Energy management methods, electronic devices, and vehicles

Abstract

This application provides an energy management method, electronic device, and vehicle, belonging to the field of vehicle energy management technology. The technical solution provided in this application, based on acquired speed information including the vehicle's average speed and maximum speed within a preset time period, determines the vehicle's target driving condition. Then, based on the target information, the relevant parameters corresponding to the equivalent energy consumption minimization strategy are discretized. On the one hand, by combining the average speed and maximum speed, the current target driving condition of the vehicle can be accurately identified. On the other hand, by associating the energy management problem in the vehicle with real-time driving conditions or other target information, the matching of the energy control strategy with the actual operating characteristics of the vehicle's power system is achieved. This allows for accurate discretization of parameters during the ECMS process, improving the vehicle's fuel economy under different driving conditions and enhancing the accuracy of energy management.

Description

Technical Field

[0001] This application relates to the field of energy management technology for vehicles, and more specifically, to energy management methods, electronic devices, and vehicles in the field of energy management technology for vehicles. Background Technology

[0002] Energy Management Strategy (EMS) is a core technology for vehicles. Its main task is to coordinate the power distribution among different power sources based on the vehicle's power requirements, the operating status of the power system, and the differences in working efficiency, transient response characteristics, and load capacity of different power sources.

[0003] Among related technologies, EMS includes the Equivalent Consumption Minimization Strategy (ECMS). ECMS can convert the electrical energy consumed by the motor into equivalent fuel consumption, and then optimize energy management by minimizing the overall fuel consumption. However, when using ECMS, the relevant parameters are not discretized in a targeted manner, resulting in low accuracy of ECMS execution.

[0004] Therefore, how to accurately discretize the parameters in the ECMS process is a hot research topic. Summary of the Invention

[0005] This application provides an energy management method, an electronic device, and a vehicle. The method can accurately discretize parameters used in the ECMS process. The technical solution is as follows:

[0006] Firstly, an energy management method is provided, the method comprising:

[0007] The vehicle's speed information and operating mode are obtained. The speed information includes the vehicle's average speed over a preset time period, the vehicle's maximum speed over the preset time period, and the vehicle's current speed.

[0008] Based on the speed information, the target driving condition of the vehicle is determined;

[0009] Based on the target information, the relevant parameters corresponding to the equivalent consumption minimization strategy are discretized. The target information includes one of the following: the target driving condition, the current speed of the vehicle, and the operating mode of the vehicle.

[0010] The above technical solution, based on acquired speed information including the vehicle's average speed and maximum speed within a preset time period, determines the vehicle's target driving condition. Then, it selects one piece of information from target information including the target driving condition, the vehicle's current speed, and the vehicle's operating mode to discretize the relevant parameters corresponding to the equivalent energy minimization strategy. On the one hand, it can accurately identify the vehicle's current target driving condition by combining average speed and maximum speed. On the other hand, it associates the vehicle's energy management problem with target information that characterizes the vehicle's current state, such as real-time driving condition, current speed, and operating mode. This achieves matching between the energy control strategy and the actual operating characteristics of the vehicle's power system, accurately discretizing parameters during ECMS usage, improving fuel economy under different driving conditions, and enhancing the accuracy of energy management.

[0011] In conjunction with the first aspect, in some possible implementations, the target information includes: the target driving condition. The discretization of relevant parameters corresponding to the equivalent consumption minimization strategy based on the target information includes: when the target driving condition is a low-speed or medium-speed driving condition, discretizing the engine power corresponding to the equivalent consumption minimization strategy, wherein the vehicle speed in the low-speed driving condition is less than the vehicle speed in the medium-speed driving condition; and when the target driving condition is a high-speed or ultra-high-speed driving condition, discretizing the engine torque corresponding to the equivalent consumption minimization strategy, wherein the vehicle speed in the medium-speed driving condition is less than the vehicle speed in the high-speed driving condition, and the vehicle speed in the high-speed driving condition is less than the vehicle speed in the ultra-high-speed driving condition.

[0012] The above technical solution discretizes engine power when the target driving condition is low-speed or medium-speed driving, and discretizes engine torque when the target driving condition is high-speed or ultra-high-speed driving. It can select different parameters for discretization under different vehicle conditions, which significantly improves the accuracy and flexibility of energy management strategy.

[0013] In conjunction with the first aspect and the above implementation methods, in some possible implementation methods, determining the target driving condition of the vehicle based on the speed information includes: determining at least one target boundary function based on the target calibration maximum speed and the target calibration average speed; determining a target reference point based on the average speed and the maximum speed, wherein one of the average speed and the maximum speed is the abscissa of the target reference point and the other is the ordinate of the target reference point; and determining the target driving condition based on the target reference point and the target boundary function.

[0014] The above technical solution determines the target boundary function by using the target calibration maximum speed and the target calibration average speed, determines the target reference point by using the average speed and the maximum speed, and then determines the target driving condition based on the target reference point and the target boundary function. This allows the target reference point based on the real-time vehicle speed to be accurately classified into the corresponding driving condition by the target boundary function, thus providing a reliable driving condition identification basis for subsequent energy management strategies.

[0015] In conjunction with the first aspect and the above implementation methods, in some possible implementation methods, determining at least one target boundary function based on the target calibration maximum speed and the target calibration average speed includes at least one of the following: determining a first boundary function based on a first calibration maximum speed and a first calibration average speed; determining a second boundary function based on a second calibration maximum speed and a second calibration average speed, wherein the second calibration maximum speed is greater than the first calibration maximum speed and the second calibration average speed is greater than the first calibration average speed; determining a third boundary function based on a third calibration maximum speed and a third calibration average speed, wherein the third calibration maximum speed is greater than the second calibration maximum speed and the third calibration average speed is greater than the second calibration average speed.

[0016] The above technical solution determines at least one target boundary function by calibrating the maximum speed and average speed of targets of different sizes. It can establish speed boundaries from low to high and construct a hierarchical driving condition discrimination standard, providing a clear basis for the identification of target driving conditions.

[0017] In conjunction with the first aspect and the above implementation methods, in some possible implementation methods, determining the target driving condition based on the target reference point and the target boundary function includes: when the target reference point is below the first boundary function, taking the low-speed driving condition as the target driving condition; when the target reference point is above the first boundary function and below the second boundary function, taking the medium-speed driving condition as the target driving condition; when the target reference point is above the second boundary function and below the third boundary function, taking the high-speed driving condition as the target driving condition; and when the target reference point is above the third boundary function, taking the ultra-high-speed driving condition as the target driving condition.

[0018] The above technical solution determines the target driving condition based on the positional relationship between the target reference point and the first boundary function, the second boundary function, and the third boundary function. It divides the driving condition into four regions through the three target boundary functions, and can achieve real-time and accurate classification of driving conditions from low speed to ultra-high speed by judging the position.

[0019] In conjunction with the first aspect and the above implementation methods, in some possible implementation methods, the method further includes: acquiring at least one test condition data; determining the average speed and the maximum speed of the test condition data; performing clustering processing on the average speed and the maximum speed of the test condition data based on a preset number of cluster centers to obtain at least one cluster center distribution; and determining the target calibration maximum speed and the target calibration average speed based on the cluster center distribution.

[0020] The above technical solution, by using a preset number of cluster centers, clusters the average speed and maximum speed of the acquired test condition data to obtain at least one cluster center distribution. Then, based on the cluster center distribution, the target calibration maximum speed and target calibration average speed are determined. This can extract representative speed data from actual driving data, ensuring the objectivity and representativeness of the calibration speed, and providing a reliable data foundation for establishing the target boundary function.

