New energy commercial vehicle driving suggestion generation method and device and storage medium

Abstract

The invention discloses a new energy commercial vehicle driving suggestion generation method, device and system, and belongs to the technical field of new energy commercial vehicle driving guidance, and the method comprises the following steps: collecting vehicle state information and vehicle driving data information of a new energy vehicle; generating a driving track according to the vehicle driving data information, comparing the driving track with a preset map, and dividing the driving track into a plurality of sections of different vehicle driving scenes; according to the vehicle driving scene, in combination with the vehicle state information and the vehicle driving data information, scoring the vehicle driving behavior to obtain a scoring result; and according to the scoring result and the vehicle driving scene, extracting a driving suggestion from a driving behavior suggestion library. The system adapts to the characteristics of the new energy commercial vehicle, can improve the driving economy, reduces the driving energy consumption of the vehicle, and trains the driver to adapt to the energy-saving driving mode of the new energy commercial vehicle on the basis.

Description

Technical Field

[0001] The present invention relates to a method, device and storage medium for generating driving advice for new energy commercial vehicles, and belongs to the technical field of driving guidance for new energy commercial vehicles. Background Art

[0002] In recent years, the market penetration rate of pure electric new energy commercial vehicles has continued to increase. More and more commercial vehicle operators are choosing to use pure electric new energy commercial vehicles as their operating tools, and the vehicle's economy and safety have become their primary considerations when purchasing a vehicle. Traditional commercial vehicle driving behavior evaluation is based on considerations of driving economy and safety, detecting the vehicle's driving status and evaluating driving operations. However, pure electric new energy commercial vehicles use a power source and drive system control method that is completely different from traditional fuel vehicles. They have electric braking and energy recovery functions. Existing commercial vehicle driving behavior evaluation strategies are not suitable for pure electric new energy commercial vehicles. Therefore, there is an urgent need for a driving behavior evaluation method that can be applied to new energy commercial vehicles to improve driving economy, reduce vehicle driving energy consumption, and use this as a basis for training drivers to adapt to the energy-saving driving style of new energy commercial vehicles. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to overcome the defects of the prior art and provide a method, device and storage medium for generating driving suggestions for new energy commercial vehicles.

[0004] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0005] In a first aspect, the present invention provides a method for generating driving suggestions for a new energy commercial vehicle, comprising the following steps:

[0006] Collect vehicle status information and vehicle driving data information of new energy vehicles;

[0007] Generate a driving trajectory based on vehicle driving data information, compare the driving trajectory with a preset map, and divide the driving trajectory into several different vehicle driving scenarios;

[0008] According to the vehicle driving scenario, combined with the vehicle status information and vehicle driving data information, the vehicle driving behavior is scored to obtain a scoring result;

[0009] Based on the scoring results and vehicle driving scenarios, driving recommendations are extracted from the driving behavior recommendation library.

[0010] The vehicle status information includes the vehicle VIN code and the vehicle GPS location; the vehicle driving data information includes the total vehicle energy consumption, cab air conditioning energy consumption, cab electric heater energy consumption, steering oil pump energy consumption, brake air pump energy consumption, body energy consumption, drive motor energy consumption, auxiliary drive system energy consumption, energy recovery ratio, sudden acceleration behavior record, sudden deceleration behavior record, sudden turn behavior record, incorrect use of turn signal record, leaving seat driving behavior record, driving behavior record without wearing seat belt, driving behavior record with door open, maximum battery cell temperature, minimum battery cell temperature, average battery cell temperature and battery SOC.

[0011] The vehicle status information and vehicle driving data information are grouped according to timestamps, and after the vehicle's single driving records are packaged, a scoring activity is carried out according to the vehicle driving scenario.

[0012] The vehicle driving behavior is scored based on the vehicle driving scenario, combined with the vehicle status information and the vehicle driving data information, and the scoring results include:

[0013] A dimension layer is set, which includes three dimensions: economic dimension, safety dimension and battery durability dimension. The scoring result is obtained by weighting the economic dimension, safety dimension and battery durability dimension.

[0014] Each dimension in the dimension layer is calculated based on the weighted scores of multiple indicators. The indicator scores are based on vehicle driving data information and are obtained according to the preset indicator scoring strategy.

[0015] The vehicle driving scenarios include plateaus, plains, mountainous areas with long slopes, mountainous areas with short slopes, mountainous areas with twisting roads, heavy-load areas, and logistics areas.

[0016] The driving suggestion library includes driving operation suggestions, vehicle function usage suggestions, vehicle performance improvement suggestions and vehicle maintenance suggestions.

