Energy management method and device and vehicle

Abstract

The invention provides an energy management method and device and a vehicle, and the method comprises the steps: determining a target driving mode of the vehicle based on the driving data of the vehicle in a first time interval, determining a target value of an oil-electricity conversion factor based on the target driving mode, and outputting the target value to an energy management control unit, the first time interval comprises a time interval before the current moment, the second time interval comprises a time interval after the current moment, and the target value of the oil-electricity conversion factor is calculated according to the target value of the oil-electricity conversion factor to trigger the energy management control unit to conduct energy management control on the vehicle within the second time interval. Therefore, the target value of the oil-electricity conversion factor in the future time interval can be pre-judged according to the driving mode of the vehicle in the historical time interval, and the energy consumption level of the whole vehicle can be effectively reduced; meanwhile, hardware equipment such as a camera, a radar and a high-precision map does not need to be added through the method, and then hardware cost of the vehicle does not need to be increased.

Description

Technical Field

[0001] The present application relates to the field of vehicle technology, and in particular to an energy management method, device and vehicle. Background Art

[0002] As the global warming problem becomes increasingly serious, reducing carbon emissions and improving energy efficiency have become important development directions in various fields. As an important means of transportation, cars consume a huge amount of energy. Therefore, strengthening car energy management and improving fuel economy and power efficiency are of great significance to reducing greenhouse gas emissions. With the rise of new energy vehicles such as electric vehicles and hybrid vehicles, how to effectively manage the energy of on-board batteries has become a key technology to improve the fuel economy and power efficiency of vehicles.

[0003] At present, the rule-based energy management strategy is usually adopted in vehicles. However, this energy management strategy has entered a bottleneck period and cannot further improve the fuel economy and power efficiency of the vehicle, and thus cannot effectively reduce the energy consumption level of the whole vehicle. In addition, the existing energy management method also obtains the road condition information ahead through cameras, radars, high-precision maps, etc., and performs predictive energy management based on the road condition information. However, this method requires the addition of hardware equipment such as cameras, radars, and high-precision maps, which greatly increases the hardware cost of the whole vehicle. Summary of the invention

[0004] In order to solve the above technical problems, the present application provides an energy management method, device and vehicle to solve the problem in the prior art that it is impossible to effectively reduce the energy consumption level of the entire vehicle without increasing the hardware cost of the vehicle.

[0005] To achieve the above technical objectives, the present application provides the following technical solutions:

[0006] In a first aspect, an embodiment of the present specification provides an energy management method, including:

[0007] Determining a target driving mode of the vehicle based on driving data of the vehicle in a first time interval; the first time interval includes a time interval before a current moment;

[0008] Based on the target driving mode, a target value of the oil-to-electricity conversion factor is determined, and the target value of the oil-to-electricity conversion factor is output to an energy management control unit to trigger the energy management control unit to perform energy management control on the vehicle based on the target value of the oil-to-electricity conversion factor within a second time interval, and the second time interval includes a time interval after the current moment.

[0009] In one embodiment, the target driving mode includes a target driving condition and a driving style of the driver; the driving data includes first data corresponding to a driving state parameter and second data corresponding to a driving control input parameter;

[0010] The determining the target driving mode of the vehicle based on the driving data of the vehicle in the first time interval includes:

[0011] The target driving condition is determined based on first statistical information corresponding to the first data, and the driving style is determined based on second statistical information corresponding to the second data.

[0012] In one implementation, determining a target value of the oil-to-electricity conversion factor based on the target driving mode includes:

[0013] Based on the target driving condition and the predetermined corresponding relationship, a preferred value space of the oil-to-electricity conversion factor under the target driving condition is determined and used as the target value space; the predetermined corresponding relationship includes a plurality of candidate driving conditions and a preferred value space of the oil-to-electricity conversion factor under each of the candidate driving conditions;

[0014] A target value of the oil-to-electricity conversion factor is determined based on the driving style and the target value space.

[0015] In one embodiment, the process of determining the preferred value space of the oil-to-electricity conversion factor under the candidate driving condition includes:

[0016] Determining a plurality of target vehicle speed sequences for the vehicle under the candidate driving conditions based on vehicle speed test information corresponding to at least one standard driving cycle;

[0017] In a process of controlling the physical model of the vehicle to perform vehicle simulation operation based on the target vehicle speed sequence, iteratively optimizing the oil-to-electricity conversion factor based on the SOC simulation value output by the physical model to obtain a correction value of the oil-to-electricity conversion factor corresponding to the target vehicle speed sequence;

[0018] Based on the oil-to-electricity conversion factor correction value corresponding to each of the target vehicle speed sequences, the preferred value space of the oil-to-electricity conversion factor under the candidate driving condition is determined.

[0019] In one implementation, the iterative optimization of the oil-to-electricity conversion factor based on the SOC simulation value output by the physical model to obtain the oil-to-electricity conversion factor correction value corresponding to the target vehicle speed sequence includes:

[0020] In the current iteration, the current value of the oil-to-electricity conversion factor is output to the energy management control unit to trigger the energy management control unit to output an energy distribution control instruction to the physical model and obtain the SOC simulation value output by the physical model; the current value of the oil-to-electricity conversion factor is the preferred value of the oil-to-electricity conversion factor or the initial oil-to-electricity conversion factor;

[0021] If the deviation between the SOC simulation value and the SOC target value corresponding to the candidate driving condition is less than or equal to a preset deviation threshold, the current value of the oil-to-electricity conversion factor is used as a correction value of the oil-to-electricity conversion factor corresponding to the target vehicle speed sequence;

[0022] If the deviation value is greater than the preset deviation threshold, a preferred value of the oil-to-electricity conversion factor in the next iteration is determined based on the SOC simulation value, and the next iteration is performed.

[0023] In one embodiment, determining the preferred value of the oil-to-electricity conversion factor in the next iteration based on the SOC simulation value includes:

[0024] Based on the magnitude relationship between the SOC simulation value and the SOC target value, and the predetermined change trend of the SOC simulation value along with the oil-to-electricity conversion factor, determine the first endpoint of the candidate value space of the oil-to-electricity conversion factor in the next iteration from the two endpoints of the candidate value space of the oil-to-electricity conversion factor in the current iteration, and use the current value of the oil-to-electricity conversion factor in the current iteration as the second endpoint of the candidate value space of the oil-to-electricity conversion factor in the next iteration;

[0025] Based on the candidate value space of the oil-to-electricity conversion factor in the next iteration, the preferred value of the oil-to-electricity conversion factor in the next iteration is determined.

[0026] In one implementation, the determining of a plurality of target vehicle speed sequences for the vehicle under the candidate driving conditions based on vehicle speed test information corresponding to at least one standard driving cycle includes:

[0027] Based on the vehicle speed interval corresponding to the candidate driving conditions, respectively obtaining target data segments in the vehicle speed test information corresponding to each of the standard driving cycles;

[0028] Each of the target data segments is spliced ​​to obtain a plurality of target vehicle speed sequences under the candidate driving conditions.

