Plateau road environment under hybrid system energy consumption test evaluation method and system

Abstract

The application discloses a kind of highland road environment under hybrid power system energy consumption test evaluation method and system, for carrying out energy consumption test evaluation to hybrid power system vehicle, the present application relates to the energy consumption test technical field of hybrid electric vehicle, this method includes: according to the road feature of highland road, highland road is divided into multiple working condition segments according to driving cycle, and the road feature of each working condition segment is recorded;The driving behavior characteristics of each working condition segment driver are obtained, and are combined with the corresponding working condition segment, to form highland working condition segment pool;The vehicle data corresponding to each driving behavior working condition segment is extracted, and the vehicle energy consumption of each driving behavior working condition segment is calculated according to vehicle data, so as to complete vehicle energy consumption test evaluation.

Description

Technical Field

[0001] This invention belongs to the field of energy consumption testing technology for hybrid vehicles, and more specifically, relates to a method and system for testing and evaluating the energy consumption of a hybrid system in a high-altitude road environment. Background Technology

[0002] The high altitude, low air pressure, and thin oxygen conditions on plateau roads reduce engine combustion efficiency and limit power output, leading to increased fuel consumption. Furthermore, the undulating terrain and numerous slopes in plateau regions significantly increase energy consumption during frequent acceleration, deceleration, and continuous uphill driving. In addition, low temperatures and high wind resistance negatively impact battery performance and overall vehicle aerodynamics, further exacerbating energy consumption levels.

[0003] Currently, vehicle energy consumption tests in high-altitude road environments generally only test pure gasoline vehicles, and there are few tests on hybrid vehicles, and the tests are not accurate enough. Therefore, there is an urgent need for a technical solution that can accurately test the energy consumption of hybrid vehicles in high-altitude road environments. Summary of the Invention

[0004] To address the above technical problems, this invention proposes a method for testing and evaluating the energy consumption of a hybrid power system in a high-altitude road environment. This method is used to evaluate the energy consumption of hybrid power system vehicles, and includes:

[0005] Based on the road characteristics of plateau roads, plateau roads are divided into multiple driving condition segments according to driving conditions, and the road characteristics of each driving condition segment are recorded.

[0006] The driving behavior characteristics of the driver in each working condition segment are obtained and combined with the corresponding working condition segment to generate multiple driving behavior working condition segments, and at the same time form a plateau working condition segment pool.

[0007] Extract vehicle data corresponding to each driving behavior segment, and calculate vehicle energy consumption for each driving behavior segment based on the vehicle data, thereby completing the vehicle energy consumption test and evaluation.

[0008] Furthermore, it also includes: acquiring road characteristics of the plateau road environment and forming a road characteristic factor library, and modeling the plateau road environment based on the road characteristic factor library.

[0009] Furthermore, the operating condition segments include: long uphill operating condition segments, long downhill operating condition segments, curve operating condition segments, and traffic jam operating condition segments.

[0010] Furthermore, acquiring the driver's driving behavior characteristics for each operating condition segment includes: collecting the driving behavior characteristics through the vehicle's OBD and / or CAN bus, which includes: rapid acceleration frequency, rapid deceleration frequency, EV mode usage ratio, SOC adjustment frequency, and throttle-brake switching frequency.

[0011] Furthermore, each driving condition segment corresponds to one or more driving behavior features.

[0012] Furthermore, obtaining the driver's driving behavior characteristics for each working condition segment includes: pre-setting multiple driving behavior characteristics, collecting vehicle data through the vehicle's OBD and / or CAN bus, and dividing the vehicle data into corresponding driving behavior characteristics through sliding window and clustering algorithms.

[0013] Furthermore, the vehicle energy consumption is displayed in the form of a bar chart, where the horizontal axis represents the serial number of multiple high-altitude operating condition segments, the vertical axis represents the corresponding vehicle energy consumption, and the corresponding operating condition segments and driving behavior characteristics are marked on each bar.

[0014] This invention also proposes a hybrid power system energy consumption testing and evaluation system for high-altitude road environments, used for energy consumption testing and evaluation of hybrid power system vehicles, including:

[0015] The driving condition segmentation module is used to divide the plateau road into multiple driving condition segments according to the road characteristics of the plateau road, and record the road characteristics of each driving condition segment.

