New energy automobile energy loss evaluation and optimization method

Abstract

The invention provides a new energy vehicle energy loss assessment and optimization method, and relates to the technical field of loss assessment, and the method comprises the steps: obtaining the driving state data and driving behavior data of a target new energy vehicle at each driving moment in real time based on a preset vehicle-mounted sensor, and obtaining first driving data; performing energy flow analysis on the first driving data based on a preset energy flow test analysis method; based on an energy flow analysis result, determining energy loss conditions of the target new energy vehicle under different working conditions, and performing energy consumption evaluation in combination with the target new energy vehicle; and formulating a loss optimization strategy of the target new energy vehicle based on an energy consumption evaluation result, thereby performing loss optimization on the target new energy vehicle. By analyzing the energy transfer process of the new energy automobile, accurate evaluation and optimization of energy loss are realized, the energy utilization efficiency of the new energy automobile is improved, and scientific basis and technical support are provided for energy management of the new energy automobile.

Description

Technical Field

[0001] The present invention relates to the field of loss assessment technology, and in particular to a new energy vehicle energy loss assessment and optimization method. Background Art

[0002] At present, new energy vehicles are an important development direction of future transportation, and their energy management efficiency directly affects the vehicle's driving range, power and economy.

[0003] However, in actual operation, new energy vehicles (NEVs) experience complex and variable energy loss due to factors such as driving style, road conditions, and climate control systems. Accurately assessing NEV energy loss and developing effective optimization strategies are pressing challenges in the field of NEV technology.

[0004] Therefore, the present invention provides a method for evaluating and optimizing energy loss of new energy vehicles. Summary of the Invention

[0005] This invention provides a method for assessing and optimizing energy loss in new energy vehicles. By analyzing the energy transfer process of new energy vehicles, this method accurately assesses and optimizes energy loss. This method not only improves the energy efficiency and driving range of new energy vehicles, but also enhances the vehicle's power, economy, and drivability. Furthermore, it provides a scientific basis and technical support for energy management in new energy vehicles, contributing to the healthy development of the new energy vehicle industry.

[0006] The present invention provides a method for evaluating and optimizing energy loss of new energy vehicles, comprising: Step 1: Acquire driving state data and driving behavior data of the target new energy vehicle at each driving moment in real time based on a preset vehicle-mounted sensor to obtain first driving data; Step 2: Performing energy flow analysis on the first driving data based on a preset energy flow test and analysis method; Step 3: Based on the energy flow analysis results, determine the energy loss of the target new energy vehicle under different operating conditions, and then conduct energy consumption assessment based on the target new energy vehicle; Step 4: Based on the energy consumption assessment results, a loss optimization strategy for the target new energy vehicle is formulated to optimize the loss of the target new energy vehicle.

[0007] According to the present invention, the driving state data and driving behavior data of the target new energy vehicle at each driving moment are obtained in real time based on the preset vehicle-mounted sensor to obtain the first driving data, including: Step 11: Acquire the driving status data of the target new energy vehicle at each driving moment in real time based on the preset vehicle-mounted sensors; Step 12: Based on the preset vehicle-mounted sensors, the vehicle behavior data and the driver behavior data of the target new energy vehicle at each driving moment are obtained in real time, and the driving behavior data is obtained by integration; Step 13: The driving state data and the driving behavior data at each driving moment are integrated to obtain the first driving data of the target new energy vehicle at each driving moment.

[0008] According to the present invention, performing energy flow analysis on the first driving data based on the preset energy flow test analysis method includes: Step 21: According to the characteristics of the target new energy vehicle, extract the energy flow test analysis method from the test analysis method database as the preset energy flow test analysis method; Step 22: performing data cleaning and data conversion on the acquired first driving data to obtain first converted driving data, and performing data segmentation on the first converted driving data to obtain first processed data; Step 23: Perform energy flow analysis on the processed first processed data based on a preset energy flow test analysis method to obtain an energy flow analysis result.

