Driving energy consumption estimation system and method based on working condition characteristic parameters
Through the driving energy consumption estimation system based on the operating condition characteristic parameters, the benchmark operating conditions are dynamically matched and the energy consumption mapping table is corrected in combination with the actual vehicle sensor data. This solves the accuracy and cost issues of energy consumption estimation in traditional methods and realizes efficient and low-cost energy consumption management.
Patent Information
- Application Number
- CN202511063951.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-07-31
- Publication Date
- 2025-10-10
AI Technical Summary
Traditional driving energy consumption estimation methods cannot accurately capture the characteristic parameters of complex driving conditions and ignore the differences between high-speed braking and low-speed braking, resulting in large deviations in the results. They also rely on actual vehicle testing and high-precision sensors, which are costly, lack a flexible matching mechanism, and cannot adapt to diverse user conditions.
The driving energy consumption estimation system based on operating condition characteristic parameters calculates the user's operating condition characteristic parameters, dynamically matches the benchmark operating conditions, and combines actual vehicle sensor data and variable learning rate to correct the energy consumption mapping table to achieve accurate energy consumption estimation.
It improves the accuracy and response speed of driving energy consumption estimation, reduces development and maintenance costs, supports energy consumption evaluation and path planning in actual road scenarios, and enhances user experience and product competitiveness.
Smart Images
Figure CN120756502A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of energy conservation for passenger vehicles, and in particular to a system and method for estimating driving energy consumption based on operating condition characteristic parameters. Background Art
[0002] Currently, the industry urgently needs intelligent energy management for passenger vehicles. Intelligent energy management can be used to simulate actual road energy consumption during the vehicle development phase, or to update range and route planning in real time based on estimated remaining mileage, thereby improving driving convenience and energy efficiency. However, traditional methods for estimating driving energy consumption have limitations: they cannot accurately capture the characteristic parameters of complex driving conditions and ignore the differences between high-speed and low-speed braking, resulting in large deviations in the estimated driving energy consumption results; they rely on extensive real-vehicle testing or high-precision sensors, which is time-consuming and costly; and fixed baseline conditions make it difficult to adapt to diverse user conditions. They lack a flexible matching mechanism and cannot automatically optimize based on the characteristic parameters of specific user conditions, resulting in driving energy consumption estimates that are not suitable for actual road scenarios. Summary of the Invention
[0003] The purpose of the present invention is to provide a driving energy consumption estimation system and method based on operating condition characteristic parameters, which improves the accuracy and efficiency of driving energy consumption estimation.
[0004] To achieve this purpose, the present invention provides a driving energy consumption estimation system based on operating condition characteristic parameters, which includes: The driving energy consumption estimation module is used to calculate the user operating condition characteristic parameters based on the vehicle speed and time curve, select a benchmark operating condition that matches the user operating condition characteristic parameters from the benchmark operating condition library, obtain the benchmark operating condition characteristic parameters and the benchmark energy consumption under the benchmark operating condition based on the selected benchmark operating condition, and calculate the estimated driving energy consumption based on the user operating condition characteristic parameters and the benchmark operating condition characteristic parameters and the benchmark energy consumption under the benchmark operating condition.
[0005] Preferably, the driving energy consumption correction module is used to calculate the actual driving energy consumption through the actual vehicle sensor data, and use a variable learning rate combined with the actual driving energy consumption and the estimated driving energy consumption to correct the original map value to obtain a corrected and updated map value, and the corrected and updated map value is used for subsequent estimated driving energy consumption optimization.
[0006] Preferably, the user operating condition characteristic parameters include speed characteristic parameters , high-speed braking characteristic parameters and low-speed braking characteristic parameters .
[0007] Preferably, the specific process of calculating the user operating condition characteristic parameters based on the vehicle speed and time curve is: Divide the vehicle's travel time into n equal time periods and calculate the braking energy based on the vehicle speed and time curve and the average speed of the vehicle at time i~i+1 , the calculation formula is as follows: Among them, t represents the driving time of the vehicle, v represents the driving speed of the vehicle, represents the speed of the vehicle at time i, represents the vehicle drag coefficient related to rolling resistance, represents the vehicle drag coefficient related to transmission resistance, represents the vehicle drag coefficient related to wind resistance, Indicates the braking energy, represents the vehicle speed at time i+1, represents the average speed of the vehicle at time i~i+1; According to the calculated braking energy and the average speed of the vehicle at time i~i+1 , the velocity characteristic parameters can be calculated , high-speed braking characteristic parameters and low-speed braking characteristic parameters ; The speed characteristic parameters The calculation formula is as follows: Where n represents the number of time periods into which the vehicle's travel time is divided. Indicates a time interval; High-speed braking characteristic parameters The calculation formula is as follows: , >15km / h Low-speed braking characteristic parameters The calculation formula is as follows: , ≤15km / h.
