Intelligent vehicle low-voltage power consumption management and control method and system

Abstract

The invention belongs to the technical field of vehicle low-voltage energy consumption, and particularly relates to an intelligent vehicle low-voltage power consumption management and control method and system. The method comprises the following steps: judging whether the vehicle type of a current vehicle is an Internet of Vehicles vehicle type or not, and obtaining a judgment result; acquiring sunrise and sunset time of the area where the current vehicle is located according to a judgment result; and performing low-voltage power consumption control on the current vehicle based on the acquired sunrise and sunset time. Based on the improvement of the endurance of the whole vehicle, the intelligence of the vehicle in any region and time can be realized, and the real vehicle using scene of the user is judged, so that the low-voltage load is regulated and controlled.

Description

Technical Field

[0001] The present invention relates to the technical field of low-voltage energy consumption of a whole vehicle, and in particular to a method and system for controlling low-voltage power consumption of a whole vehicle. Background Art

[0002] With the development of automobile intelligence, the enrichment and improvement of vehicle configuration and functions have led to the increasing power consumption of the whole vehicle, which seriously affects the vehicle's endurance and user experience. However, the actual vehicle usage scenarios of users are relatively complex, the vehicle usage areas are wide, and some exported vehicles have no vehicle network. There are generally two traditional strategies. One is to determine whether the user's vehicle usage scenario is daytime or night only by sensing the intensity of the external light source through the light and rain sensor; the other is to directly set a fixed time to determine the user's vehicle usage scenario. Traditional strategies have many restrictions and limited application scenarios. Therefore, there is a lack of more intelligent and universal low-voltage energy consumption control solutions for the whole vehicle. Summary of the invention

[0003] In view of the above problems, the present disclosure provides a method for controlling low-voltage power consumption of an intelligent vehicle, the method comprising:

[0004] Determine whether the current vehicle model is a connected vehicle model and obtain a determination result;

[0005] According to the judgment result, the sunrise and sunset time of the area where the vehicle is currently located is obtained;

[0006] The low-voltage power consumption of the current vehicle is controlled based on the acquired sunrise and sunset times.

[0007] Preferably, obtaining the sunrise and sunset times of the area where the vehicle is currently located according to the judgment result includes:

[0008] If the judgment result is a connected vehicle model, further determine whether the current vehicle has a network;

[0009] If the current vehicle has a network connection, the sunrise and sunset times of the area where the current vehicle is located are directly obtained from the network;

[0010] If the current vehicle has no network, the geographic information of the area where the current vehicle is located is obtained, and combined with the prediction function, the sunset and sunrise times are obtained.

[0011] Preferably, obtaining the sunrise and sunset times of the area where the vehicle is currently located according to the judgment result includes:

[0012] If the judgment result is that the vehicle is not a connected vehicle model, the geographic information of the current vehicle location is obtained, and combined with the prediction function, the sunset and sunrise times are obtained.

[0013] Preferably, obtaining geographic information of the area where the vehicle is currently located includes:

[0014] Collect longitude and latitude through GPS;

[0015] Collect altitude through air pressure sensor;

[0016] Collect ultraviolet light intensity through ultraviolet sensors;

[0017] Get the current date and time.

[0018] Preferably, the acquisition of the prediction function includes:

[0019] Get training data;

[0020] Perform feature processing on the training data to obtain the loss function;

[0021] Use the loss function to get the loss value and obtain the training function;

[0022] The true loss value is obtained through the gradient descent iterative formula, and the predicted loss value is obtained through the training function. The predicted loss value and the true loss value are combined to obtain the gradient descent function.

[0023] The prediction function is obtained based on the gradient descent function.

[0024] Preferably, before obtaining the geographical information of the area where the vehicle is currently located, the following steps are included:

[0025] The network is checked again at preset intervals until a network is confirmed.

[0026] Preferably, low voltage power consumption control of the current vehicle is performed based on the acquired sunrise and sunset times, including:

[0027] Determine whether the current time is within the sunrise and sunset time;

[0028] If the current time is within the sunrise and sunset time, the current vehicle usage scene is in daytime, and the daytime running lights are turned on, the steering wheel backlight and footlights are turned off, and the instrument panel central control co-pilot screen enters the daytime mode to determine whether the external light intensity is less than the threshold;

[0029] If the current time is not within the sunrise or sunset time, the current vehicle usage scene is in darkness, the low beam, steering wheel backlight and footlights are turned on, and the co-pilot screen of the instrument panel enters night mode.