[0009] According to the preset energy flow test analysis method provided by the present invention, energy flow analysis is performed on the processed first processed data to obtain energy flow analysis results, including: Step 231: determining a test analysis boundary of a preset energy flow test analysis method based on an energy flow analysis target of a target new energy vehicle; Step 232: Combine the first processed data with a preset energy flow test analysis method to draw an energy flow diagram to determine the energy transfer path; Step 233: Determine the energy loss of each path of the energy flow diagram based on the energy transfer path and the first processed data; Step 234: Analyze the energy loss cause based on the transmission type of each path, and combine the energy loss of each path to obtain the initial energy loss of each path; Step 235: Determine the energy loss efficiency based on the energy loss of each adjacent row based on the initial energy loss; Step 236: extracting the first loss link whose energy loss efficiency is lower than the preset minimum efficiency based on the energy loss efficiency, and verifying the initial energy loss in combination with the test analysis boundary conditions, thereby extracting the second loss link in the initial energy loss that exceeds the test analysis boundary conditions; Step 237: combining the first loss link and the second loss link to obtain a loss analysis result of the target new energy vehicle, thereby combining the energy flow diagram to obtain an energy flow analysis result of the target new energy vehicle.

[0010] The energy consumption assessment provided by the present invention in combination with a target new energy vehicle includes: Step 31: Based on the energy flow analysis results, determine the energy loss of the target new energy vehicle under each operating condition; Step 32: Comprehensively evaluate the energy loss based on the driving state, driving behavior, and external environmental factors of the target new energy vehicle under each operating condition to obtain an energy consumption evaluation result.

[0011] According to the present invention, the energy consumption is comprehensively evaluated by combining the driving state, driving behavior and external environmental factors of the target new energy vehicle under each operating condition to obtain the energy consumption evaluation results, including: Step 321: extract key features from the driving state data, extract driving habit features from the driving behavior data, and extract factors affecting energy loss from the external environment data; Step 322: Analyze the correlation between the key features, driving habit features, and energy loss using a machine learning algorithm to determine a first feature that has a significant impact on energy loss and a feature analysis result. Step 323: Select a model based on the feature analysis results to obtain an energy loss prediction value; Step 324: Determine the energy consumption evaluation result of the target new energy vehicle based on the energy loss prediction value.

[0012] According to the present invention, a loss optimization strategy for a target new energy vehicle is formulated based on the energy consumption evaluation result, thereby optimizing the loss of the target new energy vehicle, including: Step 41: Determine a loss optimization strategy for the target new energy vehicle based on the energy consumption evaluation result; Step 42: Determine a loss optimization plan based on the real-time driving status data and real-time driving behavior data of the target new energy vehicle; Step 43: Optimize the loss of the target new energy vehicle based on the loss optimization solution.

[0013] The loss optimization strategy is optimized based on the real-time monitoring results provided by the present invention, specifically including: Step 01: monitoring a first driving state and a first driving energy consumption of a target new energy vehicle in real time based on a preset vehicle-mounted sensor; Step 02: performing a first comparison between the first driving state and the first driving energy consumption and the driving state and driving energy consumption of the target new energy vehicle before loss optimization; If the first driving state and the first driving energy consumption in the first comparison result are both better than the driving state and driving energy consumption of the target new energy vehicle before loss optimization, then the loss optimization strategy is determined to be qualified and no adjustment to the loss optimization strategy is required; On the contrary, the corresponding sub-driving status and sub-driving energy consumption are extracted to determine the corresponding optimization point, so as to formulate the corresponding optimization adjustment plan and optimize the loss optimization strategy.