[0008] Preferably, the specific process of selecting a reference operating condition that matches the user's operating condition characteristic parameters from the reference operating condition library is as follows: The benchmark operating condition library includes NEDC, CLTC and WLTC. For each user operating condition characteristic parameter, the characteristic parameters of the benchmark operating conditions NEDC, CLTC and WLTC are compared with the user operating condition characteristic parameters. The benchmark operating condition corresponding to the characteristic parameter that minimizes the deviation from the user operating condition characteristic parameter is used as the benchmark operating condition that matches the user operating condition characteristic parameter.
[0009] Preferably, the calculation formula for calculating the estimated driving energy consumption ECR based on the user working condition characteristic parameters, the reference working condition characteristic parameters, and the reference energy consumption under the reference working condition is as follows: in, Representation and speed characteristic parameters Matching benchmark operating condition characteristic parameters, Representation and high-speed braking characteristic parameters Matching benchmark operating condition characteristic parameters, Indicates low-speed braking characteristic parameters Matching benchmark operating condition characteristic parameters, Representation and speed characteristic parameters The benchmark energy consumption corresponding to the matching benchmark working condition, Representation and high-speed braking characteristic parameters The benchmark energy consumption corresponding to the matching benchmark working condition, Indicates low-speed braking characteristic parameters The benchmark energy consumption corresponding to the matching benchmark operating conditions, and ECR represents the estimated driving energy consumption.
[0010] Preferably, the specific process of calculating the actual driving energy consumption through the actual vehicle sensor data is as follows: The vehicle battery output energy is calculated based on the vehicle battery DC current and the vehicle battery DC voltage; the vehicle compressor and high-voltage PTC heater energy consumption is calculated based on the vehicle compressor power and the vehicle high-voltage PTC heater power; the vehicle low-voltage accessory energy consumption is calculated based on the vehicle low-voltage accessory current and the vehicle low-voltage accessory voltage; and the actual driving energy consumption of the vehicle is calculated based on the vehicle battery output energy, the vehicle compressor and high-voltage PTC heater energy consumption, and the vehicle low-voltage accessory energy consumption; The calculation formula is as follows: in, Indicates the DC current of the vehicle battery, Indicates the vehicle battery DC voltage, Indicates the vehicle battery output energy, Indicates the vehicle compressor power, Indicates the vehicle's high-voltage PTC heater power, Indicates the energy consumed by the vehicle compressor and high-voltage PTC heater. Indicates the vehicle's low-voltage accessory current. Indicates the vehicle's low-voltage accessory voltage. Indicates the energy consumed by the vehicle's low-voltage accessories. Indicates actual driving energy consumption.
[0011] Preferably, a variable learning rate is used The specific process of correcting the original map value by combining the actual driving energy consumption and the estimated driving energy consumption to obtain the corrected and updated map value is as follows: in, Indicates the updated map value. Represents the original map value, Indicates actual driving energy consumption, ECR indicates estimated driving energy consumption, represents a variable learning rate.
[0012] A method for estimating driving energy consumption based on operating condition characteristic parameters comprises the following steps: The user operating condition characteristic parameters are calculated based on the vehicle speed and time curve, a benchmark operating condition that matches the user operating condition characteristic parameters is selected from the benchmark operating condition library, the benchmark operating condition characteristic parameters and the benchmark energy consumption under the benchmark operating condition are obtained based on the selected benchmark operating condition, and the estimated driving energy consumption is calculated based on the user operating condition characteristic parameters and the benchmark operating condition characteristic parameters and the benchmark energy consumption under the benchmark operating condition.
[0013] A computer program product comprises a computer program, wherein when the computer program is executed by a processor, the steps of the above method are implemented.
