A new energy vehicle energy loss evaluation and optimization method

Abstract

The application provides a new energy vehicle energy loss evaluation and optimization method, and relates to the technical field of loss evaluation, comprising: obtaining driving state data and driving behavior data of a target new energy vehicle at each driving moment based on a preset vehicle-mounted sensor to obtain first driving data; performing energy flow analysis on the first driving data based on a preset energy flow test analysis method; determining the energy loss of the target new energy vehicle under different working conditions based on the energy flow analysis result, thereby combining the target new energy vehicle to perform energy consumption evaluation; and formulating a loss optimization strategy for the target new energy vehicle based on the energy consumption evaluation result, thereby optimizing the loss of the target new energy vehicle. Through the analysis of the energy transmission process of the new energy vehicle, the accurate evaluation and optimization of the energy loss are realized, the energy utilization efficiency of the new energy vehicle is improved, and scientific basis and technical support are provided for the energy management of the new energy vehicle.

Description

Technical Field

[0001] This invention relates to the field of energy loss assessment technology, and in particular to a method for assessing and optimizing energy loss in new energy vehicles. Background Technology

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

[0003] However, in actual operation, the energy loss of new energy vehicles is complex and variable due to various factors such as driving style, road conditions, and climate control systems. How to accurately assess the energy loss of new energy vehicles and propose effective optimization strategies has become a pressing issue in the field of new energy vehicle technology.

[0004] Therefore, the present invention provides a method for assessing and optimizing energy loss in 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, it achieves accurate assessment and optimization of energy loss. This not only improves the energy utilization efficiency and increases the driving range of new energy vehicles, but also enhances the overall vehicle's power, economy, and drivability. Simultaneously, it provides a scientific basis and technical support for the energy management of new energy vehicles, contributing to the healthy development of the new energy vehicle industry.

[0006] This invention provides a method for assessing and optimizing energy loss in new energy vehicles, comprising:

[0007] Step 1: Based on preset on-board sensors, acquire real-time driving status data and driving behavior data of the target new energy vehicle at each driving moment to obtain the first driving data;

[0008] Step 2: Perform energy flow analysis on the first driving data based on the preset energy flow test and analysis method;

[0009] 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 an energy consumption assessment in conjunction with the target new energy vehicle;

[0010] Step 4: Based on the energy consumption assessment results, formulate a loss optimization strategy for the target new energy vehicle, thereby optimizing the loss of the target new energy vehicle.

[0011] According to the present invention, the driving status data and driving behavior data of a target new energy vehicle at each driving moment are acquired in real time based on preset on-board sensors to obtain first driving data, including:

[0012] Step 11: Based on preset on-board sensors, acquire real-time driving status data of the target new energy vehicle at each driving moment;

[0013] Step 12: Based on preset on-board sensors, acquire real-time vehicle behavior data and driver behavior data of the target new energy vehicle at each driving moment, and combine them to obtain driving behavior data;

[0014] Step 13: Combine the driving status data and driving behavior data at each driving moment to obtain the first driving data of the target new energy vehicle at each driving moment.

[0015] According to the energy flow analysis method based on a preset energy flow test provided by the present invention, energy flow analysis is performed on the first driving data, including:

[0016] Step 21: Based on 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;

[0017] Step 22: Clean and transform the acquired first driving data to obtain first transformed driving data, and segment the first transformed driving data to obtain first processed data;

[0018] Step 23: Perform energy flow analysis on the processed first data based on the preset energy flow test and analysis method to obtain the energy flow analysis results.

[0019] According to the present invention, an energy flow analysis is performed on the processed first data based on a preset energy flow test and analysis method to obtain energy flow analysis results, including:

[0020] Step 231: Determine the test analysis boundary of the preset energy flow test analysis method based on the energy flow analysis target of the target new energy vehicle;

[0021] Step 232: Combine the first processed data with the preset energy flow test and analysis method to draw an energy flow diagram and determine the energy transfer path;

[0022] Step 233: Determine the energy loss of each path in the energy flow graph based on the energy transfer path and the first processing data;

[0023] Step 234: Analyze the causes of energy loss 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.