[0021] In combination with the first aspect and the above implementation methods, in some possible implementation methods, the target information includes: the current speed of the vehicle, and the discretization processing of the relevant parameters corresponding to the equivalent consumption minimization strategy based on the target information includes: discretizing the engine power corresponding to the equivalent consumption minimization strategy when the current speed of the vehicle is less than the calibrated speed; and discretizing the engine torque corresponding to the equivalent consumption minimization strategy when the current speed of the vehicle is greater than or equal to the calibrated speed.

[0022] The above technical solution discretizes the engine power when the vehicle's current speed is greater than the rated speed, and discretizes the engine torque when the vehicle's current speed is less than or equal to the rated speed. This allows for the establishment of a discretization method that is related to vehicle speed, significantly optimizing the energy distribution efficiency at different vehicle speeds.

[0023] In combination with the first aspect and the above implementation methods, in some possible implementation methods, the target information includes: the operating mode of the vehicle; the discretization of the relevant parameters corresponding to the equivalent consumption minimization strategy based on the target information includes: discretizing the engine power corresponding to the equivalent consumption minimization strategy when the operating mode of the vehicle is a series mode; and discretizing the engine torque corresponding to the equivalent consumption minimization strategy when the operating mode of the vehicle is a parallel mode.

[0024] The above technical solution discretizes engine power when the vehicle is in series mode and discretizes engine torque when the vehicle is in parallel mode. This improves the adaptability of energy management strategy to different operating modes, enhances the fuel economy of the vehicle, and improves the control fault tolerance of the vehicle through various control logics.

[0025] Secondly, an energy management device is provided, the device comprising:

[0026] The acquisition module is used to acquire the vehicle's speed information and the vehicle's operating mode. The speed information includes the vehicle's average speed within a preset time period, the vehicle's maximum speed within the preset time period, and the vehicle's current speed.

[0027] The determination module is used to determine the target driving condition of the vehicle based on the speed information;

[0028] The processing module is used to discretize the relevant parameters corresponding to the equivalent consumption minimization strategy based on the target information. The target information includes one of the following: the target driving condition, the current speed of the vehicle, and the operating mode of the vehicle.

[0029] In conjunction with the second aspect, in some possible implementations, the target information includes: the target driving condition; the processing module is configured to, when the target driving condition is a low-speed driving condition or a medium-speed driving condition, discretize the engine power corresponding to the equivalent consumption minimization strategy, wherein the vehicle speed in the low-speed driving condition is less than the vehicle speed in the medium-speed driving condition; and when the target driving condition is a high-speed driving condition or an ultra-high-speed driving condition, discretize the engine torque corresponding to the equivalent consumption minimization strategy, wherein the vehicle speed in the medium-speed driving condition is less than the vehicle speed in the high-speed driving condition, and the vehicle speed in the high-speed driving condition is less than the vehicle speed in the ultra-high-speed driving condition.

[0030] In conjunction with the second aspect and the above implementation methods, in some possible implementation methods, the determining module is used to determine at least one target boundary function based on the target calibration maximum speed and the target calibration average speed; determine a target reference point based on the average speed and the maximum speed, wherein one of the average speed and the maximum speed is the abscissa of the target reference point and the other is the ordinate of the target reference point; and determine the target driving condition based on the target reference point and the target boundary function.

[0031] In conjunction with the second aspect and the above implementation methods, in some possible implementation methods, the determining module is used to determine a first boundary function based on a first calibrated maximum speed and a first calibrated average speed; determine a second boundary function based on a second calibrated maximum speed and a second calibrated average speed, wherein the second calibrated maximum speed is greater than the first calibrated maximum speed and the second calibrated average speed is greater than the first calibrated average speed; and determine a third boundary function based on a third calibrated maximum speed and a third calibrated average speed, wherein the third calibrated maximum speed is greater than the second calibrated maximum speed and the third calibrated average speed is greater than the second calibrated average speed.

[0032] In conjunction with the second aspect and the above implementation methods, in some possible implementation methods, the determining module is configured to: when the target reference point is below the first boundary function, use a low-speed driving condition as the target driving condition; when the target reference point is above the first boundary function and below the second boundary function, use a medium-speed driving condition as the target driving condition; when the target reference point is above the second boundary function and below the third boundary function, use a high-speed driving condition as the target driving condition; and when the target reference point is above the third boundary function, use an ultra-high-speed driving condition as the target driving condition.

[0033] In combination with the second aspect and the above implementation methods, in some possible implementation methods, the acquisition module is used to acquire at least one test condition data; the determination module is used to determine the average speed and the maximum speed of the test condition data; based on a preset number of cluster centers, the average speed and the maximum speed of the test condition data are clustered to obtain at least one cluster center distribution; based on the cluster center distribution, the target calibration maximum speed and the target calibration average speed are determined.

[0034] In combination with the second aspect and the above implementation methods, in some possible implementation methods, the target information includes: the current speed of the vehicle; the processing module is used to discretize the engine power corresponding to the equivalent consumption minimization strategy when the current speed of the vehicle is less than the calibrated speed; and to discretize the engine torque corresponding to the equivalent consumption minimization strategy when the current speed of the vehicle is greater than or equal to the calibrated speed.

[0035] In combination with the second aspect and the above implementation methods, in some possible implementation methods, the target information includes: the operating mode of the vehicle; the processing module is used to discretize the engine power corresponding to the equivalent consumption minimization strategy when the operating mode of the vehicle is a series mode; and to discretize the engine torque corresponding to the equivalent consumption minimization strategy when the operating mode of the vehicle is a parallel mode.

[0036] Thirdly, a vehicle is provided, comprising: a memory for storing executable program code; and a processor for calling and running the executable program code from the memory, causing the vehicle to perform the method described in the first aspect or any possible implementation thereof.

[0037] Fourthly, a computer program product is provided, comprising: computer program code, which, when run on a computer, causes the computer to perform the methods described in the first aspect or any possible implementation thereof.

[0038] Fifthly, a computer-readable storage medium is provided that stores computer program code, which, when executed on a computer, causes the computer to perform the methods described in the first aspect or any possible implementation thereof.

[0039] In a sixth aspect, an electronic device is provided, including a memory and a processor, wherein the memory is used to store executable program code; and the processor is used to call and run the executable program code from the memory, causing the electronic device to perform the data processing method in the first aspect or any possible implementation thereof.

[0040] By employing the technical solution provided in this application, combining the vehicle's average speed and maximum speed information, and using multiple delineated boundary functions, accurate and dynamic identification of the current driving condition can be achieved. Based on the identified condition, key parameters in the equivalent energy consumption minimization strategy can be adaptively selected for discretization. Deeply coupling the energy management strategy with the vehicle's actual operating state and powertrain characteristics not only significantly improves the accuracy and adaptability of parameter discretization under different conditions but also optimizes the vehicle's energy distribution efficiency, ultimately effectively improving fuel economy and enhancing the overall accuracy and robustness of the energy management system. Furthermore, different control logics can be selected according to actual needs, increasing the flexibility of energy management. Attached Figure Description

[0041] Figure 1 This is a schematic diagram of a vehicle speed distribution provided in an embodiment of this application;

[0042] Figure 2 This is a flowchart of an energy management method provided in an embodiment of this application;

[0043] Figure 3 This is a flowchart of another energy management method provided in the embodiments of this application;

[0044] Figure 4 This is a schematic diagram of the structure of an energy management device provided in an embodiment of this application;

[0045] Figure 5 This is a schematic diagram of the structure of a vehicle provided in an embodiment of this application. Detailed Implementation

[0046] The technical solutions in this application will be clearly and thoroughly described below with reference to the accompanying drawings. In the description of the embodiments of this application, unless otherwise stated, " / " means "or," for example, A / B can mean A or B. "And / or" in the text is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Furthermore, in the description of the embodiments of this application, "multiple" refers to two or more than two.

