictive energy management will be minimal, eliminating the need to compare which heating mode is more energy-efficient in the later stages. Therefore, the target temperature can also be a temperature value with a preset temperature difference from the vehicle's air conditioning set temperature.

[0072] Specifically, the preset temperature difference is a calibrable value, typically ranging from 3 to 5 degrees Celsius, which can fluctuate depending on parameters such as ambient temperature, vehicle speed, and sunlight.

[0073] S102, input vehicle operation information and traffic information into the energy consumption prediction model to obtain the predicted energy consumption of the target vehicle during the driving process when the vehicle air conditioner heats up to the target temperature under the predicted energy management function. The predicted energy consumption includes the heating power consumption of the vehicle air conditioner and the driving fuel consumption of the target vehicle.

[0074] Among them, the energy consumption prediction model predicts the energy consumption of the target vehicle during the driving process when the vehicle's air conditioning is heated to the target temperature under the predicted energy management function. This prediction is used to determine the energy consumption of the target vehicle during the driving process when the vehicle's air conditioning is heated to the target temperature under different energy management modes.

[0075] Specifically, because the target vehicle's journey to reach the target temperature with the air conditioning includes both the engine's high-efficiency and low-efficiency zones, and because the predictive energy management function is enabled, the vehicle operates in parallel charging mode during the high-efficiency zone. In this mode, the engine charges while driving, and the air conditioning uses waste heat from the engine, consuming no additional energy. However, during the low-efficiency zone, the vehicle operates in pure electric mode. In this mode, the energy consumed by driving is the additional energy gained in parallel charging mode, and the air conditioning requires additional power for heating. Therefore, with predictive energy management enabled, the predicted energy consumption during the journey to reach the target temperature includes both the energy consumed by the air conditioning in the low-efficiency zone (pure electric heating) and the fuel consumption of the engine in the high-efficiency zone (parallel charging). Thus, the predicted energy consumption comprises both the heating energy consumption of the air conditioning and the fuel consumption of the target vehicle.

[0076] Preferably, the vehicle attribute parameters of the target vehicle are obtained, and an energy consumption prediction model is obtained by simulation based on the vehicle attribute parameters; or, the driving data of the target vehicle with the on-board air conditioning heating function turned on is obtained, and the driving data with the predictive energy management function turned on is used for machine learning training to obtain an energy consumption prediction model.

[0077] The energy consumption prediction model can be obtained in two ways. One is through simulation based on the vehicle attribute parameters of the target vehicle, specifically, the vehicle attribute parameters include the structural parameters and physical characteristic parameters of the target vehicle. After the energy consumption prediction model simulation is completed, after inputting the collected vehicle operation information and traffic information, the energy consumption prediction model can simulate the heating and driving process of the target vehicle under the predicted energy management enabled state, and obtain the predicted energy consumption after the simulated driving.

[0078] Specifically, the energy consumption prediction model obtained through simulation is essentially a whole-vehicle simulation model of the target vehicle, which is necessary to simulate the energy consumption of the target vehicle during heating and driving. The whole-vehicle simulation model generally consists of simulation sub-models such as an air conditioning simulation model, an engine simulation model, a motor simulation model, a battery simulation model, a controller strategy simulation model, and a vehicle dynamics simulation model. Among these, the air conditioning simulation model can be used to predict the heating power consumption of the vehicle's air conditioning system using PTC heating when the energy management function is activated, during the process of heating to the target temperature in pure electric drive mode within the engine's inefficient range.

[0079] Another approach involves using the vehicle's driving data (when the vehicle's air conditioning is heating) with the predictive energy management function enabled as the training set to train a machine learning model. After training, this results in an energy consumption prediction model that takes vehicle operation and traffic information as input and predicts energy consumption as output. Specifically, machine learning models include deep learning models and artificial neural network models.

[0080] S103 inputs vehicle operation information and traffic information into the fuel consumption prediction model to obtain the predicted fuel consumption of the target vehicle when it is driving in parallel direct drive mode with the predicted energy management function off and the vehicle air conditioning is heating to the target temperature.

[0081] The fuel consumption prediction model predicts the fuel consumption of a target vehicle during its journey from heating the air conditioner to the target temperature when the vehicle is in parallel direct drive mode with the energy management function turned off. This prediction is then used to determine the energy consumption of the target vehicle during its journey from heating the air conditioner to the target temperature under different energy management modes.

[0082] Specifically, since the vehicle air conditioner always uses the engine's waste heat for heating in parallel direct drive mode and does not consume additional electricity, the energy consumption of the vehicle air conditioner during the driving process of heating to the target temperature in parallel direct drive mode with the predictive energy management function turned off is the predicted fuel consumption of the engine in parallel direct drive mode.

[0083] Preferably, the vehicle attribute parameters of the target vehicle are obtained, and a fuel consumption prediction model is obtained by simulation based on the vehicle attribute parameters; or, the driving data of the target vehicle in parallel direct drive mode with the on-board air conditioning heating off is obtained, and the driving data in parallel direct drive mode with the predictive energy management function off is used for machine learning training to obtain a fuel consumption prediction model.

