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

Abstract

This invention relates to a driving recommendation method, system, vehicle, and device for reducing the overall energy consumption of automobiles. The driving recommendation method for reducing the overall energy consumption of automobiles includes: determining the energy consumption of autonomous driving and the energy consumption of intelligent driving based on vehicle operating data; determining the lower of the autonomous driving energy consumption and the intelligent driving energy consumption; and pushing the driving mode corresponding to the lowest energy consumption to the user. This invention also provides a system for implementing the driving recommendation method. This invention also provides a computer device and a vehicle. Based on intelligent driving and user driving behavior, this invention achieves lower overall energy consumption for new energy vehicles and recommends corresponding driving modes to users, thereby improving the user experience.

Description

Technical Field

[0001] This invention relates to the field of automotive energy consumption control technology, specifically to driving recommendation methods, systems, vehicles, and equipment for reducing overall automotive energy consumption. Background Technology

[0002] With increasing global awareness of environmental protection and the advancement of sustainable development goals, new energy vehicles, as an important solution for reducing carbon emissions and alleviating energy pressure, are experiencing unprecedented growth in adoption. However, against the backdrop of widespread application of new energy vehicles, how to further optimize their overall energy consumption and improve energy efficiency has become a key technical challenge that urgently needs to be addressed both within and outside the industry.

[0003] Traditional energy-saving strategies for new energy vehicles mainly focus on improvements at the vehicle hardware level, such as optimizing the power system, reducing vehicle weight, and improving energy recovery efficiency. These measures reduce vehicle energy consumption to some extent. However, it is undeniable that drivers' driving habits also have a significant impact on the energy consumption of new energy vehicles, with significant differences in energy consumption under different driving modes.

[0004] With the rapid development and widespread application of intelligent driving technology, it not only improves driving safety and comfort but also opens up new paths for energy conservation and emission reduction in new energy vehicles. Intelligent driving systems, through high-precision sensors, advanced algorithms, and real-time data analysis, can achieve precise control over the vehicle's driving status, thus potentially demonstrating enormous potential in reducing energy consumption.

[0005] However, the existing vehicle-side and cloud-based technology systems still fall short in comparing the energy consumption of intelligent driving functions with that of normal driving behavior. Traditional methods for optimizing energy consumption during driving trips often focus on reducing energy consumption by regulating driving behavior (such as reducing aggressive driving operations like sudden acceleration and braking), but fail to fully consider the unique advantages of intelligent driving in reducing overall energy consumption under different road conditions.

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

[0007] CN116653962A discloses a high-efficiency energy-saving system and method for intelligent driving vehicles. This method includes: a vehicle control backend collecting road segment training data and decision speed training data; the vehicle control backend acquiring the decision speed training data and generating a training quadruple set; the vehicle control backend acquiring the road segment training data to train a machine learning model to estimate the i-th energy-saving speed value, and based on the training quadruple set, training a deep reinforcement learning model to decide whether to use an alternative driving speed when an adverse driving environment is detected. This 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 based on the magnitude of the reward value, thereby obtaining the optimal energy-saving speed value in different driving environments, realizing automatic control of the intelligent driving vehicle, and reducing energy waste. However, this energy-saving method only targets intelligent driving by training a machine learning model to determine the optimal driving speed in different driving environments to ensure the lowest energy consumption, but ignores the driver's own driving behavior in different driving environments, which has relatively lower energy consumption. Therefore, relying solely on intelligent driving is insufficient to achieve the goal of the lowest overall energy consumption for new energy 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 device for reducing the overall energy consumption of automobiles, so as to obtain lower overall energy consumption of automobile driving based on intelligent driving and user driving behavior, and recommend corresponding driving modes to users to improve user experience.

[0009] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0010] Recommended driving methods to reduce overall vehicle fuel consumption include the following steps:

[0011] Based on vehicle operation data, determine the energy consumption of autonomous driving and intelligent driving.

[0012] Determine the lowest energy consumption value between autonomous driving and intelligent driving, and push the driving mode corresponding to the lowest energy consumption to the user.

[0013] Based on the aforementioned technical means, by analyzing vehicle operation data in real time, the energy consumption under different driving modes (autonomous driving and intelligent driving) is calculated and determined, and the driving mode with the lowest energy consumption is recommended to the user. This helps the driver choose the optimal driving strategy according to the actual situation, which 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] Preferred driving recommendations for reducing overall vehicle energy consumption include the following steps:

[0015] The first current travel status is determined based on the vehicle's current energy recovery status, current road conditions, and current air conditioning status;

[0016] Determine the energy consumption for driving based on the current trip status;

[0017] Determine the second current travel status based on the vehicle's current road conditions and air conditioning status;

[0018] Determine the intelligent driving energy consumption based on the second current trip status;

[0019] Determine the lowest energy consumption value between autonomous driving and intelligent driving, and push the driving mode corresponding to the lowest energy consumption to the user.

[0020] Based on the aforementioned technologies, the current energy recovery status, road conditions, and air conditioning status of the vehicle are analyzed in real time to determine the current trip status. Then, based on this trip status, the energy consumption under different driving modes (autonomous driving and intelligent driving) is calculated, and the lowest energy consumption driving mode is recommended to the user. This helps drivers choose the optimal driving strategy based on actual conditions, effectively reducing the overall energy consumption of the vehicle, promoting energy conservation and emission reduction, and effectively meeting the driver's comfort needs and improving the driving experience. Furthermore, by combining different drivers' habits and preferences, the lowest energy consumption driving mode is recommended in a personalized and intelligent manner, achieving both personalized and intelligent recommendations, meeting the actual needs of different drivers, and demonstrating significant social and environmental benefits.

[0021] The energy recovery status includes low energy recovery intensity, medium energy recovery intensity, high energy recovery status, and off energy recovery status.

[0022] 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 represents gravitational acceleration.

[0023] Driving conditions include at least congested roads, long downhill roads, long uphill roads, and highways.

[0024] Air conditioner status includes air conditioner on and air conditioner off.

[0025] Self-driving energy consumption is determined based on the driver's driving habits.

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

[0027] Preferably, the lowest energy consumption between autonomous driving and intelligent driving is determined, and the driving mode corresponding to the lowest energy consumption is pushed to the user, including:

[0028] When the current intelligent driving status is on, if the energy consumption of autonomous driving is calculated to be lower, it is recommended to turn off intelligent driving. If the energy consumption of a certain speed range is calculated to be lower than that of the current speed range, the speed range with lower energy consumption is recommended to the user.

