Vehicle energy consumption prediction method and device

Abstract

This invention discloses a method and apparatus for predicting vehicle energy consumption. The method relates to the field of vehicle energy consumption and includes: during the vehicle's communication cycle, collecting battery state data of the vehicle's battery and power state data of the vehicle's power unit, and generating a data upload command; waking up at least one processing unit associated with the battery and power unit on the vehicle based on the data upload command; retrieving historical battery state data and historical power state data of the power unit using the at least one processing unit; and predicting the vehicle's energy consumption based on the battery state data, historical battery state data, power state data, and historical power state data to obtain a prediction result. This invention solves the technical problem of low accuracy in vehicle energy consumption prediction.

Description

Technical Field

[0001] This invention relates to the field of vehicle energy consumption, and more specifically, to a method and apparatus for predicting vehicle energy consumption. Background Technology

[0002] With the rapid development of the automotive industry, vehicles have entered a stage of widespread adoption, and their ownership is increasing rapidly. Some higher-income families have begun to own second or third vehicles. As the number of vehicles increases, users' demands for them are also rising. Among these demands, vehicle range has always been a key focus for both users and manufacturers, and predicting the remaining driving range can promptly alert users to prevent breakdowns due to insufficient energy. Energy consumption prediction is one of the main methods for forecasting the remaining driving range. However, the accuracy of energy consumption prediction in related technologies is relatively poor, often resulting in vehicles failing to reach the predicted driving range and breaking down, severely impacting the user experience.

[0003] There is currently no effective solution to the above problems. Summary of the Invention

[0004] This invention provides a method and apparatus for predicting vehicle energy consumption, thereby addressing at least the technical problem of low accuracy in predicting vehicle energy consumption.

[0005] According to one aspect of the present invention, a method for predicting the energy consumption of a vehicle is provided, comprising: collecting battery state data of a battery and power state data of a power unit in the vehicle during a communication cycle of the vehicle, and generating a data upload command; waking up at least one processing unit associated with the battery and power unit in the vehicle based on the data upload command; retrieving historical battery state data of the battery and historical power state data of the power unit using the at least one processing unit, wherein the historical battery state data represents the battery state data collected at a historical acquisition time point, and the historical power state data represents the power state data collected at a historical acquisition time point; and predicting the energy consumption of the vehicle based on the battery state data, historical battery state data, power state data, and historical power state data to obtain a prediction result, wherein the prediction result represents the remaining energy consumption of the vehicle in at least one preset time period after the current moment.

[0006] Furthermore, waking up the processing unit associated with the battery and power unit on the vehicle based on the data upload command includes: obtaining the vehicle type; determining a plurality of preset processing units contained in the electronic control unit on the vehicle; and waking up at least one processing unit associated with the battery and power unit from the plurality of preset processing units based on the vehicle type.

[0007] Furthermore, energy consumption prediction for the vehicle is performed based on battery state data, historical battery state data, power state data, and historical power state data to obtain prediction results. This includes: obtaining the current collection time point of battery state data and power state data; determining the interval between the current collection time point and the historical collection time point; and performing energy consumption prediction for the vehicle based on the interval, battery state data, historical battery state data, power state data, and historical power state data to obtain prediction results.

[0008] Furthermore, based on interval duration, battery status data, historical battery status data, power status data, and historical power status data, the vehicle's energy consumption is predicted to obtain prediction results, including: determining the remaining charge of the battery in at least one preset time period based on interval duration, battery status data, and historical battery status data; determining the remaining energy of the power unit in at least one preset time period based on interval duration, power status data, and historical power status data; and predicting the vehicle's remaining energy consumption in at least one preset time period based on the remaining charge and remaining energy in at least one preset time period.

[0009] Further, determining the remaining battery capacity in at least one preset time period based on the interval duration, battery status data, and historical battery status data includes: determining the current remaining battery capacity and historical remaining battery capacity based on the battery status data and historical battery status data, respectively; determining the difference between the current remaining battery capacity and the historical remaining battery capacity; determining the battery capacity consumption rate based on the capacity difference and the interval duration; and determining the remaining battery capacity in at least one preset time period based on the capacity consumption rate and the current remaining battery capacity.