[0084] The fuel consumption prediction model can be obtained in two ways. One is through simulation based on the vehicle attribute parameters of the target vehicle, specifically, the vehicle attribute parameters include the structural parameters and physical characteristic parameters of the target vehicle. After the fuel consumption prediction model simulation is completed, after inputting the collected vehicle operation information and traffic information, the fuel consumption prediction model can simulate the heating and driving process of the target vehicle in parallel direct drive mode with the predictive energy management function turned off, and obtain the predicted fuel consumption after simulated driving.

[0085] Specifically, the fuel consumption prediction model obtained through simulation is essentially a full-vehicle simulation model of the target vehicle, which is necessary to simulate the energy consumption of the target vehicle during heating and driving. The full-vehicle simulation model generally consists of simulation sub-models such as air conditioning simulation model, engine simulation model, motor simulation model, battery simulation model, controller strategy simulation model, and vehicle dynamics simulation model.

[0086] Another approach involves using the target vehicle's driving data (with predictive energy management function off, in parallel direct drive mode, and with the onboard air conditioning on) as a training set to train a machine learning model. After training, a fuel consumption prediction model is obtained, taking vehicle operation and traffic information as input and predicting fuel consumption as output. Specifically, machine learning models include deep learning models and artificial neural network models.

[0087] S104 compares the predicted energy consumption and predicted fuel consumption, and based on the comparison results, controls the predicted energy management function to be turned on or off during the driving process when the vehicle's air conditioning is heating to the target temperature.

[0088] For vehicle air conditioning, the most energy-intensive phase is the heating stage, where the air conditioner heats the vehicle from the current temperature to the target temperature. This is because when the vehicle cabin temperature is low, the air conditioner's electrical components, compressor operation, and other processes consume peak power during the heating phase, resulting in higher energy consumption. However, once the target temperature is reached, the air conditioner enters a low-power mode during the temperature maintenance phase, significantly reducing energy consumption. At this point, there is no significant difference in heating energy consumption between an air conditioner with predictive energy management enabled and one with it disabled.

[0089] Therefore, during the driving phase when the vehicle's air conditioning is heating, the energy consumption with the predictive energy management function enabled may be greater than the energy consumption when the predictive energy management function is disabled. This generally refers to the driving phase when the vehicle's air conditioning is heating to the target temperature.

[0090] Therefore, during the driving process when the vehicle's air conditioning is heating to the target temperature, the predicted energy consumption with the predictive energy management function on and the predicted fuel consumption with the predictive energy management function off are compared. After determining which energy management mode is more energy-efficient, the more energy-efficient energy management mode is selected during the driving process when the vehicle's air conditioning is heating to the target temperature.

[0091] Furthermore, since the energy management mode of a hybrid vehicle equipped with predictive energy management can be selected by turning the predictive energy management function on or off, by controlling the predictive energy management function to turn it on or off during the driving process when the vehicle's air conditioning is heating to the target temperature, a more energy-efficient energy management mode can be selected, thus achieving the technical effect of vehicle energy saving.

[0092] The predictive energy management function includes: based on traffic information, predicting the engine inefficiency range and engine efficiency range of the target vehicle on the driving route, and controlling the target vehicle to adopt pure electric drive mode in the engine inefficiency range and parallel charging mode in the engine efficiency range.

[0093] In this embodiment, the energy consumption of the vehicle during the heating process to the target temperature by the in-vehicle air conditioning under different energy management modes is predicted. By comparing energy consumption, it is determined which energy management mode has lower energy consumption, and the predicted energy management function of the target vehicle is turned on or off accordingly, selecting the appropriate energy management mode. Thus, in low-temperature environments, a more energy-efficient energy management mode is accurately selected, achieving the technical effect of energy saving and emission reduction in vehicles.

[0094] exist Figure 1 Based on the embodiments shown, the following is combined with Figure 2 The technical solution for the energy management method of the above-mentioned hybrid electric vehicles will be further introduced.

[0095] Figure 2 A flowchart illustrating another energy management method for a hybrid electric vehicle provided in this application embodiment is shown below. Figure 2 In some embodiments, the energy management method for this hybrid vehicle includes the following steps:

[0096] S201, if the ambient temperature of the target vehicle is lower than the preset temperature, and the difference between the current temperature of the target vehicle's cabin and the target temperature is greater than the preset temperature difference, then the steps of obtaining vehicle operation information and obtaining traffic information based on the target vehicle's navigation information are executed if the target vehicle's air conditioning is detected to be turned on for heating.

[0097] It should be noted that the execution process of "if the target vehicle is detected to have its air conditioning on for heating, then obtain the vehicle's operating information and obtain traffic information based on the target vehicle's navigation information" in step S201 is the same as in step S101, and will not be repeated here.