[0016] A high-altitude driving condition segment pool module is constructed to obtain the driving behavior characteristics of the driver for each driving condition segment and combine them with the corresponding driving condition segments to generate multiple driving behavior driving condition segments, thus forming a high-altitude driving condition segment pool.

[0017] The energy consumption test module is used to extract vehicle data corresponding to each driving behavior segment and calculate the vehicle energy consumption for each driving behavior segment based on the vehicle data, thereby completing the vehicle energy consumption test evaluation.

[0018] Furthermore, it also includes: acquiring road characteristics of the plateau road environment and forming a road characteristic factor library, and modeling the plateau road environment based on the road characteristic factor library.

[0019] Furthermore, the operating condition segments include: long uphill operating condition segments, long downhill operating condition segments, curve operating condition segments, and traffic jam operating condition segments.

[0020] In summary, the technical solutions conceived by this invention have the following beneficial effects compared with the prior art:

[0021] Through the above technical solutions, this invention can assess the energy consumption of hybrid vehicles under various working conditions and corresponding driving behaviors in plateau road environments, thereby obtaining accurate energy consumption data to support vehicle engineers in making subsequent vehicle improvements. Attached Figure Description

[0022] Figure 1 This is a flowchart of the method in Embodiment 1 of the present invention;

[0023] Figure 2 This is a system structure diagram of Embodiment 2 of the present invention. Detailed Implementation

[0024] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0025] The method provided by this invention can be implemented in a terminal environment that may include one or more of the following components: a processor, a storage medium, and a display screen. The storage medium stores at least one instruction, which is loaded and executed by the processor to implement the method described in the following embodiments.

[0026] A processor may include one or more processing cores. The processor uses various interfaces and lines to connect various parts of the terminal, and performs various functions and processes data by running or executing instructions, programs, code sets or instruction sets stored in the storage medium, and by calling data stored in the storage medium.

[0027] Storage media can include random access memory (RAM) or read-only memory (ROM). Storage media can be used to store instructions, programs, code, code sets, or instructions.

[0028] The display screen is used to show the user interface of each application.

[0029] In addition, those skilled in the art will understand that the structure of the terminal described above does not constitute a limitation on the terminal. The terminal may include more or fewer components, or combine certain components, or have different component arrangements. For example, the terminal may also include radio frequency circuits, input units, sensors, audio circuits, power supplies, and other components, which will not be described in detail here.

[0030] Example 1

[0031] like Figure 1 This embodiment proposes a method for testing and evaluating the energy consumption of a hybrid power system in a high-altitude road environment, used to evaluate the energy consumption of hybrid power system vehicles, including:

[0032] Step 101: Based on the road characteristics of plateau roads, a road feature factor library is formed. The plateau road environment is modeled based on the road feature factor library. The plateau roads are divided into multiple driving condition segments according to driving conditions, and the road characteristics of each driving condition segment are recorded.

[0033] Preferably, the road characteristics include: air density, air drag coefficient, road surface slope, rolling resistance coefficient, battery temperature, atmospheric pressure, oxygen concentration, and ambient temperature.

[0034] Step 102: Obtain the driving behavior characteristics of the driver for each working condition segment, and combine them with the corresponding working condition segments to generate multiple driving behavior working condition segments, and at the same time form a plateau working condition segment pool. The working condition segments include: long uphill working condition segments, long downhill working condition segments, curve working condition segments, and traffic jam working condition segments.

[0035] Preferably, acquiring the driver's driving behavior characteristics for each operating condition segment includes: collecting the driving behavior characteristics through the vehicle's OBD and / or CAN bus, which includes: rapid acceleration frequency, rapid deceleration frequency, EV mode usage ratio, SOC adjustment frequency, and throttle-brake switching frequency.

[0036] In addition to the examples above, other driving behavior characteristics are also listed in the table below:

[0037]

[0038] Table 1

[0039] Preferably, obtaining the driver's driving behavior characteristics for each working condition segment includes: pre-setting multiple driving behavior characteristics, collecting vehicle data through the vehicle's OBD and / or CAN bus, and dividing the vehicle data into corresponding driving behavior characteristics through a sliding window and clustering algorithm.

[0040] Preferably, each driving condition segment corresponds to one or more driving behavior features.

[0041] Step 103: Extract the vehicle data corresponding to each driving behavior segment, and calculate the vehicle energy consumption for each driving behavior segment based on the vehicle data, thereby completing the vehicle energy consumption test and evaluation.