[0024] Step 235: Determine the energy loss efficiency based on the energy loss of each adjacent row according to the initial energy loss;

[0025] Step 236: Based on the energy loss efficiency, extract the first loss link whose energy loss efficiency is lower than the preset minimum efficiency, and combine the test analysis boundary conditions to check the initial energy loss, thereby extracting the second loss link in the initial energy loss that exceeds the test analysis boundary conditions.

[0026] Step 237: Combine the first loss stage and the second loss stage to obtain the loss analysis results of the target new energy vehicle, and then combine the energy flow diagram to obtain the energy flow analysis results of the target new energy vehicle.

[0027] According to the present invention, energy consumption assessment is performed in conjunction with a target new energy vehicle, including:

[0028] Step 31: Based on the energy flow analysis results, determine the energy loss of the target new energy vehicle under each operating condition;

[0029] Step 32: Combine the driving status, driving behavior and external environmental factors of the target new energy vehicle under each working condition to comprehensively evaluate the energy loss and obtain the energy consumption evaluation result.

[0030] According to the present invention, the energy loss is comprehensively evaluated by combining the driving status, driving behavior and external environmental factors of the target new energy vehicle under each operating condition, resulting in an energy consumption evaluation result, including:

[0031] Step 321: Extract key features from driving status data, and at the same time, extract driving habit features from driving behavior data and extract factors affecting energy loss from external environment data;

[0032] Step 322: Use machine learning algorithms to analyze the correlation between key features, driving habit features and energy loss, and determine the first feature that has a significant impact on energy loss and the feature analysis results;

[0033] Step 323: Select a model based on the feature analysis results to obtain the predicted energy loss value;

[0034] Step 324: Determine the energy consumption assessment results of the target new energy vehicle based on the predicted energy loss value.

[0035] According to the present invention, a loss optimization strategy for a target new energy vehicle is formulated based on energy consumption assessment results, thereby optimizing the loss of the target new energy vehicle, including:

[0036] Step 41: Determine the loss optimization strategy for the target new energy vehicle based on the energy consumption assessment results;

[0037] Step 42: Determine the loss optimization scheme based on the real-time driving status data and real-time driving behavior data of the target new energy vehicle;

[0038] Step 43: Optimize the loss of the target new energy vehicle based on the loss optimization scheme.

[0039] The optimization of the loss optimization strategy based on real-time monitoring results according to the present invention specifically includes:

[0040] Step 01: Based on preset on-board sensors, monitor the first driving status and first driving energy consumption of the target new energy vehicle in real time;

[0041] Step 02: Compare the first driving state and the first driving energy consumption with the driving state and driving energy consumption of the target new energy vehicle before loss optimization.

[0042] 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 deemed qualified and no adjustment is needed to the loss optimization strategy.

[0043] Conversely, the corresponding sub-driving state and sub-driving energy consumption are extracted to determine the corresponding optimization points, thereby formulating corresponding optimization and adjustment schemes to optimize the loss optimization strategy.

[0044] Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention provides a method for assessing and optimizing energy loss in new energy vehicles. By analyzing the energy transfer process of new energy vehicles, it achieves accurate assessment and optimization of energy loss, which not only improves the energy utilization efficiency of new energy vehicles and increases driving range, but also enhances the power, economy and drivability of the 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. Attached Figure Description

[0045] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0046] Figure 1 This is a flowchart of a method for assessing and optimizing energy loss in new energy vehicles, provided by an embodiment of the present invention. Detailed Implementation

[0047] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0048] Example 1:

[0049] This invention provides a method for assessing and optimizing energy loss in new energy vehicles, such as... Figure 1 As shown, it includes:

[0050] Step 1: Based on preset on-board sensors, acquire real-time driving status data and driving behavior data of the target new energy vehicle at each driving moment to obtain the first driving data;

[0051] Step 2: Perform energy flow analysis on the first driving data based on the preset energy flow test and analysis method;

[0052] 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 an energy consumption assessment in conjunction with the target new energy vehicle;

[0053] Step 4: Based on the energy consumption assessment results, formulate a loss optimization strategy for the target new energy vehicle, thereby optimizing the loss of the target new energy vehicle.