[0029] In one implementation, determining the target value of the oil-to-electricity conversion factor based on the driving style and the target value space includes:

[0030] A target value of the oil-to-electricity conversion factor is determined based on the energy demand level under the driving style, a predetermined change trend of the oil-to-electricity conversion factor with the energy demand level, and the target value space.

[0031] In a second aspect, an embodiment of the present specification provides an energy management device, including:

[0032] A first processing module, configured to determine a target driving mode of the vehicle based on driving data of the vehicle in a first time interval; the first time interval includes a time interval before a current moment;

[0033] The second processing module is used to determine a target value of the oil-to-electricity conversion factor based on the target driving mode, and output the target value of the oil-to-electricity conversion factor to the energy management control unit to trigger the energy management control unit to perform energy management control on the vehicle based on the target value of the oil-to-electricity conversion factor within a second time interval, and the second time interval includes a time interval after the current moment.

[0034] In a third aspect, an embodiment of the present specification provides a vehicle, including an energy management control unit and the energy management device as described above.

[0035] In a fourth aspect, an embodiment of the present specification provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the energy management method as described in any one of the above items is implemented.

[0036] In a fifth aspect, an embodiment of the present specification provides a computer program product or a computer program, wherein the computer program product includes a computer program, and the computer program is stored in a computer-readable storage medium; the processor of the computer device reads the computer program from the computer-readable storage medium, and when the processor executes the computer program, it implements the energy management method as described in any one of the above items.

[0037] It can be seen from the above technical scheme that the embodiment of the present application provides an energy management method, device and vehicle, wherein the method determines a target driving mode of the vehicle based on the driving data of the vehicle in a first time interval, determines a target value of the oil-to-electricity conversion factor based on the target driving mode, and outputs the target value of the oil-to-electricity conversion factor to the energy management control unit to trigger the energy management control unit to perform energy management control on the vehicle based on the target value of the oil-to-electricity conversion factor in a second time interval, wherein the first time interval includes a time interval before the current moment, and the second time interval includes a time interval after the current moment, so that the target value of the oil-to-electricity conversion factor in a future time interval can be predicted according to the driving mode of the vehicle in the historical time interval, so as to realize predictive energy management of the vehicle, and then, when the vehicle is running smoothly (that is, there is no cliff-like transition in the vehicle working condition), energy management control of the vehicle according to the predicted target value of the oil-to-electricity conversion factor can effectively reduce the energy consumption level of the whole vehicle; at the same time, the method of this embodiment does not need to add hardware equipment such as cameras, radars, and high-precision maps, and thus does not need to increase the hardware cost of the vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0039] Figure 1 A schematic flow chart of an energy management method provided for an implementation of this specification.

[0040] Figure 2 A schematic diagram of a predetermined change trend of a SOC simulation value with an oil-to-electricity conversion factor provided in an embodiment of this specification.

[0041] Figure 3 A schematic diagram of target data segment interception of vehicle speed test information corresponding to a standard driving cycle is provided for an implementation manner of this specification.

[0042] Figure 4 A schematic diagram of the structure of an energy management device provided for an implementation manner of this specification. DETAILED DESCRIPTION

[0043] Unless otherwise defined, the technical terms or scientific terms used in the embodiments of this specification shall have the common meanings understood by persons with ordinary skills in the field to which this specification belongs. The words "first", "second" and similar words used in the embodiments of this specification do not indicate any order, quantity or importance, but are only used to avoid confusion of constituent elements.

[0044] Unless the context requires otherwise, throughout the specification, "plurality" means "at least two", and "including" is interpreted as an open, inclusive meaning, that is, "including, but not limited to". In the description of the specification, the terms "one embodiment", "some embodiments", "exemplary embodiments", "examples", "specific examples" or "some examples" are intended to indicate that a particular feature, structure, material or characteristic associated with the embodiment or example is included in at least one embodiment or example of the specification. The schematic representation of the above terms does not necessarily refer to the same embodiment or example.

[0045] The following will be combined with the drawings in the embodiments of this specification to clearly and completely describe the technical solutions in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments in this specification, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this specification.

[0046] Overview

[0047] As described in the background technology, with the increasingly serious problem of global warming, reducing carbon emissions and improving energy efficiency have become important development directions in various fields. As an important means of transportation, automobiles consume huge amounts of energy. Therefore, strengthening automobile energy management and improving fuel economy and electric energy efficiency are of great significance for reducing greenhouse gas emissions. With the rise of new energy vehicles such as electric vehicles and hybrid vehicles, how to effectively manage the energy of on-board batteries has become a key technology to improve the fuel economy and electric energy efficiency of vehicles. This is not only related to the vehicle's cruising range, but also directly affects the user's experience. For example, with the improvement of living standards, users pay more attention to driving pleasure, comfort and economy, and effectively managing the energy of on-board batteries can provide a technical basis for achieving this goal.

[0048] At present, rule-based energy management strategies are usually adopted in vehicles. However, this energy management strategy has entered a bottleneck period and cannot further improve the fuel economy and power efficiency of the vehicle, and thus cannot effectively reduce the energy consumption level of the whole vehicle. The inventors have found that predictive energy management can predict and optimize the energy use of the vehicle in advance, thereby effectively improving the fuel economy and power efficiency of the vehicle. However, the existing predictive energy management methods usually obtain the road condition information ahead through cameras, radars, high-precision maps, etc., and perform predictive energy management based on the road condition information. However, this method requires the addition of hardware equipment such as cameras, radars, and high-precision maps, which greatly increases the hardware cost of the whole vehicle.

[0049] In order to solve the problem that the traditional method cannot effectively reduce the energy consumption level of the whole vehicle without increasing the hardware cost of the vehicle, in the technical solution of the present application, the energy management method determines the target driving mode of the vehicle based on the driving data of the vehicle in the first time interval, determines the target value of the oil-to-electricity conversion factor based on the target driving mode, and outputs the target value of the oil-to-electricity conversion factor to the energy management control unit to trigger the energy management control unit to perform energy management control on the vehicle based on the target value of the oil-to-electricity conversion factor in the second time interval, wherein the first time interval includes the time interval before the current moment, and the second time interval includes the time interval after the current moment, so that the target value of the oil-to-electricity conversion factor in the future time interval can be predicted according to the driving mode of the vehicle in the historical time interval, so as to realize predictive energy management of the vehicle, and then when the vehicle is running smoothly (that is, there is no cliff-like transition in the vehicle working condition), energy management control of the vehicle according to the predicted target value of the oil-to-electricity conversion factor can effectively reduce the energy consumption level of the whole vehicle; at the same time, through the method of this embodiment, there is no need to add hardware equipment such as cameras, radars, and high-precision maps, and thus there is no need to increase the hardware cost of the vehicle.

[0050] Based on the above-mentioned inventive concept, the energy management method provided in the embodiment of this specification is exemplarily described below.

[0051] Exemplary Methods

[0052] The embodiments of this specification provide an energy management method, such as Figure 1 As shown, including:

[0053] S101. Determine a target driving mode of the vehicle based on driving data of the vehicle in a first time interval; the first time interval includes a time interval before a current moment.