[0098] The difference lies in the addition of two criteria in step S201: "the ambient temperature of the target vehicle is lower than the preset temperature" and "the difference between the current temperature of the target vehicle's cabin and the target temperature is greater than the preset temperature difference." The purpose of this is to:

[0099] The criterion of determining whether the ambient temperature is lower than the preset temperature threshold is because vehicles only request heating under certain low-temperature conditions. For example, vehicles will not request heating in summer. By determining the ambient temperature, unnecessary data processing and calculations are avoided in summer or other conditions. Specifically, the preset temperature threshold can be set to -10 degrees Celsius.

[0100] The purpose of determining whether the difference between the current temperature and the target temperature in the vehicle's cabin exceeds a preset temperature difference threshold is to assess whether the PTC heating power demand of the vehicle's air conditioning system will be excessive when the predictive energy management function is activated. If the temperature difference between the current and target temperatures is small, it indicates that the vehicle's air conditioning system has essentially entered the final stage of heating or the stage of maintaining a constant temperature. In this case, the heating power demand using PTC will not be too high, and there will be no situation where the predictive energy management function consumes a large amount of electricity for heating in pure electric drive mode. In this case, the energy consumption difference between PTC heating and engine waste heat heating is not significant, so there is no need to predict the energy consumption of the two energy management modes.

[0101] S202, input vehicle operation information and traffic information into the energy consumption prediction model to obtain the predicted energy consumption of the target vehicle during the driving process when the vehicle air conditioner heats up to the target temperature while the predicted energy management function is turned on; wherein, the predicted energy consumption includes the heating power consumption of the vehicle air conditioner and the driving fuel consumption of the target vehicle.

[0102] It should be noted that the execution process of step S202 is the same as that of step S102, and will not be repeated here.

[0103] S203 inputs vehicle operation information and traffic information into the fuel consumption prediction model to obtain the predicted fuel consumption of the target vehicle when it is driving in parallel direct drive mode with the predicted energy management function off and the vehicle air conditioning is heating to the target temperature.

[0104] It should be noted that the execution process of step S203 is the same as that of step S103, and will not be described again here.

[0105] S204, determine whether the remaining battery power of the target vehicle is less than the heating power consumption.

[0106] S205, if the remaining power of the target vehicle is less than the power consumption for heating, then during the driving process when the vehicle's air conditioning is heating to the target temperature, the predictive energy management function is turned off and the target vehicle is controlled to adopt parallel direct drive mode.

[0107] Specifically, due to the characteristics of the predictive energy management function, the parallel direct drive mode will switch to pure electric drive mode during the engine's inefficient range. Furthermore, the period when the vehicle's air conditioning reaches the target temperature may also include this engine inefficiency period, during which the air conditioning consumes electricity for heating. Since the additional electricity charged in parallel charging mode during the engine's efficient range will be used during the pure electric drive mode during the engine's inefficient range when predictive energy management is enabled, the energy consumed by the air conditioning for heating during the engine's inefficient range will be provided by the remaining charge of the battery. A condition has been added to determine whether the heating power consumption is less than the remaining battery charge. If the heating power consumption is greater than the remaining battery charge, it indicates that the remaining battery charge is insufficient to support the energy consumed by the vehicle's air conditioning to reach the target temperature when predictive energy management is enabled.

[0108] At this point, the predictive energy management function can be turned off, allowing the target vehicle to drive in parallel direct drive mode during this phase, ensuring that the vehicle's air conditioning can provide normal heating.

[0109] S206 If the remaining power of the target vehicle is not less than the heating power consumption, the heating power consumption is calculated by oil-to-electricity conversion to obtain the equivalent fuel consumption.

[0110] In order to compare predicted energy consumption with 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 unified unit of measurement with the predicted fuel consumption. This allows for a comparison of the energy consumption magnitude with the predicted fuel consumption, and the on / off state of the predicted energy management function is controlled based on the comparison results.

[0111] S207, based on the sum of equivalent fuel consumption and driving fuel consumption, obtains the equivalent fuel consumption.

[0112] The equivalent fuel consumption and the predicted fuel consumption are added together to obtain the total fuel consumption when the vehicle's air conditioning is heating to the target temperature with the predicted energy management function enabled.

[0113] S208 determines whether the equivalent fuel consumption is greater than the predicted fuel consumption.

[0114] S209: If the equivalent fuel consumption is not greater than the predicted fuel consumption, the predicted energy management function will be activated during the driving process when the vehicle's air conditioning is heating to the target temperature.

[0115] Among them, when the equivalent fuel consumption is no greater than the predicted fuel consumption, it indicates that during the driving stage when the air conditioning is heating to the target temperature, turning on the predictive energy management function is no more energy-consuming than turning off the predictive energy management function. When the predictive energy management function is turned on, the predictive energy management function will control the target vehicle to use pure electric drive mode in the engine inefficient range and parallel direct drive mode in the engine efficient range, according to its functional characteristics.