[0010] Further, determining the remaining energy of the power unit in at least one preset time period based on the interval duration, power status data, and historical power status data includes: determining the current remaining energy and historical remaining energy of the power unit based on the power status data and historical power status data, respectively; determining the energy difference between the current remaining energy and the historical remaining energy; determining the energy consumption rate of the power unit based on the energy difference and the interval duration; and determining the remaining energy of the power unit in at least one preset time period based on the energy consumption rate and the current remaining energy.

[0011] Furthermore, the method also includes: in response to receiving a communication connection instruction between the first terminal device and the vehicle, pushing the prediction result to the first terminal device.

[0012] Furthermore, the method also includes: when the vehicle is of a preset type, in response to receiving a remote start command for the vehicle, predicting the energy consumption of the vehicle based on power state data and historical power state data, and obtaining the prediction result.

[0013] According to another aspect of the present invention, a vehicle energy consumption prediction device is also provided, comprising: a data acquisition module, configured to acquire battery state data of a vehicle battery and power state data of a vehicle power unit during the vehicle's communication cycle, and generate a data upload command; a wake-up module, configured to wake up at least one processing unit associated with the battery and power unit on the vehicle based on the data upload command; a retrieval module, configured to retrieve historical battery state data of the battery and historical power state data of the power unit using at least one processing unit, wherein the historical battery state data represents the battery state data acquired at a historical acquisition time point, and the historical power state data represents the power state data acquired at a historical acquisition time point; and a prediction module, configured to predict the vehicle's energy consumption based on the battery state data, historical battery state data, power state data, and historical power state data, and obtain a prediction result, wherein the prediction result represents the remaining energy consumption of the vehicle for at least one preset time period after the current moment.

[0014] According to another aspect of the present invention, an electronic device is also provided, comprising: a memory storing an executable program; and a processor for running the program, wherein the program executes the methods of various embodiments of the present invention during runtime.

[0015] According to another aspect of the present invention, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored executable program, wherein, when the executable program is executed, it controls the device where the computer-readable storage medium is located to perform the methods of various embodiments of the present invention.

[0016] According to another aspect of the present invention, a computer program product is also provided, including a computer program that, when executed by a processor, implements the methods of various embodiments of the present invention.

[0017] In this embodiment of the invention, during the vehicle's communication cycle, battery status data of the vehicle's battery and power status data of the vehicle's power unit are collected, and a data upload command is generated; based on the data upload command, at least one processing unit associated with the battery and power unit on the vehicle is activated; the at least one processing unit is used to retrieve historical battery status data of the battery and historical power status data of the power unit; based on the battery status data, historical battery status data, power status data, and historical power status data, the vehicle's energy consumption is predicted to obtain the prediction result. It is noteworthy that, since the prediction focuses on the energy consumption of the battery and power unit, when waking up the vehicle, it is not necessary to wake up all processing units. At least one processing unit associated with the battery and power unit can be woken up via data upload commands. This allows for targeted waking of necessary processing units, achieving partial wake-up and reducing energy consumption. Furthermore, by retrieving historical battery status data and historical power status data of the power unit through at least one processing unit, the historical usage status of the battery and power unit can be obtained. Based on the historical usage status, the energy consumption of the vehicle in the subsequent time period can be inferred, thereby improving the accuracy of vehicle energy consumption prediction. This achieves the goal of accurately predicting vehicle energy consumption through historical and collected data, solving the technical problem of low accuracy in vehicle energy consumption prediction. Attached Figure Description

[0018] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0019] Figure 1 This is a schematic flowchart of an optional vehicle energy consumption prediction method according to an embodiment of the present invention.