[0029] When the current intelligent driving status is off, if the calculated energy consumption of autonomous driving is lower, no recommendation will be made. If the energy consumption of intelligent driving in a certain speed range is lower than that of autonomous driving, then the corresponding speed range with lower energy consumption of intelligent driving will be recommended to the user.

[0030] When a user is actually driving the vehicle, the system acquires information about the vehicle's intelligent driving status, including current speed and other vehicle data. If the vehicle is in non-intelligent driving mode, it calculates the energy consumption under the current road conditions based on the user's driving habits and compares it with the energy consumption at different speeds under the same road conditions in intelligent driving mode, providing a lower energy consumption option. If the vehicle is in intelligent driving mode, it provides a selection of the lowest energy consumption speed range. Additionally, when the user's set driving range is lower than a set distance, a low-energy driving mode is selected. When the required range is met, the system recommends the lowest energy consumption driving method to the user. If the navigation range exceeds the driving range during the user's journey, the system proactively reminds the user and recommends a lower energy consumption driving method to ensure the user completes the last mile of the trip.

[0031] Preferably, the step of determining the self-driving energy consumption based on the first current trip status is obtained by looking up a table showing the correspondence between the first historical trip status, the interval of driving behavior frequency, and the average self-driving energy consumption.

[0032] Preferably, the determination of intelligent driving energy consumption based on the second current trip status is obtained by looking up a table showing the correspondence between the second historical trip status, vehicle speed range, and average intelligent driving energy consumption.

[0033] Preferably, the self-driving energy consumption in the correspondence table between the first historical trip status, driving behavior frequency interval, and average self-driving energy consumption is obtained as follows:

[0034] The driving behavior frequency intervals are divided according to the first historical trip status and the corresponding number of driving behaviors. The average self-driving energy consumption corresponding to each driving behavior frequency interval is calculated based on the first historical trip start time, first historical trip end time, first historical trip start time mileage, first historical trip end time mileage, first historical trip start time battery and / or fuel level, and first historical trip end time battery and / or fuel level.

[0035] The first historical travel status includes historical energy recovery status, historical road conditions, and 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 correspondence table between the second historical trip status, vehicle speed range, and average intelligent driving energy consumption is obtained as follows:

[0038] The vehicle speed is divided into speed ranges based on the second historical trip status and the corresponding vehicle speed. The average intelligent driving energy consumption corresponding to each speed range is calculated based on the second historical trip start time, second historical trip end time, mileage at the second historical trip start time, mileage at the second historical trip end time, battery power and / or fuel level at the second historical trip start time, and battery power and / or fuel level at the second historical trip end time.

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

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

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

[0042] Preferably, when the vehicle is a pure gasoline car, the start time of the first historical journey is set as t1, the end time of the first historical journey is set as t2, the mileage at the start time of the first historical journey is set as m1, the mileage at the end time of the first historical journey is set as m2, the fuel quantity at the start time of the first historical journey is set as p1, and the fuel quantity at the end time of the first historical journey is set as p2. Then the average self-driving energy consumption of the corresponding driving behavior frequency interval is: (p1-p2) / [(t2-t1)*(m2-m1)].

[0043] Preferably, when the vehicle is a pure gasoline car, the start time of the second historical journey is set to t3, the end time of the second historical journey is set to t4, the mileage at the start time of the second historical journey is set to m3, the mileage at the end time of the second historical journey is set to m4, the fuel quantity at the start time of the second historical journey is set to p3, and the fuel quantity at the end time of the second historical journey is set to p4. Then the average intelligent driving energy consumption of the corresponding speed range is: (p3-p4) / [(t4-t3)*(m4-m3)].

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

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

[0046] Preferably, determining the self-driving energy consumption based on the first current trip state includes: matching the first current trip state with the first historical trip state in the correspondence table of the first historical trip state, the interval of driving behavior, and the average self-driving energy consumption; when the current energy recovery state, current road conditions, and current air conditioning state in the first current trip state are the same as the historical energy recovery state, historical road conditions, and historical air conditioning state in the first historical trip state, the matching is successful; after successful matching, the lowest value of the average self-driving energy consumption corresponding to the first historical trip state in the correspondence table of the first historical trip state, the interval of driving behavior, and the average self-driving energy consumption is found, which is the self-driving energy consumption determined for the first current trip state.

[0047] The historical energy recovery status, historical road conditions, historical air conditioning status, start time of the first historical trip, end time of the first historical trip, mileage at the start time of the first historical trip, mileage at the end time of the first historical trip, battery level and / or fuel level at the start time of the first historical trip, and battery level and / or fuel level at the end time of the first historical trip are collected by the vehicle and uploaded to the vehicle cloud at 1-second intervals. The data is then parsed by a big data program and written to the database. An offline Spark program reads the vehicle data stored in the database. For the above energy recovery status, road conditions, air conditioning status, time, mileage, battery level, and fuel level, if any of the above signals is null, it is padded with the most recent non-null value within the last 10 seconds. If the null value is more than 10 seconds later, it remains null. Rows of data where any of the aforementioned signals is null are filtered out.

[0048] When the first current trip state fails to match the first historical trip state, no driving mode is recommended to the user, and the user continues driving directly according to the current driving mode. At the same time, the energy recovery status, driving conditions, air conditioning status, trip start time, trip end time, mileage at the start time, mileage at the end time, battery level and / or fuel level at the start time, and battery level and / or fuel level at the end time are collected by the vehicle and uploaded to the vehicle cloud as historical data for the next trip state of the same type. That is, for the first current trip state, the user continues driving directly according to the current driving mode, and the current energy recovery status, current driving conditions, current air conditioning status, current trip start time, current trip end time, current trip start time, current trip end time, mileage at the start time, mileage at the end time, battery level and / or fuel level at the start time, and battery level and / or fuel level at the end time are collected by the vehicle and uploaded to the vehicle cloud as corresponding data for the first historical trip state to calculate the average self-driving energy consumption.