[0014] Compared with the existing technology, the beneficial effects of the present invention are: the present invention provides a new energy vehicle energy loss assessment and optimization method, which realizes accurate assessment and optimization of energy loss by analyzing the energy transfer process of new energy vehicles, which not only improves the energy utilization efficiency of new energy vehicles and increases the driving range, but also improves the power, economy and drivability of the whole vehicle. At the same time, it provides a scientific basis and technical support for the energy management of new energy vehicles, which helps to promote the healthy development of the new energy vehicle industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction is given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0016] Figure 1 This is a flow chart of a new energy vehicle energy loss assessment and optimization method provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0017] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0018] Example 1: The embodiment of the present invention provides a method for evaluating and optimizing energy loss of new energy vehicles, such as Figure 1 As shown, including: Step 1: Acquire driving state data and driving behavior data of the target new energy vehicle at each driving moment in real time based on a preset vehicle-mounted sensor to obtain first driving data; Step 2: Performing energy flow analysis on the first driving data based on a preset energy flow test and analysis method; Step 3: Based on the energy flow analysis results, determine the energy loss of the target new energy vehicle under different operating conditions, and then conduct energy consumption assessment based on the target new energy vehicle; Step 4: Based on the energy consumption assessment results, a loss optimization strategy for the target new energy vehicle is formulated to optimize the loss of the target new energy vehicle.

[0019] In this embodiment, the preset onboard sensors are devices pre-installed on new energy vehicles and are used to monitor and record the vehicle's driving status and driving behavior data in real time. These sensors may include speed sensors, acceleration sensors, steering angle sensors, brake pressure sensors, etc., which can capture various dynamic information of the vehicle at different time points.

[0020] In this embodiment, the driving state data refers to data related to the vehicle's motion state generated during the vehicle's driving process, such as vehicle speed, acceleration, steering angle, vehicle position, etc.

[0021] In this embodiment, the driving behavior data refers to the behavioral characteristic data exhibited by the driver during the driving process, such as the accelerator pedal's depression depth, the brake pedal's depression force, the gear shifting timing, etc.

[0022] In this embodiment, the first driving data is a collection of driving state data and driving behavior data acquired in real time by preset vehicle-mounted sensors, and is the basis for analysis and evaluation.

[0023] In this embodiment, the preset energy flow test and analysis method is a method for analyzing and evaluating the energy flow and conversion efficiency of new energy vehicles. It may include steps such as drawing an energy flow diagram, calculating energy loss, and analyzing energy efficiency, aiming to reveal the flow and conversion of energy during vehicle operation.

[0024] In this embodiment, the energy flow analysis is based on a preset energy flow test analysis method, and an in-depth analysis is performed on the first driving data to understand the flow, conversion and loss of energy of the vehicle during driving.

[0025] In this embodiment, different operating conditions refer to the driving state of the new energy vehicle under different road conditions, climate conditions, driving modes, etc. Different operating conditions will directly affect the energy loss of the vehicle.

[0026] In this embodiment, the energy loss situation is the energy loss situation of the vehicle under different working conditions obtained through energy flow analysis, including electrical energy loss, thermal energy loss, mechanical energy loss, etc.

[0027] In this embodiment, the energy consumption assessment is based on the energy loss situation and evaluates the overall energy consumption level of the target new energy vehicle.

[0028] In this embodiment, the loss optimization strategy is based on the energy consumption assessment results and is designed to address the energy loss issues experienced by the target new energy vehicle during driving. These strategies may involve improving driving habits, optimizing vehicle design, and improving energy conversion efficiency.

[0029] In this embodiment, loss optimization refers to implementing a loss optimization strategy to reduce the energy loss of the target new energy vehicle during driving and improve its energy efficiency performance.

[0030] The beneficial effects of the above technical solution are: by analyzing the energy transfer process of new energy vehicles, accurate assessment and optimization of energy loss are achieved, which not only improves the energy utilization efficiency of new energy vehicles and increases the driving range, but also improves the power, economy and drivability of the entire vehicle. At the same time, it provides a scientific basis and technical support for the energy management of new energy vehicles, which helps to promote the healthy development of the new energy vehicle industry.

