Driving recommendation method and system for reducing comprehensive energy consumption of automobile, vehicle and equipment

Abstract

The invention relates to a driving recommendation method and system for reducing comprehensive energy consumption of an automobile, the automobile and equipment. The driving recommendation method for reducing the comprehensive energy consumption of the automobile comprises the following steps: determining self-driving energy consumption and intelligent driving energy consumption according to vehicle operation data; and determining the minimum value of the self-driving energy consumption and the intelligent driving energy consumption, and pushing the minimum value to the user in a driving mode corresponding to the minimum energy consumption. The invention also provides a system for realizing the driving recommendation method. The invention further provides computer equipment and a vehicle. On the basis of intelligent driving and user driving behaviors, lower comprehensive driving energy consumption of the new energy automobile is obtained, the corresponding driving mode is recommended to the user, and the user experience is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of automobile energy consumption control, and in particular to a driving recommendation method, system, vehicle and equipment for reducing comprehensive automobile energy consumption. Background Art

[0002] With the increasing global awareness of environmental protection and the advancement of sustainable development goals, the popularity of new energy vehicles, as an important solution to reduce carbon emissions and ease energy pressure, is growing at an unprecedented rate. However, in the context of the widespread application of new energy vehicles, how to further optimize their comprehensive energy consumption and improve energy efficiency has become a key technical problem that needs to be solved urgently within and outside the industry.

[0003] Traditional energy-saving strategies for new energy vehicles mainly focus on improvements in vehicle hardware, such as optimizing the power system, reducing vehicle weight, and improving energy recovery efficiency. These measures have reduced vehicle energy consumption to a certain extent. However, it cannot be ignored that the driver's driving habits also have a significant impact on the energy consumption of new energy vehicles, and the energy consumption under different driving modes varies significantly.

[0004] With the rapid development and widespread application of intelligent driving technology, it has not only improved driving safety and comfort, but also opened up a new path for energy saving and consumption reduction of new energy vehicles. Intelligent driving systems can achieve precise control of vehicle driving status through high-precision sensors, advanced algorithms and real-time data analysis, thus showing great potential in reducing energy consumption.

[0005] However, in the existing vehicle-side and cloud-based technology systems, there is still a lack of comparison between the energy consumption of intelligent driving functions and normal driving behavior. Traditional methods for optimizing driving trip energy consumption focus on reducing energy consumption by regulating driving behavior (such as reducing violent driving operations such as sudden acceleration and sudden braking), but fail to fully consider the unique advantages of intelligent driving in reducing comprehensive energy consumption under different road conditions.

[0006] Therefore, it is necessary to use intelligent driving functions as a means to reduce the comprehensive energy consumption of driving trips, so as to reduce the comprehensive energy consumption of vehicle trips, provide users with lower energy consumption solutions for every kilometer they travel, improve user experience, and reduce users' anxiety about cruising range.

[0007] CN116653962A discloses an efficient energy-saving system and energy-saving method for intelligent driving vehicles, the energy-saving method includes the following steps: the vehicle control background collects road section training data and decision speed training data; the vehicle control background obtains the decision speed training data and generates a training quadruple set; the vehicle control background obtains the road section training data to train a machine learning model that estimates the i-th energy-saving speed value, and based on the training quadruple set, trains a deep reinforcement learning model that decides whether to use an alternative driving speed when a bad driving environment is detected; the energy-saving method uses the machine learning model to obtain speed label data, sets a reward value, and ensures that the intelligent driving vehicle, under the premise of safe driving, determines whether to replace the driving speed according to the size of the reward value, thereby obtaining the best energy-saving speed value in different driving environments, realizing automatic control of the intelligent driving vehicle, and reducing energy waste. However, the energy-saving method only trains the best driving speed in different driving environments to ensure the lowest energy consumption through the machine learning model for intelligent driving, but ignores the relatively lower energy consumption of the driving behavior of the owner in different driving environments. Therefore, it is difficult to achieve the goal of the lowest comprehensive energy consumption of new energy driving by relying solely on intelligent driving. Summary of the invention

[0008] In view of this, the purpose of the present invention is to provide a driving recommendation method, system, vehicle and equipment for reducing the comprehensive energy consumption of a car, so as to obtain lower comprehensive energy consumption of car driving based on intelligent driving and user driving behavior, and recommend the corresponding driving method to the user to improve the user experience.

[0009] In order to achieve the above object, the technical solution adopted by the present invention is as follows:

[0010] The recommended driving method for reducing the comprehensive energy consumption of the vehicle includes the following steps:

[0011] Determine the energy consumption of self-driving and intelligent driving based on vehicle operation data;

[0012] Determine the lowest value between self-driving energy consumption and intelligent driving energy consumption, and push the driving method corresponding to the lowest energy consumption to the user.

[0013] According to the above-mentioned technical means, by real-time analysis of vehicle operation data, the energy consumption under different driving modes (self-driving and intelligent driving) is calculated and determined, and the driving mode with the lowest energy consumption is recommended to users. This helps drivers choose the optimal driving strategy according to actual conditions. It not only effectively reduces the overall energy consumption of the car and promotes energy conservation and emission reduction, but also effectively meets the driver's comfort needs and improves the driving experience.

[0014] Preferably, the driving recommendation method for reducing the comprehensive energy consumption of the automobile comprises the following steps:

[0015] Determining a first current travel state according to a current energy recovery state of the vehicle, a current driving road condition, and a current air conditioning state;

[0016] Determining self-driving energy consumption according to a first current travel state;

[0017] Determining a second current travel state according to the current driving road condition and the current air conditioning state of the vehicle;

[0018] Determine the intelligent driving energy consumption according to the second current travel state;

[0019] Determine the lowest value between self-driving energy consumption and intelligent driving energy consumption, and push the driving method corresponding to the lowest energy consumption to the user.

[0020] According to the above technical means, the current energy recovery status, current driving road conditions and current air-conditioning status of the vehicle are analyzed in real time to jointly determine the current trip status, and then the energy consumption under different driving modes (self-driving and intelligent driving) is calculated and determined based on the current trip status, and the driving mode with the lowest energy consumption is recommended to the user, which helps the driver to choose the optimal driving strategy according to the actual situation. It not only effectively reduces the overall energy consumption of the car and promotes energy conservation and emission reduction, but also effectively meets the driver's comfort needs and improves the driving experience. At the same time, combined with the habits and preferences of different drivers, the driving mode with the lowest energy consumption is recommended in a personalized and intelligent manner, which not only realizes personalized recommendations, but also realizes intelligent recommendations, meets the actual needs of different drivers, and has significant social and environmental benefits.

[0021] Among them, the energy recovery status includes low energy recovery intensity, medium energy recovery intensity, high energy recovery status and energy recovery status off.

[0022] When the vehicle's deceleration is below 0.05g, it means that the energy recovery intensity is low; when the vehicle's deceleration is between 0.05g and 0.1g, it means that the energy recovery intensity is medium; when the vehicle's deceleration is between 0.1g and 0.2g, it means that the energy recovery intensity is high; g represents the acceleration due to gravity.

[0023] Driving road conditions at least include congested road conditions, long downhill road conditions, long uphill road conditions and high-speed road conditions.

[0024] The air conditioning status includes air conditioning on and air conditioning off.

[0025] Self-driving energy consumption is the energy consumption determined according to the driving habits of the car owner.

[0026] Intelligent driving energy consumption refers to the energy consumption determined when the intelligent driving function is turned on.

[0027] Preferably, determining the lowest value between the self-driving energy consumption and the intelligent driving energy consumption, and pushing the driving mode corresponding to the lowest energy consumption to the user includes:

[0028] When the current smart driving state is on, if it is calculated that self-driving consumes less energy, it is recommended to turn off smart driving. If it is calculated that the energy consumption of a certain speed range is lower than the current speed range, the speed range with lower energy consumption is recommended to the user.

[0029] When the current smart driving state is off, if it is calculated that self-driving has lower energy consumption, no recommendation will be made. If the energy consumption of smart driving in a certain speed range is lower than that of self-driving, the corresponding speed range with low smart driving energy consumption will be recommended to the user.