[0054] Specifically, the first time interval may include a time interval before the current moment, for example, the first time interval may be a time interval immediately adjacent to the current moment and before the current moment, or may be a continuous time interval including the current moment and the time interval before the current moment. The length of the first time interval may be set according to actual needs, as long as it can effectively identify the driving mode, for example, it may be set to 100 seconds.

[0055] The target driving mode of the vehicle may be the driving mode of the vehicle in the first time interval. The driving mode may include driving conditions and the driving style of the driver.

[0056] S102. Based on the target driving mode, determine a target value of the oil-to-electricity conversion factor, and output the target value of the oil-to-electricity conversion factor to an energy management control unit to trigger the energy management control unit to perform energy management control on the vehicle based on the target value of the oil-to-electricity conversion factor within a second time interval, wherein the second time interval includes a time interval after the current moment.

[0057] Specifically, the oil-to-electricity conversion factor can be used to characterize the cost of unit energy consumption, that is, the oil-to-electricity conversion factor can be understood as a virtual price factor.

[0058] In implementation, the target value of the oil-to-electricity conversion factor may be determined based on the target driving mode, for example, the target value of the oil-to-electricity conversion factor may be determined based on the target driving mode and a predetermined correspondence between the driving mode and the oil-to-electricity conversion factor. The predetermined correspondence may be determined in advance based on the target operating data of the vehicle, and the target operating data may include the historical operating data of the vehicle or the vehicle speed test information corresponding to the standard driving cycle. For example, sub-operation data under each operating condition may be determined based on the target operating data, and the physical model of the vehicle may be controlled to perform vehicle simulation operation according to the sub-operation data, so as to optimize the oil-to-electricity conversion factor corresponding to each driving condition during the vehicle simulation operation.

[0059] After obtaining the target value of the oil-to-electricity conversion factor, the target value of the oil-to-electricity conversion factor can be output to the energy management control unit to trigger the energy management control unit to perform energy management control on the vehicle based on the target value of the oil-to-electricity conversion factor within the second time interval. The second time interval may include a time interval after the current moment, for example, the second time interval may be a time interval immediately adjacent to the current moment and located after the current moment. The length of the second time interval can be set according to actual needs, as long as it can meet the energy consumption level requirements of the vehicle, for example, it can be set to 10s.

[0060] Since the target value of the oil-to-electricity conversion factor is determined based on the vehicle's driving mode in the first time interval before the current moment, the applicability of the target value of the oil-to-electricity conversion factor in the second time interval can be effectively guaranteed when the vehicle is running smoothly (that is, there is no cliff-like jump in the vehicle operating condition). Furthermore, by predicting the driving mode and determining and adjusting the oil-to-electricity conversion factor in advance according to the prediction results, the energy consumption level of the entire vehicle can be effectively reduced.

[0061] In implementation, the energy management control unit can use the target value of the oil-to-electricity conversion factor as the initial value of the oil-to-electricity conversion factor when performing energy management control on the vehicle based on the target value of the oil-to-electricity conversion factor, and use ECMS (Equivalent Consumption Minimization Strategy) to control each controlled device of the vehicle to minimize equivalent fuel consumption, achieve optimal fuel economy, and achieve energy conservation and emission reduction. Among them, the controlled devices may include engines, motors, and gearboxes.

[0062] At the same time, the oil-to-electricity conversion factor can be corrected in real time based on the SOC (State of Charge) detection value of the on-board battery during the process of energy management control of the vehicle to achieve dynamic closed-loop control of energy management, thereby further reducing the energy consumption level of the entire vehicle.

[0063] Therefore, the method of this embodiment can predict the target value of the oil-to-electricity conversion factor in the future time interval according to the driving mode of the vehicle in the historical time interval, so as to realize predictive energy management of the vehicle, and then when the vehicle is running smoothly (that is, there is no cliff-like jump in the vehicle operating condition), energy management control of the vehicle according to the predicted target value of the oil-to-electricity conversion factor can effectively reduce the energy consumption level of the whole vehicle; at the same time, the method of this embodiment does not need to add hardware equipment such as cameras, radars, and high-precision maps, and thus does not need to increase the hardware cost of the vehicle.

[0064] In a feasible implementation manner, the target driving mode includes a target driving condition and a driving style of the driver; the driving data includes first data corresponding to a driving state parameter and second data corresponding to a driving control input parameter;

[0065] The determining the target driving mode of the vehicle based on the driving data of the vehicle in the first time interval includes:

[0066] The target driving condition is determined based on first statistical information corresponding to the first data, and the driving style is determined based on second statistical information corresponding to the second data.

[0067] Specifically, the target driving mode may include a target driving condition and a driver's posture style, wherein the target driving condition may include any one of a plurality of candidate driving conditions. In implementation, the driving condition may be divided according to the vehicle speed. For example, the driving condition may be divided into eight candidate driving conditions according to the fluctuation range of the vehicle speed: 0-20 km / h, 20-40 km / h, 40-60 km / h, 60-80 km / h, 80-100 km / h, 100-120 km / h, 120-140 km / h, and above 140 km / h. The driver's driving style may be any one of a plurality of candidate driving styles, used to characterize the driver's driving habits. For example, the candidate driving styles may include aggressive driving, gentle driving, and normal driving. Aggressive driving means frequently and violently stepping on the brakes and accelerator, etc., gentle driving means slowly and steadily stepping on the brakes and accelerator, etc., and normal driving is a driving style between aggressive driving and gentle driving.

[0068] The driving data of the vehicle in the first time interval may include first data corresponding to the driving state parameter and second data corresponding to the driving control input parameter. The driving state parameter may be used to characterize the driving state of the vehicle, and may specifically include vehicle speed, acceleration, and deceleration. The driving control input parameter may be used to characterize the parameters input by the driver in the process of controlling the driving of the vehicle for controlling the driving state of the vehicle, and may include, for example, the accelerator pedal depth and the brake pedal depth.

[0069] In implementation, the driving condition within the first time interval can be determined based on the first statistical information corresponding to the first data, and used as the target driving condition. For any driving state parameter, the first statistical information corresponding to the driving state parameter may include the maximum value, minimum value, average value, etc. of the driving state parameter. For example, the first statistical information corresponding to the first data may include the maximum vehicle speed, the minimum vehicle speed, the average vehicle speed, the maximum acceleration, the minimum acceleration, the average acceleration, the maximum deceleration, the minimum deceleration and the average deceleration. Among them, based on the predetermined weight corresponding to each first statistical information, each first statistical information can be weighted and summed to obtain a first weighted value, and based on the first weighted value and the predetermined corresponding relationship between the first weighted value and the driving condition, the target driving condition is determined. It can be understood that the first data and / or the first statistical information corresponding to the first data can also be input into a pre-trained driving condition recognition model to output the target driving condition through the driving condition recognition model. The driving condition recognition model can be a machine learning model such as a convolutional neural network.