[0031] Example 2: Based on the first embodiment, the first driving data is obtained, including: Step 11: Acquire the driving status data of the target new energy vehicle at each driving moment in real time based on the preset vehicle-mounted sensors; Step 12: Based on the preset vehicle-mounted sensors, the vehicle behavior data and the driver behavior data of the target new energy vehicle at each driving moment are obtained in real time, and the driving behavior data is obtained by integration; Step 13: The driving state data and the driving behavior data at each driving moment are integrated to obtain the first driving data of the target new energy vehicle at each driving moment.

[0032] In this embodiment, the preset onboard sensors are devices pre-installed on new energy vehicles and are used to monitor various vehicle parameters and status in real time. These sensors may include speed sensors, acceleration sensors, steering angle sensors, battery status sensors, etc., and can capture real-time data from the vehicle at different driving times.

[0033] In this embodiment, driving state data refers to data related to the vehicle's state generated during driving. This data reflects key information such as the vehicle's motion state, position, speed, and acceleration at different times. For example, speed data indicates the vehicle's current speed, while acceleration data indicates how quickly the vehicle's speed changes.

[0034] In this embodiment, vehicle behavior data refers to the behavioral characteristics of a vehicle during driving, which are generally related to the vehicle's mechanical system, electronic control system, etc. For example, the vehicle's shifting behavior, braking behavior, steering behavior, etc. can all be monitored and recorded by corresponding sensors.

[0035] In this embodiment, driver behavior data refers to the behavioral characteristics of the driver during driving, which reflects the driver's driving habits and style. For example, the force and frequency with which the driver presses the accelerator and brake pedals, as well as the driver's operation of the steering wheel, can all be monitored by sensors.

[0036] In this embodiment, the driving behavior data is a comprehensive data obtained by integrating the vehicle behavior data and the driver behavior data. These data comprehensively reflect the behavioral characteristics of the vehicle during driving, including both the mechanical and electronic behavior of the vehicle itself and the driving behavior of the driver.

[0037] In this embodiment, the first driving data is a data set obtained by combining the driving state data and driving behavior data at each driving moment. This data set contains comprehensive information about the vehicle during driving and serves as the basis for subsequent tasks such as energy consumption analysis, fault diagnosis, and driving behavior assessment.

[0038] The beneficial effect of the above technical solution is: by comprehensively analyzing the vehicle behavior data and driver behavior data of the target vehicle, the energy transfer process of the new energy vehicle is determined, the energy loss is accurately evaluated and optimized, and the energy utilization efficiency of the new energy vehicle is improved.

[0039] Example 3: Based on Example 2, energy flow analysis is performed on the first driving data based on a preset energy flow test and analysis method, including: Step 21: According to the characteristics of the target new energy vehicle, extract the energy flow test analysis method from the test analysis method database as the preset energy flow test analysis method; Step 22: performing data cleaning and data conversion on the acquired first driving data to obtain first converted driving data, and performing data segmentation on the first converted driving data to obtain first processed data; Step 23: Perform energy flow analysis on the processed first processed data based on a preset energy flow test analysis method to obtain an energy flow analysis result.

[0040] In this embodiment, the characteristics of the target new energy vehicle refer to the unique properties or features of the new energy vehicle being evaluated, which may include its power type (e.g., pure electric, plug-in hybrid), battery capacity, motor performance, body weight, aerodynamic design, etc. These characteristics will affect the energy consumption and performance of the new energy vehicle.

[0041] In this embodiment, the test and analysis method database is a database that stores a variety of test and analysis methods used to evaluate the performance of different types of vehicles or systems. For new energy vehicles, this database may include methods such as energy flow testing, emissions testing, and battery performance testing.

[0042] In this embodiment, the preset energy flow test and analysis method is selected from the test and analysis method database and is suitable for the characteristics of the target new energy vehicle. This method is used to analyze the flow and conversion of energy during driving of new energy vehicles, thereby evaluating their energy efficiency performance.

[0043] In this embodiment, data cleaning is a process of preprocessing the first driving data, aiming to identify and correct errors, anomalies or missing values ​​in the data.

[0044] In this embodiment, data conversion is to convert the first driving data into a form suitable for energy flow analysis, including data format conversion, data unit unification, data normalization or standardization, etc.