[0049] Preferably, determining intelligent driving energy consumption based on the second current trip state includes: matching the second current trip state with the second historical trip state in the correspondence table of vehicle speed range and average intelligent driving energy consumption; when the current driving conditions and current air conditioning status in the second current trip state are the same as the historical driving conditions and historical air conditioning status in the second historical trip state, the matching is successful; after successful matching, the lowest value of the average intelligent driving energy consumption corresponding to the second historical trip state in the correspondence table of second historical trip state, vehicle speed range and average intelligent driving energy consumption is found, which is the intelligent driving energy consumption determined for the second current trip state.

[0050] When the second current trip state fails to match the second historical trip state, no driving mode is recommended to the user, and driving continues directly according to the current driving mode. Simultaneously, driving conditions, air conditioning status, trip start time, trip end time, mileage at the start time, mileage at the end time, battery level and / or fuel level at the start time, and battery level and / or fuel level at the end time are collected by the vehicle and uploaded to the vehicle cloud as historical data for the next similar trip state. That is, for the first second current trip state, driving continues directly according to the current driving mode, and the current driving conditions, current air conditioning status, current trip start time, current trip end time, current mileage at the start time, current mileage at the end time, battery level and / or fuel level at the start time, and battery level and / or fuel level at the end time are collected by the vehicle and uploaded to the vehicle cloud as corresponding data for 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, battery level and fuel level, when any of the above signals is null, the value is supplemented by the most recent non-null value within the past 10 seconds. If the value is null for more than 10 seconds, the data will remain null. The row data where any of the above signals is null is filtered out.

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

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

[0053] Energy consumption comparison and recommendation module: Determine the lowest energy consumption between autonomous driving and intelligent driving, and push the driving mode corresponding to the lowest energy consumption to the user.

[0054] Preferably, the system includes:

[0055] First trip status determination module: used to determine the first current trip status based on the received current energy recovery status of the vehicle, current driving conditions and current air conditioning status;

[0056] Autonomous driving energy consumption determination module: used to calculate and determine autonomous driving energy consumption based on the first current trip status;

[0057] Second trip status determination module: used to determine the second current trip status based on the received current driving conditions and current air conditioning status of the vehicle;

[0058] Intelligent driving energy consumption determination module: used to calculate and determine intelligent driving energy consumption based on the second current trip status;

[0059] Energy consumption comparison and recommendation module: Receives the output results from the autonomous driving energy consumption determination module and the intelligent driving energy consumption determination module, compares the autonomous driving energy consumption and the intelligent driving energy consumption, determines the lowest value of the autonomous driving energy consumption and the driving mode corresponding to the lowest energy consumption and pushes it to the user.

[0060] The system compares the energy consumption of autonomous driving and intelligent driving, and pushes the driving mode corresponding to the lowest energy consumption of the two to the user. Specifically, it compares the energy consumption of autonomous driving and intelligent driving. If the energy consumption of autonomous driving is less than that of intelligent driving, the autonomous driving mode is pushed to the user. If the energy consumption of autonomous driving is greater than that of intelligent driving, the intelligent driving mode is pushed to the user.

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

[0062] The first energy consumption data management module is used to store autonomous driving energy consumption data and is named as the correspondence table between the first historical trip status, the interval of driving behavior frequency and the average autonomous driving energy consumption.

[0063] The first energy consumption query module is used to query the corresponding self-driving energy consumption in the correspondence table between the first current trip status, the number of driving behaviors, and the average self-driving energy consumption, based on the first historical trip status.

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

[0065] The second energy consumption data management module is used to store intelligent driving energy consumption data and is named the correspondence table between the second historical trip status, 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 between the second current trip status, the second historical trip status, the vehicle speed range, and the average intelligent driving energy consumption, based on the second current trip status.

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

[0068] First historical data storage module: used to acquire and store the vehicle's first historical trip status data, including the number of driving behaviors corresponding to each first historical trip status, 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 battery level q1 and / or fuel level p1 at the start time of the first historical trip, and the battery level q2 and / or fuel level p2 at the end time of the first historical trip;

[0069] Driving behavior frequency interval division module: used to divide the number of driving behaviors into different intervals based on the first historical trip status and the number of driving behaviors;

[0070] The self-driving energy consumption calculation module is used to calculate the average self-driving energy consumption for each driving behavior interval based on the start time t1, end time t2, mileage m1 at the start time, mileage m2 at the end time, battery q1 and / or fuel p1 at the start time, and battery q2 and / or fuel p2 at the end time.

[0071] The formulas for calculating average self-driving energy consumption are as follows: (q1-q2) / [(t2-t1)*(m2-m1)] for pure electric vehicles; (p1-p2) / [(t2-t1)*(m2-m1)] for pure gasoline vehicles; and ([(q1-q2)+(p1-p2)] / [(t2-t1)*(m2-m1)] for hybrid electric vehicles. Here, q1 represents the amount of electricity at the start of the first historical journey, expressed in units of... The values ​​are kWh / s*km; p1 represents the fuel quantity at the start of the first historical journey, in L / s*km; q2 represents the electricity quantity at the end of the first historical journey, in kWh / s*km; p2 represents the fuel quantity at the end of the first historical journey, in L / s*km; t1 represents the start time of the first historical journey, in seconds; t2 represents the end time of the first historical journey, in seconds; m1 represents the mileage at the start time of the first historical journey, in kilometers; m2 represents the mileage at the end time of the first historical journey, in kilometers.

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

[0073] Second historical data storage module: used to acquire and store the vehicle's second historical trip status data, including the vehicle speed corresponding to each second historical trip status, 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 battery level q3 and / or fuel level p3 at the start time of the second historical trip, and the battery level q4 and / or fuel level p4 at the end time of the second historical trip;

[0074] Speed ​​range division module: used to divide the vehicle speed into different ranges based on the second historical trip status and vehicle speed;

[0075] Intelligent driving energy consumption calculation module: It is used to calculate the average intelligent driving energy consumption of each vehicle speed range based on 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 battery power q3 and / or fuel power p3 at the start time of the second historical trip, and the battery power q4 and / or fuel power p4 at the end time of the second historical trip.

[0076] The formula for calculating the average intelligent driving energy consumption is: (q3-q4) / [(t4-t3)*(m4-m3)], where q3 represents the energy consumption at the start of the second historical journey, in kWh / s*km; q4 represents the energy consumption at the end of the second historical journey, in kWh / s*km; t3 represents the start of the second historical journey, in seconds; t4 represents the end of the second historical journey, in seconds; m3 represents the mileage at the start of the second historical journey, in kilometers; and m4 represents the mileage at the end of the second historical journey, in kilometers.