[0070] At the same time, the driving style of the driver in the first time interval can be determined based on the second statistical information corresponding to the second data. For any driving control input parameter, the second statistical information corresponding to the driving control input parameter includes the maximum value, minimum value, average value, maximum change rate, minimum change rate and average change rate of the driving control input parameter in the first time interval. For example, the second statistical information corresponding to the second data may include the maximum accelerator pedal depth, the minimum accelerator pedal depth, the average accelerator pedal depth, the maximum change rate of the accelerator pedal depth, the minimum change rate of the accelerator pedal depth, the average change rate of the accelerator pedal depth, the maximum brake pedal depth, the minimum brake pedal depth, the average brake pedal depth, the maximum change rate of the brake pedal depth, the minimum change rate of the brake pedal depth and the average change rate of the brake pedal depth. Wherein, based on the predetermined weight corresponding to each second statistical information, each second statistical information can be weighted and summed to obtain a second weighted value, and the driving style of the driver can be determined based on the second weighted value and the predetermined corresponding relationship between the second weighted value and the driving style. It is understandable that the second data and / or the second statistical information corresponding to the second data can also be input into a pre-trained driving style recognition model to output the driver's driving style through the driving style recognition model. The driving style recognition model can be a machine learning model such as a convolutional neural network.

[0071] Therefore, the method of this embodiment can quickly and effectively determine the target driving condition of the vehicle and the driving style of the driver, and then accurately determine the target value of the oil-to-electricity conversion factor based on the target driving condition and the driving style of the driver.

[0072] In a feasible implementation manner, determining a target value of the oil-to-electricity conversion factor based on the target driving mode includes:

[0073] Based on the target driving condition and the predetermined corresponding relationship, a preferred value space of the oil-to-electricity conversion factor under the target driving condition is determined and used as the target value space; the predetermined corresponding relationship includes a plurality of candidate driving conditions and a preferred value space of the oil-to-electricity conversion factor under each of the candidate driving conditions;

[0074] A target value of the oil-to-electricity conversion factor is determined based on the driving style and the target value space.

[0075] Specifically, the predetermined correspondence may include multiple candidate driving conditions and the preferred value space of the oil-to-electricity conversion factor under each candidate driving condition, and the preferred value space may include one or more discrete values, and may also include one or more continuous value segments. Thus, the target driving condition may be matched with each candidate driving condition in the predetermined correspondence, and the preferred value space corresponding to the successfully matched candidate driving condition may be used as the target value space of the oil-to-electricity conversion factor, so that the target value space of the oil-to-electricity conversion factor in the future period of time may be quickly and effectively predicted based on the historical driving conditions of the vehicle.

[0076] After determining the target value space of the oil-to-electricity conversion factor, the target value of the oil-to-electricity conversion factor may be further determined based on the driving style and the target value space.

[0077] As a preferred implementation manner, determining the target value of the oil-to-electricity conversion factor based on the driving style and the target value space includes:

[0078] A target value of the oil-to-electricity conversion factor is determined based on the energy demand level under the driving style, a predetermined change trend of the oil-to-electricity conversion factor with the energy demand level, and the target value space.

[0079] Specifically, for any driving style, the energy demand under the driving style can represent the level of energy required in the process of driving based on the driving style. For example, under the same driving condition, the more aggressive the driving style, the higher the energy demand, and the more gentle the driving style, the lower the energy demand. In implementation, the energy demand under aggressive driving, normal driving, and gentle driving can be high, medium, and low, respectively.

[0080] The predetermined variation trend of the oil-to-electricity conversion factor with the energy demand level may be that under the same driving condition, the higher the energy demand level, the greater the oil-to-electricity conversion factor, and the lower the energy demand level, the smaller the oil-to-electricity conversion factor.

[0081] In implementation, the target value of the oil-to-electric conversion factor can be determined based on the energy demand level under the driving style, the predetermined change trend of the oil-to-electric conversion factor with the energy demand level, and the target value space of the oil-to-electric conversion factor. For example, if the driver's driving style is aggressive driving, the maximum value in the target value space can be used as the target value of the oil-to-electric conversion factor; if the driver's driving style is mild driving, the minimum value in the target value space can be used as the target value of the oil-to-electric conversion factor; if the driver's driving style is normal driving, the median or average value in the target value space can be used as the target value of the oil-to-electric conversion factor, thereby effectively improving the accuracy of the determination result of the target value of the oil-to-electric conversion factor.

[0082] In addition, for any preferred value space of the oil-to-electricity conversion factor, if the preferred value space includes multiple discrete values, or includes one or more continuous value segments, the preferred value space can be pre-divided into multiple subspaces, and the multiple subspaces can correspond to multiple candidate driving styles. In implementation, the target value of the oil-to-electricity conversion factor can also be determined based on the subspace corresponding to the driver's driving style in the target value space, for example, the median or average value of the subspace can be used as the target value of the oil-to-electricity conversion factor.

[0083] It is understandable that if the target value space includes only one discrete value, the discrete value can be directly used as the target value of the oil-to-electricity conversion factor.

[0084] Therefore, the method of this embodiment can quickly and effectively determine the target value of the oil-to-electricity conversion factor, and then in the process of performing energy management control on the vehicle according to the target value of the oil-to-electricity conversion factor during the second time interval, the energy consumption level of the entire vehicle can be effectively reduced.

[0085] In a feasible implementation manner, the process of determining the preferred value space of the oil-to-electricity conversion factor under the candidate driving condition includes:

[0086] Determining a plurality of target vehicle speed sequences for the vehicle under the candidate driving conditions based on vehicle speed test information corresponding to at least one standard driving cycle;

[0087] In a process of controlling the physical model of the vehicle to perform vehicle simulation operation based on the target vehicle speed sequence, iteratively optimizing the oil-to-electricity conversion factor based on the SOC simulation value output by the physical model to obtain a correction value of the oil-to-electricity conversion factor corresponding to the target vehicle speed sequence;

[0088] Based on the oil-to-electricity conversion factor correction value corresponding to each of the target vehicle speed sequences, the preferred value space of the oil-to-electricity conversion factor under the candidate driving condition is determined.

[0089] Specifically, the standard driving cycle can be a standardized test procedure for evaluating vehicle performance, fuel economy and emissions. The types of standard driving cycles may include many, such as WLTC (Worldwide harmonized Lightvehicles Test Cycles), CLTC (China Light-duty vehicle Test Cycle), NEDC (New European Driving Cycle), FTP (Federal Test Procedure), JC08 (Japanese Test Cycle), US06 (US Supplementary Test Procedure), SCC (Supplementary City Cycle), etc.

[0090] Among them, WLTC aims to provide new cars with fuel consumption and emission data that can more truly reflect actual driving conditions. WLTC consists of four different stages, each representing a different driving condition, from low-speed city driving to cruising on the highway, covering a variety of possible road conditions. The stages are carried out in sequence, namely low speed (Low), medium speed (Medium), high speed (High) and higher speed (Very High), together forming a complete test cycle.

[0091] CLTC is designed to better reflect actual driving conditions on Chinese roads and aims to provide a testing method that is closer to real-world fuel economy and emissions data.

[0092] NEDC covers driving conditions on urban, suburban and rural roads, and consists of four urban driving cycles and one suburban driving cycle.