[0047] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature.

[0048] In order to illustrate the technical solutions provided in the embodiments of this application, some terms involved in the embodiments of this application will be explained below.

[0049] ECMS (Energy Management System) is a strategy for optimizing energy management, aiming to maximize fuel economy by equating the electrical energy consumption of the battery with the fuel consumption of the internal combustion engine. The core idea of ​​ECMS is to compare and optimize the energy consumption of the battery and the internal combustion engine by defining an equivalence factor.

[0050] Figure 1 This is a schematic diagram of a vehicle speed distribution provided in an embodiment of this application.

[0051] For example, the two-dimensional coordinate system is divided into four regions by the first boundary function 11, the second boundary function 12, and the third boundary function 13. These four regions correspond to four driving conditions. The region below the first boundary function 11 represents the low-speed driving condition and has a first cluster center 14. The region above the first boundary function 11 and below the second boundary function 12 represents the medium-speed driving condition and has a second cluster center 15. The region above the second boundary function and below the third boundary function represents the high-speed driving condition and has a third cluster center 16. The region above the third boundary function represents the ultra-high-speed driving condition and has a fourth cluster center 17. Simultaneously, the horizontal axis (x-axis) of the two-dimensional coordinate system represents the average speed, and the vertical axis (y-axis) represents the maximum speed. That is, the horizontal axis of the two-dimensional coordinate system represents the average speed, and the vertical axis represents the maximum speed. The first boundary function 11, the second boundary function 12, and the third boundary function 13 are determined by the target calibration average speed located on the horizontal axis and the target calibration maximum speed located on the vertical axis, respectively.

[0052] The technical solution provided in this application determines the target driving condition of the vehicle based on the acquired speed information, including the vehicle's average speed and maximum speed within a preset time period. Then, it selects one piece of information from the target information, including the target driving condition, the vehicle's current speed, and the vehicle's operating mode, to discretize the relevant parameters corresponding to the equivalent energy minimization strategy. On the one hand, it can accurately identify the vehicle's current target driving condition by combining the average speed and maximum speed. On the other hand, it associates the energy management problem in the vehicle with target information that characterizes the vehicle's current state, such as the real-time driving condition, the vehicle's current speed, and the vehicle's operating mode. This achieves the matching of the energy control strategy with the actual operating characteristics of the vehicle's power system, and accurately discretizes the parameters used in the ECMS process, improving the vehicle's fuel economy under different driving conditions.

[0053] The application scenarios of the technical solutions provided in the embodiments of this application are described below. The technical solutions provided in the embodiments of this application can be applied to hybrid vehicles, which can be any of the above-mentioned hybrid electric vehicles, plug-in hybrid electric vehicles, range-extended hybrid electric vehicles, and mild hybrid electric vehicles. Of course, with the development of science and technology, other types of hybrid vehicles may also emerge, and the technical solutions provided in the embodiments of this application are also applicable to other types of hybrid vehicles.

[0054] After introducing the application scenarios of the embodiments of this application, the technical solutions provided by the embodiments of this application will be described below. (See also...) Figure 2 The method includes the following steps.

[0055] 201. Obtain vehicle speed information and vehicle operating mode. Speed ​​information includes the vehicle's average speed within a preset time period, the vehicle's maximum speed within a preset time period, and the vehicle's current speed.

[0056] Speed ​​information is a parameter used to evaluate vehicle speed. In some embodiments, speed information includes the vehicle's average speed over a preset time period, the vehicle's maximum speed over the preset time period, and the vehicle's current speed. The preset time period can be any suitable length, such as 100 seconds, 150 seconds, etc. Average speed refers to the average speed of the vehicle over the preset time period. Average speed can be any suitable length, such as 50 km / h, 32 km / h, etc. Maximum speed refers to the maximum speed reached by the vehicle over the preset time period. Maximum speed can be any suitable length, such as 60 km / h, 56 km / h, etc. In some embodiments, the maximum speed is greater than the average speed. Multiple vehicle speeds can be collected at fixed intervals within the preset time period, and then the average speed and maximum speed of the vehicle over the preset time period can be obtained based on the collected vehicle speeds. The fixed interval can be any suitable length, such as 1 second, 2 seconds, etc. The vehicle's current speed is used to characterize the instantaneous speed of the vehicle relative to the ground. The vehicle's current speed can be any suitable length, such as 86 km / h, 36 km / h, etc. The vehicle's operating mode is used to characterize how the vehicle's power flow is organized at the current moment. In some embodiments, the vehicle's operating mode may include a series mode or a parallel mode.

[0057] 202. Based on speed information, determine the target driving conditions of the vehicle.

[0058] The target driving condition refers to the current driving condition of the vehicle. The target driving condition may include, but is not limited to, one of the following: low-speed driving condition, medium-speed driving condition, high-speed driving condition, ultra-high-speed driving condition, etc. In some embodiments, the target driving condition is determined from at least one preset driving condition of the vehicle. The preset driving conditions may include, but are not limited to, at least one of the following: low-speed driving condition, medium-speed driving condition, high-speed driving condition, ultra-high-speed driving condition, etc. In some embodiments, the target driving condition is determined based on the vehicle's average speed and maximum speed within a preset time period from the speed information.

[0059] 203. Based on the target information, the relevant parameters corresponding to the equivalent consumption minimization strategy are discretized. The target information includes one of the following: target driving conditions, current vehicle speed, and vehicle operating mode.

[0060] The relevant parameters corresponding to the equivalent consumption minimization strategy refer to the parameters used during the ECMS process. These parameters may include, but are not limited to, engine power and engine torque. Engine power refers to the work done by the engine per unit time. Engine power can be any suitable value, such as 150kW or 220kW. Engine torque refers to the rotational torque output at the engine crankshaft. Engine torque can be any suitable value, such as 320N / m or 400N / m. In some embodiments, the relevant parameters corresponding to the equivalent consumption minimization strategy may be the same or different under different target information.

[0061] The technical solution provided in this application determines the target driving condition of the vehicle based on the acquired speed information, including the vehicle's average speed and maximum speed within a preset time period. Then, it selects one piece of information from the target information, including the target driving condition, the vehicle's current speed, and the vehicle's operating mode, to discretize the relevant parameters corresponding to the equivalent energy minimization strategy. On the one hand, it can accurately identify the vehicle's current target driving condition by combining the average speed and maximum speed. On the other hand, it associates the energy management problem in the vehicle with target information that characterizes the vehicle's current state, such as the real-time driving condition, the vehicle's current speed, and the vehicle's operating mode. This achieves the matching of the energy control strategy with the actual operating characteristics of the vehicle's power system, and accurately discretizes the parameters used in the ECMS process, improving the vehicle's fuel economy under different driving conditions.