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

[0078] The self-driving energy consumption matching module is used to match the first current trip state with the first historical trip state and the first historical trip state in the correspondence table of driving behavior frequency interval and average self-driving energy consumption. When the current energy recovery state, current driving conditions and current air conditioning state in the first current trip state are exactly the same as the historical energy recovery state, historical driving conditions and historical air conditioning state in a certain historical trip state in the correspondence table of first historical trip state, driving behavior frequency interval and average self-driving energy consumption, it means that the match is successful. After the match is successful, the module searches for and obtains the lowest value of the average self-driving energy consumption in the corresponding first historical trip state from the correspondence table of first historical trip state, driving behavior frequency interval and average self-driving energy consumption, and uses it as the determined self-driving energy consumption corresponding to the first current trip state.

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

[0080] Intelligent driving energy consumption matching module: It is used to match the second current trip state with the second historical trip state and the second historical trip state in the correspondence table of vehicle speed range and average intelligent driving energy consumption. When the current driving conditions and current air conditioning status in the second current trip state are exactly the same as the historical driving conditions and historical air conditioning status in a certain second historical trip state in the correspondence table of second historical trip state, vehicle speed range and average intelligent driving energy consumption, it means that the matching is successful. After the matching is successful, the lowest value of the average intelligent driving energy consumption in the corresponding second historical trip state is found and obtained from the correspondence table of second historical trip state, vehicle speed range and average intelligent driving energy consumption, and used as the determined intelligent driving energy consumption corresponding to the second current trip state.

[0081] Preferably, the system further includes a user interaction module: used to push recommended driving modes to the user via screen display and / or voice prompts. This allows the user to adjust the driving mode according to the recommended driving mode, thereby reducing the vehicle's overall energy consumption.

[0082] The present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the driving recommendation method as described in the present invention.

[0083] The present invention also provides a vehicle that includes the computer equipment described in the present invention.

[0084] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the driving recommendation method as described in the present invention.

[0085] The beneficial effects of this invention are:

[0086] The driving recommendation method for reducing overall vehicle energy consumption of the present invention analyzes vehicle operation data in real time, calculates and determines the energy consumption under different driving modes (autonomous driving and intelligent driving), and recommends the driving mode with the lowest energy consumption to the user. This helps drivers choose the optimal driving strategy according to the actual situation, thereby obtaining lower overall energy consumption of new energy vehicles based on intelligent driving and user driving behavior, and recommending the corresponding driving mode to the user, effectively improving the user's overall driving experience. It has promotion and application value in the field of vehicle energy consumption control technology. Attached Figure Description

[0087] Figure 1 This is a first flowchart of the driving recommendation method for reducing the overall energy consumption of a vehicle according to the present invention;

[0088] Figure 2 This is a second flowchart of the driving recommendation method for reducing the overall energy consumption of a vehicle according to the present invention;

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

[0090] Figure 4 A second structural diagram of a driving recommendation system for reducing overall vehicle energy consumption. Detailed Implementation

[0091] The embodiments of the present invention will be described below 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 content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed 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 and 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 representations of the basic concept of the present invention. Therefore, the drawings only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0093] Numerous details are explored in the following description to provide a more thorough explanation of embodiments of this application; however, it will be apparent to those skilled in the art that embodiments of this application may be implemented without these specific details.

[0094] This invention aims to disclose a driving recommendation method, system, vehicle, and device for reducing the overall energy consumption of automobiles. Based on intelligent driving and the user's own driving behavior habits, it aims to achieve lower overall energy consumption for automobile driving and recommend corresponding driving modes to the user, thereby improving the user experience and reducing the user's anxiety about driving range.

[0095] Among them, such as Figure 1 As shown, the recommended driving methods for reducing overall vehicle energy consumption include the following steps:

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

[0097] S2. Determine the lowest energy consumption between autonomous driving and intelligent driving, and push the driving mode corresponding to the lowest energy consumption to the user.

[0098] By analyzing vehicle operation data in real time, the system calculates and determines the energy consumption under different driving modes (autonomous driving and intelligent driving), and recommends the driving mode with the lowest energy consumption to users. This helps drivers choose the optimal driving strategy based on actual conditions, which not only effectively reduces the overall energy consumption of the vehicle 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 methods for reducing overall vehicle energy consumption include the following steps:

[0100] S111. Determine the first current trip status based on the vehicle's current energy recovery status, current road conditions, and current air conditioning status; S112. Determine the self-driving energy consumption based on the first current trip status;

[0101] S121. Determine the second current travel status based on the vehicle's current road conditions and current air conditioning status;

[0102] S122. Determine the intelligent driving energy consumption based on the second current trip status;

[0103] S2. Determine the lowest energy consumption between autonomous driving and intelligent driving, and push the driving mode 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 represents gravitational acceleration.

[0105] Driving conditions include at least congested roads, long downhill roads, long uphill roads, and highways.

[0106] Air conditioner status includes air conditioner on and air conditioner off.

[0107] Self-driving energy consumption is determined based on the driver's driving habits.

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

[0109] In some embodiments, determining the lower of autonomous driving energy consumption and intelligent driving energy consumption, and pushing the driving mode corresponding to the lowest energy consumption to the user, includes:

[0110] When the current intelligent driving status is on, if the energy consumption of autonomous driving is calculated to be lower, it is recommended to turn off intelligent driving. If the energy consumption of a certain speed range is calculated to be lower than that of the current speed range, the speed range with lower energy consumption is recommended to the user.

[0111] When the current intelligent driving status is off, if the calculated energy consumption of autonomous driving is lower, no recommendation will be made. If the energy consumption of intelligent driving in a certain speed range is lower than that of autonomous driving, then the corresponding speed range with lower energy consumption of intelligent driving will be recommended to the user.

[0112] In some embodiments, the self-driving energy consumption is determined based on the first current trip status by looking up the correspondence table between the first historical trip status, the interval of driving behavior frequency and the average self-driving energy consumption, i.e., by looking up Table 1.

[0113] In some embodiments, intelligent driving energy consumption is determined based on the second current trip state by referring to Table 4, which shows the correspondence between the first historical trip state, vehicle speed range and average intelligent driving energy consumption.