[0014] Beneficial effects of the present invention: The present invention can improve the accuracy and response speed of driving energy consumption estimation through dynamic matching of user operating condition characteristic parameters with benchmark operating conditions, quickly generate estimated driving energy consumption suitable for energy consumption evaluation, remaining mileage estimation and path planning in actual road scenarios, thereby enhancing product competitiveness and optimizing user experience; the present invention utilizes a variable learning rate in combination with actual vehicle sensor data to perform real-time optimization of driving energy consumption estimation, thereby improving the system's adaptability and long-term accuracy; the present invention relies on software and does not require additional hardware investment, with low technical cost, significantly reducing development and maintenance costs, while shortening the energy consumption estimation cycle, supporting vehicle energy management optimization, and enabling efficient and economical operation of passenger cars in the field of energy conservation. BRIEF DESCRIPTION OF THE DRAWINGS
[0015] Figure 1 It is a structural schematic diagram of the present invention; Figure 2 Flowchart of the present invention. DETAILED DESCRIPTION
[0016] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments: Example 1 A driving energy consumption estimation system based on working condition characteristic parameters, such as Figure 1 As shown, it includes: The driving energy consumption estimation module is used to calculate user operating condition characteristic parameters based on the vehicle speed and time curve, select a benchmark operating condition that matches the user operating condition characteristic parameters from a benchmark operating condition library (in this embodiment, the benchmark operating condition library includes three benchmark operating conditions: NEDC, CLTC, and WLTC), and obtain benchmark operating condition characteristic parameters (each benchmark operating condition has fixed benchmark operating condition characteristic parameters) and benchmark energy consumption under the benchmark operating condition (the benchmark energy consumption of NEDC, CLTC, and WLTC is derived from high-precision laboratory testing) based on the selected benchmark operating condition. Estimated driving energy consumption is calculated based on the user operating condition characteristic parameters, the benchmark operating condition characteristic parameters, and the benchmark energy consumption under the benchmark operating condition. This design, through dynamic matching of benchmark operating conditions in the benchmark operating condition library, can reduce the computational load, improve response speed, and enhance the accuracy of driving energy consumption estimation. It can also estimate driving energy consumption in real time. By dynamically updating the estimated driving energy consumption value, it can estimate the driving energy consumption for the remaining mileage, thereby providing the user with energy-saving routes and allowing the user to understand the energy consumption of the remaining route in real time. In the aforementioned driving energy consumption estimation module, the benchmark operating condition library is the core reference dataset in the vehicle energy consumption estimation and testing system. It consists of standardized driving cycles developed in different regions around the world and is used to uniformly evaluate a vehicle's energy consumption (for fuel vehicles) or range (for electric vehicles) in a laboratory environment. The NEDC stands for New European Driving Cycle (the benchmark operating condition NEDC has the limitation of not taking into account practical factors such as air conditioning and congestion, and the vehicle load during the test is fixed (driver only), resulting in an inflated range). The CLTC stands for China Light-duty Vehicle Test Cycle (the benchmark operating condition CLTC lacks ultra-high-speed sections (>130km / h) and has low applicability in high-speed scenarios). The WLTC stands for Worldwide Harmonized Light Vehicles Test Procedure (the benchmark operating condition WLTC can adapt to different vehicle models to avoid test bias).
[0017] In the above technical solution, the driving energy consumption correction module is used to calculate the actual driving energy consumption through the actual vehicle sensor data, and adopts a variable learning rate to combine the actual driving energy consumption and the estimated driving energy consumption to correct the original map value (the map value refers to the mapping relationship table of key control parameters (such as driving torque MAP, braking torque MAP) in the vehicle energy consumption management system. Its essence is to map the vehicle state (such as vehicle speed, pedal opening) to the optimal control quantity (such as torque output) through multi-dimensional table lookup) to obtain the corrected and updated map value. The corrected and updated map value is used for the subsequent estimated driving energy consumption optimization (the corrected and updated map value actively suppresses the peak torque when the vehicle makes an acceleration request). , avoiding the motor's inefficient area, which can effectively reduce energy consumption; the corrected and updated map value will trigger the linkage update of the vehicle control parameters, which will reduce the torque rise slope, avoid sudden acceleration causing instantaneous large current loss in the battery, and inject negative torque in long downhill conditions to improve the energy recovery rate. It can improve the adaptability in different scenarios and thus optimize the driving energy consumption); the above design dynamically adapts to vehicle status changes by correcting the updated map value, and compensates for slow-changing error sources such as vehicle aging, environmental changes, and driving habits in real time into a low-dimensional map table, thereby minimizing the driving energy consumption estimation error throughout the life cycle with zero hardware cost and microsecond computing overhead.