[0054] In this embodiment, the pre-installed on-board sensors are devices that are pre-installed on the new energy vehicle to monitor and record the vehicle's driving status and behavior data in real time. These sensors may include vehicle speed sensors, acceleration sensors, steering angle sensors, brake pressure sensors, etc., which can capture various dynamic information of the vehicle at different points in time.

[0055] In this embodiment, driving status 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, and vehicle position.

[0056] In this embodiment, driving behavior data refers to the behavioral characteristics data exhibited by the driver during driving, such as the depth of the accelerator pedal, the force of the brake pedal, and the timing of gear shifts.

[0057] In this embodiment, the first driving data is a collection of driving status data and driving behavior data acquired in real time by preset on-board sensors, which is the basis for analysis and evaluation.

[0058] 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 energy flow and conversion during vehicle operation.

[0059] In this embodiment, energy flow analysis is based on a preset energy flow test and analysis method to conduct in-depth analysis of the first driving data in order to understand the flow, conversion and loss of energy during vehicle driving.

[0060] In this embodiment, different operating conditions refer to the driving status of new energy vehicles under different road conditions, climate conditions, driving modes, and other environments. Different operating conditions directly affect the energy consumption of the vehicle.

[0061] In this embodiment, the energy loss situation is the energy loss of the vehicle under different operating conditions obtained through energy flow analysis, including electrical energy loss, thermal energy loss, mechanical energy loss, etc.

[0062] In this embodiment, the energy consumption assessment is based on the energy loss situation to evaluate the overall energy consumption level of the target new energy vehicle.

[0063] In this embodiment, the loss optimization strategy is an optimization measure or suggestion formulated based on energy consumption assessment results to address the energy loss problem of the target new energy vehicle during operation. 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 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 driving range, but also enhances 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.

[0066] Example 2:

[0067] Based on Example 1, the first driving data is obtained, including:

[0068] Step 11: Based on preset on-board sensors, acquire real-time driving status data of the target new energy vehicle at each driving moment;

[0069] Step 12: Based on preset on-board sensors, acquire real-time vehicle behavior data and driver behavior data of the target new energy vehicle at each driving moment, and combine them to obtain driving behavior data;

[0070] Step 13: Combine the driving status data and driving behavior data at each driving moment to obtain the first driving data of the target new energy vehicle at each driving moment.

[0071] In this embodiment, the pre-installed on-board sensors are devices that are pre-installed on the new energy vehicle to monitor various parameters and states of the vehicle in real time. These may include vehicle speed sensors, acceleration sensors, steering angle sensors, battery status sensors, etc., and are capable of capturing real-time data of the vehicle at different driving times.

[0072] In this embodiment, driving status data refers to data related to the vehicle's own 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, vehicle speed data represents the vehicle's current speed, and acceleration data represents how quickly the vehicle's speed changes.

[0073] In this embodiment, vehicle behavior data refers to the behavioral characteristics exhibited by the vehicle during driving. This data is typically related to the vehicle's mechanical and electronic control systems. For example, the vehicle's gear shifting, braking, and steering behaviors can all be monitored and recorded using corresponding sensors.

[0074] In this embodiment, driver behavior data refers to the behavioral characteristics exhibited by 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.