[0030] Preferably, determining whether the intensity of the external light source is less than a threshold value includes:

[0031] If the intensity of the external light source is less than the threshold and the duration is greater than the preset time, the current vehicle usage scene is in a tunnel or underground garage, and the low beam, steering wheel backlight and footlights are turned on;

[0032] If the external light intensity is greater than or equal to the threshold, the original load setting remains unchanged.

[0033] The present disclosure also proposes an intelligent vehicle low-voltage power consumption control system, the system comprising:

[0034] A judgment module is used to judge whether the current vehicle model is a connected vehicle model and obtain a judgment result;

[0035] An acquisition module is used to obtain the sunrise and sunset times of the current vehicle location according to the judgment result;

[0036] The control module is used to control the low-voltage power consumption of the current vehicle based on the acquired sunrise and sunset times.

[0037] Preferably, the acquisition module is used to obtain the sunrise and sunset times of the area where the current vehicle is located according to the judgment result, including:

[0038] If the judgment result is a connected vehicle model, the acquisition module further determines whether the current vehicle has a network;

[0039] If the current vehicle has a network, the acquisition module directly obtains the sunrise and sunset times of the area where the current vehicle is located from the network;

[0040] If the current vehicle has no network, the acquisition module obtains the geographic information of the area where the current vehicle is located, and combines it with the prediction function to obtain the sunset and sunrise time.

[0041] The present disclosure has the following beneficial effects:

[0042] The present invention is based on improving the endurance of the whole vehicle, can realize the intelligence of the vehicle in any region and time, and determine the user's actual vehicle usage scenario, so as to regulate the low-voltage load.

[0043] Other features and advantages of the present disclosure will be described in the following description, and partly become apparent from the description, or be understood by implementing the present disclosure. The purpose and other advantages of the present disclosure can be realized and obtained by the structures indicated in the description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the embodiments of the present disclosure or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0045] Figure 1 A diagram showing a method for controlling low-voltage power consumption of an intelligent vehicle in an embodiment of the present disclosure is shown;

[0046] Figure 2 A detailed flow chart of the low-voltage power consumption control method for an intelligent vehicle in an embodiment of the present disclosure is shown;

[0047] Figure 3 A flow chart of the Pytorch logistic regression algorithm in an embodiment of the present disclosure is shown;

[0048] Figure 4 A diagram of a low-voltage power consumption control system for an intelligent vehicle in an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0049] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as being limited to the examples set forth herein; on the contrary, these embodiments are provided so that the present disclosure will be more comprehensive and complete, and the concepts of the example embodiments are fully conveyed to those skilled in the art. The described features, structures, or characteristics may be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present disclosure. However, those skilled in the art will appreciate that the technical solutions of the present disclosure may be practiced while omitting one or more of the specific details, or other methods, components, devices, steps, etc. may be adopted. In other cases, known technical solutions are not shown or described in detail to avoid obscuring various aspects of the present disclosure.

[0050] In addition, the accompanying drawings are only schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and thus their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software form, or implemented in one or more hardware units or integrated circuits, or implemented in different networks and / or processor devices and / or microcontroller devices.

[0051] The flowcharts shown in the accompanying drawings are only exemplary and do not necessarily include all the steps. For example, some steps may be decomposed, while some steps may be combined or partially combined, so the actual execution order may change according to the actual situation.

[0052] The terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the terms used in this way can be interchanged where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein, for example.

[0053] In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or sub-modules is not necessarily limited to those steps or sub-modules explicitly listed, but may include other steps or sub-modules not explicitly listed or inherent to these processes, methods, products, or apparatuses.

[0054] like Figure 1 As shown, the present disclosure proposes a method for controlling low-voltage power consumption of an intelligent vehicle, the method comprising:

[0055] Determine whether the current vehicle model is a connected vehicle model and obtain a determination result;

[0056] According to the judgment result, the sunrise and sunset time of the area where the vehicle is currently located is obtained;

[0057] The low-voltage power consumption of the current vehicle is controlled based on the acquired sunrise and sunset times.

[0058] The detailed technical solution of this disclosure is as follows Figure 2 The specific contents are as follows:

[0059] If the current vehicle model is a connected vehicle model, execute step 1.1;

[0060] Step 1.1: Power on the vehicle and determine whether the vehicle has network access.

