Energy Management Method, Device, Range-Extended Vehicle, Medium and Product of Range-Extended Vehicle

Abstract

The present application discloses an energy management method, device, range-extended vehicle, medium and product for a range-extended vehicle, including: obtaining vehicle operation-related information during the current driving process of the range-extended vehicle, and obtaining a driver demand index; processing the vehicle operation-related information to obtain driving energy replenishment planning information during the current driving process of the range-extended vehicle; based on the vehicle operation-related information, the driver demand index and the driving energy replenishment planning information, estimating the energy consumption of the range-extended vehicle to obtain the target range-extended power generation power of the range-extended vehicle; based on the target range-extended power generation power, with the goal of minimizing fuel consumption, determining the target torque and target speed of the range extender of the range-extended vehicle, and generating a range-extended control instruction based on the target torque and target speed and sending it to the range extender for control.

Description

Technical Field

[0001] This application relates to, but is not limited to, the field of vehicle technologies, and particularly relates to an energy management method, device, range-extended vehicle, medium, and product for a range-extended vehicle. Background Art

[0002] A range-extended vehicle refers to a vehicle that adds a range extender to a pure electric vehicle. The range extender refers to a power generation system composed of an engine and a generator, and the range extender can charge the battery, thereby extending the vehicle's cruising range. However, the range extender generally operates at a fixed power, but the power demand of the whole vehicle generally does not equal the output power of the range extender. When the output power of the range extender is greater than the power required for vehicle driving, the electricity generated by the range extender in excess is charged to the battery. When the output power of the range extender is less than the power demanded by the vehicle, the battery will output power for power supplement to ensure that the output power of the power system meets the vehicle driving requirements. However, since both charging and discharging will cause energy losses, frequent battery charging and discharging will increase the energy consumption of the range-extended electric vehicle. Summary of the Invention

[0003] One of the purposes of this application is to provide an energy management method, device, range-extended vehicle, medium, and product for a range-extended vehicle.

[0004] To solve the above problems, the technical solution of the embodiments of this application is implemented as follows:

[0005] In a first aspect, this application provides an energy management method for a range-extended vehicle. The method includes:

[0006] Obtain vehicle operation-related information during the current driving process of the range-extended vehicle, and obtain the driver demand index;

[0007] Analyze and process the vehicle operation-related information to obtain driving energy replenishment planning information during the current driving process of the range-extended vehicle;

[0008] Based on the vehicle operation-related information, the driver demand index, and the driving energy replenishment planning information, estimate the energy consumption of the range-extended vehicle to obtain the target range-extended power generation power of the range-extended vehicle;

[0009] Based on the target range-extended power generation power, with the goal of minimizing fuel consumption, determine the target torque and target speed of the range extender of the range-extended vehicle, and based on the target torque and target speed, generate a range-extended control instruction and send it to the range extender for control.

[0010] According to the above technical means, the range-extended vehicle generates driving energy replenishment planning information based on the vehicle operation-related information during the current driving process, and combines the real-time road conditions, the vehicle energy consumption status, and the distribution of energy replenishment facilities along the way to reasonably plan the energy replenishment time and location; at the same time, based on multi-dimensional information, namely vehicle operation-related information, driver demand index, and driving energy replenishment planning information, etc., the energy consumption is estimated, and the target range-extended power generation power of the range-extended vehicle in time and / or space is determined to avoid energy waste or insufficient supply, and improve the reliability of the endurance. With the goal of fuel economy, the global dynamic programming is used to determine the target torque and speed of the range extender, and by systematically searching for the optimal parameter combination, the range extender is in the efficient and energy-saving operation range while meeting the power generation requirements. Precise energy consumption estimation and range extender control ensure the smooth output of vehicle power and reduce the jerks caused by energy fluctuations; at the same time, reducing fuel consumption directly reduces the vehicle use cost, and the intelligent energy replenishment planning avoids trip interruptions caused by lack of electricity and fuel, improving the driving experience and travel efficiency.

[0011] In a second aspect, the present application provides an energy management device for a range-extended vehicle, the device comprising:

[0012] An acquisition module, configured to acquire vehicle operation-related information of the range-extended vehicle during the current driving process;

[0013] The acquisition module is further configured to acquire a driver demand index;

[0014] A processing module, configured to parse and process the vehicle operation-related information to obtain driving energy replenishment planning information of the range-extended vehicle during the current driving process;

[0015] The processing module is further configured to estimate the energy consumption of the range-extended vehicle based on the vehicle operation-related information, the driver demand index, and the driving energy replenishment planning information, to obtain the target range-extended power generation power of the range-extended vehicle;

[0016] A determination module, configured to determine the target torque and target speed of the range extender of the range-extended vehicle with the lowest fuel consumption as the goal based on the target range-extended power generation power;

[0017] A generation module, configured to generate a range-extended control instruction based on the target torque and target speed;

[0018] A sending module, configured to send the range-extended control instruction to the range extender for control.

[0019] In a third aspect, the present application provides a range-extended vehicle, the range-extended vehicle comprising a processor, a memory, and a range extender,

[0020] The memory stores a computer program that can run on the processor,

[0021] When the processor executes the computer program, some or all of the steps in the extended-range vehicle energy management method described in the first aspect are implemented.

[0022] In a fourth aspect, the present application provides a computer-readable storage medium storing one or more computer programs, which can be executed by one or more processors to implement some or all of the steps in the extended-range vehicle energy management method described in the first aspect.

[0023] In a fifth aspect, an embodiment of the present application provides a computer program product including a computer program or instruction, which when executed by a processor, implements some or all of the steps in the extended-range vehicle energy management method described in the first aspect. Description of the Drawings

[0024] The drawings herein are incorporated into the specification and form a part of this specification. These drawings show embodiments consistent with the present application and are used together with the specification to illustrate the technical solutions of the present application.

[0025] Figure 1 It is an optional power system configuration of an extended-range vehicle model provided by an embodiment of the present application;

[0026] Figure 2 It is a schematic diagram of the power consumption of an optional extended-range vehicle at different stages provided by an embodiment of the present application;

[0027] Figure 3 It is a schematic diagram of an optional basic energy management strategy provided by the related art;

[0028] Figure 4 It is a system architecture of an optional extended-range vehicle energy management system provided by an embodiment of the present application;

[0029] Figure 5 It is a flowchart of an optional extended-range vehicle energy management method provided by an embodiment of the present application Figure 1 ;

[0030] Figure 6 It is a flowchart of an optional extended-range vehicle energy management method provided by an embodiment of the present application Figure 2 ;

[0031] Figure 7 It is a schematic diagram of the performance requirements involved in an optional driver demand model provided by an embodiment of the present application;

[0032] Figure 8 It is a schematic diagram of the structure of an optional extended-range vehicle energy management device provided by an embodiment of the present application;

[0033] Figure 9 A schematic diagram of the hardware entity structure of an optional range-extended vehicle provided by an embodiment of the present application. Detailed implementation manners

[0034] In order to make the objectives, technical solutions and advantages of the present application clearer, the technical solutions of the present application will be further elaborated in detail below in conjunction with the accompanying drawings and embodiments. The described embodiments should not be construed as limitations on the present application. All other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of the present application.

[0035] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments. However, it can be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments, and can be combined with each other without conflict. The terms "first / second / third" involved are only used to distinguish similar objects and do not represent a specific order for the objects. It can be understood that "first / second / third" can be interchanged with a specific order or sequence when permitted, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein.

[0036] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which this application belongs. The terms used herein are only for the purpose of describing this application and are not intended to limit this application.

[0037] In order to better understand the energy management method of the range-extended vehicle in the present application, a brief description of the energy management method of the range-extended vehicle in the related art is given.

[0038] A range-extended vehicle is a type of hybrid vehicle, and its powertrain consists of an engine, a generator, and a drive motor (also known as an electric motor). During driving, the drive motor is responsible for driving the vehicle, while the engine provides electrical energy for the drive motor through the generator. The powertrain configuration of the range-extended vehicle model is as Figure 1 shown. The drive system, like that of a pure electric vehicle model, drives the whole vehicle through the drive motor, and the engine does not participate in directly driving the vehicle. When the range-extended vehicle is driving purely electrically, it can have the advantages of environmental protection, smooth driving, low-speed power performance, and vehicle economy of a pure electric vehicle model; it can generate electricity through a range extender (including an engine and a generator) during range-extended power generation driving to supply power to the drive motor, and has the advantages of high cruising range and convenient refueling of a fuel vehicle.

[0039] In practical applications, as Figure 2As shown, the basic usage modes of range-extended vehicles include two modes: Charge Depleting (CD) stage and Charge Sustaining (CS) stage. After the range-extended vehicle is charged, it runs on pure electricity and enters the CD stage; when the power battery's charge drops to the range extender startup threshold, range-extended power generation driving (i.e., driving with depleted battery) starts and it enters the CS stage; due to the control of the range extender's own system power generation efficiency and the dynamic change characteristics of power demand during vehicle driving, the power generation of the range extender is not necessarily equal to the power consumption of the vehicle. The remaining power when the vehicle's overall power consumption is small will be stored in the power battery. When the stored power exceeds the range extender shutdown threshold, the range extender shuts down and it enters pure-electric driving again. Therefore, during the driving with depleted battery, the range extender will have changes in start-stop states.