[0114] In some embodiments, the correspondence between the first historical trip status, the interval of driving behavior frequency, and the average self-driving energy consumption, i.e., the self-driving energy consumption in Table 1, is obtained as follows:

[0115] The driving behavior frequency intervals are divided based on the first historical trip status and the corresponding number of driving behaviors. The average self-driving energy consumption corresponding to each driving behavior frequency interval is calculated based on the first historical trip start time, first historical trip end time, first historical trip start mileage, first historical trip end mileage, first historical trip start battery and / or fuel level, and first historical trip end battery and / or fuel level.

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

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

[0118] For example, when the vehicle is a pure gasoline car, the start time of the first historical journey is set to t1, the end time of the first historical journey is set to t2, the mileage at the start time of the first historical journey is set to m1, the mileage at the end time of the first historical journey is set to m2, the fuel quantity at the start time of the first historical journey is set to p1, and the fuel quantity at the end time of the first historical journey is set to p2. Then the average self-driving energy consumption of the corresponding driving behavior frequency interval is: (p1-p2) / [(t2-t1)*(m2-m1)].

[0119] For example, when the vehicle is a hybrid vehicle, the start time of the first historical trip 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 battery level at the start time of the first historical trip is set to q1 and the fuel level to p1, and the battery level at the end time of the first historical trip is set to q2 and the fuel level to p2. Then the average self-driving energy consumption of the corresponding driving behavior frequency interval is: [(q1-q2)+(p1-p2)] / [(t2-t1)*(m2-m1)].

[0120] Table 1 shows the correspondence between the first historical trip status, the interval of driving behavior frequency, and the average self-driving energy consumption.

[0121] First historical itinerary status driving behavior frequency range 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 first historical trip status is divided as shown in Table 2. The first historical trip status changes when at least one of the historical energy recovery status, historical road conditions, and historical air conditioning status changes. For example, when the road conditions change from highway to congested, it indicates the start of a congested trip; when the congested road conditions change back to highway, it indicates the end of a congested trip. The start time, end time, mileage at the start time, mileage at the end time, battery level and / or fuel level at the start time, and battery level and / or fuel level at the end time are recorded.

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

[0125]

[0126] For example, the number of driving behaviors was divided into intervals with a difference of 10 times and a difference of more than 90 times, and the division results are shown in Table 3.

[0127] Table 3. Correspondence between driving behavior frequency intervals with a difference of 10 times.

[0128]

[0129]

[0130] In some embodiments, determining the self-driving energy consumption based on the first current trip state includes: matching the first current trip state with the first historical trip state, the correspondence table of driving behavior frequency intervals and average self-driving energy consumption, i.e., the first historical trip state in Table 1; when the current energy recovery state, current driving conditions and current air conditioning state in the first current trip state are the same as the historical energy recovery state, historical driving conditions and historical air conditioning state in the first historical trip state, the matching is successful; after successful matching, the lowest value of the average self-driving energy consumption corresponding to the first historical trip state in the correspondence table of first historical trip state, driving behavior frequency intervals and average self-driving energy consumption, i.e., Table 1, is the self-driving energy consumption determined corresponding to the first current trip state.

[0131] The historical energy recovery status, historical road conditions, historical air conditioning status, start time of the first historical trip, end time of the first historical trip, mileage at the start time of the first historical trip, mileage at the end time of the first historical trip, battery level and / or fuel level at the start time of the first historical trip, and battery level and / or fuel level at the end time of the first historical trip are collected by the vehicle and uploaded to the vehicle cloud at 1-second intervals. The data is then parsed by a big data program and written to the database. An offline Spark program reads the vehicle data stored in the database. For the above energy recovery status, road conditions, air conditioning status, time, mileage, battery level, and fuel level, if any of the above signals is null, it is padded with the most recent non-null value within the last 10 seconds. If the null value is more than 10 seconds later, it remains null. Rows of data where any of the aforementioned signals is null are filtered out.

[0132] When the first current trip state fails to match the first historical trip state, no driving mode is recommended to the user, and the user continues driving directly according to the current driving mode. At the same time, the energy recovery status, driving conditions, air conditioning status, trip start time, trip end time, mileage at the start time, mileage at the end time, battery level and / or fuel level at the start time, and battery level and / or fuel level at the end time are collected by the vehicle and uploaded to the vehicle cloud as historical data for the next trip state of the same type. That is, for the first current trip state, the user continues driving directly according to the current driving mode, and the current energy recovery status, current driving conditions, current air conditioning status, current trip start time, current trip end time, current trip start time, current trip end time, mileage at the start time, mileage at the end time, battery level and / or fuel level at the start time, and battery level and / or fuel level at the end time are collected by the vehicle and uploaded to the vehicle cloud as corresponding data for the first historical trip state to calculate the average self-driving energy consumption.

[0133] In some embodiments, the correspondence between the second historical trip status, vehicle speed range, and average intelligent driving energy consumption, i.e., the intelligent driving energy consumption in Table 4, is obtained as follows:

[0134] The vehicle speed is divided into speed ranges based on the second historical trip status and the corresponding vehicle speed. The average intelligent driving energy consumption corresponding to each speed range is calculated based on the second historical trip start time, second historical trip end time, mileage at the second historical trip start time, mileage at the second historical trip end time, battery power and / or fuel level at the second historical trip start time, and battery power and / or fuel level at the second historical trip end time.

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

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

[0137] For example, when the vehicle is a pure gasoline car, the start time of the second historical journey is set to t3, the end time of the second historical journey is set to t4, the mileage at the start time of the second historical journey is set to m3, the mileage at the end time of the second historical journey is set to m4, the fuel quantity at the start time of the second historical journey is set to p3, and the fuel quantity at the end time of the second historical journey is set to p4. Then the average intelligent driving energy consumption of the corresponding speed range is: (p3-p4) / [(t4-t3)*(m4-m3)].

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

[0139] Table 4 shows the correspondence between the second historical travel status, vehicle speed range, and average intelligent driving energy consumption.

[0140]

[0141]

[0142] The division of the second historical trip status is shown in Table 5. The second historical trip status changes when either the current road condition or the current air conditioning status changes. For example, when the current road condition changes from highway to congested, it indicates the start of the congested trip; when it changes back to highway, it indicates the end of the congested trip. The start time, end time, mileage at the start time, mileage at the end time, battery level and / or fuel level at the start time, and battery level and / or fuel level at the end time are recorded.

[0143] Table 5 shows the results of the second historical journey state division.