[0042] Preferably, in order to accurately assess the actual energy consumption of hybrid vehicles in high-altitude environments, this embodiment sets up a high-altitude energy consumption assessment model for hybrid vehicles to calculate the actual energy consumption. The high-altitude energy consumption assessment model for hybrid vehicles is as follows:

[0043]

[0044] in, Here, t represents the energy consumption value of the hybrid vehicle during the current driving behavior segment, t0 represents the start time of the driving behavior segment, and t... n The end time of the driving behavior segment. P represents the overall efficiency of the hybrid power system at time t, used to describe the instantaneous output efficiency after coupling multiple energy sources such as the engine, motor, and battery. req (t) represents the power demand of the hybrid vehicle at time t under the current driving behavior segment. P is the energy recovery inhibition factor at time t, used to correct the actual energy recovery efficiency. regen (t) represents the power of energy recovery at time t, η alt (t) is the energy consumption correction factor in the plateau environment at time t.

[0045] Specifically, Used to describe the energy consumption required for effective operation in high-altitude environments. η is used to describe the energy recovered during processes such as braking or descent. alt (t) is used to describe the impact of high-altitude, low-oxygen, low-pressure, and low-temperature conditions on the overall efficiency of the drive and recovery processes.

[0046] Calculate the power demand P of the hybrid vehicle at time t under the current driving behavior segment. req (t) Specifically:

[0047]

[0048] Where ρ(t) is the air density at time t, A is the vehicle's frontal area (i.e., the vehicle's frontal projection), and C... d v(t) is the air resistance coefficient at time t, v(t) is the vehicle speed at time t, m is the total mass of the vehicle, g is the acceleration due to gravity, θ(t) is the road slope at time t, and f r (t) represents the rolling resistance coefficient at time t, and a(t) represents the vehicle acceleration at time t.

[0049] Calculate the overall efficiency of the hybrid power system at time t. Specifically:

[0050]

[0051] Where η0 is the battery efficiency under ideal conditions, γ1 is the SOC degradation sensitivity coefficient, and η eng (t) represents the engine transmission efficiency at time t, describing the energy conversion efficiency of the engine's output into mechanical work of the driving wheels; SOC(t) represents the battery state of charge at time t; γ2 is the temperature decay sensitivity coefficient; T(t) represents the battery temperature at time t; T opt For the optimal operating temperature of the battery, η motor(t) represents the motor transmission efficiency at time t, used to describe the energy conversion efficiency of the motor (or electric drive system) in obtaining energy from the battery and converting it into mechanical work for the driving wheels. The lower the SOC (State of Charge), the worse the battery output capability, therefore (1-SOC(t)) is used. 2 This indicates its decay trend, with the temperature deviating from the optimal value T. opt This will accelerate battery performance degradation, so a quadratic term is used to represent asymmetric degradation.

[0052] The methods for obtaining the SOC attenuation sensitivity coefficient γ1 and the temperature attenuation sensitivity coefficient γ2 are as follows:

[0053] First, we need to understand η0·(1-γ1·(1-SOC) 2 -γ2·(TT opt ) 2 The function of η is to calculate the battery degradation efficiency. bat (SOC, T) is a parameter used to describe the degree of energy conversion efficiency degradation of a battery under different states of charge (SOC) and temperatures (T), and is used to more realistically and dynamically reflect the performance changes of a battery in actual operating environments.

[0054] Then, a set of different SOC and temperature values ​​were designed, and the battery performance was tested:

[0055] SOC (%) T(℃) <![CDATA[Measured value η bat (SOC, T)]]> 100 25 0.98 80 25 0.95 60 25 0.91 100 0 0.93 100 40 0.9

[0056] Finally, η is analyzed using the least squares method or machine learning regression. bat By fitting (SOC, T), the SOC attenuation sensitivity coefficient γ1 and the temperature attenuation sensitivity coefficient γ2 are obtained.

[0057] Calculate the energy recovery inhibition factor at time t. Specifically:

[0058]

[0059] Where σ is the Sigmoid function, k1 is the acceleration weight, and k2 is the road slope weight. The indicator function indicates that energy recovery is only permitted during deceleration. The recovery process must be triggered during deceleration or downhill driving, hence the multiplication by the indicator function. Recovery efficiency is affected by braking intensity |a(t)| and slope magnitude |θ(t)|, which constitute recovery conditional quantities. Using the sigmoid activation function, the nonlinear expansion of recovery efficiency is avoided, and it is mapped within a reasonable range of [0,1].