[0040] Based on the above basic usage modes of range-extended vehicles, the driver needs to choose to use fuel or electricity based on the driving plan, driving experience, and driving needs. Considering driving economy, when commuting in the urban area on weekdays, the charging cost is low, the pure-electric driving energy consumption is low, the power demand is relatively low, and the subjective NVH feeling of the range extender is more prominent, so charging will be chosen as much as possible; considering power performance and driving convenience comprehensively, when the power demand on the highway is high, the charging unit price is high, and in a hurry for a long-distance trip, etc., fuel may be chosen.

[0041] In addition to the basic usage modes, driving modes such as pure electric, hybrid, and fuel are provided for the driver. This allows the driver to further enhance the driving experience of using fuel or electricity; when charging is chosen as much as possible during urban commuting on weekdays, the range extender startup threshold of the power battery will be adjusted lower, such as 15% to 20%, to ensure a higher pure-electric driving range for a single charge; when fuel is chosen on the highway, the range extender startup threshold of the power battery is adjusted higher, such as around 40% to 60%, to ensure the high-power performance demand on the highway. However, the CD / CS basic usage modes and the selection of driving modes require users to have a certain learning cost and ability requirements, and the CD / CS basic usage modes + driving mode selection cannot achieve the best driving experience in all scenarios. For example, range-extended vehicles in the market have problems such as low-speed Noise-Vibration-Harshness (NVH), insufficient power on the highway, and increased energy consumption of range-extended electric vehicles due to frequent range extender start-stop.

[0042] It should be noted that the low-speed NVH problem is caused by the operating noise of the range extender engine, and high-power power generation at low speed should be avoided; the insufficient power at high speed is due to the low state of charge (SOC, also known as the remaining battery charge) of the power battery. However, the root cause of the above problems is that the vehicle is a rule-based real-time energy management system, which real-time controls the start / stop of the range extender and the power generation power according to the current SOC of the power battery and the driver's mode selection.

[0043] The basic energy management strategy of a range-extended vehicle in the related art, such as Figure 3 shown, obtains the target SOC according to the vehicle driving mode set by the driver and the correction of weather, temperature, current and road conditions at a certain distance ahead; calculates the initial target power generation power of the range extender according to the difference between the target SOC and the actual SOC of the whole vehicle; then combines the vehicle NVH for limitation to obtain the final target power generation power; finally, controls the power generation of the range extender. However, the above methods all start from the optimization of the vehicle's own energy consumption and improve the energy consumption problem through real-time control. However, this approach cannot fully utilize the advantage of the range extender's fixed-point power generation, and may also lead to poor user experience due to insufficient scenario coverage in the development process.

[0044] Referring to Figure 4 shown, the embodiment of the present application provides a system architecture of an energy management system for a range-extended vehicle. This system architecture is established based on the service-oriented architecture (SOA). This system architecture adopts a hierarchical distributed architecture, including a vehicle-end system 100, a cloud system 200, a network 300, and a vehicle-to-everything (V2X) system 400. Here, the V2X system 400 includes, but is not limited to, mobile devices such as mobile phones, vehicles, home appliances, wearable devices, and tablets. Among them, the inside of the vehicle-end system 100, between the vehicle-end system 100 and the cloud system 200, between the cloud system 200 and the V2X system 400, and between the vehicle-end system 100 and the V2X system 400 are connected through the network 300.

[0045] Among them, the vehicle end system 100 is a system architecture established based on the data acquisition and preprocessing module 101, the central control system 102, multiple domain controllers 103, and the components 104 corresponding to each domain controller 103. Among them, the domain controller 103 uses the data acquisition and preprocessing module 101 to collect in real time the vehicle operation information related to the driver's behavior to obtain information such as the driver's intention and driving style. Of course, the domain controller 103 can also use the data acquisition and preprocessing module 101 to collect the status information and energy consumption information of key components 104 such as the battery, drive motor, and engine, so as to obtain the vehicle operation-related information with multiple modalities. Further, the collected vehicle operation-related information is reported to the central control system 102. The central control system 102 is deployed with a trained driver demand model and an energy consumption prediction model. Based on the reported historical vehicle operation-related information of the vehicle, the driver demand index is determined using the driver demand model to obtain information such as the driver's intention and driving style. Of course, based on the reported vehicle operation-related information during the current driving process of the vehicle, the driving energy supplement plan of the range-extended vehicle can be obtained, and based on the reported vehicle operation-related information during the current driving process of the vehicle, the energy consumption of the range-extended vehicle is predicted using the energy consumption prediction model to obtain the target range-extended power generation power of the range-extended vehicle. Based on the target range-extended power generation power, with the goal of minimizing fuel consumption, the target torque and target speed of the range extender of the range-extended vehicle are determined, and based on the target torque and target speed, a range-extended control instruction is generated and sent to the range extender for control.

[0046] In practical applications, the domain controller system can include a body control system, a motion control system, and a thermal management system. The vehicle end system also includes body components, power components, chassis components, and thermal management components. Of course, the vehicle end system can also include other pluggable components and corresponding management systems.

[0047] Here, the body components can execute the control instructions issued by the body control system, and the body components can also feedback their own status information to the body control system. Here, the control instructions include body instructions such as turning on / off body components such as lights, door locks, seats, aromatherapy, and spoilers, and the positions for collecting the current signals of each body component.

[0048] Here, the power components and chassis components can execute the control instructions issued by the motion control system, and the power components and chassis components can also feedback their own status information to the motion control system. The control instructions include motion control instructions such as adjusting the state control, torque, speed, power, voltage signal, and current signal. The feedback status information includes component status information such as torque, speed, power, voltage signal, current signal, temperature, and logical status.

[0049] Here, the thermal management component can execute the control instructions issued by the thermal management system, and the thermal management component can also feedback its own status information to the thermal management system. The control instructions include instructions to turn on / off the thermal management component such as the cooling fan, air conditioner, etc.

[0050] It should be noted that the body control system, the motion control system, and the thermal management system report the obtained component energy consumption information to the central control system, and perform component control according to the component capacity limits issued by the central control system. It should be noted that, continuing to refer to Figure 1 As shown, the vehicle battery and the engine fuel tank are energy storage components, the engine and the drive motor are energy conversion components, and the vehicle motion energy interacts through the wheels and the vehicle powertrain. The central control system monitors the entire life cycle of components such as the engine, generator, and drive motor, and performs energy distribution and component physical energy limitation according to the law of conservation of energy and Newton's third law.

[0051] Among them, the cloud system 200 includes a data storage and processing platform 201, an energy optimization platform 202, and an artificial intelligence (AI) optimization engine 203. The data storage and processing platform 201 stores a large amount of historical multi-modal data information of different range-extended vehicles. The energy optimization platform 202 includes an untrained energy consumption prediction model and a driver demand model. The energy optimization platform 202 can process the untrained energy consumption prediction model and the driver demand model through the AI optimization engine 203. After preprocessing the historical vehicle operation-related information such as data cleaning and classification, the energy optimization platform 202 uses the preprocessed historical vehicle operation-related information to train the network parameters of the untrained energy consumption prediction model and the driver demand model respectively, so that the trained energy consumption prediction model and the trained driver demand model meet the training conditions. The training conditions include that the number of iterations reaches the number threshold, or the loss value is lower than the loss threshold. Further, the network parameters of the trained energy consumption prediction model and the trained driver demand model are sent to the vehicle-side system. The central control system of the vehicle-side system deploys the untrained energy consumption prediction model and the driver demand model; uses the network parameters of the trained energy consumption prediction model to configure the untrained energy consumption prediction model to obtain the trained energy consumption prediction model, thereby completing the deployment of the energy consumption prediction model on the vehicle side; uses the network parameters of the trained driver demand model to configure the untrained driver demand model to obtain the trained driver demand model, thereby completing the deployment of the driver demand model on the vehicle side.

[0052] It should be noted that the energy consumption prediction model is deployed in the cloud system. The cloud system monitors the energy consumption of a large number of vehicles, uses the powerful computing power of the cloud for model training and strategy optimization, and uses the computing resources of the vehicle system for real-time control. In this way, the energy control system based on SOA service orientation adopts a hierarchical distributed architecture, solves the problem of scenario coverage inherent in the traditional development process, increases the economic optimization space of traditional energy management, reduces the traditional calibration and verification work, and shortens the vehicle development cycle.

[0053] Referring to Figure 5 As shown, an energy management method for a range-extended vehicle is provided in an embodiment of the present application. The energy management method for the range-extended vehicle can be executed by a computer device, and the computer device can be Figure 4 In the system architecture shown, the vehicle system 100 in the range-extended vehicle is the range-extended vehicle. Specifically, it can be the central control system in the vehicle system 100, or the cloud system 200. Of course, the energy management method for the range-extended vehicle can also be completed through the interaction between the vehicle system 100 and the cloud system 200. Continuing to refer to Figure 5 As shown, the energy management method for the range-extended vehicle can be implemented through the following steps:

[0054] Step 501, obtain the vehicle operation-related information of the range-extended vehicle during the current driving process, and obtain the driver demand index.