[0030] When the actual user is driving the vehicle, the vehicle intelligent driving status information, current speed and other vehicle data are obtained. If it is in a non-intelligent driving state, the driving energy consumption under this road condition is obtained according to the user's driving habits, and the energy consumption of different speeds under this road condition in the intelligent driving state is compared to provide a lower energy consumption option. If it is in an intelligent driving state, the lowest energy consumption speed range is provided. In addition, when the user independently sets the cruising range to be lower than the set kilometers, the low driving energy consumption mode is selected. When the requirements are met, the driving method with the lowest energy consumption is recommended to the user. When the navigation mileage is greater than the cruising range during the user's driving, the user is actively reminded and a lower energy consumption driving method is recommended to ensure that the user completes the last kilometer of the journey.

[0031] Preferably, the self-driving energy consumption is determined according to the first current travel state, and is obtained by looking up a correspondence table between the first historical travel state, the driving behavior frequency interval and the average self-driving energy consumption.

[0032] Preferably, the intelligent driving energy consumption is determined based on the second current travel state, and is obtained by looking up a correspondence table between the second historical travel state, the vehicle speed range and the average intelligent driving energy consumption.

[0033] Preferably, the self-driving energy consumption in the correspondence table of the first historical travel state, driving behavior frequency interval and average self-driving energy consumption is obtained in the following manner:

[0034] Divide the driving behavior number intervals according to the first historical trip state and the corresponding driving behavior number, and calculate the average self-driving energy consumption corresponding to each driving behavior number interval according to the first historical trip start time, the first historical trip end time, the mileage at the start time of the first historical trip, the mileage at the end time of the first historical trip, the power and / or fuel amount at the start time of the first historical trip, and the power and / or fuel amount at the end time of the first historical trip of the first historical trip state corresponding to each driving behavior number interval, that is, the self-driving energy consumption;

[0035] The first historical travel status includes a historical energy recovery status, a historical driving road condition, and a historical air conditioning status.

[0036] The number of driving behaviors corresponding to the first historical travel state includes the sum of the number of brake pedal travel changes and the number of accelerator pedal travel changes.

[0037] Preferably, the intelligent driving energy consumption in the corresponding relationship table of the second historical travel state, vehicle speed range and average intelligent driving energy consumption is obtained in the following manner:

[0038] Divide the vehicle speed intervals according to the second historical trip state and the corresponding vehicle speed, and calculate the average intelligent driving energy consumption corresponding to each vehicle speed interval, i.e., the intelligent driving energy consumption, according to the second historical trip start time, the second historical trip end time, the mileage at the second historical trip start time, the mileage at the second historical trip end time, the power and / or fuel amount at the second historical trip start time, and the power and / or fuel amount at the second historical trip end time of the second historical trip state corresponding to each vehicle speed interval;

[0039] The second historical travel status includes historical driving road conditions and historical air conditioning status.

[0040] Preferably, when the vehicle is a pure electric vehicle, the start time of the first historical trip of the first historical trip state is set to t1, the end time of the first historical trip is set to t2, the mileage at the start time of the first historical trip is set to m1, the mileage at the end time of the first historical trip is set to m2, the power at the start time of the first historical trip is set to q1, and the power at the end time of the first historical trip is set to q2, then the average self-driving energy consumption of the corresponding driving behavior number interval is set to: (q1-q2) / [(t2-t1)*(m2-m1)].

[0041] Preferably, when the vehicle is a pure electric vehicle, the start time of the second historical trip of the second historical trip state is set to t3, the end time of the second historical trip is set to t4, the mileage at the start time of the second historical trip is set to m3, the mileage at the end time of the second historical trip is set to m4, the power at the start time of the second historical trip is set to q3, and the power at the end time of the second historical trip is set to q4, then the average intelligent driving energy consumption in the corresponding speed range is set to: (q3-q4) / [(t4-t3)*(m4-m3)].

[0042] Preferably, when the vehicle is a pure oil vehicle, the start time of the first historical trip of the first historical trip state is set to t1, the end time of the first historical trip is set to t2, the mileage at the start time of the first historical trip is set to m1, the mileage at the end time of the first historical trip is set to m2, the amount of oil at the start time of the first historical trip is set to p1, and the amount of oil at the end time of the first historical trip is set to p2, then the average self-driving energy consumption of the corresponding driving behavior number interval is set to: (p1-p2) / [(t2-t1)*(m2-m1)].

[0043] Preferably, when the vehicle is a pure oil vehicle, the start time of the second historical trip of the second historical trip state is set to t3, the end time of the second historical trip is set to t4, the mileage at the start time of the second historical trip is set to m3, the mileage at the end time of the second historical trip is set to m4, the amount of oil at the start time of the second historical trip is set to p3, and the amount of oil at the end time of the second historical trip is set to p4, then the average intelligent driving energy consumption in the corresponding speed range is set to: (p3-p4) / [(t4-t3)*(m4-m3)].

[0044] Preferably, when the vehicle is a hybrid vehicle, the start time of the first historical trip of the first historical trip state is set to t1, the end time of the first historical trip is set to t2, the mileage at the start time of the first historical trip is set to m1, the mileage at the end time of the first historical trip is set to m2, the power at the start time of the first historical trip is set to q1 and the oil level is set to p1, and the power at the end time of the first historical trip is set to q2 and the oil level is set to p2, then the average self-driving energy consumption in the corresponding driving behavior number interval is set to: [(q1-q2)+(p1-p2)] / [(t2-t1)*(m2-m1)].

[0045] Preferably, when the vehicle is a hybrid vehicle, the start time of the second historical trip of the second historical trip state is set to t3, the end time of the second historical trip is set to t4, the mileage at the start time of the second historical trip is set to m3, the mileage at the end time of the second historical trip is set to m4, the battery level at the start time of the second historical trip is set to q3 and the fuel level p3, and the battery level at the end time of the second historical trip is set to q4 and the fuel level p4, then the average intelligent driving energy consumption in the corresponding vehicle speed range is set to: [(q3-q4)+(p3-p4)] / [(t4-t3)*(m4-m3)].

[0046] Preferably, determining the self-driving energy consumption based on the first current travel state includes: matching the first current travel state with the first historical travel state in the correspondence table of the first historical travel state, the number of driving behaviors and the average self-driving energy consumption; when the current energy recovery state, the current driving road condition and the current air-conditioning state in the first current travel state are the same as the historical energy recovery state, the historical driving road condition and the historical air-conditioning state in the first historical travel state, it indicates that the match is successful; after the match is successful, checking the correspondence table of the first historical travel state, the number of driving behaviors and the average self-driving energy consumption corresponding to the lowest value of the average self-driving energy consumption in the first historical travel state, which is the self-driving energy consumption determined corresponding to the first current travel state.

[0047] Among them, the historical energy recovery status, historical driving road conditions, historical air conditioning status, the start time of the first historical trip, the end time of the first historical trip, the mileage at the start time of the first historical trip, the mileage at the end time of the first historical trip, the power and / or fuel volume at the start time of the first historical trip, and the power and / or fuel volume at the end time of the first historical trip are collected by the vehicle end and uploaded to the vehicle cloud at a period of 1s, and the data is parsed and written into the database through the big data program. The offline spark program reads the vehicle data stored in the database. For the above energy recovery status, road conditions, air conditioning status, time, mileage, power and fuel volume, when one of the signal values ​​is null, it is complemented according to the most recent non-null value within 10s of tracing back, and it remains null for more than 10s, and the row data with any null value in the above signals is filtered.

[0048] When the first current trip state fails to match the first historical trip state, the user is not recommended a driving mode, and the vehicle continues to drive according to the current driving mode. At the same time, the energy recovery state, road conditions, air conditioning state, trip start time, trip end time, mileage at the start time of the trip, mileage at the end time of the trip, power and / or oil volume at the start time of the trip, and power and / or oil volume at the end time of the trip, obtained after the trip state ends, are collected by the vehicle end and uploaded to the vehicle cloud as historical data for the next same trip state, that is, for the first current trip state for the first time, the vehicle continues to drive according to the current driving mode, and the current energy recovery state, current road conditions, current air conditioning state, current trip start time, current trip end time, mileage at the start time of the current trip, mileage at the end time of the current trip, power and / or oil volume at the start time of the current trip, and power and / or oil volume at the end time of the current trip, obtained after the first current trip state ends, are collected by the vehicle end and uploaded to the vehicle cloud as corresponding data of the first historical trip state to calculate the average self-driving energy consumption.