[0018] In the above technical solution, the user operating condition characteristic parameters include speed characteristic parameters , high-speed braking characteristic parameters and low-speed braking characteristic parameters The above design covers the main physical causes of vehicle driving energy consumption through speed characteristics, high-speed braking characteristics and low-speed braking characteristics, which not only ensures the estimation accuracy, but also requires only three-dimensional features to calculate the driving energy consumption, compressing computing power and storage requirements. It also has the engineering advantages of being cross-model, cross-platform, explainable and scalable.
[0019] In the above technical solution, the specific process of calculating the user operating condition characteristic parameters based on the vehicle speed and time curve is as follows: Divide the vehicle's travel time into n equal time periods and calculate the braking energy based on the vehicle speed and time curve and the average speed of the vehicle at time i~i+1 , the calculation formula is as follows: Among them, t represents the driving time of the vehicle, v represents the driving speed of the vehicle, represents the speed of the vehicle at time i, represents the vehicle drag coefficient related to rolling resistance, represents the vehicle drag coefficient related to transmission resistance, represents the vehicle drag coefficient related to wind resistance, Indicates the braking energy, represents the vehicle speed at time i+1, represents the average speed of the vehicle at time i~i+1; According to the calculated braking energy and the average speed of the vehicle at time i~i+1 , the velocity characteristic parameters can be calculated , high-speed braking characteristic parameters and low-speed braking characteristic parameters ; The speed characteristic parameters The calculation formula is as follows: Where n represents the number of time periods into which the vehicle's travel time is divided. Indicates a time interval; High-speed braking characteristic parameters The calculation formula is as follows: , >15km / h in, >15km / h means All time periods above 15km / h Substitute all high-speed braking characteristic parameters Calculate by the calculation formula; Low-speed braking characteristic parameters The calculation formula is as follows: , ≤15km / h; in, ≤15km / h means All time periods corresponding to less than or equal to 15km / h Substitute the low-speed braking characteristic parameters Calculate by the calculation formula; The above design calculates the braking energy by dividing the vehicle's driving time into time periods and average speed , which can avoid the problem of transient working conditions (such as sudden acceleration / braking) being ignored and improve the accuracy of driving energy consumption estimation; by The cubic weighted design strengthens the weight of the high-speed section, can automatically adapt to congestion and high-speed cruising scenarios, and greatly improves the generalization ability; all characteristic parameters can be calculated by collecting only the vehicle speed signal, without the need for a high-precision torque sensor, reducing hardware dependence and cost; through the characteristic parameterized expression of drive energy consumption and the simplified design guided by engineering practice (such as segmented calculation and speed threshold separation), it can achieve efficient and low-cost drive energy consumption estimation while ensuring accuracy.
[0020] In the above technical solution, the specific process of selecting a reference working condition that matches the characteristic parameters of the user working condition from the reference working condition library is as follows: The benchmark operating condition library includes NEDC, CLTC, and WLTC. For each user operating condition characteristic parameter, the NEDC, CLTC, and WLTC benchmark condition characteristics are compared with the user operating condition characteristic parameter. The benchmark operating condition corresponding to the characteristic parameter with the smallest deviation compared to the user operating condition characteristic parameter is selected as the benchmark operating condition that matches the user operating condition characteristic parameter. (Using Euclidean distance as a deviation quantification metric, the multi-dimensional distance between the user operating condition characteristic parameter and the characteristic parameters of each benchmark operating condition is calculated, and the benchmark operating condition with the smallest distance is the matching result.) By independently matching the optimal benchmark operating condition (NEDC / CLTC / WLTC) for different user operating condition characteristic parameters, this design improves the adaptability of the estimation results to diverse driving behaviors (such as high-speed cruising and urban congestion) and avoids estimation errors caused by ignoring local characteristic differences when using a single benchmark operating condition (such as full-range WLTC). By comparing each user operating condition characteristic parameter with the characteristic parameters of the three benchmark operating conditions and selecting the benchmark operating condition with the characteristic parameter that minimizes the deviation, complex calculations are avoided, significantly improving response speed, and thus achieving high-precision, low-latency dynamic estimation of driving energy consumption.