[0060] The energy consumption correction factor η in the plateau environment at time t is calculated. alt (t) Specifically:

[0061]

[0062] Where δ1 is the weight of atmospheric pressure, P atm (t) represents the atmospheric pressure at time t, P0 represents the standard atmospheric pressure, and δ2 represents the weight of the oxygen concentration. Let C0 be the standard oxygen concentration at time t, δ3 be the temperature weight, T′(t) be the ambient temperature at time t, and T0 be the standard ambient temperature.

[0063] Preferably, the vehicle energy consumption is displayed in the form of a bar chart, where the horizontal axis represents the serial number of multiple high-altitude operating condition segments, the vertical axis represents the corresponding vehicle energy consumption, and the corresponding operating condition segments and driving behavior characteristics are marked on each bar.

[0064] Example 2

[0065] like Figure 2 As shown, this embodiment proposes a hybrid power system energy consumption testing and evaluation system for high-altitude road environments, used to test and evaluate the energy consumption of hybrid power system vehicles, including:

[0066] The driving condition segmentation module is used to form a road feature factor library based on the road characteristics of plateau roads, model the plateau road environment based on the road feature factor library, divide the plateau roads into multiple driving condition segments according to driving conditions, and record the road characteristics of each driving condition segment.

[0067] A plateau driving condition segment pool module is constructed to obtain the driving behavior characteristics of the driver in each driving condition segment and combine them with the corresponding driving condition segment to generate multiple driving behavior driving condition segments, and at the same time form a plateau driving condition segment pool. The driving condition segments include: long uphill driving condition segments, long downhill driving condition segments, curve driving condition segments, and traffic jam driving condition segments.

[0068] Preferably, acquiring the driver's driving behavior characteristics for each operating condition segment includes: collecting the driving behavior characteristics through the vehicle's OBD and / or CAN bus, which includes: rapid acceleration frequency, rapid deceleration frequency, EV mode usage ratio, SOC adjustment frequency, and throttle-brake switching frequency.

[0069] Preferably, obtaining the driver's driving behavior characteristics for each working condition segment includes: pre-setting multiple driving behavior characteristics, collecting vehicle data through the vehicle's OBD and / or CAN bus, and dividing the vehicle data into corresponding driving behavior characteristics through a sliding window and clustering algorithm.

[0070] Preferably, each driving condition segment corresponds to one or more driving behavior features.

[0071] The energy consumption test module is used to extract vehicle data corresponding to each driving behavior segment and calculate the vehicle energy consumption for each driving behavior segment based on the vehicle data, thereby completing the vehicle energy consumption test evaluation.

[0072] Preferably, in order to accurately assess the actual energy consumption of hybrid vehicles in high-altitude environments, this embodiment sets up a high-altitude energy consumption assessment model for hybrid vehicles to calculate the actual energy consumption. The high-altitude energy consumption assessment model for hybrid vehicles is as follows:

[0073]

[0074] in, Here, t represents the energy consumption value of the hybrid vehicle during the current driving behavior segment, t0 represents the start time of the driving behavior segment, and t... n The end time of the driving behavior segment. P represents the overall efficiency of the hybrid power system at time t, used to describe the instantaneous output efficiency after coupling multiple energy sources such as the engine, motor, and battery. req (t) represents the power demand of the hybrid vehicle at time t under the current driving behavior segment. P is the energy recovery inhibition factor at time t, used to correct the actual energy recovery efficiency. regen (t) represents the power of energy recovery at time t, η alt (t) is the energy consumption correction factor in the plateau environment at time t.

[0075] Calculate the power demand P of the hybrid vehicle at time t under the current driving behavior segment. req (t) Specifically:

[0076]

[0077] Where ρ(t) is the air density at time t, A is the vehicle's frontal area (i.e., the vehicle's frontal projection), and C... d v(t) is the air resistance coefficient at time t, v(t) is the vehicle speed at time t, m is the total mass of the vehicle, g is the acceleration due to gravity, θ(t) is the road slope at time t, and f r (t) represents the rolling resistance coefficient at time t, and a(t) represents the vehicle acceleration at time t.