[0049] Preferably, the intelligent driving energy consumption is determined according to the second current trip state, including: matching the second current trip state with the second historical trip state in the correspondence table of the vehicle speed range and the average intelligent driving energy consumption; when the current driving road condition and the current air-conditioning state under the second current trip state are the same as the historical driving road condition and the historical air-conditioning state under the second historical trip state, it indicates that the match is successful; after the match is successful, checking the correspondence table of the second historical trip state, the vehicle speed range and the average intelligent driving energy consumption corresponding to the lowest value of the average intelligent driving energy consumption under the second historical trip state, which is the intelligent driving energy consumption determined corresponding to the second current trip state.

[0050] When the second current trip state fails to match the second historical trip state, the user is not recommended a driving method, and the user continues to drive directly in the current driving method. At the same time, the driving conditions, air conditioning status, trip start time, trip end time, mileage at the start time of the trip, mileage at the end time of the trip, power and / or oil volume at the start time of the trip, and power and / or oil volume at the end time of the trip obtained after the trip state ends are collected by the vehicle end and uploaded to the vehicle cloud as historical data for the next same trip state, that is, for the first second current trip state, the user continues to drive directly in the current driving method, and the current driving conditions, current air conditioning status, current trip start time, current trip end time, mileage at the start time of the current trip, mileage at the end time of the current trip, power and / or oil volume at the start time of the current trip, and power and / or oil volume at the end time of the current trip obtained after the second current trip state ends are collected by the vehicle end and uploaded to the vehicle cloud as the corresponding data of the second historical trip state to calculate the average intelligent driving energy consumption. The offline spark program reads the vehicle data stored in the database. For the above energy recovery status, road conditions, air conditioning status, time, mileage, power and fuel level, when one of the signal values ​​is null, complement it according to the most recent non-null value within 10 seconds, and keep it as null if it exceeds 10 seconds, and filter out the row data with any null value in the above signals.

[0051] The present invention also provides a system for implementing the driving recommendation method for reducing comprehensive energy consumption of an automobile as described in the present invention, comprising:

[0052] Energy consumption determination module: used to determine the energy consumption of self-driving and intelligent driving based on vehicle operation data;

[0053] Energy consumption comparison and recommendation module: Determine the lowest value between self-driving energy consumption and intelligent driving energy consumption, and push the driving method corresponding to the lowest energy consumption to the user.

[0054] Preferably, the system comprises:

[0055] A first trip state determination module: used to determine a first current trip state according to the received current energy recovery state of the vehicle, the current driving road condition and the current air conditioning state;

[0056] Self-driving energy consumption determination module: used to calculate and determine the self-driving energy consumption according to the first current travel state;

[0057] A second trip state determination module: used to determine a second current trip state according to the received current driving road condition and current air conditioning state of the vehicle;

[0058] Intelligent driving energy consumption determination module: used to calculate and determine the intelligent driving energy consumption according to the second current travel state;

[0059] Energy consumption comparison and recommendation module: receives the output results of the self-driving energy consumption determination module and the intelligent driving energy consumption determination module, compares the self-driving energy consumption and the intelligent driving energy consumption, determines the lowest value between the self-driving energy consumption and the intelligent driving energy consumption, and pushes the driving method corresponding to the lowest energy consumption to the user.

[0060] The energy consumption of self-driving and intelligent driving are compared, and the driving mode corresponding to the lowest energy consumption of the two is pushed to the user. Specifically: the energy consumption of self-driving and intelligent driving are compared. If the energy consumption of self-driving is less than that of intelligent driving, the self-driving mode is pushed to the user; if the energy consumption of self-driving is greater than that of intelligent driving, the intelligent driving mode is pushed to the user.

[0061] Preferably, the self-driving energy consumption determination module includes:

[0062] The first energy consumption data table management module is used to store self-driving energy consumption data and is named as a corresponding relationship table of the first historical travel state, driving behavior frequency interval and average self-driving energy consumption;

[0063] The first energy consumption query module is used to query the corresponding self-driving energy consumption in the correspondence table of the first historical travel state, the driving behavior number interval and the average self-driving energy consumption according to the first current travel state.

[0064] Preferably, the intelligent driving energy consumption determination module includes:

[0065] The second energy consumption data table management module is used to store the intelligent driving energy consumption data and is named as a corresponding relationship table of the second historical travel state, vehicle speed range and average intelligent driving energy consumption;

[0066] The second energy consumption query module is used to query the corresponding intelligent driving energy consumption in the correspondence table of the second historical travel state, vehicle speed range and average intelligent driving energy consumption according to the second current travel state.

[0067] Preferably, the first energy consumption data table management module includes:

[0068] The first historical data storage module is used to obtain and store the first historical trip state data of the vehicle, including the number of driving behaviors corresponding to each first historical trip state, the start time t1 of the first historical trip, the end time t2 of the first historical trip, the mileage m1 at the start time of the first historical trip, the mileage m2 at the end time of the first historical trip, the power q1 and / or the oil level p1 at the start time of the first historical trip, and the power q2 and / or the oil level p2 at the end time of the first historical trip;

[0069] A driving behavior frequency interval division module: used for dividing the driving behavior frequency into different intervals according to the first historical travel state and the driving behavior frequency;

[0070] Self-driving energy consumption calculation module: used to calculate the average self-driving energy consumption in the corresponding interval of the driving behavior times according to each driving behavior times interval, using the first historical trip start time t1, the first historical trip end time t2, the mileage m1 at the start time of the first historical trip, the mileage m2 at the end time of the first historical trip, the power q1 and / or the oil level p1 at the start time of the first historical trip, and the power q2 and / or the oil level p2 at the end time of the first historical trip within the corresponding driving behavior times interval.

[0071] Among them, when the vehicle is a pure electric vehicle, the calculation formula for the average self-driving energy consumption is: (q1-q2) / [(t2-t1)*(m2-m1)]; when the vehicle is a pure oil vehicle, the calculation formula for the average self-driving energy consumption is: (p1-p2) / [(t2-t1)*(m2-m1)]; when the vehicle is a hybrid vehicle, the calculation formula for the average self-driving energy consumption is: [(q1-q2)+(p1-p2)] / [(t2-t1)*(m2-m1)]; q1 represents the power at the beginning of the first historical trip, unit p1 represents the fuel volume at the beginning of the first historical trip, in L / s*km; q2 represents the power volume at the end of the first historical trip, in kWh / s*km; p2 represents the fuel volume at the end of the first historical trip, in L / s*km; t1 represents the beginning of the first historical trip, in S; t2 represents the end of the first historical trip, in S; m1 represents the mileage at the beginning of the first historical trip, in km; m2 represents the mileage at the end of the first historical trip, in km.

[0072] Preferably, the second energy consumption data table management module includes:

[0073] The second historical data storage module is used to obtain and store the second historical trip state data of the vehicle, including the vehicle speed corresponding to each second historical trip state, the start time t3 of the second historical trip, the end time t4 of the second historical trip, the mileage m3 at the start time of the second historical trip, the mileage m4 at the end time of the second historical trip, the power q3 and / or the oil level p3 at the start time of the second historical trip, and the power q4 and / or the oil level p4 at the end time of the second historical trip;

[0074] Vehicle speed interval division module: used for dividing the vehicle speed into different intervals according to the second historical travel state and the vehicle speed;

[0075] Intelligent driving energy consumption calculation module: used to calculate the average intelligent driving energy consumption in the corresponding speed interval according to each vehicle speed interval, using the second historical trip start time t3, the second historical trip end time t4, the mileage m3 at the start time of the second historical trip, the mileage m4 at the end time of the second historical trip, the power q3 and / or the oil level p3 at the start time of the second historical trip, and the power q4 and / or the oil level p4 at the end time of the second historical trip within the corresponding vehicle speed interval.