[0021] In the above technical solution, the calculation formula for calculating the estimated driving energy consumption ECR based on the user working condition characteristic parameters, the reference working condition characteristic parameters, and the reference energy consumption under the reference working condition is as follows: in, Representation and speed characteristic parameters Matching benchmark operating condition characteristic parameters, Representation and high-speed braking characteristic parameters Matching benchmark operating condition characteristic parameters, Indicates low-speed braking characteristic parameters Matching benchmark operating condition characteristic parameters, Representation and speed characteristic parameters The benchmark energy consumption corresponding to the matching benchmark working condition, Representation and high-speed braking characteristic parameters The reference energy consumption corresponding to the matched reference working condition, indicates the low-speed braking characteristic parameter The reference energy consumption corresponding to the matched reference working condition, ECR represents the estimated driving energy consumption; the above design quantifies the influence of three types of characteristic parameters, i.e., speed characteristics, high-speed braking characteristics, and low-speed braking characteristics, estimates the driving energy consumption, can reduce the error rate, improve the response speed, and meet the real-time demand of the vehicle-mounted system; by separately calculating the high-speed braking characteristic parameter and the low-speed braking characteristic parameter, the high-speed braking data can be prevented from being polluted by the low-speed braking data recovery efficiency distortion; the navigation system recommends the lowest driving energy consumption route by calling the ECR values of different road sections calculated by the formula.
[0022] In the above technical solution, the specific process of calculating the actual driving energy consumption through the real vehicle sensor data is as follows: The vehicle battery output energy is calculated according to the vehicle battery direct current and the vehicle battery direct voltage, the vehicle compressor and high-voltage PTC heater consumption energy is calculated according to the vehicle compressor power (the vehicle compressor refers to the vehicle air conditioner compressor (the vehicle air conditioner compressor is used for refrigeration)) and the vehicle high-voltage PTC heater power, the vehicle low-voltage accessory consumption energy is calculated according to the vehicle low-voltage accessory current and the vehicle low-voltage accessory voltage, and the actual driving energy consumption of the vehicle is calculated according to the vehicle battery output energy, the vehicle compressor and high-voltage PTC heater consumption energy (the vehicle compressor and high-voltage PTC heater consumption energy is the vehicle high-voltage accessory consumption energy) and the vehicle low-voltage accessory consumption energy; The calculation formula is as follows: Among them, indicates the vehicle battery direct current, indicates the vehicle battery direct voltage, indicates the vehicle battery output energy, indicates the vehicle compressor power, indicates the vehicle high-voltage PTC heater power, indicates the vehicle compressor and high-voltage PTC heater consumption energy (i.e., vehicle high-voltage accessory consumption energy), indicates the vehicle low-voltage accessory current, indicates the vehicle low-voltage accessory voltage, indicates the vehicle low-voltage accessory consumption energy, Represents the actual driving energy consumption; the above design can resolve the deviation caused by mixed calculation and reduce the calculation error of the vehicle's actual driving energy consumption by decomposing the vehicle's actual driving energy consumption, that is, the total output energy of the vehicle battery, into vehicle battery output energy, vehicle high-voltage accessory consumption energy and vehicle low-voltage accessory consumption energy; by independently measuring the vehicle compressor and vehicle high-voltage PTC heater, it can automatically adapt to high and low temperature environments; by calculating the vehicle's actual driving energy consumption, it provides data support for the subsequent continuous optimization of the estimated driving energy consumption.
[0023] In the above technical solution, variable learning rate is adopted The specific process of correcting the original map value by combining the actual driving energy consumption and the estimated driving energy consumption to obtain the corrected and updated map value is as follows: in, Indicates the updated map value. Represents the original map value, Indicates actual driving energy consumption, ECR indicates estimated driving energy consumption, Represents a variable learning rate; the above design is achieved by varying the learning rate Real-time adjustment of the correction amplitude solves the problem of inaccurate estimated driving energy consumption caused by battery aging or sudden changes in road conditions, and can stabilize long-term estimation errors; the corrected and updated map value can be directly fed back to the driving energy consumption estimation module. After the next estimated driving energy consumption result comes out, the map value after the previous round of correction and update is corrected again, thereby continuously improving the accuracy of the map value and forming self-optimization.