[0055] In an embodiment of the present application, the vehicle operation-related information can be the information related to the current vehicle operation obtained by the range-extended vehicle during the current driving process. Here, the current driving process can include: the process after the range-extended vehicle starts but has not started driving, or the process when the range-extended vehicle has started driving on the road. This application does not make specific limitations on this.

[0056] In an embodiment of the present application, the vehicle operation-related information includes at least part of the driver's trip information, navigation information, environmental information, and energy consumption information of components in the range-extended vehicle.

[0057] Among them, during the driving process of the range-extended vehicle, the driver's trip information can at least include the starting point and the ending point input by the driver.

[0058] Among them, the navigation information includes the navigation driving route, the road type on the navigation driving route, and the traffic condition information. Here, the navigation driving route can be the optimal driving route planned by the navigation system for the driver within the future time period after the current moment according to the starting point, ending point, and other relevant conditions in the driver's trip information. The road type includes highway, urban road, and / or ramp road. The traffic condition information includes the traffic conditions of various roads, such as congestion and / or traffic light waiting time, and the traffic condition information is data that reflects the traffic status of each road in real time.

[0059] It should be noted that as important guidance during the driving of a range-extended vehicle, the navigation information, the road planning information and the traffic condition information complement each other. In the road planning information, highway roads are suitable for long-distance and fast travel, which can reduce the driving time, but tolls need to be paid; urban roads connect various parts of the city, with complex road conditions and large traffic flow; ramp roads have special requirements for vehicle power and energy consumption. In the traffic condition information, congestion will significantly extend the travel time and increase the energy consumption, and the waiting time at traffic lights also affects the travel efficiency. When the driver inputs the starting point and the ending point, the navigation system will, based on the road planning information, comprehensively consider the characteristics of different types of roads, plan candidate routes, and at the same time, combine the traffic condition information, such as a certain urban road section is currently congested, a certain highway section has slow traffic due to construction, and the waiting time at some intersections' traffic lights is relatively long, etc., and intelligently calculate and recommend the optimal route to help the driver reasonably plan the journey, reduce unnecessary energy consumption and time waste, and ensure that the range-extended vehicle reaches the destination in an efficient and energy-saving state.

[0060] It also should be noted that based on the navigation driving route in the navigation information, the driver's trip information can also include the driving distance and the estimated driving time determined based on the navigation driving route. Of course, during the driving process of the range-extended vehicle on the road, the driver's trip information can also include the vehicle's speed information, etc.

[0061] Among them, the environmental information includes weather information and road condition information. Here, the weather information and road condition information are to predict the weather conditions and road surface conditions when the range-extended vehicle travels along the navigation driving route to different regions according to the navigation driving route in the navigation information. Among them, the weather information includes temperature, humidity, wind speed, and rain and snow conditions, and the road condition information includes the road surface type and road slope of the road.

[0062] It should be noted that the weather and road condition factors in the environmental information are external conditions that cannot be ignored during the driving of a range-extended vehicle. For example, in the cold winter, low temperature will reduce the battery activity and significantly shorten the battery life. At this time, the driver needs to plan the journey in advance, reserve enough power or make preparations for the use of the range extender; in high-humidity weather, components in the vehicle may malfunction, and vehicle inspection and maintenance need to be strengthened. In terms of road conditions, when the vehicle is driving on a road with a large slope, the range extender may need to start more frequently to provide sufficient power, resulting in increased fuel consumption; when driving on a gravel or dirt road surface, due to the small road surface friction and large driving resistance, it will not only increase the vehicle's energy consumption, but may also affect the vehicle's passability and stability. Mastering these environmental information in real time helps the driver adjust the driving strategy and also helps the vehicle intelligent system optimize the energy distribution to ensure driving safety and efficiency.

[0063] Among them, the energy consumption information of components includes one or more of the energy consumption information of the engine, the energy consumption information of the drive motor, the energy consumption information of the battery, the energy consumption information of the air conditioner, and the energy consumption information of the lights. The energy consumption information of the engine can be the relevant data of fuel consumption (such as gasoline, diesel) by the engine during operation in a range-extended vehicle. The energy consumption information of the drive motor can be the electrical energy data consumed or generated by the drive motor during vehicle driving, energy recovery, etc. The energy consumption information of the battery is the relevant data of the electrical energy change during battery charging and discharging, including the battery discharge amount, charge amount, remaining battery capacity (SOC), pure electric remaining mileage of the vehicle (also known as the remaining mileage driven by the remaining battery capacity), charge and discharge efficiency, the impact of battery temperature on energy consumption, etc., which is used to evaluate the battery's endurance ability and health status. The energy consumption information of the air conditioner can be the power consumption situation of the vehicle air conditioning system (refrigeration, heating, ventilation, etc. functions) predicted based on environmental information, including the energy consumption data at different temperature settings and wind speed gears, reflecting the degree of power consumption of the air conditioning system on the vehicle. The energy consumption information of the lights can be the power consumption of various vehicle lights (headlights, taillights, turn signals, interior lights, etc.) predicted based on environmental information when they are working.

[0064] It should be noted that the energy consumption information of components provides an important basis for the energy management and optimization of a range-extended vehicle. Exemplarily, by analyzing the energy consumption information of the engine, the fuel consumption of the engine under different driving modes can be understood, so as to select a more economical driving method; the energy consumption information of the drive motor helps to evaluate the efficiency of pure electric drive of the vehicle and provides a reference for the improvement of the power system. The energy consumption information of the battery is directly related to the pure electric endurance mileage of the vehicle. Monitoring the battery charging and discharging process can timely detect changes in battery performance and ensure driving safety. Although the energy consumption information of the air conditioner and lights is relatively small, it will also affect the overall energy consumption of the vehicle when used for a long time.

[0065] In the embodiments of the present application, the range-extended vehicle can be a range-extended configuration vehicle or a hybrid vehicle such as a hybrid electric vehicle.

[0066] In the embodiments of the present application, the driver demand index can be a quantitative index that comprehensively reflects the driver's demand degree for various aspects of the range-extended vehicle. The driver demand index can be obtained by analyzing and processing multi-modal data information such as driver travel information, navigation information, and environmental information within a historical period before the current moment of the current range-extended vehicle. Among them, the driver demand index includes multiple sub-demand indexes such as the driver power demand index, the driver braking demand index, the driver quietness demand index, the driver vehicle use economy demand index, the driver convenience demand index, and the driver fuel economy demand index. These sub-demand indexes respectively reflect the driver's demand degree for aspects such as vehicle acceleration performance, braking performance, quietness, vehicle use economy, convenience, and fuel economy from different dimensions, providing an important reference basis for the optimized design, performance adjustment, and user experience improvement of the vehicle.

[0067] In some embodiments, the driver travel information, navigation information, and environmental information in the multi-modal data information within a historical period before the current moment of the current range-extended vehicle can be input into the trained driver demand model. The driver demand model processes the driver travel information, navigation information, and environmental information based on the acceleration performance demand, braking performance demand, quietness demand, vehicle use economy demand, convenience demand, and fuel economy demand, to obtain the driver power demand index, the driver braking demand index, the driver quietness demand index, the driver vehicle use economy demand index, the driver convenience demand index, the driver fuel economy demand index, and the driving energy replenishment planning information of the range-extended vehicle. Refer to Figure 7 As shown, the acceleration performance demand, braking performance demand, quietness demand, vehicle use economy demand, convenience demand, and fuel economy demand involved in the driver demand model.

[0068] In the embodiments of the present application, continue to refer to Figure 4 As shown, if the execution entity is the vehicle-end system, the vehicle operation-related information including driver travel information, navigation information, environmental information, and energy consumption information of the components of the range-extended vehicle can be collected through the data collection and preprocessing module in the vehicle-end system, and the driver demand index of the range-extended vehicle can be obtained in advance, and the vehicle operation-related information and the driver demand index are sent to the central control system, and the central control system processes the vehicle operation-related information and the driver demand index. Of course, if the execution entity is the cloud system, after the data collection and preprocessing module in the vehicle-end system collects the vehicle operation-related information including driver travel information, navigation information, environmental information, and energy consumption information of the components of the range-extended vehicle and the driver demand index, the vehicle operation-related information and the driver demand index are reported to the cloud system, so that the cloud system obtains the vehicle operation-related information and the driver demand index and performs subsequent processing.

[0069] Step 502: Analyze and process the vehicle operation-related information to obtain the driving energy supplement planning information of the range-extended vehicle during the current driving process.

[0070] In the embodiments of the present application, the driving energy supplement planning information can be a planning scheme for supplementing energy (such as charging or refueling) for the range-extended vehicle based on the driver's itinerary information, navigation information, environmental information, and component energy consumption information during the current driving process of the range-extended vehicle, with the requirements of convenience and vehicle use economy. Exemplarily, refer to Figure 6 as shown. This information comprehensively considers factors such as driving distance, road conditions, vehicle energy consumption prediction, and the distribution of energy supplement facilities along the way, and provides suggestions to the driver on when, where to supplement energy, and the energy supplement method (charging or refueling), etc., to ensure that the vehicle can maintain sufficient energy supply throughout the journey, avoid affecting the journey due to insufficient energy, and at the same time help the driver reasonably arrange the journey and energy supplement plan, improving travel efficiency and convenience.