[0076] Among them, the calculation formula for the average intelligent driving energy consumption is: (q3-q4) / [(t4-t3)*(m4-m3)], q3 represents the power at the start time of the second historical trip, in kWh / s*km; q4 represents the power at the end time of the second historical trip, in kWh / s*km; t3 represents the start time of the second historical trip, in S; t4 represents the end time of the second historical trip, in S; m3 represents the mileage at the start time of the second historical trip, in km; m4 represents the mileage at the end time of the second historical trip, in km.

[0077] Preferably, the self-driving energy consumption determination module further includes:

[0078] Self-driving energy consumption matching module: used to match the first current travel state with the first historical travel state in the corresponding relationship table of the first historical travel state, the number of driving behaviors and the average self-driving energy consumption. When the current energy recovery state, the current driving road condition and the current air-conditioning state in the first current travel state are exactly the same as the historical energy recovery state, the historical driving road condition and the historical air-conditioning state in a first historical travel state in the corresponding relationship table of the first historical travel state, the number of driving behaviors and the average self-driving energy consumption, it indicates that the match is successful; after the match is successful, the minimum value of the average self-driving energy consumption corresponding to the first historical travel state is searched and obtained from the corresponding relationship table of the first historical travel state, the number of driving behaviors and the average self-driving energy consumption, as the self-driving energy consumption corresponding to the first current travel state.

[0079] Preferably, the intelligent driving energy consumption determination module further includes:

[0080] Intelligent driving energy consumption matching module: used to match the second current trip state with the second historical trip state in the correspondence table between the second historical trip state, the vehicle speed range and the average intelligent driving energy consumption. When the current driving road condition and the current air-conditioning state in the second current trip state are exactly the same as the historical driving road condition and the historical air-conditioning state in a second historical trip state in the correspondence table between the second historical trip state, the vehicle speed range and the average intelligent driving energy consumption, it indicates a successful match. After the match is successful, the minimum value of the average intelligent driving energy consumption corresponding to the second historical trip state is searched and obtained from the correspondence table between the second historical trip state, the vehicle speed range and the average intelligent driving energy consumption as the intelligent driving energy consumption corresponding to the second current trip state.

[0081] Preferably, the system further comprises a user interaction module: used to push the recommended driving mode to the user through screen display and / or voice prompt, so that the user can adjust the driving mode according to the recommended driving mode, thereby achieving the purpose of reducing the comprehensive energy consumption of the car.

[0082] The present invention also provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the driving recommendation method as described in the present invention when executing the computer program.

[0083] The present invention also provides a vehicle, wherein the vehicle comprises the computer device according to the present invention.

[0084] The present invention also provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the driving recommendation method according to the present invention are implemented.

[0085] Beneficial effects of the present invention:

[0086] The driving recommendation method for reducing the comprehensive energy consumption of an automobile of the present invention calculates and determines the energy consumption under different driving modes (self-driving and intelligent driving) by analyzing the vehicle operation data in real time, and recommends the driving mode with the lowest energy consumption to the user, which helps the driver to choose the optimal driving strategy according to the actual situation, thereby obtaining lower comprehensive energy consumption of new energy vehicle driving based on intelligent driving and user driving behavior, and recommending the corresponding driving mode to the user, effectively improving the user's comprehensive driving experience. In the field of automobile energy consumption control technology, it has promotion and application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0087] Figure 1 A first flow chart of the driving recommendation method for reducing comprehensive energy consumption of an automobile according to the present invention;

[0088] Figure 2 A second flow chart of the driving recommendation method for reducing comprehensive energy consumption of an automobile according to the present invention;

[0089] Figure 3 A first structural schematic diagram of a system for a driving recommendation method for reducing comprehensive energy consumption of a vehicle;

[0090] Figure 4 A second structural schematic diagram of a system for a driving recommendation method for reducing comprehensive energy consumption of a vehicle. DETAILED DESCRIPTION

[0091] The following will describe the embodiments of the present invention with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are only for illustrating the present invention, not for limiting the scope of protection of the present invention.

[0092] It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention, and thus the drawings only show components related to the present invention rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0093] In the following description, numerous details are discussed to provide a more thorough explanation of the embodiments of the present application. However, it is obvious to those skilled in the art that the embodiments of the present application can be implemented without these specific details.

[0094] The present invention intends to disclose a driving recommendation method, system, vehicle and equipment for reducing the comprehensive energy consumption of an automobile, so as to obtain lower comprehensive energy consumption of automobile driving based on intelligent driving and the user's own driving behavior habits, and recommend the corresponding driving method to the user, thereby improving the user experience and reducing the user's anxiety about the cruising range.

[0095] Among them, Figure 1 As shown, the recommended driving method for reducing the comprehensive energy consumption of the vehicle includes the following steps:

[0096] S1. Determine the self-driving energy consumption and intelligent driving energy consumption based on vehicle operation data;

[0097] S2. Determine the lowest value between the self-driving energy consumption and the intelligent driving energy consumption, and push the driving method corresponding to the lowest energy consumption to the user.

[0098] By analyzing vehicle operation data in real time, calculating and determining the energy consumption under different driving modes (self-driving and intelligent driving), and recommending the driving mode with the lowest energy consumption to users, it helps drivers choose the optimal driving strategy based on actual conditions. It not only effectively reduces the overall energy consumption of the car and promotes energy conservation and emission reduction, but also effectively meets the driver's comfort needs and improves the driving experience.

[0099] like Figure 2 As shown, the recommended driving method for reducing the comprehensive energy consumption of the vehicle includes the following steps:

[0100] S111, determining a first current travel state according to a current energy recovery state of the vehicle, a current driving road condition, and a current air conditioning state; S112, determining a self-driving energy consumption according to the first current travel state;

[0101] S121, determining a second current travel state according to the current driving road condition and the current air conditioning state of the vehicle;

[0102] S122, determining the intelligent driving energy consumption according to the second current travel state;

[0103] S2. Determine the lowest value between the self-driving energy consumption and the intelligent driving energy consumption, and push the driving method corresponding to the lowest energy consumption to the user.

[0104] The energy recovery status includes low energy recovery intensity, medium energy recovery intensity, high energy recovery status and off energy recovery status. When the vehicle's deceleration is below 0.05g, it indicates low energy recovery intensity; when the vehicle's deceleration is between 0.05g and 0.1g, it indicates medium energy recovery intensity; when the vehicle's deceleration is between 0.1g and 0.2g, it indicates high energy recovery intensity; g indicates gravity acceleration.

[0105] Driving road conditions at least include congested road conditions, long downhill road conditions, long uphill road conditions and high-speed road conditions.

[0106] The air conditioning status includes air conditioning on and air conditioning off.

[0107] Self-driving energy consumption is the energy consumption determined according to the driving habits of the car owner.

[0108] Intelligent driving energy consumption refers to the energy consumption determined when the intelligent driving function is turned on.

[0109] In some embodiments, determining the lowest value between the self-driving energy consumption and the intelligent driving energy consumption, and pushing the driving mode corresponding to the lowest energy consumption to the user includes:

[0110] When the current smart driving state is on, if it is calculated that self-driving consumes less energy, it is recommended to turn off smart driving. If it is calculated that the energy consumption of a certain speed range is lower than the current speed range, the speed range with lower energy consumption is recommended to the user.

[0111] When the current smart driving state is off, if it is calculated that self-driving has lower energy consumption, no recommendation will be made. If the energy consumption of smart driving in a certain speed range is lower than that of self-driving, the corresponding speed range with low smart driving energy consumption will be recommended to the user.

[0112] In some embodiments, the self-driving energy consumption is determined according to the first current travel state, and is obtained by looking up a correspondence table of the first historical travel state, the driving behavior frequency interval and the average self-driving energy consumption, that is, looking up Table 1.

[0113] In some embodiments, the intelligent driving energy consumption is determined according to the second current travel state, and is obtained by looking up Table 4 through a correspondence table between the first historical travel state, the vehicle speed range and the average intelligent driving energy consumption.