[0061] If there is no network, the network will be determined again every five minutes until the network communication is restored. At the same time, the ZCU obtains the geographical information of the current vehicle location, specifically: the longitude and latitude are collected through GPS, the altitude is collected through the air pressure sensor, the ultraviolet intensity is collected through the ultraviolet sensor, and the current date and time are obtained; the sunset and sunrise time of the current vehicle location is calculated through the Pytorch logistic regression algorithm, and then step 2 is executed;

[0062] If there is a network, directly obtain the sunset and sunrise time of the current vehicle location from the network, and then execute step 2;

[0063] Step 2: Determine whether the current time is within the sunrise and sunset time;

[0064] If the current time is within the sunrise and sunset time, turn on the daytime running lights, turn off the steering wheel backlight and footlights, and the instrument panel central control co-pilot screen enters the daytime mode (white backlight). The light and rain sensor will execute step 3 according to the intensity of the external light source.

[0065] If the current time is not within the sunrise or sunset time, the vehicle is judged to be in the dark, the low beam headlight, steering wheel backlight and footlights are turned on, and the instrument panel center console passenger screen enters the night mode (black backlight);

[0066] Step 3: Determine whether the light source intensity is less than 3000 Lux;

[0067] If it is less than 3000 Lux and lasts for more than 3 seconds, it is judged that the vehicle is in a tunnel or underground garage, and the low beam, steering wheel backlight and footlights are turned on;

[0068] If the light intensity is greater than 3000 Lux, or less than 3000 Lux for less than 3 seconds, the original load setting will remain unchanged.

[0069] If the current vehicle model is not a connected vehicle model, go to step 1.2;

[0070] Step 1.2: Power on the vehicle and obtain the geographic information of the current vehicle location through the ZCU. Specifically, collect the longitude and latitude through GPS, collect the altitude through the air pressure sensor, collect the ultraviolet intensity through the ultraviolet sensor, and obtain the current date and time; calculate the sunset and sunrise time of the current vehicle location through the Pytorch logistic regression algorithm, and then execute step 2.

[0071] Pytorch logistic regression algorithm is implemented as follows Figure 3 As shown, in the logistic regression algorithm, the best fitting parameter θ is required so that the prediction function can best fit the sample data. The specific process is as follows:

[0072] Step 1. Open Pytorch.

[0073] Step 2, import toolkits, including numpy, pandas, scipy.optimize minimiz e, utils.features prepare_for_training, and utils.hypothesis sigmoid;

[0074] Step 3: Read training data;

[0075] Step 4: Define the loss function, including:

[0076] Initialization function;

[0077] Preprocessing the training data to obtain preprocessed training data;

[0078] Calculate the feature values ​​in the preprocessed training data, where the feature values ​​specifically include: longitude, latitude, altitude, ultraviolet intensity, and date and time;

[0079] Calculate the number of tags according to the number of features, wherein the number of tags specifically includes the sunset time and the sunrise time, and the number of tags in this embodiment is set at the beginning;

[0080] Define the best fit parameter θ;

[0081] Step 5: Define the training function, including:

[0082] Set the maximum number of iterations of the training function. In this embodiment, the maximum number of iterations is 1000;

[0083] The loss value is calculated through the gradient descent iterative calculation formula;

[0084] When the loss value is greater than the threshold, initialize the θ value;

[0085] Determine whether the current label (i.e., the actual result of the training data) and the data category (i.e., the calculation result obtained by calculating the variables in the training data) are consistent;

[0086] Step 6: Define the gradient descent function, including:

[0087] Select the conjugate gradient descent optimization strategy;

[0088] Select the existing gradient descent iterative calculation formula;

[0089] Use the loss function to calculate the loss during the day and night respectively;

[0090] Calculate the total loss value based on the daytime and nighttime losses and record the results;

[0091] Then calculate the difference between the predicted value (i.e. the calculated total loss value) and the true value (i.e. the result of the training data);

[0092] Use the gradient descent function to recalculate the current gradient and update the θ value;

[0093] Step 7: Define the prediction function, including:

[0094] Introduce the sigmoid function;

[0095] Convert the prediction result into percentage, calculate the prediction value, and get the label value at the current time;

[0096] The prediction accuracy is calculated. When the accuracy is greater than 97%, the trained model is deployed to the ZCU.

[0097] like Figure 4 As shown, the present disclosure also proposes an intelligent vehicle low-voltage power consumption control system, the system comprising:

[0098] A judgment module is used to judge whether the current vehicle model is a connected vehicle model and obtain a judgment result;

[0099] An acquisition module is used to obtain the sunrise and sunset times of the current vehicle location according to the judgment result;

[0100] The control module is used to control the low-voltage power consumption of the current vehicle based on the acquired sunrise and sunset times.