[0075] In this embodiment, the driving behavior data is a comprehensive dataset obtained by integrating vehicle behavior data and driver behavior data. This data comprehensively reflects the vehicle's behavioral characteristics during driving, including both the vehicle's mechanical and electronic behavior, as well as the driver's driving behavior.

[0076] In this embodiment, the first driving data is a dataset that combines driving status data and driving behavior data at each driving moment. This dataset contains comprehensive information about the vehicle during driving and serves as the foundation for subsequent energy consumption analysis, fault diagnosis, driving behavior evaluation, and other tasks.

[0077] The beneficial effects of the above technical solution are: by comprehensively analyzing the vehicle behavior data and driver behavior data of the target vehicle, the energy transfer process of the new energy vehicle can be determined, the energy loss can be accurately assessed and optimized, and the energy utilization efficiency of the new energy vehicle can be improved.

[0078] Example 3:

[0079] Based on Example 2, energy flow analysis is performed on the first driving data using a preset energy flow test and analysis method, including:

[0080] Step 21: Based on 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;

[0081] Step 22: Clean and transform the acquired first driving data to obtain first transformed driving data, and segment the first transformed driving data to obtain first processed data;

[0082] Step 23: Perform energy flow analysis on the processed first data based on the preset energy flow test and analysis method to obtain the energy flow analysis results.

[0083] 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 (such as pure electric or plug-in hybrid), battery capacity, motor performance, vehicle weight, aerodynamic design, etc. These characteristics affect the energy consumption and performance of the new energy vehicle.

[0084] In this embodiment, the test and analysis method database is a database that stores various 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.

[0085] In this embodiment, the preset energy flow test analysis method is selected from the test analysis method database and is suitable for the characteristics of the target new energy vehicle. This method is used to analyze the energy flow and conversion of the new energy vehicle during driving, thereby evaluating its energy efficiency performance.

[0086] In this embodiment, data cleaning is a process of preprocessing the first driving data, which aims to identify and correct errors, anomalies or missing values ​​in the data.

[0087] In this embodiment, data conversion involves transforming the initial driving data into a format suitable for energy flow analysis. This includes data format conversion, data unit standardization, and data normalization or standardization.

[0088] In this embodiment, the first converted driving data is driving data that has been cleaned and converted, and this data is ready for subsequent energy flow analysis.

[0089] In this embodiment, data segmentation involves dividing the first converted driving data into multiple smaller datasets based on time, distance, or other criteria to facilitate more detailed analysis. Data segmentation helps identify energy flow characteristics under different driving stages or operating conditions.

[0090] In this embodiment, the first processed data is driving data after data segmentation, which is ready for energy flow analysis.

[0091] In this embodiment, energy flow analysis is a process of in-depth analysis of the processed first-stage data based on a preset energy flow test and analysis method. Energy flow analysis aims to reveal the flow, conversion, and loss of energy in new energy vehicles during operation, thereby evaluating their energy efficiency performance.

[0092] In this embodiment, the energy flow analysis results are the output of the energy flow analysis, which typically include indicators such as energy flow graphs, 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.

[0093] 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, thereby improving the energy utilization efficiency of new energy vehicles.

[0094] Example 4:

[0095] Based on Example 3, the energy flow analysis results are obtained, including:

[0096] Step 231: Determine the test analysis boundary of the preset energy flow test analysis method based on the energy flow analysis target of the target new energy vehicle;

[0097] Step 232: Combine the first processed data with the preset energy flow test and analysis method to draw an energy flow diagram and determine the energy transfer path;

[0098] Step 233: Determine the energy loss of each path in the energy flow graph based on the energy transfer path and the first processing data;

[0099] Step 234: Analyze the causes of energy loss 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.

[0100] Step 235: Determine the energy loss efficiency based on the energy loss of each adjacent row according to the initial energy loss;

[0101] Step 236: Based on the energy loss efficiency, extract the first loss link whose energy loss efficiency is lower than the preset minimum efficiency, and combine the test analysis boundary conditions to check the initial energy loss, thereby extracting the second loss link in the initial energy loss that exceeds the test analysis boundary conditions.