[0071] In one implementable manner, the range-extended vehicle obtains the remaining navigation driving mileage during the current driving process based on the navigation driving distance (also known as the navigation driving mileage) and the already driven navigation mileage in the vehicle operation-related information. When the remaining pure-electric mileage of the vehicle in the vehicle operation-related information is less than the remaining navigation driving mileage, intelligent charging activation is recommended. At least one charging station location is recommended based on the positions, availability, and prices of multiple charging stations in the map, and the navigation paths, estimated charging duration, and charging costs of each charging location are displayed. In response to the driver's selection operation for the target charging location, the driving energy supplement planning information of the range-extended vehicle when charging at the target charging location is obtained.

[0072] Here, recommending at least one charging station location based on the positions, availability, and prices of multiple charging stations in the map can be obtained in the following way: obtain the additional travel time, charging waiting time, and charging time required for the range-extended vehicle to reach each charging station location in the map for charging, and based on the sum of the additional travel time, charging waiting time, and charging time, obtain the increased charging time for the journey; obtain the cost difference between the predicted total fuel and electricity cost of navigation without charging and the fuel and electricity cost of navigation to the preset charging point; recommend at least one charging station location where the increased charging time for the journey is less than the preset increased time threshold, and / or the cost difference is greater than the cost difference threshold. In this way, when recommending charging stations based on the charging station locations, availability, and prices in the map, each charging station location is evaluated from the aspects of economy and convenience, and at least one charging station location with economy and convenience is recommended for the user, facilitating the charging of the range-extended vehicle.

[0073] Step 503: Estimate the energy consumption of the range-extended vehicle based on the vehicle operation-related information, the driver demand index, and the driving energy supplement planning information to obtain the target range-extended power generation power of the range-extended vehicle.

[0074] In the embodiment of the present application, the vehicle operation related information, the driver demand index and the driving energy replenishment planning information can be input into the trained energy consumption estimation model, and the energy consumption of the extended-range vehicle can be estimated by using the energy consumption estimation model to obtain the target extended-range power generation power of the extended-range vehicle. Here, the energy consumption estimation model can be obtained by training a regression model based on the historical vehicle operation related information of different types of vehicles, the driver demand index and driving energy replenishment planning information corresponding to the historical vehicle operation related information, and the regression model includes but is not limited to a linear regression model, a decision tree regression model and a random forest regression model.

[0075] Step 504: Based on the target extended-range power generation power and with the goal of minimizing fuel consumption, determine the target torque and target speed of the range extender, and based on the target torque and target speed, generate a range extender control instruction and send it to the range extender for control.

[0076] In the embodiment of the present application, the target range-extending power generation power determined based on vehicle operation-related information, driver demand index, and driving energy replenishment planning information is the benchmark for the operation of the range extender. On this basis, with the lowest fuel consumption (fuel economy) as the optimization goal, a global dynamic programming algorithm is used to systematically traverse the possible combinations of torque and speed of the range extender, combined with constraints such as vehicle driving conditions and energy consumption estimation models, to comprehensively evaluate the fuel consumption under different parameter combinations, and finally select the target torque and target speed that meet the target range-extending power generation power and have the lowest fuel consumption (i.e., range-extending power generation selection point). Furthermore, based on the target torque and target speed, a range-extending control instruction is generated and sent to the range extender for control.

[0077] As can be seen from the above, the extended-range vehicle in the embodiment of the present application generates driving energy replenishment planning information based on the vehicle operation-related information during the current driving process, and reasonably plans the time and location of energy replenishment in combination with the real-time road conditions, vehicle energy consumption status and the distribution of energy replenishment facilities along the way; at the same time, based on multi-dimensional information, namely vehicle operation-related information, driver demand index and driving energy replenishment planning information, the estimated energy consumption is determined to determine the target extended-range power generation power of the extended-range vehicle in time and / or space, to avoid energy waste or insufficient supply, and to improve endurance reliability. With fuel economy as the goal, the global dynamic planning is used to determine the target torque and speed of the range extender, and by systematically searching for the optimal parameter combination, the range extender is in an efficient and energy-saving operating range while meeting the power generation needs. Accurate energy consumption estimation and range extender control ensure the smooth output of vehicle power and reduce the frustration caused by energy fluctuations; at the same time, reducing fuel consumption directly reduces the cost of using the vehicle, and intelligent energy replenishment planning avoids trip interruptions due to lack of power and oil, and improves driving experience and travel efficiency.

[0078] In some embodiments, obtaining the driver demand index in step 501 may be achieved through the following process.

[0079] Obtain historical vehicle operation-related information of a range-extended vehicle under at least one standard condition, where the historical vehicle operation-related information includes historical driver trip information, historical navigation information, historical environment information, and historical energy consumption information of components in the range-extended vehicle; process the historical vehicle operation-related information to obtain a driver demand index of the range-extended vehicle.

[0080] In an embodiment of the present application, the historical vehicle operation-related information may be vehicle operation-related information of the range-extended vehicle under at least one standard condition before the current moment, and the historical vehicle operation-related information includes historical driver trip information, historical navigation information, historical environment information, and historical energy consumption information of components in the range-extended vehicle.

[0081] Among them, the historical driver trip information may include a starting point, an ending point, a driving distance, a driving time, a real-time vehicle speed, and a braking frequency. Among them, based on the change amount of the real-time speed and the corresponding time interval, an acceleration can be obtained. Acceleration is a physical quantity used to characterize the speed change of a vehicle. The acceleration may be the acceleration at different times. The acceleration reflects the rate of speed change of the vehicle during acceleration or deceleration. A positive acceleration indicates that the vehicle is accelerating, and a negative acceleration indicates that the vehicle is decelerating. The magnitude of the acceleration reflects the dynamic performance of the vehicle and the driving operation style of the driver. The braking frequency can be understood as the frequency at which the driver uses the brake pedal to decelerate or stop the vehicle, usually measured by the number of brakes within a unit time. The braking frequency can reflect the driver's judgment of the road conditions and driving habits.

[0082] Among them, the historical navigation information includes a historical navigation driving route planned based on the above starting point and ending point, the road type on the historical navigation driving route, and historical traffic condition information.

[0083] Here, the historical environment information includes historical weather information and historical road condition information of the areas passed through on the historical navigation driving route. Among them, the historical weather information includes temperature, humidity, wind speed, and rain and snow conditions, and the historical road condition information includes the road surface type and road gradient of the road.

[0084] Here, the historical energy consumption information of the components includes one or more of the historical energy consumption information of the engine, the historical energy consumption information of the drive motor, the historical energy consumption information of the battery, the historical energy consumption information of the air conditioner, and the historical energy consumption information of the lights corresponding to each navigation driving route.

[0085] In the embodiments of the present application, the standard driving conditions may be different types of road conditions involved in the actual driving process of a range-extended vehicle. Different types of road conditions include, but are not limited to, urban road conditions, suburban road conditions, highway road conditions, etc. Of course, the standard driving conditions may also be the Worldwide Harmonized Light Vehicles Test Cycle (WLTC). WLTC is a test driving condition used to evaluate the fuel economy, exhaust emissions, power consumption and other performance of light vehicles.

[0086] It should be noted that the standard driving conditions include a variety of different speed segments, such as from low speed to high speed, and then to complex driving states such as rapid acceleration and rapid deceleration. For example, the standard driving conditions cover scenarios such as low-speed crawling in urban congestion, medium-speed driving on suburban roads, and high-speed driving on highways, which can more comprehensively analyze various speed changes of the vehicle in actual use. Of course, the standard driving conditions also include the acceleration changes of the vehicle during actual driving, including rapid acceleration, slow acceleration, rapid deceleration and slow deceleration. By precisely controlling the numerical values and change frequencies of the acceleration and deceleration, the driver demand index during the process of the driver driving the vehicle can be obtained, such as the power performance demand and the braking performance demand.

[0087] In the embodiments of the present application, the historical vehicle operation-related information under the standard driving conditions of the range-extended vehicle is obtained, and the historical vehicle operation-related information under different road conditions is analyzed and processed to obtain the driver demand index of the range-extended vehicle.

[0088] In some embodiments, after obtaining the historical vehicle operation-related information, it may further include: performing data preprocessing on the historical vehicle operation-related information to obtain the processed historical vehicle operation-related information.

[0089] In the embodiments of the present application, the historical vehicle operation-related information includes, but is not limited to, speed (also known as vehicle speed), energy consumption information of the engine, energy consumption information of the motor, energy consumption information of the battery, energy consumption information of the air conditioner, and energy consumption information of the lights.