[0116] S210 If the equivalent fuel consumption is greater than the predicted fuel consumption, then during the driving process when the vehicle's air conditioning is heating to the target temperature, the predicted energy management function is turned off and the target vehicle is controlled to adopt parallel direct drive mode.

[0117] When the equivalent fuel consumption is greater than the predicted fuel consumption, it indicates that during the driving phase when the air conditioning is heating to the target temperature, turning on the predictive energy management function consumes more energy than turning off the predictive energy management function. In this case, it is recommended to turn off the predictive energy management function and drive in parallel direct drive mode.

[0118] S211, when the vehicle air conditioning is detected to have reached the target temperature, the predictive energy management function is activated.

[0119] When the vehicle's air conditioning reaches the target temperature, it will enter a constant temperature maintenance phase. At this time, the energy consumption of the vehicle's air conditioning using pure electric heating or engine waste heat heating is not much different. Therefore, after the vehicle's air conditioning reaches the target temperature, the predictive energy management function will resume normal operation.

[0120] Specifically, there are two scenarios for controlling the activation of the predictive energy management function: one is that the predictive energy management function was already enabled before, in which case it will remain enabled; the other is that the predictive energy management function was previously disabled, in which case it will be re-enabled.

[0121] In this embodiment, by converting the predicted power consumption into an equivalent fuel consumption, the predicted energy consumption and predicted fuel consumption can be compared under the same unit of measurement to accurately determine which one has lower energy consumption. Based on the comparison results, a more energy-efficient energy management method can be selected.

[0122] exist Figure 2 Based on the illustrated embodiment, it is necessary to convert the heating fuel consumption into equivalent fuel consumption. The following will explain... Figure 3 The technical solution for energy management of hybrid electric vehicles described above will be further introduced regarding the conversion of heating power consumption into equivalent fuel consumption.

[0123] Figure 3 A schematic diagram illustrating the process of converting heating power consumption into equivalent fuel consumption is provided in this application embodiment. (See attached diagram.) Figure 3In some embodiments, the process of converting heating power consumption into equivalent fuel consumption includes the following steps:

[0124] S301, obtain the thermal efficiency of the engine and the calorific value of the fuel in the target vehicle.

[0125] Among them, power generation thermal efficiency refers to the conversion efficiency of an engine in converting the heat energy from fuel combustion into electrical energy. It is a dimensionless indicator and is generally expressed as a percentage.

[0126] The calorific value of fuel is the amount of heat produced when one kilogram of fuel is completely burned.

[0127] S302, divide the heating power consumption by the product of the power generation thermal efficiency and the fuel calorific value to obtain the equivalent heating power consumption as the equivalent fuel consumption.

[0128] Specifically, based on the calculated relationship between the engine's power generation thermal efficiency, fuel calorific value, and fuel consumption, the heating power consumption is divided by the product of the power generation thermal efficiency and the fuel calorific value to obtain the equivalent heating fuel consumption as the equivalent fuel consumption.

[0129] In this embodiment, by converting the heating power consumption into equivalent fuel consumption, the measurement units for predicted energy consumption and predicted fuel consumption can be unified, making it easier to compare them.

[0130] exist Figure 2 Based on the illustrated embodiment, it is necessary to convert the heating fuel consumption into equivalent fuel consumption. The following will explain... Figure 4 The technical solution for energy management of hybrid electric vehicles described above will be further introduced regarding the conversion of heating power consumption into equivalent fuel consumption.

[0131] Figure 4 A schematic diagram illustrating another process for converting heating power consumption into equivalent fuel consumption, as provided in this application embodiment, is shown below. Figure 4 In some embodiments, the process of converting heating power consumption into equivalent fuel consumption includes the following steps:

[0132] S401, obtain the thermal efficiency of the engine and the calorific value of the fuel in the target vehicle.

[0133] S402, divide the heating power consumption by the product of the power generation thermal efficiency and the fuel calorific value to obtain the equivalent heating fuel consumption.

[0134] It should be noted that the execution process of steps S401-S402 is the same as that of steps S301-S302, and will not be repeated here.

[0135] S403 multiplies the equivalent heating fuel consumption by the heating energy consumption correction factor to obtain the comprehensive heating fuel consumption as the equivalent fuel consumption.

[0136] Specifically, the heating power consumption is calculated when the vehicle's air conditioning is in pure electric drive mode with the predictive energy management function enabled and the engine operating in its inefficient range. This power consumption is related to the heating performance of the air conditioning system. Therefore, it is calculated in conjunction with the heating energy consumption coefficient to obtain the heating energy consumption with the predictive energy management function disabled, achieving the same heating effect. In particular, the higher the heating power consumption to reach the target temperature, the higher the heating rate, the better the heating effect, and the shorter the time to reach the target temperature.