[0093] FTP is a standard established by the U.S. Environmental Protection Agency (EPA) for certifying emissions of light-duty vehicles, including both cold start and hot start conditions.

[0094] JC08 takes into account more actual driving conditions, such as frequent acceleration and deceleration.

[0095] US06 is used to simulate the performance of a vehicle under high-speed driving conditions and is usually used in conjunction with FTP.

[0096] The SCC, also known as the Los Angeles Cycle, simulates a more demanding urban driving environment and is often used as a supplement to FTP.

[0097] The vehicle speed test information corresponding to the standard driving cycle may include the vehicle speed data of the tested vehicle at each time during the standard driving cycle test, for example, a curve of the vehicle speed changing over time. In implementation, the target vehicle speed sequence of the vehicle under each candidate driving condition may be determined based on the vehicle speed test information corresponding to at least one standard driving cycle. For example, for any standard driving cycle, the vehicle speed test information corresponding to the standard driving cycle may include the vehicle speed test information of any tested vehicle that has passed the test under the standard driving cycle.

[0098] Among them, for any candidate driving condition, the target speed sequence under the candidate driving condition may include multiple. The target speed sequence may be continuous data of the vehicle speed changing over time, and the value interval of the vehicle speed in the target speed sequence is the same as the vehicle speed interval corresponding to the candidate driving condition. In implementation, based on the vehicle speed interval corresponding to the candidate driving condition, target data segments may be intercepted from each vehicle speed test information, and each target data segment may be spliced ​​and processed to obtain multiple target speed sequences, wherein the duration of the target speed sequence may be a predetermined duration, for example, 1800s.

[0099] For any target vehicle speed sequence, the oil-to-electricity conversion factor correction value corresponding to the target vehicle speed sequence may be a preferred value of the oil-to-electricity conversion factor obtained by optimizing the oil-to-electricity conversion factor when the vehicle or the physical model of the vehicle is running according to the target vehicle speed sequence.

[0100] The physical model of the vehicle may include a powertrain system and an aerodynamic system. The powertrain system may include components such as an engine, a gearbox, and a differential to simulate the acceleration performance under different gears; the aerodynamic system is used to simulate the air resistance of the vehicle when driving at high speed. The power transmission efficiency of each component in the powertrain system can be determined by conducting a real vehicle test on the vehicle, and the drag coefficient of the aerodynamic system can be obtained by conducting a real vehicle wind tunnel test on the vehicle or CFD (Computational Fluid Dynamics) simulation, so as to ensure the validity of the determination result of the preferred value space of the oil-to-electricity conversion factor under each candidate driving condition.

[0101] During the process of vehicle simulation operation, the SOC simulation value output by the physical model of the vehicle is the SOC of the vehicle battery obtained by the physical model simulation when the physical model of the vehicle is subjected to speed following control based on the target vehicle speed sequence and when the physical model of the vehicle is subjected to energy management control simulation based on the oil-to-electric conversion factor. In implementation, the oil-to-electric conversion factor can be iteratively optimized based on the SOC simulation value to obtain the oil-to-electric conversion factor correction value corresponding to the target vehicle speed sequence. For example, when the deviation between the SOC simulation value and the SOC target value corresponding to the candidate driving condition is less than or equal to a preset deviation threshold, the optimization result of the oil-to-electric conversion factor can be used as the oil-to-electric conversion factor correction value corresponding to the target vehicle speed sequence. Therefore, by repeatedly executing the method under different target vehicle speed sequences, the oil-to-electric conversion factor correction value corresponding to each target vehicle speed sequence under the candidate driving condition can be obtained, thereby effectively ensuring the validity of the determination result of the oil-to-electric conversion factor correction value corresponding to each target vehicle speed sequence.

[0102] Among them, the preferred value space of the oil-to-electricity conversion factor under the candidate driving condition can be determined based on the oil-to-electricity conversion factor correction value corresponding to each target speed sequence under the candidate driving condition. For example, the preferred value space may include the oil-to-electricity conversion factor correction value corresponding to each target speed sequence under the candidate driving condition, and may also include a curve segment or a straight line segment fitted based on the oil-to-electricity conversion factor correction value corresponding to some or all target speed sequences under the candidate driving condition, etc., which can be specifically set according to actual needs, so as to achieve offline optimization of the value space of the oil-to-electricity conversion factor under different candidate driving conditions, ensure the robustness of the determination result of the preferred value space of the oil-to-electricity conversion factor under each candidate driving condition, and then in the actual operation of the vehicle, predictive energy management control is performed according to the offline optimization result of the oil-to-electricity conversion factor under the corresponding driving condition, which can effectively reduce the energy consumption level of the vehicle.

[0103] In a feasible implementation manner, the iterative optimization of the oil-to-electricity conversion factor based on the SOC simulation value output by the physical model to obtain the oil-to-electricity conversion factor correction value corresponding to the target vehicle speed sequence includes:

[0104] In the current iteration, the current value of the oil-to-electricity conversion factor is output to the energy management control unit to trigger the energy management control unit to output an energy distribution control instruction to the physical model and obtain the SOC simulation value output by the physical model; the current value of the oil-to-electricity conversion factor is the preferred value of the oil-to-electricity conversion factor or the initial oil-to-electricity conversion factor;

[0105] If the deviation between the SOC simulation value and the SOC target value corresponding to the candidate driving condition is less than or equal to a preset deviation threshold, the current value of the oil-to-electricity conversion factor is used as a correction value of the oil-to-electricity conversion factor corresponding to the target vehicle speed sequence;

[0106] If the deviation value is greater than the preset deviation threshold, a preferred value of the oil-to-electricity conversion factor in the next iteration is determined based on the SOC simulation value, and the next iteration is performed.

[0107] Specifically, the current iteration may be any iteration in the iterative optimization process of the oil-to-electricity conversion factor. In the current iteration, the current value of the oil-to-electricity conversion factor may be output to the energy management control unit to trigger the energy management control unit to output the energy distribution control instruction to the physical model according to the current value of the oil-to-electricity conversion factor, thereby outputting the SOC simulation value through the physical model. Wherein, when the current iteration is the first iteration, the current value of the oil-to-electricity conversion factor may be the initial oil-to-electricity conversion factor, and the initial oil-to-electricity conversion factor may be a predetermined value, for example, the initial oil-to-electricity conversion factor may be randomly selected within the interval formed by the upper limit value and the lower limit value of the oil-to-electricity conversion factor, and the median value of the interval may also be used as the initial oil-to-electricity conversion factor. When the current iteration is not the first iteration, the current value of the oil-to-electricity conversion factor may be the preferred value of the oil-to-electricity conversion factor in the current iteration determined based on the SOC simulation value output by the physical model in the previous iteration.

[0108] After obtaining the SOC simulation value output by the physical model, the deviation value between the SOC simulation value and the SOC target value corresponding to the candidate driving condition can be obtained, and based on the comparison result of the deviation value and the preset deviation threshold, it is determined whether the condition for ending the iteration is met. The deviation value between the SOC simulation value and the SOC target value corresponding to the candidate driving condition is the absolute value of the difference between the SOC simulation value and the SOC target value corresponding to the candidate driving condition.