[0024] Example 2 A driving energy consumption estimation method based on operating condition characteristic parameters, such as Figure 2 As shown, the user operating condition characteristic parameters are calculated based on the vehicle speed and time curve, a benchmark operating condition that matches the user operating condition characteristic parameters is selected from the benchmark operating condition library, the benchmark operating condition characteristic parameters and the benchmark energy consumption under the benchmark operating condition are obtained based on the selected benchmark operating condition, and the estimated driving energy consumption is calculated based on the user operating condition characteristic parameters, the benchmark operating condition characteristic parameters and the benchmark energy consumption under the benchmark operating condition.
[0025] The specific method for estimating drive energy consumption includes the following steps: The user operating condition characteristic parameters are calculated based on the vehicle speed and time curve, a benchmark operating condition that matches the user operating condition characteristic parameters is selected from the benchmark operating condition library, the benchmark operating condition characteristic parameters and the benchmark energy consumption under the benchmark operating condition are obtained based on the selected benchmark operating condition, and the estimated driving energy consumption is calculated based on the user operating condition characteristic parameters and the benchmark operating condition characteristic parameters and the benchmark energy consumption under the benchmark operating condition.
[0026] Example 3 A computer program product comprising a computer program which, when executed by a processor, implements the steps of the method described in embodiment 2.
[0027] Those skilled in the art will appreciate that embodiments of the application can be supplied as a method, a system, or a computer program product. Accordingly, the application can be embodied in the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the application can be embodied in the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk memory, CD-ROMs, optical storage media, etc.) having computer usable program code embodied therein.
[0028] The present application is described in reference to the flowchart and / or block diagrams of the method, apparatus (system) and computer program product according to embodiments of the application. It will be understood that each block of the flowchart and / or block diagrams, and combinations of blocks in the flowchart and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks.
[0029] These computer program instructions can also be stored in a computer- readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks.
[0030] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks.
[0031] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit its scope of protection. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that after reading the present invention, those skilled in the art may still make various changes, modifications or equivalent substitutions to the specific implementation methods of the invention, but these changes, modifications or equivalent substitutions are all within the scope of protection of the pending claims of the invention.
[0032] The contents not described in detail in this specification belong to the prior art known to professional and technical personnel in this field.
Claims
1. A driving energy consumption estimation system based on operating condition characteristic parameters, characterized in that: It includes: The driving energy consumption estimation module is used to calculate the user operating condition characteristic parameters based on the vehicle speed and time curve, select a benchmark operating condition that matches the user operating condition characteristic parameters from the benchmark operating condition library, obtain the benchmark operating condition characteristic parameters and the benchmark energy consumption under the benchmark operating condition based on the selected benchmark operating condition, and calculate the estimated driving energy consumption based on the user operating condition characteristic parameters and the benchmark operating condition characteristic parameters and the benchmark energy consumption under the benchmark operating condition.
2. The driving energy consumption estimation system based on operating condition characteristic parameters according to claim 1 is characterized in that: It also includes: The driving energy consumption correction module is used to calculate the actual driving energy consumption through the actual vehicle sensor data, and use a variable learning rate to combine the actual driving energy consumption and the estimated driving energy consumption to correct the original map value to obtain a corrected and updated map value. The corrected and updated map value is used for subsequent estimated driving energy consumption optimization.
3. The driving energy consumption estimation system based on operating condition characteristic parameters according to claim 1 is characterized in that: The user operating condition characteristic parameters include speed characteristic parameters , high-speed braking characteristic parameters and low-speed braking characteristic parameters .