[0144]

[0145]

[0146] For example, the vehicle speed range is divided into two equal ranges with a range difference of 10 km / h and a range greater than 120 km / h. The division results are shown in Table 6.

[0147] Table 6 shows the corresponding relationships for speed ranges with a difference of 10 km / h.

[0148]

[0149]

[0150] In some embodiments, determining intelligent driving energy consumption based on the second current trip state includes: matching the second current trip state with the first historical trip state, the correspondence table of driving behavior frequency intervals and average self-driving energy consumption, i.e., the second historical trip state in Table 4. When the current driving conditions and current air conditioning status in the second current trip state are the same as the historical driving conditions and historical air conditioning status in the second historical trip state, the matching is successful. After successful matching, the correspondence table of the first historical trip state, the correspondence table of driving behavior frequency intervals and average self-driving energy consumption is looked up, i.e., the lowest value of the average intelligent driving energy consumption corresponding to the second historical trip state in Table 4 is the intelligent driving energy consumption determined for the second current trip state.

[0151] When the second current trip state fails to match the second historical trip state, no driving mode is recommended to the user, and driving continues directly according to the current driving mode. Simultaneously, driving conditions, air conditioning status, trip start time, trip end time, mileage at the start time, mileage at the end time, battery level and / or fuel level at the start time, and battery level and / or fuel level at the end time are collected by the vehicle and uploaded to the vehicle cloud as historical data for the next similar trip state. That is, for the first second current trip state, driving continues directly according to the current driving mode, and the current driving conditions, current air conditioning status, current trip start time, current trip end time, current mileage at the start time, current mileage at the end time, battery level and / or fuel level at the start time, and battery level and / or fuel level at the end time are collected by the vehicle and uploaded to the vehicle cloud as corresponding data for 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, battery level and fuel level, when any of the above signals is null, the value is supplemented by the most recent non-null value within the past 10 seconds. If the value is null for more than 10 seconds, the data will remain null. The row data where any of the above signals is null is filtered out.

[0152] In some embodiments, such as Figure 3 As shown, a system for a driving recommendation method to reduce the overall energy consumption of a vehicle is also provided, including: an energy consumption determination module 10: used to determine autonomous driving energy consumption and intelligent driving energy consumption based on vehicle operating data;

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

[0154] In some embodiments, such as Figure 4 As shown, a system for a driving recommendation method to reduce the overall energy consumption of a vehicle is also provided, comprising:

[0155] First trip status determination module 1011: used to determine the first current trip status based on the received current energy recovery status of the vehicle, current driving conditions and current air conditioning status;

[0156] Autonomous driving energy consumption determination module 1012: used to calculate and determine autonomous driving energy consumption based on the first current trip status;

[0157] Second trip status determination module 1021: used to determine the second current trip status based on the received current driving conditions and current air conditioning status of the vehicle;

[0158] Intelligent driving energy consumption determination module 1022: used to calculate and determine intelligent driving energy consumption based on the second current trip status;

[0159] Energy consumption comparison and recommendation module 20: Receives the output results of the autonomous driving energy consumption determination module and the intelligent driving energy consumption determination module, compares the autonomous driving energy consumption and the intelligent driving energy consumption, determines the lowest value of the autonomous driving energy consumption and the intelligent driving energy consumption, and pushes the driving mode corresponding to the lowest energy consumption to the user.

[0160] The system compares the energy consumption of autonomous driving and intelligent driving, and pushes the driving mode corresponding to the lowest energy consumption of the two to the user. Specifically, it compares the energy consumption of autonomous driving and intelligent driving. If the energy consumption of autonomous driving is less than that of intelligent driving, the autonomous driving mode is pushed to the user. If the energy consumption of autonomous driving is greater than that of intelligent driving, the intelligent driving mode is pushed to the user.

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

[0162] The first energy consumption data management module is used to store autonomous driving energy consumption data and is named the correspondence table between the first historical trip status, the interval of driving behavior frequency and the average autonomous driving energy consumption, i.e., Table 1;

[0163] First energy consumption query module: used to query the corresponding self-driving energy consumption in the first historical trip status, the correspondence table of driving behavior frequency interval and average self-driving energy consumption, i.e., Table 1, based on the first current trip status.

[0164] The second energy consumption data management module is used to store intelligent driving energy consumption data and is named the correspondence table between the second historical trip status, 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 table of correspondence between the second current trip status, the second historical trip status, the vehicle speed range and the average intelligent driving energy consumption, i.e., Table 4, based on the second current trip status.

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

[0167] First historical data storage module: used to acquire and store the vehicle's first historical trip status data, including the number of driving behaviors corresponding to each first historical trip status, 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 battery level q1 and / or fuel level p1 at the start time of the first historical trip, and the battery level q2 and / or fuel level p2 at the end time of the first historical trip;

[0168] Driving behavior frequency interval division module: used to divide the number of driving behaviors into different intervals based on the first historical trip status and the number of driving behaviors;

[0169] The self-driving energy consumption calculation module is used to calculate the average self-driving energy consumption for each driving behavior interval based on the start time t1, end time t2, mileage m1 at the start time, mileage m2 at the end time, battery q1 and / or fuel p1 at the start time, and battery q2 and / or fuel p2 at the end time.

[0170] The formulas for calculating average self-driving energy consumption are as follows: (q1-q2) / [(t2-t1)*(m2-m1)] for pure electric vehicles; (p1-p2) / [(t2-t1)*(m2-m1)] for pure gasoline vehicles; and ([(q1-q2)+(p1-p2)] / [(t2-t1)*(m2-m1)] for hybrid electric vehicles. Here, q1 represents the amount of electricity at the start of the first historical journey, expressed in units of... The values ​​are kWh / s*km; p1 represents the fuel quantity at the start of the first historical journey, in L / s*km; q2 represents the electricity quantity at the end of the first historical journey, in kWh / s*km; p2 represents the fuel quantity at the end of the first historical journey, in L / s*km; t1 represents the start time of the first historical journey, in seconds; t2 represents the end time of the first historical journey, in seconds; m1 represents the mileage at the start time of the first historical journey, in kilometers; m2 represents the mileage at the end time of the first historical journey, in kilometers.