[0109] When the deviation value is less than or equal to the preset deviation threshold, it can be determined that the condition for ending the iteration is met, and the current value of the oil-to-electricity conversion factor is used as the oil-to-electricity conversion factor correction value corresponding to the target vehicle speed sequence.

[0110] When the deviation value is greater than the preset deviation threshold, it can be determined that the condition for ending the iteration is not met, and the oil-to-electricity conversion factor is updated based on the SOC simulation value output by the physical model in the current iteration to obtain the preferred value of the oil-to-electricity conversion factor in the next iteration, and the next iteration is executed, thereby effectively ensuring the validity of the determination result of the oil-to-electricity conversion factor correction value corresponding to each target vehicle speed sequence. Therefore, in the process of determining the target value of the oil-to-electricity conversion factor based on the preferred value space determined by the oil-to-electricity conversion factor correction value corresponding to each target vehicle speed sequence, and performing predictive energy management control of the vehicle based on the target value of the oil-to-electricity conversion factor, effective energy management of the vehicle-mounted battery can be achieved, thereby further improving the fuel economy and power utilization of the vehicle, and reducing the energy consumption level of the entire vehicle.

[0111] In a feasible implementation manner, determining the preferred value of the oil-to-electricity conversion factor in the next iteration based on the SOC simulation value includes:

[0112] Based on the magnitude relationship between the SOC simulation value and the SOC target value, and the predetermined change trend of the SOC simulation value along with the oil-to-electricity conversion factor, determine the first endpoint of the candidate value space of the oil-to-electricity conversion factor in the next iteration from the two endpoints of the candidate value space of the oil-to-electricity conversion factor in the current iteration, and use the current value of the oil-to-electricity conversion factor in the current iteration as the second endpoint of the candidate value space of the oil-to-electricity conversion factor in the next iteration;

[0113] Based on the candidate value space of the oil-to-electricity conversion factor in the next iteration, the preferred value of the oil-to-electricity conversion factor in the next iteration is determined.

[0114] Specifically, the predetermined variation trend of the SOC simulation value with the oil-to-electric conversion factor may be that under the same driving condition, the greater the oil-to-electric conversion factor, the greater the SOC simulation value. For example, the predetermined variation trend of the SOC simulation value with the oil-to-electric conversion factor may be as follows: Figure 2 As shown, Figure 2 In the equation, λ is the oil-to-electricity conversion factor. It is understandable that the predetermined variation trend of the SOC simulation value with the oil-to-electricity conversion factor may be the same under different driving conditions. In implementation, the predetermined variation trend of the SOC simulation value with the oil-to-electricity conversion factor may be predetermined by experiment. For example, multiple sampling points may be obtained in the threshold interval of the oil-to-electricity conversion factor, and the SOC simulation value corresponding to each sampling point may be obtained during the simulation operation of the physical model of the vehicle under the same driving condition to obtain the predetermined variation trend of the SOC simulation value with the oil-to-electricity conversion factor. For any sampling point, the SOC simulation value corresponding to the sampling point may be the SOC of the vehicle battery obtained by the physical model simulation when the energy management control simulation is performed on the physical model of the vehicle based on the sampling point.

[0115] In the process of iterative optimization of the oil-to-electricity conversion factor, for any iteration, in the process of determining the preferred value of the oil-to-electricity conversion factor in the next iteration, the candidate value space of the oil-to-electricity conversion factor in the next iteration can be determined first. Among them, based on the size relationship between the SOC simulation value and the SOC target value in the current iteration and the predetermined change trend of the SOC simulation value with the oil-to-electricity conversion factor, the first endpoint of the candidate value space of the oil-to-electricity conversion factor in the next iteration can be determined from the two endpoints of the candidate value space of the oil-to-electricity conversion factor in the current iteration, and the current value of the oil-to-electricity conversion factor in the current iteration is used as the second endpoint of the candidate value space of the oil-to-electricity conversion factor in the next iteration. That is, the candidate value space of the oil-to-electricity conversion factor in the next iteration is one of the two sub-value spaces in the candidate value space of the oil-to-electricity conversion factor in the current iteration.

[0116] Among them, the two sub-value spaces in the candidate value space of the oil-to-electricity conversion factor in the current iteration can be determined based on the candidate value space of the oil-to-electricity conversion factor in the current iteration and the current value of the oil-to-electricity conversion factor in the current iteration, and the first endpoints of the two sub-value spaces can be the two endpoints of the candidate value space of the oil-to-electricity conversion factor in the current iteration, respectively, and the first endpoints of the two sub-value spaces can both be the current value of the oil-to-electricity conversion factor in the current iteration.

[0117] In implementation, the method for determining the first endpoint of the candidate value space of the oil-to-electricity conversion factor in the next iteration may include: if the SOC simulation value in the current iteration is greater than the SOC target value, then, of the two endpoints of the candidate value space of the oil-to-electricity conversion factor in the current iteration, the endpoint in the downward direction of the SOC simulation value in the predetermined change trend is used as the first endpoint of the candidate value space of the oil-to-electricity conversion factor in the next iteration, for example, the endpoint with the smaller value of the two endpoints of the candidate value space of the oil-to-electricity conversion factor in the current iteration is used as the first endpoint of the candidate value space of the oil-to-electricity conversion factor in the next iteration. ; If the SOC simulation value in the current iteration is less than the SOC target value, then of the two endpoints of the candidate value space of the oil-to-electricity conversion factor in the current iteration, the endpoint in the rising direction of the SOC simulation value in the predetermined change trend is used as the first endpoint of the candidate value space of the oil-to-electricity conversion factor in the next iteration. For example, of the two endpoints of the candidate value space of the oil-to-electricity conversion factor in the current iteration, the endpoint with the larger value is used as the first endpoint of the candidate value space of the oil-to-electricity conversion factor in the next iteration, thereby effectively ensuring the validity of the determination result of the candidate value space of the oil-to-electricity conversion factor in the next iteration.

[0118] Among them, the preferred value of the oil-to-electricity conversion factor in the next iteration can be determined based on the candidate value space of the oil-to-electricity conversion factor in the next iteration. For example, any value other than the first endpoint and the second endpoint in the candidate value space can be used as the preferred value of the oil-to-electricity conversion factor in the next iteration. Therefore, in the next iteration, the search space of the preferred value of the oil-to-electricity conversion factor can be effectively reduced compared with the current iteration, thereby effectively improving the optimization efficiency of the oil-to-electricity conversion factor while ensuring the validity of the determination result of the preferred value of the oil-to-electricity conversion factor in the next iteration.

[0119] As a preferred implementation, the median of the candidate value space of the oil-to-electricity conversion factor in the next iteration can be used as the preferred value of the oil-to-electricity conversion factor in the next iteration, so that in each iteration, the search space of the preferred value of the oil-to-electricity conversion factor can be reduced by half compared to the previous iteration, thereby maximizing the optimization efficiency of the oil-to-electricity conversion factor. It can be understood that in the first iteration, the candidate value space of the oil-to-electricity conversion factor can be the interval formed by the upper limit and lower limit of the oil-to-electricity conversion factor, for example, [2,3], and at this time, the initial oil-to-electricity conversion factor can be selected as 2.5.