[0137] Specifically, by introducing a heating energy consumption correction coefficient, which serves as a discount factor for pure electric heating, the additional energy costs incurred by customers due to the heating effect during pure electric heating are reflected. The purpose is to unify the heating effect with and without predictive energy management, thus obtaining energy consumption under different energy management modes for the same heating effect. For example, a heating energy consumption correction coefficient of 1 indicates that the heating effect of pure electric heating is consistent with that of engine waste heat heating. However, a heating energy consumption correction coefficient of 0.5 indicates that pure electric heating is more effective and has a faster heating rate. To unify the heating effect of pure electric heating with that of engine waste heat heating, the extra energy consumed by pure electric heating to achieve the desired heating effect needs to be discounted using the heating energy consumption correction coefficient before comparing the energy consumption of the two modes.

[0138] In pure electric drive mode, the vehicle's air conditioning primarily uses a PTC (Power Transmission Control) system for heating. The heating effect of the PTC is controllable; for example, increasing its heating power can control its performance. However, the heating effect from engine waste heat is stable and cannot be adjusted, as it utilizes the residual heat from the engine coolant. Therefore, the introduced heating energy consumption correction coefficient is mainly used to correct the equivalent heating fuel consumption in pure electric drive mode. The corrected comprehensive heating fuel consumption is then used as the equivalent fuel consumption for vehicle air conditioning heating when the predictive energy management function is enabled. This is used for subsequent comparisons of energy consumption, and based on the comparison results, the on / off state of the predictive energy management function is controlled when the vehicle air conditioning reaches the target temperature.

[0139] Specifically, if the equivalent fuel consumption is no greater than the predicted fuel consumption, it means that during the driving phase when the air conditioning is heating to the target temperature, under the same heating effect, turning on the predictive energy management function does not consume more energy than turning off the predictive energy management function. Keeping the predictive energy management function on means that when the target vehicle is in the engine inefficiency range, the predictive energy management function will control the target vehicle to use pure electric drive mode in the engine inefficiency range and parallel direct drive mode in the engine high efficiency range, according to its functional characteristics.

[0140] Specifically, if the overall fuel consumption is greater than the predicted fuel consumption, it indicates that during the driving phase when the air conditioning is heating to the target temperature, under the same heating effect, turning on the predictive energy management function consumes more energy than turning it off. In this case, it is recommended to turn off the predictive energy management function and drive in parallel direct drive mode.

[0141] In this embodiment, by introducing a 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 unit of measurement to accurately determine which one has lower energy consumption. Based on the comparison results, a more energy-efficient energy management method can be selected.

[0142] Preferably, when the energy prediction management function is enabled, the heating temperature rise rate during the process of the vehicle air conditioner heating to the target temperature is obtained; the heating temperature rise rate is normalized to obtain the heating energy consumption correction coefficient; wherein, the heating energy consumption correction coefficient is inversely proportional to the heating temperature rise rate.

[0143] The heating energy consumption correction coefficient is obtained by normalizing the heating temperature rise rate. A higher heating temperature rise rate indicates a faster heating rate, better heating effect, better user experience, and the more energy users are willing to expend for it. This is because when the predictive energy management function is off, the vehicle uses engine waste heat for heating throughout the journey, resulting in a stable heating effect. However, when the predictive energy management function is on, the vehicle's air conditioning uses the PTC (Power Transmission Control Center) to consume additional electricity for heating in areas where the engine is less efficient. Therefore, the heating effect of pure electric heating varies depending on the user's choices. For example, a user can increase the PTC's heating power, choosing to consume more energy to improve the heating effect. Therefore, to achieve the same heating effect between pure electric heating and engine waste heat heating, a discount coefficient for pure electric heating can be obtained by normalizing the heating temperature rise rate when the predictive energy management function is on. This coefficient reflects the additional energy cost incurred by the user due to the improved heating effect during pure electric heating.

[0144] Therefore, the higher the heating rate, the better the heating effect, but the more additional energy costs for the user. Consequently, the cost that needs to be discounted during calculation is greater, and the smaller the discount factor becomes. Therefore, when normalizing the heating rate, the heating rate is inversely proportional to the heating energy consumption correction factor.

[0145] Specifically, a data acquisition window can be selected for the heating temperature rise rate, such as the average temperature rise rate when the vehicle's air conditioning system heats to 50% of the target temperature difference. By selecting 50% of the target temperature difference as the window for calculating the heating rate, we will record the time and energy required to reach the first 50% of the target temperature during the heating process, thus calculating the heating rate. This window reflects the response speed and stability of the heating system and helps predict the changing trends of time and energy required for future heating processes, thereby better controlling the heating process and determining the optimal operating parameters. Of course, this window can also be set to other values, such as 70%, 80%, etc.

[0146] Specifically, the range of the heating energy consumption correction factor is (0,1).

[0147] Figure 5 This is a schematic diagram of the energy management device for a hybrid electric vehicle provided in this application embodiment. (See attached diagram.) Figure 5 The energy management device of the hybrid electric vehicle includes various functional modules for implementing the aforementioned energy management method of the hybrid electric vehicle, and any functional module can be implemented by software and / or hardware.