[0017] The construction of the driving behavior suggestion library includes:

[0018] Establish a geographical scenario library based on common driving environments of new energy commercial vehicles;

[0019] Based on the impact of driving behaviors in each scenario on driving economy, safety, and battery durability in the geographical region scenario library, the scoring strategy is weighted and the corresponding evaluation indicators for economy, safety, and battery durability in each scenario are determined;

[0020] A driving behavior recommendation library is compiled based on the weights, evaluation indicators, and the scoring strategy score segments of each data in the evaluation indicators.

[0021] In a second aspect, the present invention provides a device for generating driving suggestions for energy commercial vehicles, comprising:

[0022] Information collection module, used to collect vehicle status information and vehicle driving data information of new energy vehicles;

[0023] The vehicle driving scene analysis module is used to generate a driving trajectory based on the vehicle driving data information, compare the driving trajectory with a preset map, and divide the driving trajectory into several different vehicle driving scenes;

[0024] The scoring calculation module is used to score the vehicle driving behavior based on the vehicle driving scene, combined with the vehicle status information and vehicle driving data information, and obtain the scoring result;

[0025] The driving suggestion generation module is used to extract driving suggestions from the driving behavior suggestion library based on the scoring results and vehicle driving scenarios.

[0026] In a third aspect, the present invention provides a computer-readable storage medium having a computer program / instruction stored thereon. When the computer program / instruction is executed by a processor, the above-mentioned method for generating driving recommendations for new energy commercial vehicles is implemented.

[0027] The beneficial effects of the present invention are as follows: the method, device and storage medium for generating driving suggestions for new energy commercial vehicles provided by the present invention generate a driving trajectory based on vehicle driving data information, compare the driving trajectory with a preset map, and divide the driving trajectory into several different vehicle driving scenarios; based on the vehicle driving scenario, the vehicle state information and the vehicle driving data information are combined to score the vehicle driving behavior and obtain a scoring result; based on the scoring result and the vehicle driving scenario, driving suggestions are extracted from a driving behavior suggestion library, focusing on the driving and control characteristics of new energy commercial vehicles, adapting to the characteristics of new energy commercial vehicles, and being able to improve driving economy and reduce vehicle driving energy consumption, and on this basis, train drivers to adapt to the energy-saving driving method of new energy commercial vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 This is a flow chart of a method for generating driving suggestions for new energy commercial vehicles according to the present invention;

[0029] Figure 2 It is a schematic diagram of the construction process of the driving behavior suggestion library in the present invention. DETAILED DESCRIPTION

[0030] The present invention will be further described below. The following examples are only used to more clearly illustrate the technical solutions of the present invention and are not intended to limit the scope of protection of the present invention.

[0031] Example 1

[0032] like Figure 1 As shown, the present invention discloses a method for generating driving suggestions for new energy commercial vehicles, comprising the following steps:

[0033] Step 1: The vehicle status information and vehicle driving data of the new energy vehicle are collected through the vehicle-side supporting device and sent to the computer product. Among them, the vehicle status information includes the vehicle VIN code and the vehicle GPS location; the vehicle driving data information includes the total vehicle energy consumption, cab air conditioning energy consumption, cab electric heater energy consumption, steering oil pump energy consumption, brake air pump energy consumption, body energy consumption, drive motor energy consumption, auxiliary drive system energy consumption, energy recovery ratio, sudden acceleration behavior record, sudden deceleration behavior record, sudden turning behavior record, incorrect use of turn signal record, driving behavior record without seat belt fastened, driving behavior record with door open, battery cell maximum temperature, battery cell minimum temperature, battery cell average temperature, battery SOC, etc.

[0034] Step 2: Generate a driving trajectory based on the vehicle driving data information, compare the driving trajectory with a preset map, and divide the driving trajectory into several different vehicle driving scenarios.

[0035] In step three, the computer product uses the vehicle's driving data and status information based on the driving scenario to score the driving behavior and send it to the user application. Vehicle information and driving data are grouped by timestamp, and individual vehicle trip records are packaged. The route's geographic characteristics are analyzed to perform the scoring.

[0036] The present invention provides a set of driving behavior scoring rules, specifically:

[0037] 1) Set up dimension layer, indicator layer, and data layer to describe and score driving behavior;

[0038] 2) The dimension layer is divided into three parts: economy, safety, and battery durability;

[0039] 3) Each indicator in the indicator layer is described by specific driving behavior and vehicle status;

[0040] 4) Each piece of data in the data layer contains information and storage and transmission format instructions.

[0041] The indicator layer includes the following indicators that reflect driving safety and driving economy: sudden acceleration, sudden deceleration, sharp turns, incorrect use of turn signals, driving without a seat, driving without a seat belt, driving with the door open, energy recovery ratio, total energy consumption, drive motor energy consumption, auxiliary drive energy consumption, superstructure energy consumption, air conditioning and electric heating energy consumption, steering and braking energy consumption, battery depletion, battery overheating, fatigue driving, etc.