[0114] In some embodiments, the correspondence table of the first historical travel state, the driving behavior frequency interval and the average self-driving energy consumption, that is, the self-driving energy consumption in Table 1 is obtained in the following manner:

[0115] Divide the driving behavior number intervals according to the first historical trip state and the corresponding driving behavior number, and calculate the average self-driving energy consumption corresponding to each driving behavior number interval, that is, the self-driving energy consumption, according to the first historical trip start time, the first historical trip end time, the mileage at the start time of the first historical trip, the mileage at the end time of the first historical trip, the power and / or fuel amount at the start time of the first historical trip, and the power and / or fuel amount at the end time of the first historical trip of the first historical trip state corresponding to each driving behavior number interval;

[0116] The first historical travel status includes historical energy recovery status, historical driving road conditions and historical air conditioning status.

[0117] Exemplarily, when the vehicle is a pure electric vehicle, the start time of the first historical trip of the first historical trip state is set to t1, the end time of the first historical trip is set to t2, the mileage at the start time of the first historical trip is set to m1, the mileage at the end time of the first historical trip is set to m2, the power at the start time of the first historical trip is set to q1, and the power at the end time of the first historical trip is set to q2, then the average self-driving energy consumption of the corresponding driving behavior number interval is set to: (q1-q2) / [(t2-t1)*(m2-m1)].

[0118] Exemplarily, when the vehicle is a pure oil vehicle, the start time of the first historical trip of the first historical trip state is set to t1, the end time of the first historical trip is set to t2, the mileage at the start time of the first historical trip is set to m1, the mileage at the end time of the first historical trip is set to m2, the amount of oil at the start time of the first historical trip is set to p1, and the amount of oil at the end time of the first historical trip is set to p2, then the average self-driving energy consumption of the corresponding driving behavior number interval is set to: (p1-p2) / [(t2-t1)*(m2-m1)].

[0119] Exemplarily, when the vehicle is a hybrid vehicle, the start time of the first historical trip of the first historical trip state is set to t1, the end time of the first historical trip is set to t2, the mileage at the start time of the first historical trip is set to m1, the mileage at the end time of the first historical trip is set to m2, the power level at the start time of the first historical trip is set to q1 and the fuel level is set to p1, and the power level at the end time of the first historical trip is set to q2 and the fuel level is set to p2, then the average self-driving energy consumption in the corresponding driving behavior number interval is set to: [(q1-q2)+(p1-p2)] / [(t2-t1)*(m2-m1)].

[0120] Table 1 is the corresponding relationship between the first historical trip status, driving behavior frequency interval and average self-driving energy consumption

[0121] First historical trip status Driving behavior frequency interval Average self-driving energy consumption A1 (0，x1] a1 A1 (x1, x2] a2 A1 (x2, x3] a3 A1 (x3, x4] a4 A1 (x4, x5] a5 A1 (x5, x6] a6 A1 (x6, x7] a7 A1 (x7, x8] a8 A1 (x8, x9] a9 A1 (x9, x10] a10 A2 (0，x1] a11 A2 (x1, x2] a12 A2 (x2, x3] a13 A2 (x3, x4] a14 A2 (x4, x5] a15 … … … B1 (0，x1] b1 B1 (x1, x2] b2 B1 (x2, x3] b3 B1 (x3, x4] b4 B1 (x4, x5] b5 B1 (x5, x6] b6 B1 (x6, x7] b7 B1 (x7, x8] b8 B1 (x8, x9] b9 B1 (x9, x10] b10 B2 (0，x1] b11 B2 (x1, x2] b12 B2 (x2, x3] b13 B2 (x3, x4] b14 B2 (x4, x5] b15 … … …

[0122] The number of driving behaviors corresponding to the first historical travel state includes the sum of the number of brake pedal travel changes and the number of accelerator pedal travel changes.

[0123] The division of the first historical trip state is shown in Table 2. Among them, when at least one of the historical energy recovery state, the historical driving road condition and the historical air conditioning state changes, the first historical trip state changes. For example, when the driving road condition changes from the highway condition to the congested road condition, it indicates that the congested trip has started, and when the congested road condition changes to the highway condition, it indicates that the congested trip has ended, and the start time of the congested trip, the end time of the congested trip, the mileage at the start time of the congested trip, the mileage at the end time of the congested trip, the power and / or fuel amount at the start time of the congested trip, and the power and / or fuel amount at the end time of the congested trip are recorded.

[0124] Table 2 shows the division results of the first historical trip state

[0125]

[0126] Exemplarily, the number of driving behaviors is divided into intervals with a difference of 10 times, and the number of driving behaviors greater than 90 times is evenly divided into one interval. The division results are shown in Table 3.

[0127] Table 3 Corresponding relationship table of driving behavior frequency interval difference of 10 times

[0128]

[0129]

[0130] In some embodiments, the self-driving energy consumption is determined according to the first current travel state, including: matching the first current travel state with the first historical travel state, the correspondence table of the number of driving behaviors and the average self-driving energy consumption, that is, the first historical travel state in Table 1; when the current energy recovery state, the current driving road condition and the current air-conditioning state in the first current travel state are the same as the historical energy recovery state, the historical driving road condition and the historical air-conditioning state in the first historical travel state, it indicates that the match is successful; after the match is successful, checking the correspondence table of the first historical travel state, the number of driving behaviors and the average self-driving energy consumption, that is, the lowest value of the average self-driving energy consumption under the first historical travel state in Table 1 is the self-driving energy consumption determined corresponding to the first current travel state.

[0131] Among them, the historical energy recovery status, historical driving road conditions, historical air conditioning status, the start time of the first historical trip, the end time of the first historical trip, the mileage at the start time of the first historical trip, the mileage at the end time of the first historical trip, the power and / or fuel volume at the start time of the first historical trip, and the power and / or fuel volume at the end time of the first historical trip are collected by the vehicle end and uploaded to the vehicle cloud at a period of 1s, and the data is parsed and written into the database through the big data program. The offline spark program reads the vehicle data stored in the database. For the above energy recovery status, road conditions, air conditioning status, time, mileage, power and fuel volume, when one of the signal values ​​is null, it is complemented according to the most recent non-null value within 10s of tracing back, and it remains null for more than 10s, and the row data with any null value in the above signals is filtered.

[0132] When the first current trip state fails to match the first historical trip state, the user is not recommended a driving mode, and the vehicle continues to drive according to the current driving mode. At the same time, the energy recovery state, road conditions, air conditioning state, trip start time, trip end time, mileage at the start time of the trip, mileage at the end time of the trip, power and / or oil volume at the start time of the trip, and power and / or oil volume at the end time of the trip, obtained after the trip state ends, are collected by the vehicle end and uploaded to the vehicle cloud as historical data for the next same trip state, that is, for the first current trip state for the first time, the vehicle continues to drive according to the current driving mode, and the current energy recovery state, current road conditions, current air conditioning state, current trip start time, current trip end time, mileage at the start time of the current trip, mileage at the end time of the current trip, power and / or oil volume at the start time of the current trip, and power and / or oil volume at the end time of the current trip, obtained after the first current trip state ends, are collected by the vehicle end and uploaded to the vehicle cloud as corresponding data of the first historical trip state to calculate the average self-driving energy consumption.

[0133] In some embodiments, the corresponding relationship table of the second historical travel state, the vehicle speed range and the average intelligent driving energy consumption, that is, the intelligent driving energy consumption in Table 4 is obtained as follows:

[0134] The vehicle speed intervals are divided according to the second historical trip state and the corresponding vehicle speed, and the average intelligent driving energy consumption corresponding to each vehicle speed interval is calculated according to the second historical trip start time, the second historical trip end time, the mileage at the second historical trip start time, the mileage at the second historical trip end time, the power and / or fuel amount at the second historical trip start time, and the power and / or fuel amount at the second historical trip end time of the second historical trip state corresponding to each vehicle speed interval, that is, the intelligent driving energy consumption;

[0135] The second historical travel status includes historical driving road conditions and historical air conditioning status.

[0136] Exemplarily, when the vehicle is a pure electric vehicle, the start time of the second historical trip of the second historical trip state is set to t3, the end time of the second historical trip is set to t4, the mileage at the start time of the second historical trip is set to m3, the mileage at the end time of the second historical trip is set to m4, the power at the start time of the second historical trip is set to q3, and the power at the end time of the second historical trip is set to q4, then the average intelligent driving energy consumption in the corresponding speed range is set to: (q3-q4) / [(t4-t3)*(m4-m3)].