[0078] Calculate the overall efficiency of the hybrid power system at time t. Specifically:

[0079]

[0080] Where η0 is the battery efficiency under ideal conditions, γ1 is the SOC degradation sensitivity coefficient, and η eng(t) represents the engine transmission efficiency at time t, describing the energy conversion efficiency of the engine's output into mechanical work of the driving wheels; SOC(t) represents the battery state of charge at time t; γ2 is the temperature decay sensitivity coefficient; T(t) represents the battery temperature at time t; T opt For the optimal operating temperature of the battery, η motor (t) represents the motor transmission efficiency at time t, which describes the energy conversion efficiency of the motor (or electric drive system) in obtaining energy from the battery and converting it into mechanical work for the driving wheels.

[0081] Calculate the energy recovery inhibition factor at time t. Specifically:

[0082]

[0083] Where σ is the Sigmoid function, k1 is the acceleration weight, and k2 is the road slope weight. This is an indicator function that indicates that energy recovery is allowed only when the deceleration phase is in progress.

[0084] The energy consumption correction factor η in the plateau environment at time t is calculated. alt (t) Specifically:

[0085]

[0086] Where δ1 is the weight of atmospheric pressure, P atm (t) represents the atmospheric pressure at time t, P0 represents the standard atmospheric pressure, and δ2 represents the weight of the oxygen concentration. δ3 represents the oxygen concentration at time t, C0 represents the standard oxygen concentration, δ3 represents the weight of temperature, and T0 represents the standard temperature.

[0087] Preferably, the vehicle energy consumption is displayed in the form of a bar chart, where the horizontal axis represents the serial number of multiple high-altitude operating condition segments, the vertical axis represents the corresponding vehicle energy consumption, and the corresponding operating condition segments and driving behavior characteristics are marked on each bar.

[0088] Example 3

[0089] This invention also proposes a storage medium storing multiple instructions for implementing the aforementioned method for testing and evaluating the energy consumption of a hybrid power system in a high-altitude road environment.

[0090] Optionally, in this embodiment, the storage medium may be located in any computer terminal in a group of computer terminals in a computer network, or in any mobile terminal in a group of mobile terminals.

[0091] Optionally, in this embodiment, the storage medium is configured to store program code for performing the following steps: Step 101, based on the road characteristics of the plateau road, a road feature factor library is formed, the plateau road environment is modeled based on the road feature factor library, the plateau road is divided into multiple working condition segments according to driving conditions, and the road characteristics of each working condition segment are recorded.

[0092] Step 102: Obtain the driving behavior characteristics of the driver for each working condition segment, and combine them with the corresponding working condition segments to generate multiple driving behavior working condition segments, and at the same time form a plateau working condition segment pool. The working condition segments include: long uphill working condition segments, long downhill working condition segments, curve working condition segments, and traffic jam working condition segments.

[0093] Preferably, acquiring the driver's driving behavior characteristics for each operating condition segment includes: collecting the driving behavior characteristics through the vehicle's OBD and / or CAN bus, which includes: rapid acceleration frequency, rapid deceleration frequency, EV mode usage ratio, SOC adjustment frequency, and throttle-brake switching frequency.

[0094] Preferably, obtaining the driver's driving behavior characteristics for each working condition segment includes: pre-setting multiple driving behavior characteristics, collecting vehicle data through the vehicle's OBD and / or CAN bus, and dividing the vehicle data into corresponding driving behavior characteristics through a sliding window and clustering algorithm.

[0095] Preferably, each driving condition segment corresponds to one or more driving behavior features.

[0096] Step 103: Extract the vehicle data corresponding to each driving behavior segment, and calculate the vehicle energy consumption for each driving behavior segment based on the vehicle data, thereby completing the vehicle energy consumption test and evaluation.

[0097] Preferably, the vehicle energy consumption is displayed in the form of a bar chart, where the horizontal axis represents the serial number of multiple high-altitude operating condition segments, the vertical axis represents the corresponding vehicle energy consumption, and the corresponding operating condition segments and driving behavior characteristics are marked on each bar.

[0098] Example 4

[0099] This invention also proposes an electronic device, including a processor and a storage medium connected to the processor. The storage medium stores multiple instructions, which can be loaded and executed by the processor to enable the processor to execute the aforementioned method for testing and evaluating the energy consumption of a hybrid power system under high-altitude road conditions.