[0045] In this embodiment, the first converted driving data is driving data that has undergone data cleaning and data conversion, and these data are ready for subsequent energy flow analysis.

[0046] In this embodiment, data segmentation is to divide the first converted driving data into multiple small data sets according to time, distance or other criteria to facilitate more detailed analysis. Data segmentation helps identify energy flow characteristics under different driving stages or working conditions.

[0047] In this embodiment, the first processed data is the driving data after data segmentation, and these data are ready for energy flow analysis.

[0048] In this embodiment, energy flow analysis is a process of performing an in-depth analysis of the processed first processed data based on a preset energy flow test analysis method. Energy flow analysis aims to reveal the flow, conversion, and loss of energy during the driving process of new energy vehicles, thereby evaluating their energy efficiency performance.

[0049] In this embodiment, the energy flow analysis results are the output of the energy flow analysis, which usually include indicators such as energy flow diagram, energy loss distribution, and energy conversion efficiency. These results are used to evaluate the energy efficiency performance of new energy vehicles and provide a basis for subsequent optimization and improvement.

[0050] The beneficial effect of the above technical solution is: by analyzing the energy transfer process of new energy vehicles, accurate evaluation and optimization of energy loss are achieved, and the energy utilization efficiency of new energy vehicles is improved.

[0051] Example 4: Based on Example 3, the energy flow analysis results are obtained, including: Step 231: determining a test analysis boundary of a preset energy flow test analysis method based on an energy flow analysis target of a target new energy vehicle; Step 232: Combine the first processed data with a preset energy flow test analysis method to draw an energy flow diagram to determine the energy transfer path; Step 233: Determine the energy loss of each path of the energy flow diagram based on the energy transfer path and the first processed data; Step 234: Analyze the energy loss cause based on the transmission type of each path, and combine the energy loss of each path to obtain the initial energy loss of each path; Step 235: Determine the energy loss efficiency based on the energy loss of each adjacent row based on the initial energy loss; Step 236: extracting the first loss link whose energy loss efficiency is lower than the preset minimum efficiency based on the energy loss efficiency, and verifying the initial energy loss in combination with the test analysis boundary conditions, thereby extracting the second loss link in the initial energy loss that exceeds the test analysis boundary conditions; Step 237: combining the first loss link and the second loss link to obtain a loss analysis result of the target new energy vehicle, thereby combining the energy flow diagram to obtain an energy flow analysis result of the target new energy vehicle.

[0052] In this embodiment, energy flow analysis is a process of performing an in-depth analysis of the processed first processed data based on a preset energy flow test analysis method. Energy flow analysis aims to reveal the flow, conversion, and loss of energy during the driving process of new energy vehicles, thereby evaluating their energy efficiency performance.

[0053] In this embodiment, the energy flow analysis results are the output of the energy flow analysis, which usually include indicators such as energy flow diagram, energy loss distribution, and energy conversion efficiency. These results are used to evaluate the energy efficiency performance of new energy vehicles and provide a basis for subsequent optimization and improvement.

[0054] The beneficial effect of the above technical solution is: by analyzing the energy transfer process of new energy vehicles, accurate evaluation and optimization of energy loss are achieved, and the energy utilization efficiency of new energy vehicles is improved.

[0055] Example 5: Based on Example 3, energy consumption evaluation is performed in combination with the target new energy vehicle, including: Step 31: Based on the energy flow analysis results, determine the energy loss of the target new energy vehicle under each operating condition; Step 32: Comprehensively evaluate the energy loss based on the driving state, driving behavior, and external environmental factors of the target new energy vehicle under each operating condition to obtain an energy consumption evaluation result.

[0056] In this embodiment, the energy flow analysis is based on a preset energy flow test analysis method, and an in-depth analysis is performed on the first driving data to understand the flow, conversion and loss of energy of the vehicle during driving.