[0148] In some embodiments, the energy management device 500 for a 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:

[0149] The information acquisition module 501 is used to acquire vehicle operation information and traffic information based on the navigation information of the target vehicle if it is detected that the target vehicle has turned on the vehicle air conditioning for heating.

[0150] The energy consumption prediction module 502 is used to input vehicle operation information and traffic information into the energy consumption prediction model to obtain the predicted energy consumption of the target vehicle during the driving process when the vehicle air conditioner heats up to the target temperature under the predicted energy management function. The predicted energy consumption includes the heating power consumption of the vehicle air conditioner and the driving fuel consumption of the target vehicle.

[0151] The fuel consumption prediction module 503 is used to input vehicle operation information and traffic information into the fuel consumption prediction model to obtain the predicted fuel consumption of the target vehicle during the process of heating the vehicle air conditioner to the target temperature in parallel direct drive mode.

[0152] The energy management module 504 is used to compare predicted energy consumption and predicted fuel consumption, and based on the comparison results, controls the predicted energy management function to be turned on or off during driving when the vehicle's air conditioning is heating to the target temperature.

[0153] The predictive energy management function includes: based on traffic information, predicting the engine inefficiency range and engine efficiency range of the target vehicle on the driving route, and controlling the target vehicle to adopt pure electric drive mode in the engine inefficiency range and parallel charging mode in the engine efficiency range.

[0154] In some embodiments, the energy management module 504 is specifically used for:

[0155] The heating power consumption is calculated by converting oil to electricity to obtain the equivalent oil consumption.

[0156] The equivalent fuel consumption is obtained by summing the equivalent fuel consumption and the driving fuel consumption.

[0157] If the equivalent fuel consumption is not greater than the predicted fuel consumption, the predicted energy management function will be activated during the driving process when the vehicle's air conditioning is heating to the target temperature.

[0158] If the equivalent fuel consumption is greater than the predicted fuel consumption, then during the driving process when the vehicle's air conditioning is heating to the target temperature, the predictive energy management function will be turned off and the target vehicle will be controlled to adopt parallel direct drive mode.

[0159] In some embodiments, the energy management module 504 is specifically used for:

[0160] Obtain the thermal efficiency of the engine and the calorific value of the fuel in the target vehicle;

[0161] Divide the heating power consumption by the product of the power generation thermal efficiency and the fuel calorific value to obtain the equivalent heating fuel consumption as the equivalent fuel consumption.

[0162] In some embodiments, the energy management module 504 is specifically used for:

[0163] The equivalent heating fuel consumption is multiplied by the heating energy consumption correction factor to obtain the comprehensive heating fuel consumption as the equivalent fuel consumption.

[0164] In some embodiments, the energy management module 504 is specifically used for:

[0165] With the energy prediction management function enabled, the heating temperature rise rate during the process of the vehicle's air conditioning heating up to the target temperature is obtained.

[0166] The heating temperature rise rate is normalized to obtain the heating energy consumption correction coefficient; the heating energy consumption correction coefficient is inversely proportional to the heating temperature rise rate.

[0167] In some embodiments, the energy management module 504 is specifically used for:

[0168] If the remaining battery power of the target vehicle is less than the heating power consumption, then during the driving process when the vehicle's air conditioning is heating to the target temperature, the predictive energy management function will be turned off and the target vehicle will be controlled to adopt parallel direct drive mode.

[0169] In some embodiments, the energy consumption prediction module 502 is specifically used for:

[0170] Obtain the vehicle attribute parameters of the target vehicle, and perform simulation based on the vehicle attribute parameters to obtain an energy consumption prediction model; or,

[0171] Acquire driving data of the target vehicle with the predictive energy management function enabled when the vehicle's air conditioning is on, and use machine learning to train an energy consumption prediction model from the driving data with the predictive energy management function enabled.

[0172] In some embodiments, the fuel consumption prediction module 503 is specifically used for:

[0173] Obtain the vehicle attribute parameters of the target vehicle, and perform simulation based on the vehicle attribute parameters to obtain a fuel consumption prediction model; or,

[0174] Acquire driving data of the target vehicle in engine drive mode with the onboard air conditioning on and the predictive energy management function turned off. Use the driving data in engine drive mode with the predictive energy management function turned off to train a fuel consumption prediction model through machine learning.

[0175] In some embodiments, the information acquisition module 501 is specifically used for:

[0176] If the ambient temperature of the target vehicle is lower than the preset temperature, and the difference between the current temperature of the target vehicle's cabin and the target temperature is greater than the preset temperature difference, then the steps of obtaining vehicle operation information and obtaining traffic information based on the target vehicle's navigation information are executed.

[0177] In some embodiments, the energy management module 504 is specifically used for:

[0178] When the vehicle's air conditioning system detects that it has reached the target temperature, the predictive energy management function is activated.