[0062] It should be noted that steps 201-203 above are a simplified description of the energy management method provided in the embodiments of this application. The energy management method provided in the embodiments of this application will be described in more detail below with reference to some examples. See [link to relevant documentation]. Figure 3 The method includes the following steps.

[0063] 301. Obtain vehicle speed information and vehicle operating mode. Speed ​​information includes the vehicle's average speed within a preset time period, the vehicle's maximum speed within a preset time period, and the vehicle's current speed.

[0064] Speed ​​information is a parameter used to evaluate vehicle speed. In some embodiments, speed information includes the vehicle's average speed over a preset time period, the vehicle's maximum speed over the preset time period, and the vehicle's current speed. The preset time period is a period of time preceding and adjacent to the current moment. The preset time period can be any suitable size, such as 100s, 150s, etc. The average speed refers to the average speed of the vehicle over the preset time period. The average speed can be any suitable size, such as 50km / h, 32km / h, etc. The maximum speed refers to the maximum speed reached by the vehicle over the preset time period. The maximum speed can be any suitable size, such as 60km / h, 56km / h, etc. In some embodiments, the maximum speed is greater than the average speed. Vehicle speed and distance traveled can be continuously collected over the preset time period. Then, the average speed is determined based on the ratio of the distance traveled to the preset time period, and the maximum value is selected from the continuously collected vehicle speeds as the maximum speed. The vehicle's current speed is used to characterize the instantaneous speed of the vehicle relative to the ground. The vehicle's current speed can be any suitable size, such as 86km / h, 36km / h, etc. The vehicle's operating mode is used to characterize how the vehicle's power flow is organized at the current moment. In some embodiments, the vehicle's operating mode may include a series mode or a parallel mode.

[0065] In some embodiments, multiple vehicle speeds can be collected at fixed time intervals within a preset time period. Then, the average speed and the maximum speed of the vehicle within the preset time period can be obtained based on the collected vehicle speeds. The time interval can be any suitable size, such as 1 second, 2 seconds, etc.

[0066] In some embodiments, the vehicle control unit (VCU) stores 100 past vehicle speeds at 1-second intervals. The maximum speed is selected from these 100 speeds, and the average speed is obtained by averaging the 100 speeds. This allows the acquisition of vehicle speed information. The VCU is the central control unit of the new energy vehicle. It can make corresponding judgments based on the driver's intentions and monitor the actions of lower-level component controllers. It is responsible for the vehicle's normal operation, regenerative braking, and the vehicle's drive system.

[0067] 302. Based on the target calibration maximum speed and the target calibration average speed, determine at least one target boundary function.

[0068] The target calibration maximum speed and target calibration average speed are used to determine the target boundary function. The target calibration maximum speed can be any suitable value, such as 100 km / h, 150 km / h, etc. The target calibration average speed can be any suitable value, such as 80 km / h, 135 km / h, etc. The target boundary function is a function used to classify the driving conditions of the vehicle. In some embodiments, there is at least one target calibration maximum speed and target calibration average speed; therefore, at least one target boundary function can be determined by using different target calibration maximum speeds and different target calibration average speeds.

[0069] In some embodiments, a two-dimensional coordinate system can be established, with one of the target calibration average speed and the target calibration maximum speed as the x-axis and the other as the y-axis. Then, at least one target calibration maximum speed is mapped as a coordinate point onto the corresponding coordinate axis, and at least one target calibration average speed is mapped as a coordinate point onto the corresponding coordinate axis. Then, two coordinate points are selected from multiple coordinate points, and the two coordinate points are connected to obtain at least one target boundary function.

[0070] In one possible implementation, a first boundary function is determined based on a first calibrated maximum speed and a first calibrated average speed. A second boundary function is determined based on a second calibrated maximum speed and a second calibrated average speed, wherein the second calibrated maximum speed is greater than the first calibrated maximum speed, and the second calibrated average speed is greater than the first calibrated average speed. A third boundary function is determined based on a third calibrated maximum speed and a third calibrated average speed, wherein the third calibrated maximum speed is greater than the second calibrated maximum speed, and the third calibrated average speed is greater than the second calibrated average speed.

[0071] The first calibrated maximum speed and the first calibrated average speed are used to determine the first boundary function. The first calibrated maximum speed can be any suitable value, such as 85 km / h, 90 km / h, etc. The first calibrated average speed can be any suitable value, such as 80 km / h, 83 km / h, etc. In some embodiments, the first calibrated maximum speed is greater than the first calibrated average speed. A two-dimensional coordinate system can be established, with one of the first calibrated maximum speed and the first calibrated average speed as a coordinate point on the horizontal axis and the other as a coordinate point on the vertical axis. Then, the first calibrated maximum speed is mapped onto the corresponding coordinate axis, and the first calibrated average speed is mapped onto the corresponding coordinate axis. Finally, the first boundary function is obtained by connecting the two coordinate points. In some embodiments, the first calibrated maximum speed is defined as... Define the first calibration average velocity as The first boundary function can be expressed by the following formula:

[0072]

[0073] Wherein, the first boundary function is a linear function, y is the dependent variable of the first boundary function, x is the independent variable of the first boundary function, the slope of the first boundary function is the ratio of the first calibrated maximum speed to the first calibrated average speed, and the intercept of the first boundary function is the first calibrated maximum speed.

[0074] The second calibrated maximum speed and the second calibrated average speed are used to determine the second boundary function. The second calibrated maximum speed can be any suitable value, such as 135 km / h, 140 km / h, etc. The second calibrated average speed can be any suitable value, such as 130 km / h, 125 km / h, etc. In some embodiments, the second calibrated maximum speed is greater than the second calibrated average speed, the second calibrated maximum speed is greater than the first calibrated maximum speed, and the second calibrated average speed is greater than the first calibrated average speed. A two-dimensional coordinate system can be established, with one of the second calibrated maximum speed and the second calibrated average speed as a coordinate point on the horizontal axis and the other as a coordinate point on the vertical axis. Then, the second calibrated maximum speed and the second calibrated average speed are mapped onto the corresponding coordinate axes, and the second boundary function is obtained by connecting the two coordinate points. In some embodiments, the second calibrated maximum speed is defined as... The second calibration average velocity is defined as The second boundary function can be expressed by the following formula:

[0075]

[0076] Wherein, the second boundary function is a linear function, y is the dependent variable of the second boundary function, x is the independent variable of the second boundary function, the slope of the second boundary function is the ratio of the second calibrated maximum speed to the second calibrated average speed, and the intercept of the second boundary function is the second calibrated maximum speed.

[0077] The third calibration maximum speed and the third calibration average speed are used to determine the third boundary function. The third calibration maximum speed can be any suitable value, such as 200 km / h, 190 km / h, etc. The third calibration average speed can be any suitable value, such as 185 km / h, 183 km / h, etc. In some embodiments, the third calibration maximum speed is greater than the third calibration average speed, the third calibration maximum speed is greater than the second calibration maximum speed, and the third calibration average speed is greater than the second calibration average speed. A two-dimensional coordinate system can be established, with one of the third calibration maximum speed and the third calibration average speed as a coordinate point on the horizontal axis and the other as a coordinate point on the vertical axis. Then, the third calibration maximum speed and the third calibration average speed are mapped onto the corresponding coordinate axes, and the third boundary function is obtained by connecting the two coordinate points. In some embodiments, the third calibration maximum speed is defined as... The third calibration average velocity is defined as The third boundary function can be expressed by the following formula:

[0078]

[0079] Wherein, the third boundary function is a linear function, y is the dependent variable of the third boundary function, x is the independent variable of the third boundary function, the slope of the third boundary function is the ratio of the third calibration maximum speed to the third calibration average speed, and the intercept of the third boundary function is the first calibration maximum speed.