[0057] In this embodiment, different operating conditions refer to the driving state of the new energy vehicle under different road conditions, climate conditions, driving modes, etc. Different operating conditions will directly affect the energy loss of the vehicle.

[0058] In this embodiment, the energy loss situation is the energy loss situation of the vehicle under different working conditions obtained through energy flow analysis, including electrical energy loss, thermal energy loss, mechanical energy loss, etc.

[0059] In this embodiment, the energy consumption assessment is to assess the overall energy consumption level of the target new energy vehicle based on the energy loss situation.

[0060] Example 6: Based on Example 5, the energy consumption evaluation results are obtained, including: Step 321: extract key features from the driving state data, extract driving habit features from the driving behavior data, and extract factors affecting energy loss from the external environment data; Step 322: Analyze the correlation between the key features, driving habit features, and energy loss using a machine learning algorithm to determine a first feature that has a significant impact on energy loss and a feature analysis result. Step 323: Select a model based on the feature analysis results to obtain an energy loss prediction value; Step 324: Determine the energy consumption evaluation result of the target new energy vehicle based on the energy loss prediction value.

[0061] The beneficial effects of the above technical solution are: through accurate evaluation and optimization of energy loss, it not only improves the energy utilization efficiency of new energy vehicles and increases the driving range, but also improves the power, economy and drivability of the entire vehicle.

[0062] Example 7: Based on Example 6, loss optimization is performed on the target new energy vehicle, including: Step 41: Determine a loss optimization strategy for the target new energy vehicle based on the energy consumption evaluation result; Step 42: Determine a loss optimization plan based on the real-time driving status data and real-time driving behavior data of the target new energy vehicle; Step 43: Optimize the loss of the target new energy vehicle based on the loss optimization solution.

[0063] In this embodiment, the loss optimization strategy is based on the energy consumption assessment results and is designed to address the energy loss issues experienced by the target new energy vehicle during driving. These strategies may involve improving driving habits, optimizing vehicle design, and improving energy conversion efficiency.

[0064] In this embodiment, loss optimization refers to implementing a loss optimization strategy to reduce the energy loss of the target new energy vehicle during driving and improve its energy efficiency performance.

[0065] The beneficial effects of the above technical solution are: by optimizing the losses of new energy vehicles, the energy utilization efficiency of new energy vehicles can be improved, the driving range can be increased, and the power, economy and drivability of the entire vehicle can be improved.

[0066] Example 8: Based on Example 7, the loss optimization strategy is optimized based on the real-time monitoring results, specifically including: Step 01: monitoring a first driving state and a first driving energy consumption of a target new energy vehicle in real time based on a preset vehicle-mounted sensor; Step 02: performing a first comparison between the first driving state and the first driving energy consumption and the driving state and driving energy consumption of the target new energy vehicle before loss optimization; If the first driving state and the first driving energy consumption in the first comparison result are both better than the driving state and driving energy consumption of the target new energy vehicle before loss optimization, then the loss optimization strategy is determined to be qualified and no adjustment to the loss optimization strategy is required; On the contrary, the corresponding sub-driving status and sub-driving energy consumption are extracted to determine the corresponding optimization point, so as to formulate the corresponding optimization adjustment plan and optimize the loss optimization strategy.

[0067] In this embodiment, the first driving state refers to the state of the target new energy vehicle at a certain moment or during a certain driving process, including vehicle speed, acceleration, steering angle, battery power, etc. These state data reflect the actual operation of the vehicle.

[0068] In this embodiment, the first driving energy consumption refers to the energy consumed by the target new energy vehicle at a certain moment or during a certain driving period, which is usually measured in terms of power or fuel consumption. This data is used to evaluate the energy efficiency performance of the vehicle.

[0069] In this embodiment, the driving state and driving energy consumption refer to the state and energy consumption of the vehicle during driving, including vehicle speed, acceleration, battery consumption, etc. This corresponds to the "first driving state" and "first driving energy consumption", but here refers to the state before loss optimization is performed.