[0100] Specifically, the electronic device in this embodiment can be a computer terminal, which may include one or more processors and a storage medium.

[0101] The storage medium can be used to store software programs and modules, such as the energy consumption testing and evaluation method for a hybrid power system under high-altitude road conditions in this embodiment of the invention. The corresponding program instructions / modules allow the processor to execute various functional applications and data processing by running the software programs and modules stored in the storage medium, thus realizing the aforementioned energy consumption testing and evaluation method for a hybrid power system under high-altitude road conditions. The storage medium may include high-speed random access storage media, and may also include non-volatile storage media, such as one or more magnetic storage systems, flash memory, or other non-volatile solid-state storage media. In some instances, the storage medium may further include storage media remotely configured relative to the processor, which can be connected to the terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0102] The processor can call the information and application stored in the storage medium through the transmission system to perform the following steps: Step 101, based on the road characteristics of the plateau road, form a road feature factor library, model the plateau road environment according to the road feature factor library, divide the plateau road into multiple working condition segments according to driving conditions, and record the road characteristics of each working condition segment.

[0103] Step 102: Obtain the driving behavior characteristics of the driver for each working condition segment, and combine them with the corresponding working condition segments to generate multiple driving behavior working condition segments, and at the same time form a plateau working condition segment pool. The working condition segments include: long uphill working condition segments, long downhill working condition segments, curve working condition segments, and traffic jam working condition segments.

[0104] Preferably, acquiring the driver's driving behavior characteristics for each operating condition segment includes: collecting the driving behavior characteristics through the vehicle's OBD and / or CAN bus, which includes: rapid acceleration frequency, rapid deceleration frequency, EV mode usage ratio, SOC adjustment frequency, and throttle-brake switching frequency.

[0105] Preferably, obtaining the driver's driving behavior characteristics for each working condition segment includes: pre-setting multiple driving behavior characteristics, collecting vehicle data through the vehicle's OBD and / or CAN bus, and dividing the vehicle data into corresponding driving behavior characteristics through a sliding window and clustering algorithm.

[0106] Preferably, each driving condition segment corresponds to one or more driving behavior features.

[0107] Step 103: Extract the vehicle data corresponding to each driving behavior segment, and calculate the vehicle energy consumption for each driving behavior segment based on the vehicle data, thereby completing the vehicle energy consumption test and evaluation.

[0108] Preferably, the vehicle energy consumption is displayed in the form of a bar chart, where the horizontal axis represents the serial number of multiple high-altitude operating condition segments, the vertical axis represents the corresponding vehicle energy consumption, and the corresponding operating condition segments and driving behavior characteristics are marked on each bar.

[0109] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0110] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0111] In the several embodiments provided by this invention, it should be understood that the disclosed technical content can be implemented in other ways. The system embodiments described above are merely illustrative; for example, the division of 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 system, 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, indirect coupling or communication connection between units or modules, and may be electrical or other forms.

[0112] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0113] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0114] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes: USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, optical disks, and other media capable of storing program code.

[0115] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A method for testing and evaluating the energy consumption of a hybrid power system in a high-altitude road environment, used for testing and evaluating the energy consumption of hybrid power system vehicles, characterized in that... include: Based on the road characteristics of plateau roads, plateau roads are divided into multiple driving condition segments according to driving conditions, and the road characteristics of each driving condition segment are recorded. The driving behavior characteristics of the driver in each working condition segment are obtained and combined with the corresponding working condition segment to generate multiple driving behavior working condition segments, and at the same time form a plateau working condition segment pool. Extract vehicle data corresponding to each driving behavior segment, and calculate vehicle energy consumption for each driving behavior segment based on the vehicle data, thereby completing the vehicle energy consumption test and evaluation. A high-altitude energy consumption assessment model for hybrid electric vehicles was established to calculate the vehicle energy consumption for each driving behavior segment: in, This represents the energy consumption value of a hybrid electric vehicle during the current driving behavior segment. The start time of the driving behavior segment. The end time of the driving behavior segment. For time The overall efficiency of the hybrid power system For time The power demand of a hybrid vehicle under the current driving behavior segment. For time Time-based energy recovery inhibitor, For time Power of energy recovery at that time For time Energy consumption correction factor in high-altitude environments; Calculation time Time-energy recovery inhibitor include: in, For the Sigmoid function, For acceleration weights, As the road surface slope weight, This is an indicator function, indicating that energy recovery is only allowed during the deceleration phase. The recovery process must be triggered during deceleration or downhill. For time Vehicle acceleration at that time.