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

[0172] Second historical data storage module: used to acquire and store the vehicle's second historical trip status data, including the vehicle speed corresponding to each second historical trip status, 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 battery level q3 and / or fuel level p3 at the start time of the second historical trip, and the battery level q4 and / or fuel level p4 at the end time of the second historical trip;

[0173] Speed ​​range division module: used to divide the vehicle speed into different ranges based on the second historical trip status and vehicle speed; Intelligent driving energy consumption calculation module: used to calculate the average intelligent driving energy consumption of the corresponding speed range based on the second historical trip start time t3, second historical trip end time t4, mileage m3 at the start time of the second historical trip, mileage m4 at the end time of the second historical trip, battery power q3 and / or fuel level p3 at the start time of the second historical trip, and battery power q4 and / or fuel level p4 at the end time of the second historical trip.

[0174] The formulas for calculating the average intelligent driving energy consumption are as follows: (q3-q4) / [(t4-t3)*(m4-m3)] for a pure electric vehicle; (p3-p4) / [(t4-t3)*(m4-m3)] for a pure gasoline vehicle; and ([(q3-q4)+(p3-p4)] / [(t4-t3)*(m4-m3)] for a hybrid vehicle. Here, q3 represents the battery level at the start of the second historical journey, in units of... The values ​​are kWh / s*km; p3 represents the fuel quantity at the start of the second historical journey, in L / s*km; q4 represents the electricity quantity at the end of the second historical journey, in kWh / s*km; p4 represents the fuel quantity at the end of the second historical journey, in L / s*km; t3 represents the start time of the second historical journey, in seconds; t4 represents the end time of the second historical journey, in seconds; m3 represents the mileage at the start time of the second historical journey, in kilometers; m4 represents the mileage at the end time of the second historical journey, in kilometers.

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

[0176] The autonomous driving energy consumption matching module is used to match the first current trip state with the first historical trip state, the correspondence table of driving behavior frequency intervals and average autonomous driving energy consumption, i.e., the first historical trip state in Table 1. When the current energy recovery state, current driving conditions and current air conditioning state of the first current trip state are exactly the same as the historical energy recovery state, historical driving conditions and historical air conditioning state of a certain first historical trip state in Table 1, it means that the matching is successful. After the matching is successful, the lowest value of the average autonomous driving energy consumption of the corresponding first historical trip state is found and obtained from Table 1, which is used as the determined autonomous driving energy consumption corresponding to the first current trip state.

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

[0178] Intelligent driving energy consumption matching module: It is used to match the second current trip state with the second historical trip state, the speed range with the average intelligent driving energy consumption, i.e., the second historical trip state in Table 4. When the current driving conditions and current air conditioning status in the second current trip state are exactly the same as the historical driving conditions and historical air conditioning status in a certain second historical trip state in Table 4, it means that the matching is successful. After the matching is successful, the lowest value of the average intelligent driving energy consumption in the corresponding second historical trip state is found from Table 4 and used as the determined 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 recommended driving modes to the user via screen display and / or voice prompts, so that the user can adjust the driving mode according to the recommended driving mode, thereby reducing the overall energy consumption of the vehicle.

[0180] In some embodiments, a computer device is also provided, including a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the steps of the driving recommendation method as described in any of the above embodiments.

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

[0182] In some embodiments, a computer-readable storage medium is also provided, having a computer program stored thereon that, when executed by a processor, implements the steps of the driving recommendation method as described in any of the above embodiments.

[0183] In summary, the advantages of the driving recommendation method for reducing overall vehicle energy consumption of the present invention include: significantly reducing overall vehicle energy consumption: by analyzing the vehicle's current energy recovery status, driving conditions, and air conditioning usage in real time, the method accurately calculates the energy consumption under different driving modes (autonomous driving and intelligent driving) and recommends the driving mode with the lowest energy consumption to the user. This helps drivers choose the optimal driving strategy based on actual conditions, thereby effectively reducing the overall energy consumption of the vehicle and promoting energy conservation and emission reduction; improving driving experience and comfort: when recommending driving modes, this method not only considers energy consumption factors but also implicitly considers driving convenience and comfort (such as air conditioning status). Therefore, the recommended driving mode reduces energy consumption while also meeting the driver's comfort needs as much as possible, improving the driving experience; enhancing driving intelligence and personalization: by intelligently analyzing vehicle status and the external environment, this method achieves intelligent recommendation of driving modes, making vehicle driving more in line with the trend of modern intelligent travel. At the same time, this method can also achieve personalized recommendations to a certain extent based on the habits and preferences of different drivers, meeting the needs of different drivers. Promoting Energy Conservation and Emission Reduction Awareness: By recommending the lowest energy consumption driving style to drivers, this method helps cultivate their awareness of energy conservation and emission reduction, encouraging them to adopt more environmentally friendly and energy-efficient driving behaviors in their daily driving. This is of great significance for promoting the transformation of the entire society towards low-carbon, green, and sustainable travel modes. Improving Energy Efficiency: By accurately calculating and analyzing energy consumption under different driving styles, this method helps optimize vehicle energy utilization strategies and improve energy 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 style in an intelligent and personalized way. It not only helps reduce the overall energy consumption of automobiles and improve driving experience and comfort, but also promotes the popularization of energy conservation and emission reduction awareness and improves energy efficiency, demonstrating significant social and environmental benefits. It has promotional and application value in the field of automotive 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 this application. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of this application. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in this application should still be covered by the claims of this application.

Claims

1. A driving recommendation method for reducing the overall energy consumption of a vehicle, characterized in that, Includes the following steps: Based on vehicle operation data, determine the energy consumption of autonomous driving and intelligent driving. Determine the lowest energy consumption value between autonomous driving and intelligent driving, and push the driving mode corresponding to the lowest energy consumption to the user, including: When the current intelligent driving status is on, if the energy consumption of autonomous driving is calculated to be lower, it is recommended to turn off intelligent driving. If the energy consumption of a certain speed range is calculated to be lower than that of the current speed range, the speed range with lower energy consumption is recommended to the user. When the current intelligent driving status is off, if the calculated energy consumption of autonomous driving is lower, no recommendation will be made. If the energy consumption of intelligent driving in a certain speed range is lower than that of autonomous driving, then the corresponding speed range with lower energy consumption of intelligent driving will be recommended to the user.

2. The driving recommendation method for reducing overall vehicle energy consumption according to claim 1, characterized in that, Includes the following steps: The first current travel status is determined based on the vehicle's current energy recovery status, current road conditions, and current air conditioning status; Determine the energy consumption for driving based on the current trip status; Determine the second current travel status based on the vehicle's current road conditions and air conditioning status; Determine the intelligent driving energy consumption based on the second current trip status; Determine the lowest energy consumption value between autonomous driving and intelligent driving, and push the driving mode corresponding to the lowest energy consumption to the user.