[0102] Step 237: Combine the first loss stage and the second loss stage to obtain the loss analysis results of the target new energy vehicle, and then combine the energy flow diagram to obtain the energy flow analysis results of the target new energy vehicle.

[0103] In this embodiment, energy flow analysis is a process of in-depth analysis of the processed first-stage data based on a preset energy flow test and analysis method. Energy flow analysis aims to reveal the flow, conversion, and loss of energy in new energy vehicles during operation, thereby evaluating their energy efficiency performance.

[0104] In this embodiment, the energy flow analysis results are the output of the energy flow analysis, which typically include indicators such as energy flow graphs, 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.

[0105] 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, thereby improving the energy utilization efficiency of new energy vehicles.

[0106] Example 5:

[0107] Based on Example 3, an energy consumption assessment is conducted in conjunction with the target new energy vehicle, including:

[0108] Step 31: Based on the energy flow analysis results, determine the energy loss of the target new energy vehicle under each operating condition;

[0109] Step 32: Combine the driving status, driving behavior and external environmental factors of the target new energy vehicle under each working condition to comprehensively evaluate the energy loss and obtain the energy consumption evaluation result.

[0110] In this embodiment, energy flow analysis is based on a preset energy flow test and analysis method to conduct in-depth analysis of the first driving data in order to understand the flow, conversion and loss of energy during vehicle driving.

[0111] In this embodiment, different operating conditions refer to the driving status of new energy vehicles under different road conditions, climate conditions, driving modes, and other environments. Different operating conditions directly affect the energy consumption of the vehicle.

[0112] In this embodiment, the energy loss situation is the energy loss of the vehicle under different operating conditions obtained through energy flow analysis, including electrical energy loss, thermal energy loss, mechanical energy loss, etc.

[0113] In this embodiment, the energy consumption assessment is based on the energy loss situation to evaluate the overall energy consumption level of the target new energy vehicle.

[0114] Example 6:

[0115] Based on Example 5, the energy consumption assessment results are obtained, including:

[0116] Step 321: Extract key features from driving status data, and at the same time, extract driving habit features from driving behavior data and extract factors affecting energy loss from external environment data;

[0117] Step 322: Use machine learning algorithms to analyze the correlation between key features, driving habit features and energy loss, and determine the first feature that has a significant impact on energy loss and the feature analysis results;

[0118] Step 323: Select a model based on the feature analysis results to obtain the predicted energy loss value;

[0119] Step 324: Determine the energy consumption assessment results of the target new energy vehicle based on the predicted energy loss value.

[0120] The beneficial effects of the above technical solution are: by accurately assessing and optimizing energy loss, it not only improves the energy utilization efficiency of new energy vehicles and increases driving range, but also enhances the overall vehicle's power, economy, and drivability.

[0121] Example 7:

[0122] Based on Example 6, loss optimization is performed on the target new energy vehicle, including:

[0123] Step 41: Determine the loss optimization strategy for the target new energy vehicle based on the energy consumption assessment results;

[0124] Step 42: Determine the loss optimization scheme based on the real-time driving status data and real-time driving behavior data of the target new energy vehicle;

[0125] Step 43: Optimize the loss of the target new energy vehicle based on the loss optimization scheme.

[0126] In this embodiment, the loss optimization strategy is an optimization measure or suggestion formulated based on energy consumption assessment results to address the energy loss problem of the target new energy vehicle during operation. These strategies may involve improving driving habits, optimizing vehicle design, and improving energy conversion efficiency.

[0127] 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.

[0128] The beneficial effects of the above technical solution are: by optimizing the loss 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 driving performance of the whole vehicle can be improved.