[0070] In this embodiment, the first comparison is a process of comparing the "first driving state" and "first driving energy consumption" with the "driving state and driving energy consumption of the target new energy vehicle before loss optimization." This comparison is intended to evaluate whether the loss optimization strategy is effective.

[0071] In this embodiment, the loss optimization strategy is an optimization measure or suggestion formulated to address the energy loss problem of new energy vehicles during driving. These strategies are intended to improve the energy efficiency performance of the vehicle.

[0072] In this embodiment, if both the "first driving state" and the "first driving energy consumption" are better than the state before loss optimization, the loss optimization strategies are considered qualified, that is, these strategies effectively improve the energy efficiency of the vehicle.

[0073] In this embodiment, if the loss optimization strategy fails, that is, if the "first driving state" and / or "first driving energy consumption" are not better than the state before loss optimization, then the loss optimization strategy needs to be adjusted. This usually involves modifying certain parameters or measures in the strategy to better adapt to the actual operating conditions of the vehicle.

[0074] In this embodiment, the sub-driving state and sub-driving energy consumption data are extracted from the "first driving state" and "first driving energy consumption" data to determine the optimization point when the loss optimization strategy fails. This sub-data helps to more accurately identify the problem and formulate corresponding optimization and adjustment plans.

[0075] In this embodiment, the optimization points are specific points or aspects that need to be optimized based on the analysis of "sub-driving states and sub-driving energy consumption". These optimization points may be a certain system, a certain component, or a certain driving behavior of the vehicle.

[0076] In this embodiment, the optimization adjustment plan is a specific optimization measure or suggestion formulated for the determined optimization point. These plans are intended to further improve the loss optimization strategy and enhance the energy efficiency performance of the vehicle.

[0077] The beneficial effects of the above technical solution are: through real-time monitoring, comparison and analysis, the loss optimization strategy is continuously optimized to improve the energy efficiency level of new energy vehicles.

[0078] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for evaluating and optimizing energy loss of new energy vehicles, characterized in that: include: Step 1: Acquire driving state data and driving behavior data of the target new energy vehicle at each driving moment in real time based on a preset vehicle-mounted sensor to obtain first driving data; Step 2: Performing energy flow analysis on the first driving data based on a preset energy flow test and analysis method; Step 3: Based on the energy flow analysis results, determine the energy loss of the target new energy vehicle under different operating conditions, and then conduct energy consumption assessment based on the target new energy vehicle; Step 4: Based on the energy consumption assessment results, a loss optimization strategy for the target new energy vehicle is formulated to optimize the loss of the target new energy vehicle.

2. A new energy vehicle energy loss assessment and optimization method according to claim 1, characterized in that: Based on the preset vehicle-mounted sensors, driving state data and driving behavior data of the target new energy vehicle at each driving moment are acquired in real time to obtain first driving data, including: Step 11: Acquire the driving status data of the target new energy vehicle at each driving moment in real time based on the preset vehicle-mounted sensors; Step 12: Based on the preset vehicle-mounted sensors, the vehicle behavior data and the driver behavior data of the target new energy vehicle at each driving moment are obtained in real time, and the driving behavior data is obtained by integration; Step 13: The driving state data and the driving behavior data at each driving moment are integrated to obtain the first driving data of the target new energy vehicle at each driving moment.

3. A new energy vehicle energy loss assessment and optimization method according to claim 2, characterized in that: Performing energy flow analysis on the first driving data based on a preset energy flow test and analysis method includes: Step 21: According to the characteristics of the target new energy vehicle, extract the energy flow test analysis method from the test analysis method database as the preset energy flow test analysis method; Step 22: performing data cleaning and data conversion on the acquired first driving data to obtain first converted driving data, and performing data segmentation on the first converted driving data to obtain first processed data; Step 23: Perform energy flow analysis on the processed first processed data based on a preset energy flow test analysis method to obtain an energy flow analysis result.