[0179] The energy management device 500 for hybrid electric vehicles provided in this application embodiment is used to execute the technical solution provided in the aforementioned embodiment of the energy management method for hybrid electric vehicles. Its implementation principle and technical effects are similar to those in the aforementioned embodiment of the method, and will not be repeated here.

[0180] It should be noted that the division of the various modules in the above device is merely a logical functional division. In actual implementation, they can be fully or partially integrated into a single physical entity, or they can be physically separated. Furthermore, these modules can be implemented entirely in software via processing elements, entirely in hardware, or partially in software via processing elements and partially in hardware. For example, the energy management module can be a separate processing element, or it can be integrated into a chip within the device. Alternatively, it can be stored as program code in the device's memory, invoked and executed by a processing element. The implementation of other modules is similar. Moreover, these modules can be fully or partially integrated together, or implemented independently. The processing element here can be an integrated circuit with signal processing capabilities. During implementation, each step of the above method or each of the above modules can be completed through integrated logic circuits in the processor element or through software instructions.

[0181] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. (See attached diagram.) Figure 6 The electronic device 600 includes: a processor 601, and a memory 602 communicatively connected to the processor 601;

[0182] Memory 602 stores computer-executed instructions;

[0183] The processor 601 executes computer execution instructions stored in the memory 602 to implement the aforementioned technical solution of the energy management method for hybrid electric vehicles.

[0184] In the aforementioned electronic device 600, the memory 602 and the processor 601 are electrically connected directly or indirectly to achieve data transmission or interaction. For example, these components can be electrically connected to each other through one or more communication buses or signal lines, such as bus connections. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be classified as address buses, data buses, control buses, etc., but this does not mean that there is only one bus or one type of bus. The memory 602 stores computer execution instructions for implementing the aforementioned energy management method for hybrid electric vehicles, including at least one software functional module that can be stored in the memory 602 in the form of software or firmware. The processor 601 executes various functional applications and data processing by running the software programs and modules stored in the memory 602.

[0185] The memory 602 includes at least one type of readable storage medium, not limited to Random Access Memory (RAM), Read Only Memory (ROM), Programmable Read-Only Memory (PROM), Erasable Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), etc. The memory 602 stores programs, and the processor 601 executes the programs after receiving execution instructions. Furthermore, 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 can communicate with various hardware or software components to provide an operating environment for other software components.

[0186] Processor 601 can be an integrated circuit chip with signal processing capabilities. The aforementioned processor 601 can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), etc. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor, or processor 601 can be any conventional processor.

[0187] The electronic device 600 is used to execute the technical solution provided in the aforementioned embodiment of the energy management method for hybrid electric vehicles. Its implementation principle and technical effects are similar to those in the aforementioned method embodiment, and will not be repeated here.

[0188] This application also provides a computer-readable storage medium storing computer-executable instructions. When a processor executes the computer-executable instructions, it implements the aforementioned energy management method for hybrid electric vehicles.

[0189] The aforementioned computer-readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, 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 storage, flash memory, magnetic disk, or optical disk. The computer-readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0190] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the control unit of a hybrid electric vehicle's energy management system.

[0191] This application also provides a computer program product, including a computer program that, when executed by a processor, is used to implement the aforementioned energy management method for hybrid electric vehicles.

[0192] In the above embodiments, those skilled in the art will understand that the above method embodiments can be implemented entirely or partially by software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented entirely or partially in the form of a computer program product. A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of the present invention is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless network, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state drives (SSDs)).

[0193] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the following claims.

[0194] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. An energy management method for a hybrid electric vehicle, characterized in that, include: If the target vehicle is detected to have its air conditioning on for heating, the vehicle's operating information is obtained, and traffic information is obtained based on the target vehicle's navigation information. The vehicle operation information and traffic information are input into the energy consumption prediction model to obtain the predicted energy consumption of the target vehicle during the driving process when the vehicle air conditioner heats up to the target temperature while the predicted energy management function is turned on; wherein, the predicted energy consumption includes the heating power consumption of the vehicle air conditioner and the driving fuel consumption of the target vehicle. The vehicle operation information and the traffic information are input into the fuel consumption prediction model to obtain the predicted fuel consumption of the target vehicle when it is driving in parallel direct drive mode with the predicted energy management function off and the vehicle air conditioning is heating to the target temperature. The predicted energy consumption and the predicted fuel consumption are compared, and based on the comparison results, the predicted energy management function is controlled to be turned on or off during the driving process when the vehicle's air conditioning is heating to the target temperature. The predictive energy management function includes: based on the traffic information, predicting the engine inefficiency range and engine efficiency range of the target vehicle on the driving route, controlling the target vehicle to adopt a pure electric drive mode in the engine inefficiency range, and controlling the target vehicle to adopt a parallel charging mode in the engine efficiency range.