[0129] Example 8:

[0130] Based on Example 7, the loss optimization strategy is further optimized based on real-time monitoring results, specifically including:

[0131] Step 01: Based on preset on-board sensors, monitor the first driving status and first driving energy consumption of the target new energy vehicle in real time;

[0132] Step 02: Compare the first driving state and the first driving energy consumption with the driving state and driving energy consumption of the target new energy vehicle before loss optimization.

[0133] 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 deemed qualified and no adjustment is needed to the loss optimization strategy.

[0134] Conversely, the corresponding sub-driving state and sub-driving energy consumption are extracted to determine the corresponding optimization points, thereby formulating corresponding optimization and adjustment schemes to optimize the loss optimization strategy.

[0135] 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 period of driving, including vehicle speed, acceleration, steering angle, battery charge, etc. These state data reflect the actual operating conditions of the vehicle.

[0136] 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 period of driving, usually measured in terms of electricity or fuel consumption. This data is used to evaluate the vehicle's energy efficiency performance.

[0137] In this embodiment, driving state and driving energy consumption refer to the state and energy consumption of the vehicle during driving, including vehicle speed, acceleration, battery power consumption, etc. This corresponds to "first driving state" and "first driving energy consumption," but here it refers to the state before loss optimization.

[0138] 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 aims to evaluate whether the loss optimization strategy is effective.

[0139] In this embodiment, the loss optimization strategy refers to optimization measures or suggestions formulated to address the energy loss problem that occurs in new energy vehicles during operation. These strategies aim to improve the energy efficiency of the vehicle.

[0140] In this embodiment, "qualified" means that if both the "first driving state" and the "first driving energy consumption" are better than the state before loss optimization, then the loss optimization strategy is considered qualified, that is, these strategies effectively improve the energy efficiency of the vehicle.

[0141] In this embodiment, adjustment is required if the loss optimization strategy is unsatisfactory, i.e., the "first driving state" and / or "first driving energy consumption" are not better than the state before loss optimization. This typically involves modifying certain parameters or measures in the strategy to better adapt to the actual operating conditions of the vehicle.

[0142] In this embodiment, the sub-driving state and sub-driving energy consumption are specific driving state and energy consumption data extracted from the "first driving state" and "first driving energy consumption" when the loss optimization strategy is unsatisfactory, and are used to determine the optimization point. This sub-data helps to more accurately identify the problem and formulate corresponding optimization and adjustment schemes.

[0143] In this embodiment, the optimization points are derived from the analysis of "sub-driving states and sub-driving energy consumption," and are specific points or aspects that need to be optimized. These optimization points may be a certain system, a certain component, or a certain driving behavior of the vehicle.

[0144] In this embodiment, the optimization and adjustment scheme refers to specific optimization measures or suggestions formulated for a determined optimization point. These schemes aim to further improve the loss optimization strategy and enhance the vehicle's energy efficiency performance.

[0145] The beneficial effects of the above technical solution are: by continuously optimizing the loss optimization strategy through real-time monitoring, comparison and analysis, the energy efficiency level of new energy vehicles can be improved.