4. A new energy vehicle energy loss assessment and optimization method according to claim 3, characterized in that: Performing energy flow analysis on the processed first processed data based on a preset energy flow test analysis method to obtain energy flow analysis results, including: Step 231: determining a test analysis boundary of a preset energy flow test analysis method based on an energy flow analysis target of a target new energy vehicle; Step 232: Combine the first processed data with a preset energy flow test analysis method to draw an energy flow diagram to determine the energy transfer path; Step 233: Determine the energy loss of each path of the energy flow diagram based on the energy transfer path and the first processed data; Step 234: Analyze the energy loss cause based on the transmission type of each path, and combine the energy loss of each path to obtain the initial energy loss of each path; Step 235: Determine the energy loss efficiency based on the energy loss of each adjacent row based on the initial energy loss; Step 236: extracting the first loss link whose energy loss efficiency is lower than the preset minimum efficiency based on the energy loss efficiency, and verifying the initial energy loss in combination with the test analysis boundary conditions, thereby extracting the second loss link in the initial energy loss that exceeds the test analysis boundary conditions; Step 237: combining the first loss link and the second loss link to obtain a loss analysis result of the target new energy vehicle, thereby combining the energy flow diagram to obtain an energy flow analysis result of the target new energy vehicle.

5. The method for evaluating and optimizing energy loss of new energy vehicles according to claim 3, characterized in that: Based on the energy flow analysis results, determine the energy loss of the target new energy vehicle under different operating conditions, and then conduct energy consumption assessment based on the target new energy vehicle, including: Step 31: Based on the energy flow analysis results, determine the energy loss of the target new energy vehicle under each operating condition; Step 32: Comprehensively evaluate the energy loss based on the driving state, driving behavior, and external environmental factors of the target new energy vehicle under each operating condition to obtain an energy consumption evaluation result.

6. A new energy vehicle energy loss assessment and optimization method according to claim 5, characterized in that: Combined with the target new energy vehicle's driving status, driving behavior and external environmental factors under each operating condition, a comprehensive assessment of energy loss is conducted to obtain energy consumption assessment results, including: Step 321: extract key features from the driving state data, extract driving habit features from the driving behavior data, and extract factors affecting energy loss from the external environment data; Step 322: Analyze the correlation between the key features, driving habit features, and energy loss using a machine learning algorithm to determine a first feature that has a significant impact on energy loss and a feature analysis result. Step 323: Select a model based on the feature analysis results to obtain an energy loss prediction value; Step 324: Determine the energy consumption evaluation result of the target new energy vehicle based on the energy loss prediction value.

7. The method for evaluating and optimizing energy loss of new energy vehicles according to claim 5, characterized in that: Based on the energy consumption assessment results, a loss optimization strategy for the target new energy vehicle is formulated to optimize the loss of the target new energy vehicle, including: Step 41: Determine a loss optimization strategy for the target new energy vehicle based on the energy consumption evaluation result; Step 42: Determine a loss optimization plan based on the real-time driving status data and real-time driving behavior data of the target new energy vehicle; Step 43: Optimize the loss of the target new energy vehicle based on the loss optimization solution.

8. A new energy vehicle energy loss assessment and optimization method according to claim 7, characterized in that: After the loss optimization of the target new energy vehicle is performed, the process also includes: real-time monitoring of the target new energy vehicle, and optimizing the loss optimization strategy based on the real-time monitoring results, specifically including: Step 01: monitoring a first driving state and a first driving energy consumption of a target new energy vehicle in real time based on a preset vehicle-mounted sensor; Step 02: performing a first comparison between the first driving state and the first driving energy consumption and the driving state and driving energy consumption of the target new energy vehicle before loss optimization; If the first driving state and the first driving energy consumption in the first comparison result are both better than the driving state and driving energy consumption of the target new energy vehicle before loss optimization, then the loss optimization strategy is determined to be qualified and no adjustment to the loss optimization strategy is required; On the contrary, the corresponding sub-driving status and sub-driving energy consumption are extracted to determine the corresponding optimization point, so as to formulate the corresponding optimization adjustment plan and optimize the loss optimization strategy.

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