[0020] Figure 2 This is a schematic diagram of an optional vehicle energy consumption prediction device according to an embodiment of the present invention. Detailed Implementation

[0021] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0022] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0023] Example 1

[0024] According to an embodiment of the present invention, an embodiment of a method for predicting the energy consumption of a vehicle is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0025] Figure 1 This is a schematic flowchart of an optional vehicle energy consumption prediction method according to an embodiment of the present invention, as shown below. Figure 1 As shown, the method includes the following steps:

[0026] Step S102: During the vehicle's communication cycle, collect the battery status data of the vehicle's battery and the power status data of the vehicle's power unit, and generate a data upload command.

[0027] The aforementioned vehicles refer to electric vehicles, fuel vehicles, or hybrid vehicles that are equipped with internal batteries and power devices, including but not limited to cars, trucks, vans, and trains.

[0028] The communication cycle of a vehicle refers to the interval between information exchanges between the vehicle and other vehicles or infrastructure. Within one communication cycle, the vehicle completes one data transmission and reception process.

[0029] The aforementioned battery status data refers to the status data of the vehicle's internal battery, including key indicators such as battery performance, health status, and remaining lifespan. Among these, battery status data includes, but is not limited to, capacity, voltage, current, internal resistance, and temperature.

[0030] The aforementioned power unit refers to a system or device that provides power to a vehicle, enabling the vehicle to operate and travel. Different types of vehicles have different power units. The power units referred to in this application include, but are not limited to, the power battery of an electric vehicle and the internal combustion engine of a fuel vehicle.

[0031] The aforementioned power status data refers to the power status data of the vehicle's power unit, which refers to parameters related to the power unit. Power status data can be used to assess the performance, health, and operating status of the power unit. The power status data in this application includes, but is not limited to: engine speed, engine load, fuel consumption, power battery charge, and power battery voltage.

[0032] The aforementioned data upload command refers to the command used to wake up the processing unit associated with the battery and power unit on the vehicle, and to upload the battery status data of the battery and the power status data of the power unit on the vehicle to predict the energy consumption of the vehicle.

[0033] Within a single communication cycle of the vehicle, the battery management system can collect battery status data, and sensors such as those for vibration, temperature, and engine speed can collect power system status data. After collecting the battery status data and the power system status data, a data upload command is generated.

[0034] During the vehicle's communication cycle, battery status data can be acquired via data acquisition devices or wireless monitoring systems, and power status data of the vehicle's power unit can be collected via sensors such as vibration, temperature, and speed sensors. After acquiring the battery status data and the power unit's power status data, a data upload command is generated.

[0035] Within a single communication cycle of the vehicle, the battery management system can collect battery status data, and the data acquisition system can collect power status data of the vehicle's power unit. After collecting the battery status data and the power status data of the vehicle's power unit, a data upload command is generated.

[0036] Step S104: Based on the data upload command, wake up at least one processing unit on the vehicle associated with the battery and power unit.

[0037] The aforementioned processing unit is a processing unit within the vehicle associated with the battery and power unit, including but not limited to the battery management system and data acquisition system. The processing unit is used to retrieve historical battery status data of the battery and historical power status data of the power unit.

[0038] In one optional embodiment, the battery management unit associated with the battery and the engine control unit associated with the power unit can be woken up based on a data upload command. After waking up the battery management unit and the engine control unit, the battery management unit retrieves historical battery status data, and the engine control unit retrieves historical power status data. Since the energy consumption of the battery and the power unit is predicted, it is not necessary to wake up all processing units when waking up the vehicle. The battery management unit associated with the battery and the engine control unit associated with the power unit can be woken up by a data upload command, and the required processing units can be woken up selectively to achieve the purpose of partial wake-up, thereby reducing the energy consumption of the vehicle.