2. The energy consumption testing and evaluation method for a hybrid power system in a high-altitude road environment as described in claim 1, characterized in that, Also includes: The road characteristics of the plateau road environment are obtained and a road characteristic factor library is formed. The plateau road environment is modeled based on the road characteristic factor library.

3. The energy consumption testing and evaluation method for a hybrid power system in a high-altitude road environment as described in claim 1, characterized in that, The operating conditions segments include: long uphill operating conditions segment, long downhill operating conditions segment, curve operating conditions segment, and traffic jam operating conditions segment.

4. The energy consumption testing and evaluation method for a hybrid power system in a high-altitude road environment as described in claim 1, characterized in that, Acquiring the driver's driving behavior characteristics for each operating condition segment includes: collecting the driving behavior characteristics through the vehicle's OBD and / or CAN bus, which include: rapid acceleration frequency, rapid deceleration frequency, EV mode usage ratio, SOC adjustment frequency, and throttle-brake switching frequency.

5. The energy consumption testing and evaluation method for a hybrid power system in a high-altitude road environment as described in claim 1, characterized in that, Each driving condition segment corresponds to one or more driving behavior characteristics.

6. The energy consumption testing and evaluation method for a hybrid power system in a high-altitude road environment as described in claim 1, characterized in that, Acquiring the driver's driving behavior characteristics for each working condition segment includes: pre-setting multiple driving behavior characteristics, collecting vehicle data through the vehicle's OBD and / or CAN bus, and dividing the vehicle data into corresponding driving behavior characteristics through sliding window and clustering algorithms.

7. The energy consumption testing and evaluation method for a hybrid power system in a high-altitude road environment as described in claim 1, characterized in that, Vehicle energy consumption is displayed in the form of a bar chart, where the horizontal axis represents the serial number of multiple high-altitude operating condition segments, the vertical axis represents the corresponding vehicle energy consumption, and the corresponding operating condition segments and driving behavior characteristics are marked on each bar.

8. A hybrid power system energy consumption testing and evaluation system for high-altitude road environments, used for energy consumption testing and evaluation of hybrid power system vehicles, characterized in that, include: The driving condition segmentation module is used to divide the plateau road into multiple driving condition segments according to the road characteristics of the plateau road, and record the road characteristics of each driving condition segment. A high-altitude driving condition segment pool module is constructed to obtain the driving behavior characteristics of the driver for each driving condition segment and combine them with the corresponding driving condition segments to generate multiple driving behavior driving condition segments, thus forming a high-altitude driving condition segment pool. The energy consumption test module is used to extract vehicle data corresponding to each driving behavior segment and calculate the vehicle energy consumption for each driving behavior segment based on the vehicle data, thereby completing the vehicle energy consumption test evaluation. A high-altitude energy consumption assessment model for hybrid electric vehicles was established to calculate the vehicle energy consumption for each driving behavior segment: in, This represents the energy consumption value of a hybrid electric vehicle during the current driving behavior segment. The start time of the driving behavior segment. The end time of the driving behavior segment. For time The overall efficiency of the hybrid power system For time The power demand of a hybrid vehicle under the current driving behavior segment. For time Time-based energy recovery inhibitor, For time Power of energy recovery at that time For time Energy consumption correction factor in high-altitude environments; Calculation time Time-energy recovery inhibitor include: in, For the Sigmoid function, For acceleration weights, As the road surface slope weight, This is an indicator function, indicating that energy recovery is only allowed during the deceleration phase. The recovery process must be triggered during deceleration or downhill. For time Vehicle acceleration at that time.

9. The energy consumption testing and evaluation system for a hybrid power system in a high-altitude road environment as described in claim 8, characterized in that, Also includes: The road characteristics of the plateau road environment are obtained and a road characteristic factor library is formed. The plateau road environment is modeled based on the road characteristic factor library.

10. The energy consumption testing and evaluation system for a hybrid power system in a high-altitude road environment as described in claim 8, characterized in that, The operating conditions segments include: long uphill operating conditions segment, long downhill operating conditions segment, curve operating conditions segment, and traffic jam operating conditions segment.

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