[0080] It should be noted that when different target calibration maximum speeds and target calibration average speeds are mapped onto the coordinate axes to determine the target boundary function, since the second calibration maximum speed is greater than the first calibration maximum speed, the second calibration average speed is greater than the first calibration average speed, the third calibration maximum speed is greater than the second calibration maximum speed, and the third calibration average speed is greater than the second calibration average speed, the intercept of the third boundary function is greater than the intercept of the second boundary function, and the intercept of the second boundary function is greater than the intercept of the first boundary function.

[0081] In this implementation, by calibrating the maximum speed and average speed of targets of different sizes, at least one target boundary function is determined, which can establish speed boundaries from low to high and construct a hierarchical driving condition discrimination standard, providing a clear basis for the identification of target driving conditions.

[0082] In one possible implementation, at least one set of test condition data is acquired. The average speed and the maximum speed of the test condition data are determined. Based on a preset number of cluster centers, the average speed and the maximum speed of the test condition data are clustered to obtain at least one cluster center distribution. Based on the cluster center distribution, the target calibration maximum speed and the target calibration average speed are determined.

[0083] To provide a clearer explanation of the above implementation methods, the process of determining the target calibration maximum speed and the target calibration average speed in the above implementation methods will be described in four parts below.

[0084] Part 1: Obtain at least one test condition data.

[0085] The test cycle data refers to representative driving condition data. In some embodiments, test cycle data can be obtained from open-source platforms. Test cycle data may include, but is not limited to, standard cycle road condition data and / or custom cycle data. Standard cycle road condition data may include, but is not limited to, at least one of the following: the Worldwide Harmonized Light Vehicles Test Cycle (WLTC), the New European Driving Cycle (NEDC), the China Light-dutyvehicle Test Cycle (CLTC), the Federal Test Procedure (FTP-75), and the Highway Fuel Economy Test (HWFET). WLTC is used to evaluate the fuel economy and emissions performance of vehicles. NEDC is primarily used for evaluating vehicle fuel consumption and emissions performance in the European market. CLTC is used for testing vehicle fuel economy, emissions performance, and range of new energy vehicles in the Chinese market. FTP-75 is a standard for testing the economy and emissions of passenger cars in urban driving conditions, used to evaluate the emissions and fuel economy of light-duty vehicles and light-duty trucks. HWFET is used to evaluate vehicle fuel economy under highway conditions. Custom operating condition data can include, but is not limited to, at least one of the following: urban operating condition data, highway data, road spectrum data, etc. Urban operating condition data is used to simulate vehicle performance in urban driving environments, evaluating fuel economy, emissions, and electric vehicle energy consumption. Highway data is used to simulate vehicle performance in highway driving environments, evaluating high-speed fuel economy, stability, and thermal management. Road spectrum data refers to the power spectral density curve of road surface roughness, used to describe the statistical characteristics of road surface roughness in response to vehicle vibration input.

[0086] Part Two: Determine the average speed and the maximum speed of the test condition data.

[0087] Specifically, for each data point in at least one test condition data set, the average speed and maximum speed are determined. The average speed of the test condition data can be any suitable value, such as 120 km / h, 86 km / h, etc. The maximum speed of the test condition data can be any suitable value, such as 135 km / h, 140 km / h, etc. In some embodiments, for the same test condition data set, the maximum speed of the test condition data is greater than or equal to the average speed of the test condition data.

[0088] In some embodiments, the test condition data may include, but is not limited to, at least one of: total driving distance, total driving time, and speed points. The average speed of the test condition data can be determined based on the ratio of total driving distance to total driving time, or the average speed of the test condition data can be obtained by averaging all speed points. Simultaneously, the maximum value among all speed points is selected as the highest speed of the test condition data.

[0089] Part Three: Based on the preset number of cluster centers, the average speed and the highest speed of the test condition data are clustered to obtain at least one cluster center distribution.

[0090] The preset number of cluster centers can be any suitable size, such as 3 or 4. In some embodiments, the number of driving conditions included in the vehicle can be used as the preset number of cluster centers. The cluster center distribution is obtained through clustering processing and is used to characterize the position and distribution of each cluster center in the feature space during cluster analysis. In some embodiments, the number of cluster center distributions is at least one, and the number of cluster center distributions is the same as the preset number of cluster centers.

[0091] In some embodiments, the method for clustering the average speed and the highest speed of the test condition data may include, but is not limited to: clustering the average speed and the highest speed of the test condition data using the K-Means clustering algorithm, or clustering the average speed and the highest speed of the test condition data using a hierarchical clustering algorithm. For example, K-Means clustering can, based on a preset number of cluster centers, iteratively assign the average speed and the highest speed of each test condition data point to the cluster represented by the nearest cluster center to obtain at least one cluster center distribution. As another example, hierarchical clustering can, based on a preset number of cluster centers, calculate the similarity between each test condition data point using a bottom-up aggregation method, and gradually merge the most similar clusters until a preset number of clusters are formed, thereby obtaining at least one cluster center distribution.

[0092] Part Four: Based on the cluster center distribution, determine the target calibration maximum speed and target calibration average speed.

[0093] The target calibration maximum speed and target calibration average speed are used to determine the target boundary function. The target calibration maximum speed can be any suitable size, such as 100 km / h, 150 km / h, etc. The target calibration maximum speed can include, but is not limited to, at least one of a first calibration maximum speed, a second calibration maximum speed, a third calibration maximum speed, etc. The target calibration average speed can be any suitable size, such as 80 km / h, 135 km / h, etc. The target calibration average speed can include, but is not limited to, at least one of a first calibration average speed, a second calibration average speed, a third calibration average speed, etc. The target boundary function is a function used to classify the driving conditions of the vehicle. In some embodiments, the coordinate values ​​of at least one cluster center distribution are used as the target calibration maximum speed and the target calibration average speed, respectively.

[0094] In this implementation, the average speed and maximum speed of the acquired test condition data are clustered by a preset number of cluster centers to obtain at least one cluster center distribution. Then, based on the cluster center distribution, the target calibration maximum speed and target calibration average speed are determined. This allows for the extraction of representative speed data from actual driving data, ensuring the objectivity and representativeness of the calibration speed and providing a reliable data foundation for establishing the target boundary function.

[0095] 303. Based on the average speed and the maximum speed, determine the target reference point. One of the average speed and the maximum speed is the x-coordinate of the target reference point, and the other is the y-coordinate of the target reference point.

[0096] The average speed can be any suitable value, such as 50 km / h, 32 km / h, etc. The maximum speed can also be any suitable value, such as 60 km / h, 56 km / h, etc. In some embodiments, the maximum speed is greater than the average speed. The target reference point is a coordinate point used to characterize the average speed and the maximum speed. In some embodiments, one of the average speed and the maximum speed is the x-coordinate of the target reference point, and the other is the y-coordinate. Specifically, when the average speed is the x-coordinate of the target reference point, the maximum speed is the y-coordinate. When the maximum speed is the x-coordinate of the target reference point, the average speed is the y-coordinate.

[0097] 304. Determine the target driving conditions based on the target reference point and the target boundary function.