[0120] In a feasible implementation manner, the determining of a plurality of target vehicle speed sequences for the vehicle under the candidate driving condition based on vehicle speed test information corresponding to at least one standard driving cycle includes:

[0121] Based on the vehicle speed interval corresponding to the candidate driving conditions, respectively obtaining target data segments in the vehicle speed test information corresponding to each of the standard driving cycles;

[0122] Each of the target data segments is spliced ​​to obtain a plurality of target vehicle speed sequences under the candidate driving conditions.

[0123] Specifically, for any candidate driving condition, based on the speed range corresponding to the candidate driving condition, data segments that meet the speed range corresponding to the candidate driving condition can be intercepted from the speed test information corresponding to each standard driving cycle, and the intercepted data segments are used as target data segments, that is, the value range of the vehicle speed in each target data segment is the speed range corresponding to the candidate driving condition. Taking the speed range corresponding to the candidate driving condition as 20 to 40 km / h, the target data segments intercepted from the speed test information corresponding to the standard driving cycle can be as follows: Figure 3 shown.

[0124] After obtaining the target data segments corresponding to the candidate driving condition, the target data segments can be spliced ​​to obtain multiple target vehicle speed sequences under the candidate driving condition. It can be understood that in order to achieve vehicle speed following control of the physical model of the vehicle, the target vehicle speed sequence needs to ensure the continuity of the vehicle speed in time. For example, some target data segments can be mirrored and then spliced ​​to achieve the continuity of the vehicle speed in time.

[0125] During implementation, in the process of splicing the target data segments, multiple target data segments can be randomly arranged and then spliced. In a target vehicle speed sequence, there may be repeated target data segments, or only part of the target data segments may be included, as long as the duration of the target vehicle speed sequence is greater than or equal to the predetermined duration.

[0126] Since the vehicle speed test information corresponding to the standard driving cycle represents the vehicle speed test information under different driving conditions, optimizing the preferred value space of the oil-to-electricity conversion factor under the target vehicle speed sequence can meet the energy management control for different driving conditions under the driving condition, and further improves the robustness of the determination result of the preferred value space of the oil-to-electricity conversion factor.

[0127] Exemplary Devices

[0128] In an exemplary embodiment of the present specification, an energy management device is also provided, such as Figure 4 As shown, including:

[0129] A first processing module 401 is used to determine a target driving mode of the vehicle based on driving data of the vehicle in a first time interval; the first time interval includes a time interval before a current moment;

[0130] The second processing module 402 is used to determine a target value of the oil-to-electricity conversion factor based on the target driving mode, and output the target value of the oil-to-electricity conversion factor to the energy management control unit to trigger the energy management control unit to perform energy management control on the vehicle based on the target value of the oil-to-electricity conversion factor within a second time interval, and the second time interval includes a time interval after the current moment.

[0131] In a feasible implementation manner, the target driving mode includes a target driving condition and a driving style of the driver; the driving data includes first data corresponding to a driving state parameter and second data corresponding to a driving control input parameter; the first processing module 401 is specifically used for:

[0132] The target driving condition is determined based on first statistical information corresponding to the first data, and the driving style is determined based on second statistical information corresponding to the second data.

[0133] In a feasible implementation manner, the second processing module 402 is specifically configured to:

[0134] Based on the target driving condition and the predetermined corresponding relationship, a preferred value space of the oil-to-electricity conversion factor under the target driving condition is determined and used as the target value space; the predetermined corresponding relationship includes a plurality of candidate driving conditions and a preferred value space of the oil-to-electricity conversion factor under each of the candidate driving conditions;

[0135] A target value of the oil-to-electricity conversion factor is determined based on the driving style and the target value space.

[0136] In a feasible embodiment, a third processing module is further included, wherein the third processing module is used to:

[0137] Determining a plurality of target vehicle speed sequences for the vehicle under the candidate driving conditions based on vehicle speed test information corresponding to at least one standard driving cycle;

[0138] In a process of controlling the physical model of the vehicle to perform vehicle simulation operation based on the target vehicle speed sequence, iteratively optimizing the oil-to-electricity conversion factor based on the SOC simulation value output by the physical model to obtain a correction value of the oil-to-electricity conversion factor corresponding to the target vehicle speed sequence;

[0139] Based on the oil-to-electricity conversion factor correction value corresponding to each of the target vehicle speed sequences, the preferred value space of the oil-to-electricity conversion factor under the candidate driving condition is determined.

[0140] In a feasible implementation manner, the third processing module is specifically used for:

[0141] In the current iteration, the current value of the oil-to-electricity conversion factor is output to the energy management control unit to trigger the energy management control unit to output an energy distribution control instruction to the physical model and obtain the SOC simulation value output by the physical model; the current value of the oil-to-electricity conversion factor is the preferred value of the oil-to-electricity conversion factor or the initial oil-to-electricity conversion factor;

[0142] If the deviation between the SOC simulation value and the SOC target value corresponding to the candidate driving condition is less than or equal to a preset deviation threshold, the current value of the oil-to-electricity conversion factor is used as a correction value of the oil-to-electricity conversion factor corresponding to the target vehicle speed sequence;

[0143] If the deviation value is greater than the preset deviation threshold, a preferred value of the oil-to-electricity conversion factor in the next iteration is determined based on the SOC simulation value, and the next iteration is performed.

[0144] In a feasible implementation manner, the third processing module is specifically used for:

[0145] Based on the magnitude relationship between the SOC simulation value and the SOC target value, and the predetermined change trend of the SOC simulation value along with the oil-to-electricity conversion factor, determine the first endpoint of the candidate value space of the oil-to-electricity conversion factor in the next iteration from the two endpoints of the candidate value space of the oil-to-electricity conversion factor in the current iteration, and use the current value of the oil-to-electricity conversion factor in the current iteration as the second endpoint of the candidate value space of the oil-to-electricity conversion factor in the next iteration;

[0146] Based on the candidate value space of the oil-to-electricity conversion factor in the next iteration, the preferred value of the oil-to-electricity conversion factor in the next iteration is determined.

[0147] In a feasible implementation manner, the third processing module is specifically used for:

[0148] Based on the vehicle speed interval corresponding to the candidate driving conditions, respectively obtaining target data segments in the vehicle speed test information corresponding to each of the standard driving cycles;

[0149] Each of the target data segments is spliced ​​to obtain a plurality of target vehicle speed sequences under the candidate driving conditions.

[0150] In a feasible implementation manner, the second processing module 402 is specifically configured to:

[0151] A target value of the oil-to-electricity conversion factor is determined based on the energy demand level under the driving style, a predetermined change trend of the oil-to-electricity conversion factor with the energy demand level, and the target value space.

[0152] The energy management device provided in this embodiment belongs to the same application concept as the energy management method provided in the above embodiments of this application, can execute the energy management method provided in any of the above embodiments of this application, and has the corresponding functional modules and beneficial effects of executing the energy management method. For technical details not fully described in this embodiment, please refer to the specific processing content of the energy management method provided in the above embodiments of this application, which will not be repeated here.