3. The driving recommendation method for reducing overall vehicle energy consumption according to claim 2, characterized in that, The self-driving energy consumption is determined based on the first current trip status by looking up the correspondence table between the first historical trip status, the interval of driving behavior frequency and the average self-driving energy consumption. And / or, the determination of intelligent driving energy consumption based on the second current trip state is obtained by looking up the correspondence table between the second historical trip state, vehicle speed range and average intelligent driving energy consumption.

4. The driving recommendation method for reducing overall vehicle energy consumption according to claim 3, characterized in that, The self-driving energy consumption in the table corresponding to the first historical trip status, driving behavior frequency interval, and average self-driving energy consumption is obtained as follows: The driving behavior frequency intervals are divided according to the first historical trip status and the corresponding number of driving behaviors. The average self-driving energy consumption corresponding to each driving behavior frequency interval is calculated based on the first historical trip start time, first historical trip end time, first historical trip start time mileage, first historical trip end time mileage, first historical trip start time battery and / or fuel level, and first historical trip end time battery and / or fuel level. The first historical travel status includes historical energy recovery status, historical road conditions, and historical air conditioning status; And / or, the intelligent driving energy consumption in the correspondence table between the second historical trip status, vehicle speed range, and average intelligent driving energy consumption is obtained in the following way: The vehicle speed is divided into speed ranges based on the second historical trip status and the corresponding vehicle speed. The average intelligent driving energy consumption corresponding to each speed range is calculated based on the second historical trip start time, second historical trip end time, mileage at the second historical trip start time, mileage at the second historical trip end time, battery power and / or fuel level at the second historical trip start time, and battery power and / or fuel level at the second historical trip end time. The second historical travel status includes historical road conditions and historical air conditioning status.

5. The driving recommendation method for reducing overall vehicle energy consumption according to claim 4, characterized in that, When the vehicle is a pure electric vehicle, the start time of the first historical trip is set as t1, the end time of the first historical trip is set as t2, the mileage at the start time of the first historical trip is set as m1, the mileage at the end time of the first historical trip is set as m2, the battery level at the start time of the first historical trip is set as q1, and the battery level at the end time of the first historical trip is set as q2. Then the average self-driving energy consumption of the corresponding driving behavior frequency interval is: (q1-q2) / [(t2-t1)*(m2-m1)]; And / or, when the vehicle is a pure electric vehicle, if the start time of the second historical journey is set to t3, the end time of the second historical journey is set to t4, the mileage at the start time of the second historical journey is set to m3, the mileage at the end time of the second historical journey is set to m4, the battery level at the start time of the second historical journey is set to q3, and the battery level at the end time of the second historical journey is set to q4, then the average intelligent driving energy consumption in the corresponding speed range is: (q3-q4) / [(t4-t3)*(m4-m3)]; Alternatively, when the vehicle is a pure gasoline car, the start time of the first historical journey is set to t1, the end time of the first historical journey is set to t2, the mileage at the start time of the first historical journey is set to m1, the mileage at the end time of the first historical journey is set to m2, the fuel quantity at the start time of the first historical journey is set to p1, and the fuel quantity at the end time of the first historical journey is set to p2. Then the average self-driving energy consumption of the corresponding driving behavior frequency interval is: (p1-p2) / [(t2-t1)*(m2-m1)]; And / or, when the vehicle is a pure gasoline car, the start time of the second historical journey is set to t3, the end time of the second historical journey is set to t4, the mileage at the start time of the second historical journey is set to m3, the mileage at the end time of the second historical journey is set to m4, the fuel quantity at the start time of the second historical journey is set to p3, and the fuel quantity at the end time of the second historical journey is set to p4. Then the average intelligent driving energy consumption of the corresponding speed range is: (p3-p4) / [(t4-t3)*(m4-m3)]; Alternatively, when the vehicle is a hybrid vehicle, the start time of the first historical trip in 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 battery level at the start time of the first historical trip is set to q1 and the fuel level to p1, and the battery level at the end time of the first historical trip is set to q2 and the fuel level to p2. Then the average self-driving energy consumption of the corresponding driving behavior frequency interval is: [(q1-q2)+(p1-p2)] / [(t2-t1)*(m2-m1)]; And / or, when the vehicle is a hybrid vehicle, the second historical trip start time is set to t3, the second historical trip end time is set to t4, the mileage at the second historical trip start time is set to m3, the mileage at the second historical trip end time is set to m4, the battery level at the second historical trip start time is set to q3 and the fuel level to p3, and the battery level at the second historical trip end time is set to q4 and the fuel level to p4. Then the average intelligent driving energy consumption of the corresponding speed range is: [(q3-q4)+(p3-p4)] / [(t4-t3)*(m4-m3)].

6. A system for implementing the driving recommendation method for reducing the overall energy consumption of a vehicle as described in any one of claims 1 to 5, characterized in that, include: Energy consumption determination module: used to determine autonomous driving energy consumption and intelligent driving energy consumption based on vehicle operation data; Energy consumption comparison and recommendation module: Determine the lowest energy consumption between autonomous driving and intelligent driving, and push the driving mode corresponding to the lowest energy consumption to the user.

7. The system according to claim 6, characterized in that, include: First trip status determination module: used to determine the first current trip status based on the received current energy recovery status of the vehicle, current driving conditions and current air conditioning status; Autonomous driving energy consumption determination module: used to calculate and determine autonomous driving energy consumption based on the first current trip status; Second trip status determination module: used to determine the second current trip status based on the received current driving conditions and current air conditioning status of the vehicle; Intelligent driving energy consumption determination module: used to calculate and determine intelligent driving energy consumption based on the second current trip status; Energy consumption comparison and recommendation module: Receives the output results from the autonomous driving energy consumption determination module and the intelligent driving energy consumption determination module, compares the autonomous driving energy consumption and the intelligent driving energy consumption, determines the lowest value of the autonomous driving energy consumption and the driving mode corresponding to the lowest energy consumption and pushes it to the user.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the driving recommendation method as described in any one of claims 1 to 5.

9. A vehicle, characterized in that, The vehicle includes the computer equipment as described in claim 8.

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