4. The driving energy consumption estimation system based on operating condition characteristic parameters according to claim 3 is characterized in that: The specific process of calculating the user operating condition characteristic parameters based on the vehicle speed and time curve is as follows: Divide the vehicle's travel time into n equal time periods and calculate the braking energy based on the vehicle speed and time curve and the average speed of the vehicle at time i~i+1 , the calculation formula is as follows: Among them, t represents the driving time of the vehicle, v represents the driving speed of the vehicle, represents the speed of the vehicle at time i, represents the vehicle drag coefficient related to rolling resistance, represents the vehicle drag coefficient related to transmission resistance, represents the vehicle drag coefficient related to wind resistance, Indicates the braking energy, represents the vehicle speed at time i+1, represents the average speed of the vehicle at time i~i+1; According to the calculated braking energy and the average speed of the vehicle at time i~i+1 , the velocity characteristic parameters can be calculated , high-speed braking characteristic parameters and low-speed braking characteristic parameters ; The speed characteristic parameters The calculation formula is as follows: Where n represents the number of time periods into which the vehicle's travel time is divided. Indicates a time interval; High-speed braking characteristic parameters The calculation formula is as follows: , >15km / h Low-speed braking characteristic parameters The calculation formula is as follows: , ≤15km / h。 5. The driving energy consumption estimation system based on operating condition characteristic parameters according to claim 4 is characterized in that: The specific process of selecting a benchmark operating condition that matches the user's operating condition characteristic parameters from the benchmark operating condition library is as follows: The benchmark operating condition library includes NEDC, CLTC and WLTC. For each user operating condition characteristic parameter, the characteristic parameters of the benchmark operating conditions NEDC, CLTC and WLTC are compared with the user operating condition characteristic parameters. The benchmark operating condition corresponding to the characteristic parameter that minimizes the deviation from the user operating condition characteristic parameter is used as the benchmark operating condition that matches the user operating condition characteristic parameter.
6. The driving energy consumption estimation system based on operating condition characteristic parameters according to claim 5, characterized in that: The calculation formula for calculating the estimated driving energy consumption ECR based on the user operating condition characteristic parameters, the reference operating condition characteristic parameters, and the reference energy consumption under the reference operating condition is as follows: in, Representation and speed characteristic parameters Matching benchmark operating condition characteristic parameters, Representation and high-speed braking characteristic parameters Matching benchmark operating condition characteristic parameters, Indicates low-speed braking characteristic parameters Matching benchmark operating condition characteristic parameters, Representation and speed characteristic parameters The benchmark energy consumption corresponding to the matching benchmark working condition, Representation and high-speed braking characteristic parameters The benchmark energy consumption corresponding to the matching benchmark working condition, Indicates low-speed braking characteristic parameters The benchmark energy consumption corresponding to the matching benchmark operating conditions, and ECR represents the estimated driving energy consumption.
7. The driving energy consumption estimation system based on operating condition characteristic parameters according to claim 2, characterized in that: The specific process of calculating the actual driving energy consumption through real vehicle sensor data is as follows: The vehicle battery output energy is calculated based on the vehicle battery DC current and the vehicle battery DC voltage; the vehicle compressor and high-voltage PTC heater energy consumption is calculated based on the vehicle compressor power and the vehicle high-voltage PTC heater power; the vehicle low-voltage accessory energy consumption is calculated based on the vehicle low-voltage accessory current and the vehicle low-voltage accessory voltage; and the actual driving energy consumption of the vehicle is calculated based on the vehicle battery output energy, the vehicle compressor and high-voltage PTC heater energy consumption, and the vehicle low-voltage accessory energy consumption; The calculation formula is as follows: in, Indicates the DC current of the vehicle battery, Indicates the vehicle battery DC voltage, Indicates the vehicle battery output energy, Indicates the vehicle compressor power, Indicates the vehicle's high-voltage PTC heater power, Indicates the energy consumed by the vehicle compressor and high-voltage PTC heater. Indicates the vehicle's low-voltage accessory current. Indicates the vehicle's low-voltage accessory voltage. Indicates the energy consumed by the vehicle's low-voltage accessories. Indicates actual driving energy consumption.
8. The driving energy consumption estimation system based on operating condition characteristic parameters according to claims 6 and 7 is characterized in that: Using variable learning rate The specific process of correcting the original map value by combining the actual driving energy consumption and the estimated driving energy consumption to obtain the corrected and updated map value is as follows: in, Indicates the updated map value. Represents the original map value, Indicates actual driving energy consumption, ECR indicates estimated driving energy consumption, represents a variable learning rate.
9. A method for estimating driving energy consumption based on operating condition characteristic parameters, characterized in that: It includes the following steps: The user operating condition characteristic parameters are calculated based on the vehicle speed and time curve, a benchmark operating condition that matches the user operating condition characteristic parameters is selected from the benchmark operating condition library, the benchmark operating condition characteristic parameters and the benchmark energy consumption under the benchmark operating condition are obtained based on the selected benchmark operating condition, and the estimated driving energy consumption is calculated based on the user operating condition characteristic parameters and the benchmark operating condition characteristic parameters and the benchmark energy consumption under the benchmark operating condition.
10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to claim 9 are implemented.