[0090] In the embodiments of the present application, data preprocessing includes data screening and / or data normalization processing. Data preprocessing can process historical vehicle operation-related information, such as removing outliers, filling in missing values, and unifying data of different magnitudes into a suitable range for subsequent analysis. It should be noted that the normalization processing can be based on the performance of the whole vehicle, and perform normalization processing on some of the historical vehicle operation-related information. Exemplarily, based on the speed in the historical vehicle operation-related information, the acceleration is determined. The normalization processing of the acceleration includes determining the maximum acceleration performance of the whole vehicle, that is, the maximum acceleration, according to the design requirements of the whole vehicle, and normalizing the acceleration at each moment during the vehicle driving process with the maximum acceleration performance of the whole vehicle to obtain the normalized acceleration.

[0091] In some embodiments, the driver demand index includes a driver power demand index, a driver braking demand index, and a driver quiet demand index. For different driver demand indexes, the following methods can be adopted.

[0092] The obtaining processes of the driver power demand index and the driver braking demand index can be realized by the following methods:

[0093] Input the historical driver trip information, historical navigation information, and historical environment information into a pre-trained driver demand model. Using the driver demand model, based on the maximum acceleration of the range-extended vehicle, the speed change amount in the historical driver trip information, and the time interval corresponding to the speed change amount, normalize the acceleration of the range-extended vehicle when driving under standard conditions; count the distribution law of the normalized positive acceleration to obtain the driver power demand index of the range-extended vehicle, and count the distribution law of the normalized negative acceleration to obtain the driver braking demand index of the range-extended vehicle, where the driver demand index includes the driver power demand index.

[0094] It can be understood that with reference to Figure 6As shown, the historical driver trip information, historical navigation information, and historical environment information are input into the trained driver demand model. The driver demand model obtains the speeds at different times in the historical driver trip information. Based on the speeds at different times, it determines at least one speed change amount and the time interval corresponding to the speed change amount during the driving process of the range-extended vehicle; based on at least one speed change amount and the corresponding time interval, it determines the accelerations at different times; after normalizing the accelerations at different times based on the maximum acceleration of the range-extended vehicle, it statistically analyzes the distribution law of the normalized positive accelerations generated during the driving process of the range-extended vehicle. For example, if most of the normalized positive accelerations are distributed at 0.7, the driver power demand index is obtained as 0.7; it statistically analyzes the distribution law of the normalized negative accelerations generated during the driving process of the range-extended vehicle. For example, if most of the normalized negative accelerations are distributed at -0.5, the driver braking demand index is obtained as 0.5.

[0095] In this way, by fusing multi-source information and performing acceleration normalization in combination with the maximum acceleration of the vehicle, fully considering the vehicle performance limit and the actual driving scenario, it can accurately reflect the driver's real power demand. Based on the distribution laws of the normalized positive accelerations and the normalized negative accelerations, the power demand index and the braking demand index are reasonably determined, which can avoid unnecessary excessive output of the vehicle power system and the vehicle braking system, reduce energy waste, and improve energy utilization efficiency.

[0096] In some embodiments, for the driver power demand index, it can also be achieved through other means.

[0097] Based on the speed change amount in the historical driver trip information and the time interval corresponding to the speed change amount, the accelerations during the driving of the range-extended vehicle under standard conditions are averaged to obtain the average acceleration; if the average acceleration is greater than the average acceleration threshold, the different road types, the weights corresponding to the different road types, and the road proportion of the roads corresponding to the different road types in the historical navigation driving route are obtained for the range-extended vehicle in the historical navigation driving route, where the historical navigation information includes the historical navigation driving route and the road types in the historical navigation driving route; based on the average acceleration, the weights corresponding to the different road types, and the road proportion, the driver power demand index is determined; among them, the driver demand index includes the driver power demand index.

[0098] In the embodiments of the present application, the historical navigation information includes the historical navigation driving route, the road types in the historical navigation driving route, and the historical traffic condition information. The road types include but are not limited to highway roads, urban roads, and ramp roads, and the historical traffic condition information includes but is not limited to congestion and traffic light waiting time.

[0099] It can be understood that according to the speed data in the historical navigation driving route and the time intervals corresponding to the speed changes, the acceleration data is determined, and indicators such as average acceleration and maximum acceleration are calculated. For example, the greater the average acceleration, the higher the possible demand of the driver for the acceleration performance. At the same time, the road types in the historical navigation driving route are considered, namely highway, urban road, and ramp road. When overtaking on a highway or driving on a ramp, the demand for power is usually high; while frequent starts and stops on urban roads also require a certain acceleration performance. Corresponding weights can be assigned according to different road types. For example, the weight of the highway can be 0.4, the weight of the urban road can be 0.3, and the weight of the ramp road can be 0.3. In addition, the road proportion of the roads corresponding to different road types in the historical navigation driving route can also be calculated. Further, by analyzing the average acceleration, road type weights, and road proportions of the range-extended vehicle during the entire driving journey, the driver's power demand index can be determined. Exemplarily, the acceleration index, road type weights, and road proportions are combined, and the driver's power demand index is calculated through methods such as weighted average, that is, the product of the average acceleration, the weights of each road type, and the corresponding road proportions is calculated, and the sum of all products is obtained as the driver's power demand index.

[0100] In this way, by screening out the trips with high power demand through the average acceleration, and combining the different road types, weights, and proportions in the navigation route, the power demand characteristics of the driver in the actual driving scenario can be accurately captured. At the same time, reasonably determining the power demand index can avoid unnecessary excessive output of the vehicle power system, reduce energy waste, and improve energy utilization efficiency.

[0101] In some embodiments, the process of obtaining the driver's braking demand index can be achieved in the following manner:

[0102] If the braking frequency in the historical driver trip information is greater than the braking frequency threshold, based on the historical traffic condition information and the historical road condition information in the historical environment information, obtain the proportions of the congested roads and ramp roads in the historical navigation driving route respectively, and the weights corresponding to the congested roads and ramp roads respectively; wherein, the historical navigation information includes the historical navigation driving route, the road types in the historical navigation driving route, and the historical traffic condition information; based on the braking frequency, the proportions and weights corresponding to the congested roads and ramp roads respectively, determine the driver's braking demand index; wherein, the driver demand index includes the driver's braking demand index.

[0103] In the embodiments of the present application, the historical navigation information includes the historical navigation driving route, the road types in the historical navigation driving route, and the traffic condition information. The road types include but are not limited to highways, urban roads, and ramp roads. The traffic condition information includes but is not limited to congestion and traffic light waiting time.

[0104] In the embodiments of the present application, the historical environment information includes the weather information and road condition information of the areas passed through on the historical navigation driving route. Among them, the weather information includes temperature, humidity, wind speed, and rain and snow conditions, and the road condition information includes the road surface type and road slope of the road.

[0105] It can be understood that, as shown in Figure 6 , the braking frequency in the historical navigation driving route in the historical driver travel information is statistically analyzed. The higher the braking frequency, the greater the possible demand for braking performance by the driver. At the same time, the congestion situation in the traffic condition information and the road slope of the ramp road in the road condition information are combined. When braking frequently on congested roads and when the vehicle speed needs to be controlled on downhill sections, the requirement for braking performance is relatively high. Similarly, corresponding weights are assigned to congested roads and ramp roads respectively. For example, the weight of congested roads can be 0.5, and the weight of ramp roads can be 0.5. At the same time, the road occupancy ratios of congested roads and ramp roads in the navigation driving route can also be calculated. Further, the driver braking demand index can be determined according to the braking frequency, road condition weight, and road occupancy ratio. Exemplarily, calculate the product of the braking frequency, the weight of congested roads, and the occupancy ratio of congested roads, calculate the product of the braking frequency, the weight of ramp roads, and the occupancy ratio of ramp roads, and sum up each product to obtain the driver braking demand index.

[0106] In this way, by combining the braking frequency with the traffic condition and road condition information, it can more comprehensively and accurately reflect the braking demand of the driver in the actual driving scenario. When the braking frequency is greater than the threshold, further analyze the occupancy ratio and weight of congested roads and ramp roads to accurately quantify the braking demand of the driver and avoid the one-sidedness of single-index evaluation. There are differences in the driving habits and braking demands of different drivers, and this index can provide personalized driving settings for drivers. According to their own braking demand index, drivers can adjust parameters such as the sensitivity of the vehicle's brake pedal and the intensity of energy recovery to meet personalized driving preferences and improve the driving experience.

[0107] In some embodiments, for the determination of the driver's quietness demand index, the driver demand model can be used to judge according to information such as the vehicle speed and window opening frequency in the driver's itinerary in the historical vehicle operation-related information. At the same vehicle speed and the same ambient temperature, the higher the window opening frequency, the higher the quietness demand.

[0108] In some embodiments, for the determination of the driver's vehicle use economy demand index, the driver demand model can be used to determine according to the proportion of the historical total pure-electric driving mileage in the sum of the total pure-electric driving mileage and the total fuel driving mileage in the historical vehicle operation-related information. The higher the proportion, the higher the vehicle use economy demand.

[0109] In some embodiments, for the determination of the driver convenience demand index, the driver demand model can be used to determine it according to the charging execution situation selected in the historical trips and the driver's charging selection preferences.

[0110] For the determination of the driver fuel economy demand index, in the normal driving modes (such as pure electric mode, fuel mode, and hybrid mode), according to the normal driving mode, using the driver demand model, the best fuel economy of the components is followed. In the track mode or off-road mode, to ensure fast responsiveness, the driver fuel economy demand can be appropriately reduced.