2. The method according to claim 1, characterized in that, The predicted energy consumption and the predicted fuel consumption are compared, and based on the comparison result, the predicted energy management function is controlled to be turned on or off during the driving process when the vehicle's air conditioning is heating to the target temperature, including: The heating power consumption is calculated by oil-to-electricity conversion to obtain the equivalent oil consumption; The equivalent fuel consumption is obtained based on the sum of the equivalent fuel consumption and the driving fuel consumption. If the equivalent fuel consumption is not greater than the predicted fuel consumption, then during the driving process when the vehicle air conditioning is heating to the target temperature, the predicted energy management function is activated. If the equivalent fuel consumption is greater than the predicted fuel consumption, then during the driving process when the vehicle's air conditioning is heating to the target temperature, the predicted energy management function is turned off and the target vehicle is controlled to adopt a parallel direct drive mode.

3. The method according to claim 2, characterized in that, The heating power consumption is calculated using an oil-to-electricity conversion method to obtain the equivalent oil consumption, including: Obtain the thermal efficiency of the engine and the calorific value of the fuel in the target vehicle; The equivalent fuel consumption for heating is obtained by dividing the heating power consumption by the product of the power generation thermal efficiency and the fuel calorific value.

4. The method according to claim 3, characterized in that, The method further includes: Multiply the equivalent heating fuel consumption by the heating energy consumption correction factor to obtain the comprehensive heating fuel consumption as the equivalent fuel consumption.

5. The method according to claim 4, characterized in that, The method further includes: With the predictive energy management function enabled, the heating temperature rise rate during the process of the vehicle air conditioner heating to the target temperature is obtained. The heating temperature rise rate is normalized to obtain the heating energy consumption correction coefficient; wherein the heating energy consumption correction coefficient is inversely proportional to the heating temperature rise rate.

6. The method according to any one of claims 1-5, characterized in that, The method further includes: If the remaining battery power of the target vehicle is less than the heating power consumption, then during the driving process when the vehicle air conditioner heats up to the target temperature, the predictive energy management function is turned off and the target vehicle is controlled to adopt parallel direct drive mode.

7. The method according to any one of claims 1-5, characterized in that, The method further includes: Obtain the vehicle attribute parameters of the target vehicle, and perform simulation based on the vehicle attribute parameters to obtain the energy consumption prediction model; or, The target vehicle's driving data is obtained when the vehicle's air conditioning is heating and the predictive energy management function is turned on. The driving data under the predicted energy management function's on state is then used for machine learning training to obtain the energy consumption prediction model.

8. The method according to any one of claims 1-5, characterized in that, The method further includes: Obtain the vehicle attribute parameters of the target vehicle, and perform simulation based on the vehicle attribute parameters to obtain the fuel consumption prediction model; or, The target vehicle's driving data in parallel direct drive mode with the onboard air conditioning in heating mode turned off and the predictive energy management function turned off is obtained, and the driving data in parallel direct drive mode with the predictive energy management function turned off is used for machine learning training to obtain the fuel consumption prediction model.

9. The method according to any one of claims 1-5, characterized in that, Before acquiring vehicle operation information and traffic information for the engine's inefficient range, the process also includes: If the ambient temperature of the target vehicle is lower than the preset temperature, and the difference between the current temperature of the target vehicle's cabin and the target temperature is greater than the preset temperature difference, then the steps of obtaining vehicle operation information and obtaining traffic information based on the target vehicle's navigation information are executed.

10. The method according to any one of claims 1-5, characterized in that, The method further includes: When the vehicle air conditioner detects that it has reached the target temperature, the predictive energy management function is activated.

11. An energy management device for a hybrid electric vehicle, characterized in that, include: The information acquisition module is used to acquire vehicle operation information and traffic information based on the navigation information of the target vehicle if the target vehicle is detected to have its air conditioning on for heating. The energy consumption prediction module is used to input the vehicle operation information and the traffic information into the energy consumption prediction model to obtain the predicted energy consumption of the target vehicle during the driving process when the vehicle air conditioner heats up to the target temperature while the predicted energy management function is turned on; wherein, the predicted energy consumption includes the heating power consumption of the vehicle air conditioner and the driving fuel consumption of the target vehicle. The fuel consumption prediction module is used to input the vehicle operation information and the traffic information into the fuel consumption prediction model to obtain the predicted fuel consumption of the target vehicle when it is driving in parallel direct drive mode with the on-board air conditioning heating to the target temperature under the predicted energy management function off state. An energy management module is used to compare the predicted energy consumption and the predicted fuel consumption, and based on the comparison results, control the predicted energy management function to be turned on or off during the driving process when the vehicle's air conditioning is heating to the target temperature; The predictive energy management function includes: based on the traffic information, predicting the engine inefficiency range and engine efficiency range of the target vehicle on the driving route, controlling the target vehicle to adopt a pure electric drive mode in the engine inefficiency range, and controlling the target vehicle to adopt a parallel charging mode in the engine efficiency range.

12. An electronic device, characterized in that, Includes a processor and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method as described in any one of claims 1 to 10.

13. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1 to 10.

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