[0042] In the present invention, the geographical area scene library includes: plateaus, plains, long slope mountainous areas, short slope mountainous areas, twisting road mountainous areas, heavy load areas, logistics areas, etc.; the driving suggestion library includes: driving operation suggestions, vehicle function usage suggestions, vehicle performance improvement suggestions, vehicle maintenance suggestions, etc.

[0043] Step 4: The user-side application delivers the scoring results to the user and makes driving recommendations from the driving behavior recommendation library based on the scores and driving scenarios. Figure 2 As shown, the construction of the driving behavior suggestion library in the present invention includes:

[0044] Establish a geographical scenario library based on common driving environments of new energy commercial vehicles;

[0045] Based on the impact of driving behaviors in each scenario on driving economy, safety, and battery durability in the geographical region scenario library, the scoring strategy is weighted and the corresponding evaluation indicators for economy, safety, and battery durability in each scenario are determined;

[0046] A driving behavior recommendation library is compiled based on the weights, evaluation indicators, and the scoring strategy score segments of each data in the evaluation indicators.

[0047] The present invention focuses on the driving and control characteristics of new energy commercial vehicles, adapts to the characteristics of new energy commercial vehicles, can improve driving economy, reduce vehicle driving energy consumption, and on this basis train drivers to adapt to the energy-saving driving style of new energy commercial vehicles.

[0048] Example 2

[0049] This embodiment, based on Example 1, uses specific cases to illustrate how the driving behavior scoring method and its supporting devices, computer products, and applications work together.

[0050] For example, when scoring the driving behavior of a new energy commercial vehicle in the southwest region, the following steps are performed in sequence.

[0051] Step 1: The vehicle-side supporting device collects vehicle information and driving data of the new energy vehicle and sends it to the computer product.

[0052] In step 2, the computer product analyzes the vehicle's trajectory and route during the driving, and divides the driving record into several segments according to geographical boundaries.

[0053] Step three: Score the vehicle's driving based on the mileage, driving time, and geographical area scenario described in each record, according to the driving behavior scoring rules.

[0054] For the explanation of step 3, please refer to the following case for easier understanding:

[0055] The vehicle traveled 30 kilometers in the southwest region. It passed through a long, hilly mountainous area for 15 kilometers, taking 30 minutes, and a short, hilly mountainous area for 15 kilometers, taking 45 minutes. The long-hill driving scenario scored a, the short-hill driving scenario scored b, the long-hill driving time percentage was m, the short-hill driving time percentage was n, the long-hill driving distance percentage was k, and the short-hill driving distance percentage was s.

[0056] Total score calculation: Score=0.5(ka+sb)+0.5(ma+nb).

[0057] The calculation of scenario driving scores a and b is based on the corresponding data segments and scoring methods:

[0058] For long slope driving score a: a = economy score × 0.5 + safety score × 0.4 + battery durability score × 0.1;

[0059] The dimension scores can be calculated based on the weighted indicator scores, for example:

[0060] Safety score = sudden acceleration score × 0.2 + sudden deceleration score × 0.1 + sharp turn score × 0.2 + incorrect use of turn signal × 0.2 + fatigue driving × 0.2 + driving without a seat belt × 0.1;

[0061] Indicator scores are based on data and are derived according to the indicator scoring strategy, for example:

[0062] Sudden acceleration score: 100 points if it occurs 0 times in a single driving, 50 points if it occurs 1-2 times, 25 points if it occurs 3-4 times, and 0 points if it occurs more than 4 times.

[0063] For short hill driving score b:

[0064] b = economic score × 0.4 + safety score × 0.5 + battery durability score × 0.1;

[0065] The dimension scores can be calculated based on the weighted indicator scores, for example:

[0066] Safety score = sudden acceleration score × 0.1 + sudden deceleration score × 0.1 + sharp turn score × 0.3 + incorrect use of turn signal × 0.3 + fatigue driving × 0.1 + driving without a seat belt × 0.1;

[0067] Indicator scores are based on data and are derived according to the indicator scoring strategy, for example:

[0068] Fatigue driving score = number of fatigue driving times in a single driving session × 0.5 + percentage of fatigue driving time in a single driving session × 0.5.

[0069] Step 4: The driving behavior score is sent to the user-side application, and effective driving suggestions are made based on the score and driving scenario, combined with the suggestion library.

[0070] Example 3

[0071] This embodiment discloses a device for generating driving suggestions for energy commercial vehicles, comprising:

[0072] Information collection module, used to collect vehicle status information and vehicle driving data information of new energy vehicles;

[0073] The vehicle driving scene analysis module is used to generate a driving trajectory based on the vehicle driving data information, compare the driving trajectory with a preset map, and divide the driving trajectory into several different vehicle driving scenes;

[0074] The scoring calculation module is used to score the vehicle driving behavior based on the vehicle driving scene, combined with the vehicle status information and vehicle driving data information, and obtain the scoring result;

[0075] The driving suggestion generation module is used to extract driving suggestions from the driving behavior suggestion library based on the scoring results and vehicle driving scenarios.