[0111] In some embodiments, step 503 estimates the energy consumption of the range-extended vehicle based on the vehicle operation-related information, the driver demand index, and the driving energy replenishment planning information, and obtains the target range-extended power generation power of the range-extended vehicle, which can be achieved through the following process:

[0112] According to the vehicle operation-related information and the driver demand index, estimate the energy consumption demand of each energy consumption control system respectively to obtain the estimated sub-energy consumption of each energy consumption control system; based on the estimated sub-energy consumption of each energy consumption control system, determine the estimated total energy consumption of the range-extended vehicle; according to the estimated total energy consumption and the driving energy replenishment planning information, use the N charging points in the driving energy replenishment planning information to divide the navigation driving route in the vehicle operation-related information into corresponding N + 1 range-extended cycle mileage, where N is a positive integer greater than or equal to 1; for the nth range-extended cycle mileage, based on the estimated total energy consumption and the available battery power of the range-extended vehicle, obtain the target range-extended power generation amount, and perform range-extended cycle mileage path space allocation on the target range-extended power generation amount to obtain the target range-extended power generation power of each space point, where n is a positive integer greater than or equal to 1 and less than or equal to N + 1.

[0113] In the embodiments of the present application, the vehicle operation-related information covers multi-dimensional data such as the driver's trip information, navigation information, environmental information, and energy consumption information of components, while the driver demand index reflects the driver's demands in aspects such as vehicle power, braking, quietness, vehicle use economy, convenience, and fuel economy. Combining these information, for different energy consumption control systems of the vehicle, such as the engine, motor, battery, air conditioner, lights, etc., analyze their energy consumption laws in the current driving situation and demands. For example, according to the congested road conditions in the navigation information and the relatively high driver power demand index, estimate the energy consumption when the motor starts and stops frequently and accelerates, so as to obtain the estimated sub-energy consumption of each energy consumption control system. In this way, the actual driving scenario and the driver's demands are fully considered, making the energy consumption estimation more in line with the actual situation.

[0114] Further, after obtaining the estimated sub-energy consumption of each energy consumption control system, the estimated sub-energy consumptions of each energy consumption control system are summed up to obtain the estimated total energy consumption of the range-extended vehicle during the entire journey. In this way, an overall quantitative index is provided for the energy management of the vehicle, helping the vehicle system and the driver to understand in advance the total amount of energy required for the journey and providing basic data support for subsequent energy allocation and planning.

[0115] Then, based on the estimated total energy consumption and the driving energy replenishment plan information, according to the number N of charging points in the driving energy replenishment plan information, the navigation driving route in the vehicle operation related information is segmented into corresponding N + 1 range-extended cycle mileage. Exemplarily, there are 2 charging points (N = 2) in the driving energy replenishment plan information, and the navigation driving route (including the complete navigation driving route or the remaining navigation driving route) is from place A to place B. Then, the navigation driving route can be segmented into 3 (N + 1 = 3) range-extended cycle mileage. For example, from place A to the first charging point is the first range-extended cycle mileage, from the first charging point to the second charging point is the second range-extended cycle mileage, and from the second charging point to place B is the third range-extended cycle mileage. In this way, the energy use of the vehicle in different sections is reasonably planned, and it is determined in which sections range-extended power generation is needed to meet the energy consumption requirements.

[0116] Finally, for the nth range-extended cycle mileage, based on the estimated total energy consumption and the available battery power, the amount of power that needs to be generated by range extension within this mileage, that is, the target range-extended power generation amount, is determined. Then, this target range-extended power generation amount is distributed according to the spatial points (also called position points) on the route of the range-extended cycle mileage, and the target range-extended power generation power of each spatial point is calculated. Exemplarily, for the second range-extended cycle mileage, it is known that the estimated total energy consumption within this mileage is 20 kilowatt-hours (kWh), and the available battery power is 10 kWh. Then the target range-extended power generation amount is 20 - 10 = 10 kWh. Assuming there are 10 spatial points on the path of this range-extended cycle mileage, the 10 kWh target range-extended power generation amount is evenly distributed to these 10 spatial points, and the target range-extended power generation power of each spatial point can be calculated according to corresponding factors such as time and distance. In this way, the power generation power of the range extender at different positions can be controlled more precisely to meet the energy consumption requirements of the vehicle in this section and optimize the energy utilization efficiency.

[0117] In some embodiments, the estimated sub-energy consumption of each energy consumption control system can be obtained by estimating the energy consumption requirements of each energy consumption control system respectively according to the vehicle operation related information and the driver demand index, and the process can be as follows:

[0118] Step A1: Based on the driver's trip information, navigation information, environmental information, driver power demand index, and driver braking demand index, analyze the speed information in the navigation driving route, use the energy consumption prediction model to determine the sum of the energy consumption of the sliding group and the acceleration energy consumption, and obtain the first predicted sub-energy consumption of the motion control system based on the energy consumption of the sliding group and the acceleration energy consumption; among them, the vehicle operation-related information includes the driver's trip information, navigation information, and environmental information, and the driver demand index includes the driver power demand index and the driver braking demand index.

[0119] In the embodiment of the present application, the navigation information includes the navigation driving route, the road type and traffic condition information in the navigation driving route. The road type includes, but is not limited to, highway roads, urban roads, and ramp roads. The traffic condition information includes, but is not limited to, congestion and traffic light waiting time.

[0120] In the embodiment of the present application, the environmental information includes the weather information and road condition information of the area passed by the navigation driving route. Among them, the weather information includes temperature, humidity, wind speed, and rain and snow conditions, and the road condition information includes the road surface type and road slope of the road.

[0121] In the embodiment of the present application, refer to Figure 6 As shown, the first predicted sub-energy consumption of the motion control system can be realized in the following way:

[0122] According to the road type and traffic condition information in the navigation information, count the predicted vehicle speed distribution information of each spatial point in the navigation driving route to obtain the vehicle speed model of the navigation driving route; according to the road condition information in the navigation information and environmental information, count the slope distribution information of each position point in the navigation driving route to obtain the slope model of the navigation driving route; according to the weather information in the navigation information and environmental information, count the temperature distribution information of each position point in the navigation driving route to obtain the temperature model of the navigation driving route; based on the vehicle speed model, slope model, temperature model, driver power demand index, and driver braking demand index, use the trained energy consumption prediction model to determine the energy consumption of the sliding group and the acceleration energy consumption, and determine the sum of the energy consumption of the sliding group and the acceleration energy consumption as the first predicted sub-energy consumption of the motion control system. It should be noted that the driver power demand index and the driver braking demand index affect the acceleration energy consumption. The larger the index, the greater the energy consumption. The specific influence is learned by the cloud AI optimization system through a large amount of historical trip information.

[0123] Step A2: Based on the environmental information, estimate the energy consumption of the thermal management system to obtain the second predicted sub-energy consumption of the thermal management system.

[0124] In the embodiments of the present application, based on the environmental information and the driver's demand for the in-vehicle environmental temperature, the energy consumption of the thermal management system during the navigation driving route can be estimated by using the thermal management system model to obtain the second estimated sub-energy consumption of the thermal management system. It should be noted that whether the environmental temperature is too high or too low, the energy consumption will be higher. The specific quantification index is obtained by the cloud AI optimization system through learning a large amount of historical trip information.

[0125] Step A3: Based on the energy consumption information of the components in the range-extended vehicle, estimate the energy consumption of the body system and the power system components respectively to obtain the third estimated sub-energy consumption of the body system and the fourth estimated sub-energy consumption of the power system components; wherein, the vehicle operation-related information further includes the energy consumption information of the components.

[0126] In the embodiments of the present application, according to the status information and environmental information (such as day or night) of each body component, estimate the on-time of each body component to obtain the estimated time. Based on the estimated time and the energy consumption information of the body component, estimate the energy consumption of each body component on the subsequent navigation driving route, so as to obtain the third estimated sub-energy consumption of the body system. According to the corresponding energy consumption information of the power system components, estimate the energy consumption of the power system components on the subsequent navigation driving route, so as to obtain the fourth estimated sub-energy consumption of the power system components.

[0127] Step A4: Determine the estimated total energy consumption of the vehicle based on the first estimated sub-energy consumption, the second estimated sub-energy consumption, the third estimated sub-energy consumption, and the fourth estimated sub-energy consumption.

[0128] In the embodiments of the present application, after obtaining the first estimated sub-energy consumption, the second estimated sub-energy consumption, the third estimated sub-energy consumption, and the fourth estimated sub-energy consumption, sum up the first estimated sub-energy consumption, the second estimated sub-energy consumption, the third estimated sub-energy consumption, and the fourth estimated sub-energy consumption to obtain the estimated total energy consumption of the vehicle.