[0137] Exemplarily, when the vehicle is a pure oil vehicle, the start time of the second historical trip of the second historical trip state is set to t3, the end time of the second historical trip is set to t4, the mileage at the start time of the second historical trip is set to m3, the mileage at the end time of the second historical trip is set to m4, the amount of fuel at the start time of the second historical trip is set to p3, and the amount of fuel at the end time of the second historical trip is set to p4, then the average intelligent driving energy consumption in the corresponding speed range is set to: (p3-p4) / [(t4-t3)*(m4-m3)].

[0138] Exemplarily, when the vehicle is a hybrid vehicle, the start time of the second historical trip of the second historical trip state is set to t3, the end time of the second historical trip is set to t4, the mileage at the start time of the second historical trip is set to m3, the mileage at the end time of the second historical trip is set to m4, the battery level at the start time of the second historical trip is set to q3 and the fuel level is set to p3, and the battery level at the end time of the second historical trip is set to q4 and the fuel level is set to p4, then the average intelligent driving energy consumption in the corresponding vehicle speed range is set to: [(q3-q4)+(p3-p4)] / [(t4-t3)*(m4-m3)].

[0139] Table 4 is the corresponding relationship between the second historical travel state, vehicle speed range and average intelligent driving energy consumption

[0140]

[0141]

[0142] The division of the second historical trip state is shown in Table 5. Among them, when one of the current driving road condition and the current air conditioning state changes, the second historical trip state changes. For example, when the current driving road condition switches from a high-speed road condition to a congested road condition, it indicates that the congested trip has started, and when the congested road condition switches to a high-speed road condition, it indicates that the congested trip has ended, and the start time of the congested trip, the end time of the congested trip, the mileage at the start time of the congested trip, the mileage at the end time of the congested trip, the power and / or fuel at the start time of the congested trip, and the power and / or fuel at the end time of the congested trip are recorded.

[0143] Table 5 shows the division results of the second historical trip status

[0144]

[0145]

[0146] Exemplarily, the vehicle speed intervals are divided into intervals with an interval difference of 10 km / h, and the intervals greater than 120 km / h are divided into one interval. The division results are shown in Table 6.

[0147] Table 6 is the corresponding relationship table for the speed interval difference of 10km / h

[0148]

[0149]

[0150] In some embodiments, the intelligent driving energy consumption is determined according to the second current travel state, including: matching the second current travel state with the first historical travel state, the correspondence table of the number of driving behaviors and the average self-driving energy consumption, that is, the second historical travel state in Table 4; when the current driving road conditions and the current air-conditioning state under the second current travel state are the same as the historical driving road conditions and the historical air-conditioning state under the second historical travel state, it indicates that the match is successful; after the match is successful, checking the correspondence table of the first historical travel state, the number of driving behaviors and the average self-driving energy consumption, that is, checking Table 4 for the lowest value of the average intelligent driving energy consumption under the second historical travel state, which is the intelligent driving energy consumption determined corresponding to the second current travel state.

[0151] When the second current trip state fails to match the second historical trip state, the user is not recommended a driving method, and the user continues to drive directly in the current driving method. At the same time, the driving conditions, air conditioning status, trip start time, trip end time, mileage at the start time of the trip, mileage at the end time of the trip, power and / or oil volume at the start time of the trip, and power and / or oil volume at the end time of the trip obtained after the trip state ends are collected by the vehicle end and uploaded to the vehicle cloud as historical data for the next same trip state, that is, for the first second current trip state, the user continues to drive directly in the current driving method, and the current driving conditions, current air conditioning status, current trip start time, current trip end time, mileage at the start time of the current trip, mileage at the end time of the current trip, power and / or oil volume at the start time of the current trip, and power and / or oil volume at the end time of the current trip obtained after the second current trip state ends are collected by the vehicle end and uploaded to the vehicle cloud as the corresponding data of the second historical trip state to calculate the average intelligent driving energy consumption. The offline spark program reads the vehicle data stored in the database. For the above energy recovery status, road conditions, air conditioning status, time, mileage, power and fuel level, when one of the signal values ​​is null, complement it according to the most recent non-null value within 10 seconds, and keep it as null if it exceeds 10 seconds, and filter out the row data with any null value in the above signals.

[0152] In some embodiments, Figure 3 As shown, a system for a driving recommendation method for reducing comprehensive energy consumption of an automobile is also provided, comprising: an energy consumption determination module 10: for determining self-driving energy consumption and intelligent driving energy consumption according to vehicle operation data;

[0153] Energy consumption comparison and recommendation module 20: Determine the lowest value between the self-driving energy consumption and the intelligent driving energy consumption, and push the driving method corresponding to the lowest energy consumption to the user.

[0154] In some embodiments, Figure 4 As shown, a system for recommending a driving method for reducing comprehensive energy consumption of an automobile is also provided, comprising:

[0155] The first trip state determination module 1011 is used to determine the first current trip state according to the received current energy recovery state of the vehicle, the current driving road condition and the current air conditioning state;

[0156] Self-driving energy consumption determination module 1012: used to calculate and determine self-driving energy consumption according to the first current travel state;

[0157] The second trip state determination module 1021 is used to determine the second current trip state according to the received current driving road condition and current air conditioning state of the vehicle;

[0158] Intelligent driving energy consumption determination module 1022: used to calculate and determine the intelligent driving energy consumption according to the second current travel state;

[0159] Energy consumption comparison and recommendation module 20: receives the output results of the self-driving energy consumption determination module and the intelligent driving energy consumption determination module, compares the self-driving energy consumption and the intelligent driving energy consumption, determines the lowest value between the self-driving energy consumption and the intelligent driving energy consumption, and pushes the driving method corresponding to the lowest energy consumption to the user.

[0160] The energy consumption of self-driving and intelligent driving are compared, and the driving mode corresponding to the lowest energy consumption of the two is pushed to the user. Specifically: the energy consumption of self-driving and intelligent driving are compared. If the energy consumption of self-driving is less than that of intelligent driving, the self-driving mode is pushed to the user; if the energy consumption of self-driving is greater than that of intelligent driving, the intelligent driving mode is pushed to the user.

[0161] In some embodiments, the self-driving energy consumption determination module includes:

[0162] The first energy consumption data table management module is used to store the self-driving energy consumption data and is named as a corresponding relationship table of the first historical travel state, driving behavior frequency interval and average self-driving energy consumption, i.e., Table 1;

[0163] The first energy consumption query module is used to query the corresponding self-driving energy consumption in the first historical travel state, the driving behavior number interval and the average self-driving energy consumption in Table 1 according to the first current travel state;

[0164] The second energy consumption data table management module is used to store the intelligent driving energy consumption data and is named as the corresponding relationship table of the second historical travel state, vehicle speed range and average intelligent driving energy consumption, i.e., Table 4;

[0165] The second energy consumption query module: is used to query the corresponding intelligent driving energy consumption in the correspondence table between the second historical travel state, the vehicle speed range and the average intelligent driving energy consumption, i.e. Table 4, according to the second current travel state.

[0166] In some embodiments, the first energy consumption data table management module includes:

[0167] The first historical data storage module is used to obtain and store the first historical trip state data of the vehicle, including the number of driving behaviors corresponding to each first historical trip state, the start time t1 of the first historical trip, the end time t2 of the first historical trip, the mileage m1 at the start time of the first historical trip, the mileage m2 at the end time of the first historical trip, the power q1 and / or the oil level p1 at the start time of the first historical trip, and the power q2 and / or the oil level p2 at the end time of the first historical trip;

[0168] A driving behavior frequency interval division module: used for dividing the driving behavior frequency into different intervals according to the first historical travel state and the driving behavior frequency;

[0169] Self-driving energy consumption calculation module: used to calculate the average self-driving energy consumption in the corresponding interval of the driving behavior times according to each driving behavior times interval, using the first historical trip start time t1, the first historical trip end time t2, the mileage m1 at the start time of the first historical trip, the mileage m2 at the end time of the first historical trip, the power q1 and / or the oil level p1 at the start time of the first historical trip, and the power q2 and / or the oil level p2 at the end time of the first historical trip within the corresponding driving behavior times interval.