[0153] Example Vehicles

[0154] In an exemplary embodiment of the present specification, a vehicle is further provided, comprising an energy management control unit and an energy management device as described in any one of the above embodiments.

[0155] Exemplary computer program products and storage media

[0156] In addition to the above-mentioned methods and devices, the energy management method provided in the embodiments of this specification may also be a computer program product, which includes computer program instructions, which, when executed by a processor, enable the processor to execute the steps of the energy management method according to various embodiments of this specification described in the above "Exemplary Method" section of this specification.

[0157] The computer program product can be written in any combination of one or more programming languages ​​to write program codes for executing the operations of the embodiments of this specification, and the programming languages ​​include object-oriented programming languages, such as Java, C++, etc., and also include conventional procedural programming languages, such as "C" language or similar programming languages.

[0158] In addition, an embodiment of the present specification also provides a computer-readable storage medium on which a computer program is stored, and the computer program is executed by a processor to execute the steps of the energy management method according to various embodiments of the present specification described in the above "Exemplary Method" section of the present specification.

[0159] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this specification can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0160] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0161] The above-mentioned embodiments only express several implementation methods of this specification, and the descriptions are relatively specific and detailed, but they cannot be understood as limiting the scope of the solutions provided by the embodiments of this specification. It should be pointed out that for ordinary technicians in this field, several modifications and improvements can be made without departing from the concept of this specification, which all belong to the protection scope of this specification. Therefore, the protection scope of the patent of this specification shall be based on the attached claims.

Claims

1. An energy management method, characterized in that: include: Determining a target driving mode of the vehicle based on driving data of the vehicle in a first time interval; The first time interval includes a time interval before the current moment; Based on the target driving mode, a target value of the oil-to-electricity conversion factor is determined, and the target value of the oil-to-electricity conversion factor is output to an energy management control unit to trigger the energy management control unit to perform energy management control on the vehicle based on the target value of the oil-to-electricity conversion factor within a second time interval, and the second time interval includes a time interval after the current moment.

2. The method according to claim 1, characterized in that The target driving mode includes a target driving condition and a driving style of the driver; the driving data includes first data corresponding to a driving state parameter and second data corresponding to a driving control input parameter; The determining the target driving mode of the vehicle based on the driving data of the vehicle in the first time interval includes: The target driving condition is determined based on first statistical information corresponding to the first data, and the driving style is determined based on second statistical information corresponding to the second data.

3. The method according to claim 2, characterized in that The determining a target value of the oil-to-electricity conversion factor based on the target driving mode includes: Based on the target driving condition and the predetermined corresponding relationship, a preferred value space of the oil-to-electricity conversion factor under the target driving condition is determined and used as the target value space; the predetermined corresponding relationship includes a plurality of candidate driving conditions and a preferred value space of the oil-to-electricity conversion factor under each of the candidate driving conditions; A target value of the oil-to-electricity conversion factor is determined based on the driving style and the target value space.

4. The method according to claim 3, characterized in that The process of determining the optimal value space of the oil-to-electricity conversion factor under the candidate driving condition includes: Determining a plurality of target vehicle speed sequences for the vehicle under the candidate driving conditions based on vehicle speed test information corresponding to at least one standard driving cycle; In a process of controlling the physical model of the vehicle to perform vehicle simulation operation based on the target vehicle speed sequence, iteratively optimizing the oil-to-electricity conversion factor based on the SOC simulation value output by the physical model to obtain a correction value of the oil-to-electricity conversion factor corresponding to the target vehicle speed sequence; Based on the oil-to-electricity conversion factor correction value corresponding to each of the target vehicle speed sequences, the preferred value space of the oil-to-electricity conversion factor under the candidate driving condition is determined.

5. The method according to claim 4, characterized in that The iterative optimization of the oil-to-electricity conversion factor based on the SOC simulation value output by the physical model to obtain the oil-to-electricity conversion factor correction value corresponding to the target vehicle speed sequence includes: In the current iteration, the current value of the oil-to-electricity conversion factor is output to the energy management control unit to trigger the energy management control unit to output an energy distribution control instruction to the physical model and obtain the SOC simulation value output by the physical model; the current value of the oil-to-electricity conversion factor is the preferred value of the oil-to-electricity conversion factor or the initial oil-to-electricity conversion factor; If the deviation between the SOC simulation value and the SOC target value corresponding to the candidate driving condition is less than or equal to a preset deviation threshold, the current value of the oil-to-electricity conversion factor is used as a correction value of the oil-to-electricity conversion factor corresponding to the target vehicle speed sequence; If the deviation value is greater than the preset deviation threshold, a preferred value of the oil-to-electricity conversion factor in the next iteration is determined based on the SOC simulation value, and the next iteration is performed.

6. The method according to claim 5, characterized in that The determining the preferred value of the oil-to-electricity conversion factor in the next iteration based on the SOC simulation value includes: Based on the magnitude relationship between the SOC simulation value and the SOC target value, and the predetermined change trend of the SOC simulation value along with the oil-to-electricity conversion factor, determine the first endpoint of the candidate value space of the oil-to-electricity conversion factor in the next iteration from the two endpoints of the candidate value space of the oil-to-electricity conversion factor in the current iteration, and use the current value of the oil-to-electricity conversion factor in the current iteration as the second endpoint of the candidate value space of the oil-to-electricity conversion factor in the next iteration; Based on the candidate value space of the oil-to-electricity conversion factor in the next iteration, the preferred value of the oil-to-electricity conversion factor in the next iteration is determined.

7. The method according to claim 4, characterized in that The determining, based on vehicle speed test information corresponding to at least one standard driving cycle, a plurality of target vehicle speed sequences for the vehicle under the candidate driving conditions comprises: Based on the vehicle speed interval corresponding to the candidate driving conditions, respectively obtaining target data segments in the vehicle speed test information corresponding to each of the standard driving cycles; Each of the target data segments is spliced ​​to obtain a plurality of target vehicle speed sequences under the candidate driving conditions.

8. The method according to any one of claims 3 to 7, characterized in that: The determining the target value of the oil-to-electricity conversion factor based on the driving style and the target value space includes: A target value of the oil-to-electricity conversion factor is determined based on the energy demand level under the driving style, a predetermined change trend of the oil-to-electricity conversion factor with the energy demand level, and the target value space.

9. An energy management device, characterized in that: include: A first processing module, configured to determine a target driving mode of the vehicle based on driving data of the vehicle within a first time interval; The first time interval includes a time interval before the current moment; The second processing module is used to determine a target value of the oil-to-electricity conversion factor based on the target driving mode, and output the target value of the oil-to-electricity conversion factor to the energy management control unit to trigger the energy management control unit to perform energy management control on the vehicle based on the target value of the oil-to-electricity conversion factor within a second time interval, and the second time interval includes a time interval after the current moment.

10. A vehicle, characterized in that: The device comprises an energy management control unit and the energy management device as claimed in claim 9.

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