[0129] In this way, multi-source information such as the driver's itinerary, navigation, and environment is integrated, combined with the power and braking demand index, and key influencing factors such as driving behavior, road conditions, and environmental temperature are fully considered. The vehicle energy consumption is disassembled into subsystems such as motion control, thermal management, body system, and power system for separate estimation. The accurate energy consumption estimation of each system helps the vehicle to intelligently allocate energy. In the case of low power demand and low temperature environment, reduce the output power of the power system, and at the same time reasonably allocate the energy consumption of the thermal management system for battery preheating to avoid energy waste and improve the overall energy utilization efficiency. The accurate energy consumption estimation provides a reliable basis for the driving energy replenishment plan, avoiding problems such as power exhaustion caused by energy consumption estimation deviation. At the same time, based on the energy consumption estimation of the thermal management system, it can ensure that the battery and motor work at a suitable temperature, extend the life of the components, and improve the reliability and driving safety of the vehicle.

[0130] In some embodiments, the range extender includes a first range extender and a second range extender. In step 504, based on the target torque and the target speed, a range extension control instruction is generated and sent to the range extender for control, including:

[0131] Based on the target torque, a range extension control instruction for the first range extender is generated and sent to the first range extender for control. The first range extender is an engine or a generator. Based on the target speed, a range extension control instruction for the second range extender is generated and sent to the second range extender for control. The second range extender is a generator or an engine.

[0132] In the embodiments of the present application, after determining the target torque, for the first range extender (engine or generator), the target torque parameter is converted into a specific control instruction, which includes the execution parameters and operation logic for adjusting the torque output. After being sent to the first range extender, it can drive its internal adjustment mechanism (such as the engine throttle opening, generator excitation current, etc.) to adjust the working state and achieve accurate torque output. Similarly, according to the target speed, a control instruction for the second range extender (generator or engine) is generated, and by adjusting its speed control components (such as the engine fuel injection frequency, generator speed adjustment device), the second range extender is stably operated at the target speed, ensuring that the range extender system works in coordination with the optimal efficiency, reducing fuel consumption while ensuring the vehicle power supply, and improving the economy and endurance of the range-extended vehicle.

[0133] The embodiments of the present application provide an energy management device for a range-extended vehicle. Refer to Figure 8 as shown. Figure 8 FIG. is a schematic structural diagram of an energy management device for a range-extended vehicle provided by the embodiments of the present application. The energy management device 8 of the range-extended vehicle includes:

[0134] An acquisition module 801, configured to acquire vehicle operation-related information during the current driving process of the range-extended vehicle;

[0135] The acquisition module 801 is further configured to acquire a driver demand index;

[0136] A processing module 802, configured to parse and process the vehicle operation-related information to obtain driving energy replenishment planning information during the current driving process of the range-extended vehicle;

[0137] The processing module 802 is further configured to estimate the energy consumption of the range-extended vehicle based on the vehicle operation-related information, the driver demand index, and the driving energy replenishment planning information, to obtain the target range extension power generation power of the range-extended vehicle;

[0138] A determination module 803, configured to determine the target torque and the target speed of the range extender of the range-extended vehicle with the lowest fuel consumption as the goal based on the target range extension power generation power;

[0139] A generation module 804, configured to generate a range extender control instruction based on a target torque and a target rotational speed;

[0140] A sending module 805, configured to send the range extender control instruction to a range extender for control.

[0141] An embodiment of the present application provides a range-extended vehicle, where the range-extended vehicle includes: a memory, a processor, and a range extender,

[0142] The memory stores a computer program that can run on the processor;

[0143] When the processor executes the computer program, it implements some or all of the steps in the energy management method of the range-extended vehicle as described in the above embodiments.

[0144] An embodiment of the present application provides a storage medium that stores one or more computer programs, and the one or more computer programs can be executed by one or more processors to implement some or all of the steps in the above method. The storage medium can be transient or non-transient.

[0145] An embodiment of the present application provides a computer program, including computer-readable code. When the computer-readable code runs in a computer device, a processor in the computer device executes to implement some or all of the steps in the above method.

[0146] An embodiment of the present application provides a computer program product, where the computer program product includes a non-transient computer-readable storage medium storing a computer program. When the computer program is read and executed by a computer, it implements some or all of the steps in the above method. The computer program product can be specifically implemented in a manner of hardware, software, or a combination thereof. In some embodiments, the computer program product is specifically embodied as a computer storage medium, and in other embodiments, the computer program product is specifically embodied as a software product, such as a Software Development Kit (SDK), etc.

[0147] It should be noted here that: the descriptions of the above embodiments tend to emphasize the differences between the embodiments, and their similarities or similarities can be referred to each other. The descriptions of the above device, storage medium, computer program, and computer program product embodiments are similar to the descriptions of the above method embodiments and have beneficial effects similar to those of the method embodiments. For the technical details not disclosed in the device, storage medium, computer program, and computer program product embodiments of the present application, please refer to the descriptions of the method embodiments of the present application for understanding.

[0148] It should be noted here that the descriptions of the above storage medium and device embodiments are similar to those of the above method embodiments and have similar beneficial effects to the method embodiments. For the technical details not disclosed in the storage medium and device embodiments of this application, please refer to the descriptions of the method embodiments of this application for understanding.

[0149] A schematic diagram of the hardware entities of an extended-range vehicle provided by an embodiment of this application is shown as Figure 9 shown. The hardware entities of the extended-range vehicle 9 include: a processor 901, a memory 902, and a range extender 903. Among them, the memory 902 stores a computer program that can run on the processor 901, and the processor 901 executes the following steps:

[0150] Obtain the vehicle operation-related information during the current driving process of the extended-range vehicle, and obtain the driver demand index;

[0151] Analyze and process the vehicle operation-related information to obtain the driving energy replenishment planning information during the current driving process of the extended-range vehicle;

[0152] Based on the vehicle operation-related information, the driver demand index, and the driving energy replenishment planning information, estimate the energy consumption of the extended-range vehicle to obtain the target range-extending power generation power of the extended-range vehicle;

[0153] Based on the target range-extending power generation power, with the goal of minimizing fuel consumption, determine the target torque and target speed of the range extender of the extended-range vehicle, and based on the target torque and target speed, generate a range-extending control instruction and send it to the range extender 903 for control.

[0154] Among them, the memory 902 stores a computer program that can run on the processor. The memory 902 is configured to store instructions and applications executable by the processor 901, and can also cache data to be processed or already processed by the processor 901 and each module in the extended-range vehicle 9 (for example, image data, audio data, voice communication data, and video communication data), and can be implemented by flash memory (FLASH) or random access memory (Random Access Memory, RAM).

[0155] Among them, when the processor 901 executes the program, it implements the steps of the energy management method of the extended-range vehicle in any one of the above. The processor 901 generally controls the overall operation of the extended-range vehicle 9.

[0156] The above-mentioned processor may be at least one of an Application Specific Integrated Circuit (ASIC), a Digital Signal Processor (DSP), a Digital Signal Processing Device (DSPD), a Programmable Logic Device (PLD), a Field Programmable Gate Array (FPGA), a Central Processing Unit (CPU), a controller, a microcontroller, and a microprocessor. It can be understood that the electronic device implementing the functions of the above-mentioned processor may also be other devices, which are not specifically limited in the embodiments of the present application.

[0157] The above-mentioned computer storage medium / memory may be a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Programmable Read-Only Memory (EPROM), an Electrically Erasable Programmable Read-Only Memory (EEPROM), a Ferromagnetic Random Access Memory (FRAM), a Flash Memory, a magnetic surface memory, an optical disc, or a Compact Disc Read-Only Memory (CD-ROM), etc.; it may also be various terminals including one or any combination of the above-mentioned memories, such as a mobile phone, a computer, a tablet device, a personal digital assistant, etc.

[0158] It should be understood that the "one embodiment" or "an embodiment" mentioned throughout the specification means that the specific features, structures or characteristics related to the embodiment are included in at least one embodiment of the present application. Therefore, the "in one embodiment" or "in an embodiment" that appears throughout the specification does not necessarily refer to the same embodiment. In addition, these specific features, structures or characteristics may be combined in one or more embodiments in any suitable manner. It should be understood that in various embodiments of the present application, the magnitudes of the serial numbers of the above steps / processes do not mean the sequence of execution. The execution sequence of each step / process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application. The serial numbers of the embodiments of the present application above are only for description and do not represent the advantages or disadvantages of the embodiments.

[0159] It should be noted that in this article, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device. Without further limitation, the element defined by the statement "including a..." does not exclude the existence of additional identical elements in the process, method, article or device including the element.

[0160] In several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored, or not executed. In addition, the coupling, direct coupling or communication connection between the components shown or discussed with each other can be through some interfaces. The indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0161] The units described as separate components above may or may not be physically separated, and the components shown as units may or may not be physical units; they can be located in one place or distributed to multiple network units; some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0162] In addition, each functional unit in the embodiments of the present application can be all integrated in a processing unit, or each unit can be separately used as a unit, or two or more units can be integrated in a unit; the above integrated units can be implemented in the form of hardware, or in the form of a combination of hardware and software functional units.

[0163] Those of ordinary skill in the art will understand that all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions. The foregoing program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps including those of the above method embodiments; and the foregoing storage medium includes: various media such as removable storage devices, read-only memory (ROM), magnetic disks, or optical discs that can store program codes.