[0101] Those skilled in the art should understand that although the present disclosure is described in detail with reference to the aforementioned embodiments, it is still possible to modify the technical solutions recorded in the aforementioned embodiments, or to make equivalent replacements for some of the technical features therein; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present disclosure.

Claims

1. A method for controlling low-voltage power consumption of an intelligent vehicle, characterized in that: The method comprises: Determine whether the current vehicle model is a connected vehicle model and obtain a determination result; According to the judgment result, the sunrise and sunset time of the area where the vehicle is currently located is obtained; The low-voltage power consumption of the current vehicle is controlled based on the acquired sunrise and sunset times.

2. The low-voltage power consumption control method for an intelligent vehicle according to claim 1 is characterized in that: Get the sunrise and sunset times of the current vehicle location based on the judgment results, including: If the judgment result is a connected vehicle model, further determine whether the current vehicle has a network; If the current vehicle has a network connection, the sunrise and sunset times of the area where the current vehicle is located are directly obtained from the network; If the current vehicle has no network, the geographic information of the area where the current vehicle is located is obtained, and combined with the prediction function, the sunset and sunrise times are obtained.

3. The low-voltage power consumption control method for an intelligent vehicle according to claim 1 is characterized in that: Get the sunrise and sunset times of the current vehicle location based on the judgment results, including: If the judgment result is that the vehicle is not a connected vehicle model, the geographic information of the current vehicle location is obtained, and combined with the prediction function, the sunset and sunrise times are obtained.

4. The low-voltage power consumption control method for an intelligent vehicle according to claim 2 or 3, characterized in that: Get the geographic information of the current vehicle location, including: Collect longitude and latitude through GPS; Collect altitude through air pressure sensor; Collect ultraviolet light intensity through ultraviolet sensors; Get the current date and time.

5. The low-voltage power consumption control method for an intelligent vehicle according to claim 2 or 3, characterized in that: The acquisition of the prediction function includes: Get training data; Perform feature processing on the training data to obtain the loss function; Use the loss function to get the loss value and obtain the training function; The true loss value is obtained through the gradient descent iterative formula, and the predicted loss value is obtained through the training function. The predicted loss value and the true loss value are combined to obtain the gradient descent function. The prediction function is obtained based on the gradient descent function.

6. The method for controlling low-voltage power consumption of an intelligent vehicle according to claim 2, characterized in that: Before obtaining the geographic information of the current vehicle location, include: The network is checked again at preset intervals until a network is confirmed.

7. The method for controlling low-voltage power consumption of an intelligent vehicle according to claim 2, characterized in that: Based on the acquired sunrise and sunset times, the low-voltage power consumption of the current vehicle is controlled, including: Determine whether the current time is within the sunrise and sunset time; If the current time is within the sunrise and sunset time, the current vehicle usage scene is in daytime, and the daytime running lights are turned on, the steering wheel backlight and footlights are turned off, and the instrument panel central control co-pilot screen enters the daytime mode to determine whether the external light intensity is less than the threshold; If the current time is not within the sunrise or sunset time, the current vehicle usage scene is in darkness, the low beam, steering wheel backlight and footlights are turned on, and the instrument panel and central control passenger screen enter night mode.

8. The method for controlling low-voltage power consumption of an intelligent vehicle according to claim 7, characterized in that: Determine whether the intensity of the external light source is less than a threshold, including: If the intensity of the external light source is less than the threshold and the duration is greater than the preset time, the current vehicle usage scene is in a tunnel or underground garage, and the low beam, steering wheel backlight and footlights are turned on; If the external light intensity is greater than or equal to the threshold, the original load setting remains unchanged.

9. An intelligent vehicle low-voltage power consumption control system, characterized in that: The system comprises: A judgment module is used to judge whether the current vehicle model is a connected vehicle model and obtain a judgment result; An acquisition module is used to obtain the sunrise and sunset times of the current vehicle location according to the judgment result; The control module is used to control the low-voltage power consumption of the current vehicle based on the acquired sunrise and sunset times.

10. The intelligent vehicle low-voltage power consumption control system according to claim 9, characterized in that: The acquisition module is used to obtain the sunrise and sunset times of the current vehicle location based on the judgment results, including: If the judgment result is a connected vehicle model, the acquisition module further determines whether the current vehicle has a network; If the current vehicle has a network, the acquisition module directly obtains the sunrise and sunset times of the area where the current vehicle is located from the network; If the current vehicle has no network, the acquisition module obtains the geographic information of the area where the current vehicle is located, and combines it with the prediction function to obtain the sunset and sunrise time.

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