[0098] The target boundary function is a function used to classify the driving conditions of the vehicle. The target boundary function may include, but is not limited to, at least one of the following: a first boundary function, a second boundary function, a third boundary function, etc. In some embodiments, different target boundary functions are used to represent different driving conditions. At least one target boundary function and a target reference point can be mapped to a two-dimensional coordinate system. The distance between the target reference point and the target boundary function is calculated to obtain at least one reference distance. Then, the driving condition set by the target boundary function corresponding to the smallest reference distance is taken as the target driving condition.

[0099] In this implementation, the target boundary function is determined by the target calibration maximum speed and the target calibration average speed, and the target reference point is determined by the average speed and the maximum speed. Then, the target driving condition is determined based on the target reference point and the target boundary function, so that the target reference point based on the real-time vehicle speed can be accurately classified into the corresponding driving condition by the target boundary function, thereby providing a reliable driving condition identification basis for subsequent energy management strategies.

[0100] In one possible implementation, if the target reference point is below the first boundary function, the low-speed driving condition is taken as the target driving condition. If the target reference point is above the first boundary function and below the second boundary function, the medium-speed driving condition is taken as the target driving condition. If the target reference point is above the second boundary function and below the third boundary function, the high-speed driving condition is taken as the target driving condition. If the target reference point is above the third boundary function, the ultra-high-speed driving condition is taken as the target driving condition.

[0101] The first boundary function distinguishes between low-speed and medium-speed driving conditions. The second boundary function distinguishes between medium-speed and high-speed driving conditions. The third boundary function distinguishes between high-speed and ultra-high-speed driving conditions. Low-speed driving condition represents a vehicle traveling at low speed. Medium-speed driving condition represents a vehicle traveling at medium speed. High-speed driving condition represents a vehicle traveling at high speed. Ultra-high-speed driving condition represents a vehicle traveling at ultra-high speed.

[0102] It should be noted that when mapping the three target boundary functions to the same two-dimensional coordinate system, since the intercept of the third boundary function is greater than the intercept of the second boundary function, and the intercept of the second boundary function is greater than the intercept of the first boundary function, the coordinate axis can be divided into four regions by the third boundary function, the second boundary function, and the first boundary function. These four regions can be mapped to four driving conditions, and the target driving conditions can be determined based on the positional relationship between the target reference point and the target boundary functions.

[0103] In some embodiments, when the target reference point is below the first boundary function, it can be characterized that the target reference point is located in the region corresponding to a low-speed driving condition, and the low-speed driving condition is taken as the target driving condition. When the target reference point is above the first boundary function and below the second boundary function, it can be characterized that the target reference point is located in the region corresponding to a medium-speed driving condition, and the medium-speed driving condition is taken as the target driving condition. When the target reference point is above the second boundary function and below the third boundary function, it can be characterized that the target reference point is located in the region corresponding to a high-speed driving condition, and the high-speed driving condition is taken as the target driving condition. When the target reference point is above the third boundary function, it can be characterized that the target reference point is located in the region corresponding to an ultra-high-speed driving condition, and the ultra-high-speed driving condition is taken as the target driving condition.

[0104] In this implementation, the target driving condition is determined based on the positional relationship between the target reference point and the first boundary function, the second boundary function and the third boundary function. Four driving condition regions are divided by the three target boundary functions. Real-time and accurate classification of driving conditions from low speed to ultra-high speed can be achieved by judging the position.

[0105] 305. Based on the target information, the relevant parameters corresponding to the equivalent consumption minimization strategy are discretized. The target information includes one of the following: target driving conditions, current vehicle speed, and vehicle operating mode.

[0106] The relevant parameters corresponding to the equivalent consumption minimization strategy refer to the parameters used during the ECMS process. These parameters may include, but are not limited to, engine power and engine torque. Engine power refers to the work done by the engine per unit time. Engine power can be any suitable value, such as 150kW or 220kW. Engine torque refers to the rotational torque output at the crankshaft end of the engine. Engine torque can be any suitable value, such as 320N / m or 400N / m. In some embodiments, the relevant parameters corresponding to the equivalent consumption minimization strategy may be the same or different under different target information. For example, the relevant parameters corresponding to the equivalent consumption minimization strategy may be the same or different under different target driving conditions. Discretization processing refers to the process of dividing continuous relevant parameters into discrete candidate values ​​according to a preset range of discrete points.

[0107] In one possible implementation, the target information includes the target driving condition. When the target driving condition is a low-speed or medium-speed driving condition, the engine power corresponding to the equivalent energy minimization strategy is discretized, and the vehicle speed in the low-speed driving condition is less than the vehicle speed in the medium-speed driving condition. When the target driving condition is a high-speed or ultra-high-speed driving condition, the engine torque corresponding to the equivalent energy minimization strategy is discretized, and the vehicle speed in the medium-speed driving condition is less than the vehicle speed in the high-speed driving condition, and the vehicle speed in the high-speed driving condition is less than the vehicle speed in the ultra-high-speed driving condition.

[0108] The driving conditions are categorized as follows: Low-speed driving condition represents the vehicle traveling at low speed. Medium-speed driving condition represents the vehicle traveling at medium speed. In some embodiments, when the target driving condition is low-speed or medium-speed, the engine power corresponding to the equivalent energy minimization strategy is discretized to obtain a discrete engine power sequence containing at least one engine power. High-speed driving condition represents the vehicle traveling at high speed. Ultra-high-speed driving condition represents the vehicle traveling at ultra-high speed. In some embodiments, when the target driving condition is high-speed or ultra-high-speed, the engine torque corresponding to the equivalent energy minimization strategy is discretized to obtain a discrete engine torque sequence containing at least one engine torque. The vehicle speed in low-speed driving condition is lower than the vehicle speed in medium-speed driving condition, the vehicle speed in medium-speed driving condition is lower than the vehicle speed in high-speed driving condition, and the vehicle speed in high-speed driving condition is lower than the vehicle speed in ultra-high-speed driving condition.

[0109] It should be noted that when the target driving conditions are low-speed or medium-speed, the vehicle's power demand is relatively low and fluctuates significantly. In this case, the engine is not mechanically connected to the wheels, and its speed can be freely controlled to maintain the most efficient power output. Therefore, the engine power corresponding to the equivalent power minimization strategy needs to be discretized. When the target driving conditions are high-speed or ultra-high-speed, the vehicle requires continuous and stable high power output. The engine is rigidly connected to the wheels via the transmission, and its speed is determined by vehicle speed and gear position, which cannot be freely controlled. Engine torque is the only freely controllable variable directly related to dynamics; therefore, the engine torque corresponding to the equivalent power minimization strategy needs to be discretized.

[0110] In this implementation, engine power is discretized when the target driving condition is low-speed or medium-speed, and engine torque is discretized when the target driving condition is high-speed or ultra-high-speed. Different parameters can be selected for discretization under different vehicle conditions, which significantly improves the accuracy and flexibility of the energy management strategy.

[0111] In one possible implementation, the target information includes the vehicle's current speed. If the vehicle's current speed is less than the calibrated speed, the engine power corresponding to the equivalent energy minimization strategy is discretized. If the vehicle's current speed is greater than or equal to the calibrated speed, the engine torque corresponding to the equivalent energy minimization strategy is discretized.