[0164] Alternatively, if the above integrated units of the present application are implemented in the form of software function modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present application, in essence or the part that contributes to the related art, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a vehicle-mounted terminal (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in the various embodiments of the present application. And the foregoing storage medium includes: various media such as removable storage devices, ROM, magnetic disks, or optical discs that can store program codes.

[0165] As described above, only the implementation manners of the present application are provided, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered by the protection scope of the present application.

Claims

1. An energy management method for a range-extended vehicle, characterized in that, The method includes: Obtaining vehicle operation - related information during the current driving process of the range - extended vehicle, and obtaining a driver demand index; Analyzing and processing the vehicle operation - related information to obtain driving energy - supplementing planning information of the range - extended vehicle during the current driving process; Based on the vehicle operation - related information, the driver demand index, and the driving energy - supplementing planning information, estimating the energy consumption of the range - extended vehicle to obtain the target range - extending power generation power of the range - extended vehicle; Based on the target range - extending power generation power, with the goal of minimizing fuel consumption, determining the target torque and target speed of the range - extender of the range - extended vehicle, and generating a range - extending control instruction based on the target torque and target speed and sending it to the range - extender for control; Among them, the estimating the energy consumption of the range - extended vehicle based on the vehicle operation - related information, the driver demand index, and the driving energy - supplementing planning information to obtain the target range - extending power generation power of the range - extended vehicle includes: According to the vehicle operation - related information and the driver demand index, respectively estimating the energy consumption demands of each energy - consumption control system to obtain the estimated sub - energy consumption of each energy - consumption control system; Based on the estimated sub - energy consumption of each energy - consumption control system, determining the estimated total energy consumption of the range - extended vehicle; According to the estimated total energy consumption and the driving energy - supplementing planning information, using N charging points in the driving energy - supplementing planning information, splitting the navigation driving route in the vehicle operation - related information into corresponding N + 1 range - extending cycle mileage, where N is a positive integer greater than or equal to 1; For the nth range - extending cycle mileage, based on the estimated total energy consumption and the available battery power of the range - extended vehicle, obtaining the target range - extending power generation amount, and performing range - extending cycle mileage path - space allocation on the target range - extending power generation amount to obtain the target range - extending power generation power of each space point, where n is a positive integer greater than or equal to 1 and less than or equal to N + 1; 2. The energy management method for the range-extended vehicle according to claim 1, wherein The obtaining the driver demand index includes: Obtaining the historical vehicle operation - related information of the range - extended vehicle under at least one standard condition, where the historical vehicle operation - related information includes historical driver travel information, historical navigation information, historical environmental information, and historical energy consumption information of components in the range - extended vehicle; Analyzing and processing the historical vehicle operation - related information to obtain the driver demand index of the range - extended vehicle; 3. The energy management method of the range-extended vehicle according to claim 2, characterized in that, The analyzing and processing the historical vehicle operation - related information to obtain the driver demand index of the range - extended vehicle includes: Inputting the historical driver travel information, the historical navigation information, and the historical environmental information into a pre - trained driver demand model, and using the driver demand model to normalize the acceleration of the range - extended vehicle during driving within the standard condition based on the maximum acceleration of the range - extended vehicle, the speed change amount in the historical driver travel information, and the time interval corresponding to the speed change amount; Statistically analyze the distribution law of the normalized positive acceleration to obtain the driver power demand index of the range-extended vehicle, and statistically analyze the distribution law of the normalized negative acceleration to obtain the driver braking demand index of the range-extended vehicle, where the driver demand index includes the driver power demand index and the driver braking demand index.

4. The energy management method for a range-extended vehicle according to claim 2, characterized in that, The parsing and processing of the historical vehicle operation-related information to obtain the driver demand index of the range-extended vehicle includes: Based on the speed change amount in the historical driver travel information and the time interval corresponding to the speed change amount, perform average processing on the acceleration of the range-extended vehicle during driving within the standard working condition to obtain the average acceleration. If the average acceleration is greater than the average acceleration threshold, obtain different road types in the historical navigation driving route of the range-extended vehicle, the weights corresponding to different road types, and the road proportion of the roads corresponding to different road types in the historical navigation driving route, where the historical navigation information includes the historical navigation driving route and the road types in the historical navigation driving route. Based on the average acceleration, the weights and road proportions corresponding to different road types, determine the driver power demand index of the range-extended vehicle; where the driver demand index includes the driver power demand index.

5. The energy management method of the range-extended vehicle according to claim 2, characterized in that, The parsing and processing of the historical vehicle operation-related information to obtain the driver demand index of the range-extended vehicle includes: If the braking frequency in the historical driver travel information is greater than the braking frequency threshold, based on the historical traffic condition information and the historical road condition information in the historical environment information, obtain the proportions of the congested roads and the ramp roads in the historical navigation driving route respectively, and the weights corresponding to the congested roads and the ramp roads respectively, where the historical navigation information includes the historical navigation driving route, the road types in the historical navigation driving route, and the historical traffic condition information. Based on the braking frequency, the proportions and weights corresponding to the congested roads and the ramp roads respectively, determine the driver braking demand index of the range-extended vehicle; where the driver demand index includes the driver braking demand index.

6. The energy management method of the range-extended vehicle according to any one of claims 1 to 5, characterized in that The estimation of the energy consumption demand of each energy consumption control system according to the vehicle operation-related information and the driver demand index to obtain the estimated sub-energy consumption of each energy consumption control system includes: According to the driver travel information, navigation information, environment information, driver power demand index and driver braking demand index, from the speed information distribution in the navigation driving route, use the energy consumption estimation model to determine the sliding resistance energy consumption and acceleration energy consumption, and based on the sliding resistance energy consumption and the acceleration energy consumption, obtain the first estimated sub-energy consumption of the motion control system; where the vehicle operation-related information includes the driver travel information, the navigation information and the environment information, and the driver demand index includes the driver power demand index and the driver braking demand index. Based on the environmental information, estimate the energy consumption of the thermal management system to obtain the second estimated sub-energy consumption of the thermal management system; Based on the energy consumption information of the components in the range-extended vehicle, estimate the energy consumption of the body system and the power system components respectively to obtain the third estimated sub-energy consumption of the body system and the fourth estimated sub-energy consumption of the power system components; wherein, the vehicle operation related information further includes the energy consumption information of the components; Based on the first estimated sub-energy consumption, the second estimated sub-energy consumption, the third estimated sub-energy consumption and the fourth estimated sub-energy consumption, determine the estimated total energy consumption of the vehicle.

7. The energy management method of the range-extended vehicle according to any one of claims 1 to 5, characterized in that, The range extender includes a first range extender and a second range extender. The generating and sending the range extension control instruction to the range extender for control based on the target torque and the target speed includes: Based on the target torque, generate the range extension control instruction of the first range extender and send it to the first range extender for control. The first range extender is an engine or a generator; Based on the target speed, generate the range extension control instruction of the second range extender and send it to the second range extender for control. The second range extender is the generator or the engine.

8. An energy management device for a range-extended vehicle, characterized in that, The device includes: An obtaining module, configured to obtain the vehicle operation related information of the range-extended vehicle during the current driving process; The obtaining module is further configured to obtain the driver demand index; A processing module, configured to analyze and process the vehicle operation related information to obtain the driving energy replenishment planning information of the range-extended vehicle during the current driving process; The processing module is further configured to estimate the energy consumption of the range-extended vehicle based on the vehicle operation related information, the driver demand index and the driving energy replenishment planning information to obtain the target range extension power generation power of the range-extended vehicle; A determining module, configured to determine the target torque and the target speed of the range extender of the range-extended vehicle with the lowest fuel consumption as the goal based on the target range extension power generation power; A generating module, configured to generate a range extension control instruction based on the target torque and the target speed; A sending module, configured to send the range extension control instruction to the range extender for control; Wherein, the processing module is further configured to estimate the energy consumption requirements of each energy consumption control system respectively according to the vehicle operation related information and the driver demand index to obtain the estimated sub-energy consumption of each energy consumption control system; determine the estimated total energy consumption of the range-extended vehicle based on the estimated sub-energy consumption of each energy consumption control system; according to the estimated total energy consumption and the driving energy replenishment planning information, use the N charging points in the driving energy replenishment planning information to divide the navigation driving route in the vehicle operation related information into corresponding N + 1 range extension cycle mileage, where N is a positive integer greater than or equal to 1; for the nth range extension cycle mileage, obtain the target range extension power generation amount based on the estimated total energy consumption and the available battery power of the range-extended vehicle, and perform range extension cycle mileage path space allocation on the target range extension power generation amount to obtain the target range extension power generation power of each spatial point, where n is a positive integer greater than or equal to 1 and less than or equal to N + 1.

9. An extended-range vehicle, characterized in that, The range-extended vehicle includes a processor, a memory, and a range extender, the memory stores a computer program that can run on the processor, when the processor executes the computer program, it implements the energy management method of the range-extended vehicle according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores one or more computer programs, and the one or more computer programs can be executed by one or more processors to implement the energy management method of the range-extended vehicle according to any one of claims 1 to 7.

11. A computer program product, characterized in that, It includes a computer program or instruction, and when the computer program or instruction is executed by the processor, it implements the energy management method of the range-extended vehicle according to any one of claims 1 to 7.

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