[0170] Among them, when the vehicle is a pure electric vehicle, the calculation formula for the average self-driving energy consumption is: (q1-q2) / [(t2-t1)*(m2-m1)]; when the vehicle is a pure oil vehicle, the calculation formula for the average self-driving energy consumption is: (p1-p2) / [(t2-t1)*(m2-m1)]; when the vehicle is a hybrid vehicle, the calculation formula for the average self-driving energy consumption is: [(q1-q2)+(p1-p2)] / [(t2-t1)*(m2-m1)]; q1 represents the power at the beginning of the first historical trip, unit p1 represents the fuel volume at the beginning of the first historical trip, in L / s*km; q2 represents the power volume at the end of the first historical trip, in kWh / s*km; p2 represents the fuel volume at the end of the first historical trip, in L / s*km; t1 represents the beginning of the first historical trip, in S; t2 represents the end of the first historical trip, in S; m1 represents the mileage at the beginning of the first historical trip, in km; m2 represents the mileage at the end of the first historical trip, in km.

[0171] In some embodiments, the second energy consumption data table management module includes:

[0172] The second historical data storage module is used to obtain and store the second historical trip state data of the vehicle, including the vehicle speed corresponding to each second historical trip state, the start time t3 of the second historical trip, the end time t4 of the second historical trip, the mileage m3 at the start time of the second historical trip, the mileage m4 at the end time of the second historical trip, the power q3 and / or the oil level p3 at the start time of the second historical trip, and the power q4 and / or the oil level p4 at the end time of the second historical trip;

[0173] Vehicle speed interval division module: used to divide the vehicle speed into different intervals according to the second historical trip status and the vehicle speed; intelligent driving energy consumption calculation module: used to calculate the average intelligent driving energy consumption in the corresponding speed interval according to each vehicle speed interval, using the second historical trip start time t3, the second historical trip end time t4, the mileage m3 at the start time of the second historical trip, the mileage m4 at the end time of the second historical trip, the power q3 and / or the oil level p3 at the start time of the second historical trip, and the power q4 and / or the oil level p4 at the end time of the second historical trip within the corresponding vehicle speed interval.

[0174] Among them, when the vehicle is a pure electric vehicle, the calculation formula for the average intelligent driving energy consumption is: (q3-q4) / [(t4-t3)*(m4-m3)]; when the vehicle is a pure oil vehicle, the calculation formula for the average intelligent driving energy consumption is: (p3-p4) / [(t4-t3)*(m4-m3)]; when the vehicle is a hybrid vehicle, the calculation formula for the average intelligent driving energy consumption is: [(q3-q4)+(p3-p4)] / [(t4-t3)*(m4-m3)]; q3 represents the power at the beginning of the second historical trip, unit p3 represents the fuel volume at the beginning of the second historical trip, in L / s*km; q4 represents the power volume at the end of the second historical trip, in kWh / s*km; p4 represents the fuel volume at the end of the second historical trip, in L / s*km; t3 represents the beginning of the second historical trip, in S; t4 represents the end of the second historical trip, in S; m3 represents the mileage at the beginning of the second historical trip, in km; m4 represents the mileage at the end of the second historical trip, in km.

[0175] In some embodiments, the self-driving energy consumption determination module further includes:

[0176] Self-driving energy consumption matching module: used to match the first current travel state with the first historical travel state, the correspondence table of the number of driving behaviors and the average self-driving energy consumption, that is, the first historical travel state in Table 1. When the current energy recovery state, the current driving road condition and the current air-conditioning state in the first current travel state are exactly the same as the correspondence table of the first historical travel state, the number of driving behaviors and the average self-driving energy consumption, that is, the historical energy recovery state, the historical driving road condition and the historical air-conditioning state in a first historical travel state in Table 1, it indicates that the match is successful; after the match is successful, the minimum value of the average self-driving energy consumption under the corresponding first historical travel state is searched and obtained from Table 1 as the self-driving energy consumption corresponding to the first current travel state.

[0177] In some embodiments, the intelligent driving energy consumption determination module further includes:

[0178] Intelligent driving energy consumption matching module: used to match the second current trip state with the second historical trip state, the correspondence table between the vehicle speed range and the average intelligent driving energy consumption, that is, the second historical trip state in Table 4. When the current driving road condition and the current air-conditioning state in the second current trip state are exactly the same as the correspondence table between the second historical trip state, the vehicle speed range and the average intelligent driving energy consumption, that is, the historical driving road condition and the historical air-conditioning state under a second historical trip state in Table 4, it indicates a successful match. After the match is successful, the minimum value of the average intelligent driving energy consumption corresponding to the second historical trip state is searched and obtained from Table 4 as the intelligent driving energy consumption corresponding to the second current trip state.

[0179] In some embodiments, the system further includes a user interaction module: used to push the recommended driving method to the user through screen display and / or voice prompt, so that the user can adjust the driving mode according to the recommended driving method, thereby achieving the purpose of reducing the comprehensive energy consumption of the car.

[0180] In some embodiments, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the driving recommendation method in any of the above embodiments are implemented.

[0181] In some embodiments, a vehicle is also provided, the vehicle comprising the computer device in any of the above embodiments.

[0182] In some embodiments, a computer-readable storage medium is also provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the driving recommendation method in any of the above embodiments are implemented.

[0183] In summary, the advantages of the driving recommendation method for reducing the comprehensive energy consumption of automobiles of the present invention include: significantly reducing the comprehensive energy consumption of automobiles: by real-time analysis of the current energy recovery state of the vehicle, the road conditions and the air conditioning use state, the energy consumption under different driving modes (self-driving and intelligent driving) can be accurately calculated, and the driving mode with the lowest energy consumption can be recommended to the user, which helps the driver to choose the optimal driving strategy according to the actual situation, thereby effectively reducing the overall energy consumption of the automobile and promoting energy conservation and emission reduction; improving driving experience and comfort: when recommending driving methods, the method not only considers energy consumption factors, but also implicitly considers the convenience and comfort of driving (such as considering the air conditioning state). Therefore, the recommended driving method not only reduces energy consumption, but also tries to meet the driver's comfort needs and improve the driving experience; enhancing driving intelligence and personalization: through intelligent analysis of vehicle status and external environment, the method realizes intelligent recommendation of driving methods, making automobile driving more in line with the trend of modern intelligent travel. At the same time, according to the habits and preferences of different drivers, the method can also realize personalized recommendations to a certain extent to meet the needs of different drivers. Promoting energy conservation and emission reduction awareness: By recommending the lowest energy consumption driving mode to drivers, this method helps to cultivate drivers' energy conservation and emission reduction awareness and encourages them to adopt more environmentally friendly and energy-saving driving behaviors in daily driving. This is of great significance for promoting the entire society to transform to a low-carbon, green and sustainable travel mode. Improving energy utilization efficiency: By accurately calculating and analyzing the energy consumption under different driving modes, this method helps to optimize the vehicle's energy utilization strategy and improve energy utilization efficiency. This has a positive effect on alleviating energy shortages and reducing environmental pollution. In summary, this driving recommendation method recommends the lowest energy consumption driving mode in an intelligent and personalized way, which not only helps to reduce the comprehensive energy consumption of the car, improve the driving experience and comfort, but also promotes the popularization of energy conservation and emission reduction awareness and improves energy utilization efficiency. It has significant social and environmental benefits and has promotion and application value in the field of automobile energy consumption control technology.

[0184] The above embodiments are merely illustrative of the principles and effects of the present invention, and are not intended to limit the present application. Anyone familiar with the technology may modify or change the above embodiments without violating the spirit and scope of the present application. Therefore, all equivalent modifications or changes made by a person of ordinary skill in the art without departing from the spirit and technical ideas disclosed in the present application shall still be covered by the claims of the present application.