[0039] In another optional embodiment, the electronic control unit associated with the battery and the transmission control unit associated with the power unit can be woken up based on a data upload command. The electronic control unit retrieves historical battery status data, and the transmission control unit retrieves historical power status data. Since the energy consumption of the battery and power unit is predicted, it is not necessary to wake up all processing units when waking up the vehicle. The electronic control unit associated with the battery and the transmission control unit associated with the power unit can be woken up via a data upload command, allowing for targeted waking of the necessary processing units and achieving partial wake-up, thereby reducing vehicle energy consumption.

[0040] Step S106: Use at least one processing unit to retrieve historical battery status data of the storage battery and historical power status data of the power unit. The historical battery status data is used to represent the battery status data collected at the historical acquisition time point, and the historical power status data is used to represent the power status data collected at the historical acquisition time point.

[0041] The aforementioned historical battery status data refers to the historical status data of the battery, including but not limited to data such as capacity, voltage, current, internal resistance, and temperature collected at historical points in time.

[0042] The aforementioned historical power status data refers to the historical status data of the power unit, including but not limited to data such as engine speed, engine load, fuel consumption, power battery charge, and power battery voltage collected at historical points in time.

[0043] In one optional embodiment, the historical battery status data of the battery is retrieved using the battery management unit associated with the battery after being woken up, and the historical power status data of the power unit is retrieved using the engine control unit associated with the power unit after being woken up. The historical battery status data represents the battery status data collected at a historical acquisition time point, and the historical power status data represents the power status data collected at the same historical acquisition time point. Based on the historical battery status data and the historical power status data of the power unit, the remaining charge and energy within a preset time period can be predicted more accurately, resulting in more precise predictions of vehicle energy consumption.

[0044] Step S108: Based on the battery state data, the historical battery state data, the power state data, and the historical power state data, the energy consumption of the vehicle is predicted to obtain a prediction result, wherein the prediction result is used to represent the remaining energy consumption of the vehicle in at least one preset time period after the current moment.

[0045] The energy consumption mentioned above refers to the vehicle's consumption of electricity and fuel.

[0046] The aforementioned preset time period is at least one pre-set time period. The vehicle energy consumption prediction method provided in this application is used to predict the remaining energy consumption of the vehicle during the preset time period. The preset time period can also be set according to the actual application scenario.

[0047] In one optional embodiment, since the energy consumption of a vehicle includes the power consumption of the battery and the energy consumption of the power unit, the vehicle's energy consumption can be predicted more accurately based on the remaining battery power and the remaining power unit energy within a preset time period. This application predicts vehicle energy consumption based on battery state data, historical battery state data, power unit power state data, and historical power unit power state data. Specifically, the method for predicting vehicle energy consumption is as follows: calculating the remaining battery power of the vehicle within a preset time period after the current moment based on the current remaining battery power and historical remaining battery power; calculating the remaining energy of the vehicle within a preset time period after the current moment based on the current remaining energy and historical remaining energy; and predicting the remaining energy consumption within a preset time period after the current moment based on the remaining battery power and remaining energy.

[0048] For example, if a preset time period of two hours is selected as needed, the remaining power after two hours is calculated based on the remaining power two hours ago and the remaining power before that, the remaining energy after two hours is calculated based on the remaining energy two hours ago and the remaining energy before that, and the remaining energy consumption after two hours is predicted based on the remaining power and remaining energy after that.

[0049] During the vehicle's communication cycle, battery status data of the vehicle's battery and power status data of the vehicle's power unit are collected, and a data upload command is generated. Based on the data upload command, at least one processing unit associated with the battery and power unit on the vehicle is activated. The at least one processing unit is used to retrieve historical battery status data of the battery and historical power status data of the power unit. Based on the battery status data, historical battery status data, power status data, and historical power status data, the vehicle's energy consumption is predicted, and the prediction result is obtained. It is noteworthy that, since the prediction focuses on the energy consumption of the battery and power unit, when waking up the vehicle, it is not necessary to wake up all processing units. At least one processing unit associated with the battery and power unit can be woken up via data upload commands. This allows for targeted waking of necessary processing units, achieving partial wake-up and reducing energy consumption. Furthermore, by retrieving historical battery status data and historical power status data of the power unit through at least one processing unit, the historical usage status of the battery and power unit can be obtained. Based on the historical usage status, the energy consumption of the vehicle in the subsequent time period can be inferred, thereby improving the accuracy of vehicle energy consumption prediction. This achieves the goal of accurately predicting vehicle energy consumption through historical and collected data, solving the technical problem of low accuracy in vehicle energy consumption prediction.