[0076] Example 4

[0077] This embodiment discloses a computer-readable storage medium having a computer program / instruction stored thereon. When the computer program / instruction is executed by a processor, the method for generating driving recommendations for energy commercial vehicles is implemented.

[0078] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0079] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0080] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0081] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for generating driving suggestions for new energy commercial vehicles, characterized by: The following steps are involved: Collect vehicle status information and vehicle driving data information of new energy vehicles; Generate a driving trajectory based on vehicle driving data information, compare the driving trajectory with a preset map, and divide the driving trajectory into several different vehicle driving scenarios; According to the vehicle driving scenario, combined with the vehicle status information and vehicle driving data information, the vehicle driving behavior is scored to obtain a scoring result; Based on the scoring results and vehicle driving scenarios, driving recommendations are extracted from the pre-built driving behavior recommendation library.

2. The method for generating driving advice for new energy commercial vehicles according to claim 1, characterized in that: The vehicle status information includes the vehicle VIN code and the vehicle GPS location; the vehicle driving data information includes the total vehicle energy consumption, cab air conditioning energy consumption, cab electric heater energy consumption, steering oil pump energy consumption, brake air pump energy consumption, body energy consumption, drive motor energy consumption, auxiliary drive system energy consumption, energy recovery ratio, sudden acceleration behavior record, sudden deceleration behavior record, sudden turn behavior record, incorrect use of turn signal record, leaving seat driving behavior record, driving behavior record without wearing seat belt, driving behavior record with door open, maximum battery cell temperature, minimum battery cell temperature, average battery cell temperature and battery SOC.

3. The method for generating driving advice for new energy commercial vehicles according to claim 1, characterized in that: The vehicle status information and vehicle driving data information are grouped according to timestamps, and after the vehicle's single driving records are packaged, a scoring activity is carried out according to the vehicle driving scenario.

4. The method for generating driving advice for new energy commercial vehicles according to claim 1, characterized in that: The vehicle driving behavior is scored based on the vehicle driving scenario, combined with the vehicle status information and the vehicle driving data information, and the scoring results include: A dimension layer is set, which includes three dimensions: economic dimension, safety dimension and battery durability dimension. The scoring result is obtained by weighting the economic dimension, safety dimension and battery durability dimension.

5. The method for generating driving advice for new energy commercial vehicles according to claim 4, characterized in that: Each dimension in the dimension layer is calculated based on the weighted scores of multiple indicators. The indicator scores are based on vehicle driving data information and are obtained according to the preset indicator scoring strategy.

6. The method for generating driving advice for new energy commercial vehicles according to claim 1, characterized in that: The vehicle driving scenarios include plateaus, plains, mountainous areas with long slopes, mountainous areas with short slopes, mountainous areas with twisting roads, heavy-load areas, and logistics areas.

7. The method for generating driving advice for new energy commercial vehicles according to claim 1, characterized in that: The driving suggestion library includes driving operation suggestions, vehicle function usage suggestions, vehicle performance improvement suggestions and vehicle maintenance suggestions.

8. The method for generating driving advice for new energy commercial vehicles according to claim 1, characterized in that: The construction of the driving behavior suggestion library includes: Establish a geographical scenario library based on common driving environments of new energy commercial vehicles; Based on the impact of driving behaviors in each scenario on driving economy, safety, and battery durability in the geographical region scenario library, the scoring strategy is weighted and the corresponding evaluation indicators for economy, safety, and battery durability in each scenario are determined; A driving behavior recommendation library is compiled based on the weights, evaluation indicators, and the scoring strategy score segments of each data in the evaluation indicators.

9. A device for generating driving suggestions for energy commercial vehicles, characterized by: include: Information collection module, used to collect vehicle status information and vehicle driving data information of new energy vehicles; The vehicle driving scene analysis module is used to generate a driving trajectory based on the vehicle driving data information, compare the driving trajectory with a preset map, and divide the driving trajectory into several different vehicle driving scenes; The scoring calculation module is used to score the vehicle driving behavior based on the vehicle driving scene, combined with the vehicle status information and vehicle driving data information, and obtain the scoring result; The driving suggestion generation module is used to extract driving suggestions from the pre-built driving behavior suggestion library based on the scoring results and vehicle driving scenarios.

10. A computer-readable storage medium having a computer program / instruction stored thereon, characterized in that: When the computer program / instruction is executed by a processor, the method for generating driving advice for a new energy commercial vehicle described in any one of claims 1 to 8 is implemented.

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