Claims

1. A driving recommendation method for reducing comprehensive energy consumption of a vehicle, characterized in that: The following steps are involved: Determine the energy consumption of self-driving and intelligent driving based on vehicle operation data; Determine the lowest value between self-driving energy consumption and intelligent driving energy consumption, and push the driving method corresponding to the lowest energy consumption to the user.

2. The driving recommendation method for reducing comprehensive energy consumption of automobiles according to claim 1, characterized in that: The following steps are involved: Determining a first current travel state according to a current energy recovery state of the vehicle, a current driving road condition, and a current air conditioning state; Determining self-driving energy consumption according to a first current travel state; Determining a second current travel state according to the current driving road condition and the current air conditioning state of the vehicle; Determine the intelligent driving energy consumption according to the second current travel state; Determine the lowest value between self-driving energy consumption and intelligent driving energy consumption, and push the driving method corresponding to the lowest energy consumption to the user.

3. The driving recommendation method for reducing comprehensive automobile energy consumption according to claim 2, characterized in that: Determine the lowest value between self-driving energy consumption and intelligent driving energy consumption, and push the driving method corresponding to the lowest energy consumption to the user, including: When the current smart driving state is on, if it is calculated that self-driving consumes less energy, it is recommended to turn off smart driving. If it is calculated that the energy consumption of a certain speed range is lower than the current speed range, the speed range with lower energy consumption is recommended to the user. When the current smart driving state is off, if it is calculated that self-driving has lower energy consumption, no recommendation will be made. If the energy consumption of smart driving in a certain speed range is lower than that of self-driving, the corresponding speed range with low smart driving energy consumption will be recommended to the user.

4. The driving recommendation method for reducing comprehensive energy consumption of automobiles according to claim 2, characterized in that: The self-driving energy consumption is determined according to the first current travel state, and is obtained by looking up a corresponding relationship table between the first historical travel state, the driving behavior frequency interval and the average self-driving energy consumption; And / or, the intelligent driving energy consumption is determined according to the second current travel state, and is obtained by looking up a correspondence table between the second historical travel state, the vehicle speed range and the average intelligent driving energy consumption.

5. The driving recommendation method for reducing comprehensive automobile energy consumption according to claim 4, characterized in that: The self-driving energy consumption in the corresponding relationship table of the first historical travel state, driving behavior frequency interval and average self-driving energy consumption is obtained as follows: Divide the driving behavior number intervals according to the first historical trip state and the corresponding driving behavior number, and calculate the average self-driving energy consumption corresponding to each driving behavior number interval according to the first historical trip start time, the first historical trip end time, the mileage at the start time of the first historical trip, the mileage at the end time of the first historical trip, the power and / or fuel amount at the start time of the first historical trip, and the power and / or fuel amount at the end time of the first historical trip of the first historical trip state corresponding to each driving behavior number interval, that is, the self-driving energy consumption; The first historical travel status includes historical energy recovery status, historical driving road conditions and historical air conditioning status; And / or, the intelligent driving energy consumption in the corresponding relationship table of the second historical travel state, vehicle speed range and average intelligent driving energy consumption is obtained in the following manner: Divide the vehicle speed intervals according to the second historical trip state and the corresponding vehicle speed, and calculate the average intelligent driving energy consumption corresponding to each vehicle speed interval, i.e., the intelligent driving energy consumption, according to the second historical trip start time, the second historical trip end time, the mileage at the second historical trip start time, the mileage at the second historical trip end time, the power and / or fuel amount at the second historical trip start time, and the power and / or fuel amount at the second historical trip end time of the second historical trip state corresponding to each vehicle speed interval; The second historical travel status includes historical driving road conditions and historical air conditioning status.

6. The driving recommendation method for reducing comprehensive energy consumption of automobiles according to claim 5, characterized in that: When the vehicle is a pure electric vehicle, the start time of the first historical trip of the first historical trip state is set to t1, the end time of the first historical trip is set to t2, the mileage at the start time of the first historical trip is set to m1, the mileage at the end time of the first historical trip is set to m2, the power at the start time of the first historical trip is set to q1, and the power at the end time of the first historical trip is set to q2, then the average self-driving energy consumption of the corresponding driving behavior number interval is: (q1-q2) / [(t2-t1)*(m2-m1)]; And / or, when the vehicle is a pure electric vehicle, the second historical trip start time of the second historical trip state is set to t3, the second historical trip end time is set to t4, the mileage at the start time of the second historical trip is set to m3, the mileage at the end time of the second historical trip is set to m4, the power at the start time of the second historical trip is set to q3, and the power at the end time of the second historical trip is set to q4, then the average intelligent driving energy consumption in the corresponding vehicle speed range is: (q3-q4) / [(t4-t3)*(m4-m3)]; Alternatively, when the vehicle is a pure oil vehicle, the first historical trip start time of the first historical trip state is set to t1, the first historical trip end time is set to t2, the mileage at the start time of the first historical trip is set to m1, the mileage at the end time of the first historical trip is set to m2, the oil volume at the start time of the first historical trip is set to p1, and the oil volume at the end time of the first historical trip is set to p2, then the average self-driving energy consumption of the corresponding driving behavior number interval is: (p1-p2) / [(t2-t1)*(m2-m1)]; And / or, when the vehicle is a pure oil vehicle, the second historical trip start time of the second historical trip state is set to t3, the second historical trip end time is set to t4, the mileage at the start time of the second historical trip is set to m3, the mileage at the end time of the second historical trip is set to m4, the fuel volume at the start time of the second historical trip is set to p3, and the fuel volume at the end time of the second historical trip is set to p4, then the average intelligent driving energy consumption of the corresponding vehicle speed range is: (p3-p4) / [(t4-t3)*(m4-m3)]; Alternatively, when the vehicle is a hybrid vehicle, the first historical trip start time of the first historical trip state is set to t1, the first historical trip end time is set to t2, the mileage at the start time of the first historical trip is set to m1, the mileage at the end time of the first historical trip is set to m2, the power at the start time of the first historical trip is set to q1 and the oil level is set to p1, and the power at the end time of the first historical trip is set to q2 and the oil level is set to p2, then the average self-driving energy consumption of the corresponding driving behavior number interval is: [(q1-q2)+(p1-p2)] / [(t2-t1)*(m2-m1)]; And / or, when the vehicle is a hybrid vehicle, the start time of the second historical trip of the second historical trip state is set to t3, the end time of the second historical trip is set to t4, the mileage at the start time of the second historical trip is set to m3, the mileage at the end time of the second historical trip is set to m4, the battery level at the start time of the second historical trip is set to q3 and the fuel level is set to p3, and the battery level at the end time of the second historical trip is set to q4 and the fuel level is set to p4, then the average intelligent driving energy consumption in the corresponding vehicle speed range is set to: [(q3-q4)+(p3-p4)] / [(t4-t3)*(m4-m3)].

7. A system for implementing the driving recommendation method for reducing comprehensive energy consumption of an automobile as claimed in any one of claims 1 to 6, characterized in that: include: Energy consumption determination module: used to determine the energy consumption of self-driving and intelligent driving based on vehicle operation data; Energy consumption comparison and recommendation module: Determine the lowest value between self-driving energy consumption and intelligent driving energy consumption, and push the driving method corresponding to the lowest energy consumption to the user.

8. The system according to claim 7, characterized in that include: A first trip state determination module: used to determine a first current trip state according to the received current energy recovery state of the vehicle, the current driving road condition and the current air conditioning state; Self-driving energy consumption determination module: used to calculate and determine the self-driving energy consumption according to the first current travel state; A second trip state determination module: used to determine a second current trip state according to the received current driving road condition and current air conditioning state of the vehicle; Intelligent driving energy consumption determination module: used to calculate and determine the intelligent driving energy consumption according to the second current travel state; Energy consumption comparison and recommendation module: receives the output results of the self-driving energy consumption determination module and the intelligent driving energy consumption determination module, compares the self-driving energy consumption and the intelligent driving energy consumption, determines the lowest value between the self-driving energy consumption and the intelligent driving energy consumption, and pushes the driving method corresponding to the lowest energy consumption to the user.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the driving recommendation method according to any one of claims 1 to 6 are implemented.

10. A vehicle, characterized in that: The vehicle comprises the computer device of claim 9.

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