[0112] The vehicle's current speed characterizes its instantaneous speed relative to the ground. The current speed can be any suitable value, such as 86 km / h, 36 km / h, etc. The calibration speed can also be any suitable value, such as 90 km / h, 85 km / h, etc. When the vehicle's current speed is less than the calibration speed, the engine power corresponding to the equivalent energy minimization strategy is discretized. When the vehicle's current speed is greater than or equal to the calibration speed, the engine torque corresponding to the equivalent energy minimization strategy is discretized.

[0113] It should be noted that when the vehicle's current speed is less than the rated speed, the overall power demand is relatively low and fluctuates greatly. At this time, the engine is not mechanically connected to the wheels, and its speed can be freely controlled to maintain the most efficient power output point. Therefore, the engine power corresponding to the equivalent power minimization strategy needs to be discretized. When the vehicle's current speed is greater than or equal to the rated speed, the vehicle requires continuous and stable high power output. The engine is rigidly connected to the wheels through the transmission, and its speed is determined by the vehicle speed and gear, which cannot be freely controlled. Engine torque is the only freely controllable variable directly related to dynamics; therefore, the engine torque corresponding to the equivalent power minimization strategy needs to be discretized.

[0114] In this implementation, the engine power is discretized when the current speed of the vehicle is greater than the rated speed, and the engine torque is discretized when the current speed of the vehicle is less than or equal to the rated speed. This enables the establishment of a discretization method related to vehicle speed, which significantly optimizes the energy distribution efficiency at different vehicle speeds.

[0115] In one possible implementation, the target information includes the vehicle's operating mode. If the vehicle's operating mode is series mode, the engine power corresponding to the equivalent power consumption minimization strategy is discretized. If the vehicle's operating mode is parallel mode, the engine torque corresponding to the equivalent power consumption minimization strategy is discretized.

[0116] The vehicle's operating mode characterizes the power flow organization of the vehicle at the current moment. In some embodiments, the vehicle's operating mode may include a series mode or a parallel mode. A series mode connects the engine and electric motor in series, allowing them to drive the vehicle together or individually. A parallel mode connects the engine and electric motor in parallel, enabling them to drive the vehicle simultaneously and providing stronger power output.

[0117] In some embodiments, when the vehicle operates in series mode, the engine power demand is discretized to obtain at least one initial engine power. Then, the initial engine power is corrected to obtain a target engine power corresponding to the initial engine power. The target engine power is then used as the engine power in the discrete engine power sequence. When the vehicle operates in parallel mode, the engine torque demand is discretized to obtain at least one initial engine torque. Then, the initial engine torque is corrected to obtain a target engine torque corresponding to the initial engine torque. The target engine torque is then used as the engine power in the discrete engine power sequence.

[0118] It should be noted that when the vehicle operates in series mode, the overall power demand is relatively low and fluctuates significantly. In this mode, the engine is not mechanically connected to the wheels, and its speed can be freely controlled to maintain the most efficient power output. Therefore, the engine power corresponding to the equivalent power minimization strategy needs to be discretized. When the vehicle operates in parallel mode, it requires continuous and stable high power output. The engine is rigidly connected to the wheels via the transmission, and its speed is determined by vehicle speed and gear position, making it uncontrollable. Engine torque is the only freely controllable variable directly related to dynamics; therefore, the engine torque corresponding to the equivalent power minimization strategy needs to be discretized.

[0119] Meanwhile, the three technical solutions for discretizing the relevant parameters of the equivalent consumption minimization strategy based on the vehicle's target driving condition, current speed, and operating mode are independent of each other. A calibration switch can be set on the vehicle to select the control logic currently used.

[0120] In this implementation, engine power is discretized when the vehicle is operating in series mode, and engine torque is discretized when the vehicle is operating in parallel mode. This improves the adaptability of the energy management strategy to different operating modes, enhances the overall vehicle fuel economy, and improves the vehicle's control fault tolerance through various control logics.

[0121] Figure 4 This is a schematic diagram of the structure of an energy management device provided in an embodiment of this application. See also... Figure 4 The energy management device 400 includes:

[0122] The acquisition module 401 is used to acquire the vehicle's speed information and the vehicle's operating mode. The speed information includes the vehicle's average speed within a preset time period, the vehicle's maximum speed within the preset time period, and the vehicle's current speed.

[0123] The determination module 402 is used to determine the target driving condition of the vehicle based on speed information;

[0124] The processing module 403 is used to discretize the relevant parameters corresponding to the equivalent consumption minimization strategy based on the target information. The target information includes one of the following: target driving conditions, current vehicle speed, and vehicle operating mode.

[0125] In one possible implementation, the target information includes the target driving condition. The processing module 403 is used to discretize the engine power corresponding to the equivalent consumption minimization strategy when the target driving condition is a low-speed driving condition or a medium-speed driving condition, wherein the vehicle speed in the low-speed driving condition is less than the vehicle speed in the medium-speed driving condition; and to discretize the engine torque corresponding to the equivalent consumption minimization strategy when the target driving condition is a high-speed driving condition or an ultra-high-speed driving condition, wherein the vehicle speed in the medium-speed driving condition is less than the vehicle speed in the high-speed driving condition, and the vehicle speed in the high-speed driving condition is less than the vehicle speed in the ultra-high-speed driving condition.

[0126] In one possible implementation, the determining module 402 is used to determine at least one target boundary function based on the target calibration maximum speed and the target calibration average speed; determine a target reference point based on the average speed and the maximum speed, wherein one of the average speed and the maximum speed is the abscissa of the target reference point and the other is the ordinate of the target reference point; and determine a target driving condition based on the target reference point and the target boundary function.

[0127] In one possible implementation, the determining module 402 is configured to: determine a low-speed driving condition as the target driving condition when the target reference point is below a first boundary function; determine a medium-speed driving condition as the target driving condition when the target reference point is above the first boundary function and below a second boundary function; determine a high-speed driving condition as the target driving condition when the target reference point is above the second boundary function and below a third boundary function; and determine an ultra-high-speed driving condition as the target driving condition when the target reference point is above the third boundary function.

[0128] In one possible implementation, the determining module 402 is configured to determine a first boundary function based on a first calibrated maximum speed and a first calibrated average speed; determine a second boundary function based on a second calibrated maximum speed and a second calibrated average speed, wherein the second calibrated maximum speed is greater than the first calibrated maximum speed and the second calibrated average speed is greater than the first calibrated average speed; and determine a third boundary function based on a third calibrated maximum speed and a third calibrated average speed, wherein the third calibrated maximum speed is greater than the second calibrated maximum speed and the third calibrated average speed is greater than the second calibrated average speed.

[0129] In one possible implementation, the acquisition module 401 is used to acquire at least one test condition data; the determination module 402 is used to determine the average speed and the maximum speed of the test condition data; based on a preset number of cluster centers, the average speed and the maximum speed of the test condition data are clustered to obtain at least one cluster center distribution; based on the cluster center distribution, the target calibration maximum speed and the target calibration average speed are determined.

[0130] In one possible implementation, the target information includes the vehicle's current speed. The processing module 403 is used to discretize the engine power corresponding to the equivalent consumption minimization strategy when the vehicle's current speed is less than the calibrated speed; and to discretize the engine torque corresponding to the equivalent consumption minimization strategy when the vehicle's current speed is greater than or equal to the calibrated speed.

[0131] In one possible implementation, the target information includes the vehicle's operating mode. The processing module 403 is used to discretize the engine power corresponding to the equivalent consumption minimization strategy when the vehicle's operating mode is in series mode; and to discretize the engine torque corresponding to the equivalent consumption minimization strategy when the vehicle's operating mode is in parallel mode.