[0146] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for assessing and optimizing energy loss in new energy vehicles, characterized in that, include: Step 1: Based on preset on-board sensors, acquire real-time driving status data and driving behavior data of the target new energy vehicle at each driving moment to obtain the first driving data; Step 2: Perform energy flow analysis on the first driving data based on the 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 an energy consumption assessment in conjunction with the target new energy vehicle; Step 4: Based on the energy consumption assessment results, formulate a loss optimization strategy for the target new energy vehicle, thereby optimizing the loss of the target new energy vehicle; Energy flow analysis is performed on the first driving data based on a preset energy flow test and analysis method, including: Step 21: Based on 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: Clean and transform the acquired first driving data to obtain first transformed driving data, and segment the first transformed driving data to obtain first processed data; Step 23: Perform energy flow analysis on the processed first data based on the preset energy flow test and analysis method to obtain the energy flow analysis results; Based on a pre-defined energy flow test and analysis method, energy flow analysis is performed on the first processed data to obtain energy flow analysis results, including: Step 231: Determine the test analysis boundary of the preset energy flow test analysis method based on the energy flow analysis target of the target new energy vehicle; Step 232: Combine the first processed data with the preset energy flow test and analysis method to draw an energy flow diagram and determine the energy transfer path; Step 233: Determine the energy loss of each path in the energy flow graph based on the energy transfer path and the first processing data; Step 234: Analyze the causes of energy loss 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 according to the initial energy loss; Step 236: Based on the energy loss efficiency, extract the first loss link whose energy loss efficiency is lower than the preset minimum efficiency, and combine the test analysis boundary conditions to check the initial energy loss, thereby extracting the second loss link in the initial energy loss that exceeds the test analysis boundary conditions. Step 237: Combine the first loss stage and the second loss stage to obtain the loss analysis results of the target new energy vehicle, and then combine the energy flow diagram to obtain the energy flow analysis results of the target new energy vehicle.

2. The method for assessing and optimizing energy loss in new energy vehicles according to claim 1, characterized in that, Based on pre-set onboard sensors, real-time data on the driving status and behavior of the target new energy vehicle at each driving moment is acquired to obtain the first driving data, including: Step 11: Based on preset on-board sensors, acquire real-time driving status data of the target new energy vehicle at each driving moment; Step 12: Based on preset on-board sensors, acquire real-time vehicle behavior data and driver behavior data of the target new energy vehicle at each driving moment, and combine them to obtain driving behavior data; Step 13: Combine the driving status data and driving behavior data at each driving moment to obtain the first driving data of the target new energy vehicle at each driving moment.

3. The method for assessing and optimizing energy loss in new energy vehicles according to claim 2, characterized in that, Based on the energy flow analysis results, the energy loss of the target new energy vehicle under different operating conditions is determined, and an energy consumption assessment is then conducted in conjunction 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: Combine the driving status, driving behavior and external environmental factors of the target new energy vehicle under each working condition to comprehensively evaluate the energy loss and obtain the energy consumption evaluation result.

4. The method for assessing and optimizing energy loss in new energy vehicles according to claim 3, characterized in that, A comprehensive assessment of energy loss is conducted by combining the target new energy vehicle's driving status, driving behavior, and external environmental factors under each operating condition, resulting in an energy consumption assessment result, including: Step 321: Extract key features from driving status data, and at the same time, extract driving habit features from driving behavior data and extract factors affecting energy loss from external environment data; Step 322: Use machine learning algorithms to analyze the correlation between key features, driving habit features and energy loss, and determine the first feature that has a significant impact on energy loss and the feature analysis results; Step 323: Select a model based on the feature analysis results to obtain the predicted energy loss value; Step 324: Determine the energy consumption assessment results of the target new energy vehicle based on the predicted energy loss value.

5. The method for assessing and optimizing energy loss in new energy vehicles according to claim 4, characterized in that, Based on the energy consumption assessment results, a loss optimization strategy is formulated for the target new energy vehicle, thereby optimizing the loss of the target new energy vehicle, including: Step 41: Determine the loss optimization strategy for the target new energy vehicle based on the energy consumption assessment results; Step 42: Determine the loss optimization scheme 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 scheme.

6. The method for assessing and optimizing energy loss in new energy vehicles according to claim 5, characterized in that, After optimizing the losses of the target new energy vehicle, 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: Based on preset on-board sensors, monitor the first driving status and first driving energy consumption of the target new energy vehicle in real time; Step 02: Compare the first driving state and the first driving energy consumption with 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 deemed qualified and no adjustment is needed to the loss optimization strategy. Conversely, the corresponding sub-driving state and sub-driving energy consumption are extracted to determine the corresponding optimization points, thereby formulating corresponding optimization and adjustment schemes to optimize the loss optimization strategy.

Images