[0132] It should be noted that the energy management device provided in the above embodiments is only illustrated by the division of the above functional modules when controlling a vehicle. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the computer device can be divided into different functional modules to complete all or part of the functions described above. In addition, the energy management device and the energy management method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0133] The technical solution provided in this application determines the target driving condition of the vehicle based on the acquired speed information, including the vehicle's average speed and maximum speed within a preset time period. Then, it selects one piece of information from the target information, including the target driving condition, the vehicle's current speed, and the vehicle's operating mode, to discretize the relevant parameters corresponding to the equivalent energy minimization strategy. On the one hand, it can accurately identify the vehicle's current target driving condition by combining the average speed and maximum speed. On the other hand, it associates the energy management problem in the vehicle with target information that characterizes the vehicle's current state, such as the real-time driving condition, the vehicle's current speed, and the vehicle's operating mode. This achieves the matching of the energy control strategy with the actual operating characteristics of the vehicle's power system, and accurately discretizes the parameters used in the ECMS process, improving the vehicle's fuel economy under different driving conditions.

[0134] This application also provides a vehicle. Figure 5 This is a schematic diagram of the structure of a vehicle provided in an embodiment of this application.

[0135] Typically, vehicle 500 includes one or more processors 501 and one or more memories 502.

[0136] Processor 501 may include one or more processing cores, such as a quad-core processor, a penta-core processor, etc. Processor 501 may be implemented using at least one hardware form selected from DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). Processor 501 may also include a main processor and a coprocessor. The main processor, also known as a CPU (Central Processing Unit), is used to process data in the wake-up state; the coprocessor is a low-power processor used to process data in the standby state. In some embodiments, processor 501 may integrate a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the screen. In some embodiments, processor 501 may also include an AI (Artificial Intelligence) processor, which is used to handle computational operations related to machine learning.

[0137] The memory 502 may include one or more computer-readable storage media, which may be non-transitory. The memory 502 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices or flash memory devices. In some embodiments, the non-transitory computer-readable storage media in the memory 502 are used to store at least one computer program, which is executed by the processor 501 to implement the energy management method provided in the method embodiments of this application.

[0138] Those skilled in the art will understand that Figure 5 The structure shown does not constitute a limitation on vehicle 500 and may include more or fewer components than shown, or combine certain components, or use different component arrangements.

[0139] In addition, the device provided in the embodiments of this application may specifically be a chip, component or module. The chip may include a connected processor and a memory. The memory is used to store instructions. When the processor calls and executes the instructions, the chip can execute a vehicle control method provided in the above embodiments.

[0140] This embodiment also provides a computer-readable storage medium storing computer program code. When the computer program code is run on a computer, the computer executes the above-described method steps to implement a vehicle control method provided in the above embodiment.

[0141] This embodiment also provides a computer program product that, when run on a computer, causes the computer to perform the aforementioned steps to implement a vehicle control method provided in the above embodiment.

[0142] In this embodiment, the device, computer-readable storage medium, computer program product, or chip are all used to execute the corresponding methods provided above. Therefore, the beneficial effects they can achieve can be referred to the beneficial effects in the corresponding methods provided above, and will not be repeated here.

[0143] Through the above description of the embodiments, those skilled in the art will understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0144] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another apparatus, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0145] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. An energy management method, characterized by, The method is applied to a vehicle, and the method comprises: obtaining speed information of the vehicle and an operating mode of the vehicle, the speed information comprising an average speed of the vehicle in a preset time period, a maximum speed of the vehicle in the preset time period, and a current speed of the vehicle; based on the speed information, determining a target driving mode of the vehicle; based on target information, discretizing relevant parameters corresponding to an equivalent consumption minimization strategy, the target information comprising one of the target driving mode, the current speed of the vehicle, and the operating mode of the vehicle; the method further comprises: obtaining at least one test driving mode data; determining an average speed of the test driving mode data and a maximum speed of the test driving mode data; based on a preset number of cluster centers, clustering the average speed of the test driving mode data and the maximum speed of the test driving mode data to obtain at least one cluster center distribution; based on the cluster center distribution, determining the target calibration maximum speed and the target calibration average speed. the target information comprises the target driving mode, and the discretizing, based on the target information, the relevant parameters corresponding to the equivalent consumption minimization strategy comprises: in a case where the target driving mode is a low-speed driving mode or a medium-speed driving mode, discretizing an engine power corresponding to the equivalent consumption minimization strategy, the vehicle speed of the vehicle in the low-speed driving mode being less than the vehicle speed in the medium-speed driving mode; in a case where the target driving mode is a high-speed driving mode or an ultra-high-speed driving mode, discretizing an engine torque corresponding to the equivalent consumption minimization strategy, the vehicle speed of the vehicle in the medium-speed driving mode being less than the vehicle speed in the high-speed driving mode, and the vehicle speed of the vehicle in the high-speed driving mode being less than the vehicle speed in the ultra-high-speed driving mode. the determining, based on the target calibration maximum speed and the target calibration average speed, of at least one target boundary function comprises at least one of:

2. The method of claim 1, wherein, determining a first boundary function based on a first calibration maximum speed and a first calibration average speed; determining a second boundary function based on a second calibration maximum speed and a second calibration average speed, the second calibration maximum speed being greater than the first calibration maximum speed, and the second calibration average speed being greater than the first calibration average speed; determining a third boundary function based on a third calibration maximum speed and a third calibration average speed, the third calibration maximum speed being greater than the second calibration maximum speed, and the third calibration average speed being greater than the second calibration average speed.

3. The method of claim 1, wherein, ​ ​ ​ ​ 4. The method of claim 3, wherein, The target driving condition is determined based on the target reference point and the target boundary function, including: in the case that the target reference point is below the first boundary function, a low-speed driving condition is taken as the target driving condition; in the case that the target reference point is above the first boundary function and below the second boundary function, a medium-speed driving condition is taken as the target driving condition; in the case that the target reference point is above the second boundary function and below the third boundary function, a high-speed driving condition is taken as the target driving condition; in the case that the target reference point is above the third boundary function, an ultra-high-speed driving condition is taken as the target driving condition.

5. The method of claim 1, wherein, The target information includes a current speed of the vehicle, and the discretization of the relevant parameters corresponding to the equivalent consumption minimization strategy based on the target information includes: in the case that the current speed of the vehicle is less than a rated speed, the engine power corresponding to the equivalent consumption minimization strategy is discretized; in the case that the current speed of the vehicle is greater than or equal to the rated speed, the engine torque corresponding to the equivalent consumption minimization strategy is discretized.

6. The method of claim 1, wherein, The target information includes a working mode of the vehicle, and the discretization of the relevant parameters corresponding to the equivalent consumption minimization strategy based on the target information includes: in the case that the working mode of the vehicle is a series mode, the engine power corresponding to the equivalent consumption minimization strategy is discretized; in the case that the working mode of the vehicle is a parallel mode, the engine torque corresponding to the equivalent consumption minimization strategy is discretized.

7. An electronic device, comprising: including: a memory for storing executable program code; a processor for calling and running the executable program code from the memory, so that the electronic device executes the method of any one of claims 1 to 6.

8. A vehicle characterized by comprising: The vehicle includes: a memory for storing executable program code; a processor for calling and running the executable program code from the memory, so that the vehicle executes the method of any one of claims 1 to 6.

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