[0050] Optionally, waking up a processing unit associated with the battery and power unit on the vehicle based on a data upload command includes: obtaining the vehicle type; determining a plurality of preset processing units contained in the electronic control unit on the vehicle; and waking up at least one processing unit associated with the battery and power unit from the plurality of preset processing units based on the vehicle type.

[0051] The aforementioned vehicle types include electric vehicles, gasoline vehicles, and hybrid vehicles.

[0052] The aforementioned electronic control unit refers to the electronic control unit that controls the vehicle's battery or power unit.

[0053] The aforementioned preset processing unit refers to the preset processing unit in the pre-selected electronic control unit used to collect historical battery status data of the battery and historical power status data of the power unit. It includes preset processing units of any vehicle type, and different preset processing power supplies are used to retrieve the historical data of the corresponding battery or power unit.

[0054] In one optional embodiment, since different vehicles have different energy consumption patterns—for example, the energy consumption of a hybrid vehicle consists of battery power consumption and internal combustion engine energy consumption, while the energy consumption of an electric vehicle consists of battery power consumption and power battery power consumption—it is necessary to predict the energy consumption based on the different vehicle types. After obtaining the vehicle type, different electronic control units are selected for different vehicles to obtain different historical power state data.

[0055] For example, when the vehicle type is an electric vehicle, the acquired historical power state data is the historical power state data of the power battery; when the vehicle type is a gasoline vehicle, the acquired historical power state data is the historical power state data of the internal combustion engine. Multiple preset processing units included in the vehicle's electronic control unit are identified, and based on the vehicle type, a preset processing unit that can be woken up in the current vehicle is selected from these preset processing units. The processing units associated with the battery and power unit in the current vehicle are then woken up. The waking up of the processing unit can be performed by only waking up the parts of the processing unit related to calculating the remaining charge duration and remaining energy duration, i.e., partial wake-up.

[0056] Optionally, energy consumption prediction of the vehicle is performed based on battery state data, historical battery state data, power state data, and historical power state data to obtain prediction results, including: obtaining the current collection time point of battery state data and power state data; determining the interval between the current collection time point and the historical collection time point; and performing energy consumption prediction of the vehicle based on the interval, battery state data, historical battery state data, power state data, and historical power state data to obtain prediction results.

[0057] The aforementioned current data collection point is the time point for energy consumption prediction. At the current data collection point, battery status data of the storage battery and power unit power status data are collected.

[0058] The aforementioned historical data collection points refer to the times when historical battery status data and historical power status data of the storage battery were collected.

[0059] The current interval is the time interval between the current data collection point and the historical data collection point. The battery power consumption rate and the power unit energy consumption rate can be determined by the interval.

[0060] In one optional embodiment, the current acquisition time point of battery status data and power status data is obtained. The interval between the current acquisition time point and the historical acquisition time point is calculated based on the acquisition time points of historical battery status data and historical power status data of the power unit. Based on the interval, battery status data, and historical battery status data, the remaining battery charge within a preset time period is calculated. Based on the interval, power status data, and historical power status data, the remaining energy of the power unit within the preset time period is calculated. Based on the remaining charge and remaining energy, the vehicle's energy consumption is predicted to obtain the prediction result.

[0061] By comparing the current and historical data collection points, the time interval between the current and historical time points can be calculated. From this interval and the amount of electricity consumed, the rate of electricity consumption can be calculated. Similarly, from the interval and the amount of energy consumed, the rate of energy consumption can be calculated. By analyzing both the rate of electricity consumption and the rate of energy consumption, vehicle energy consumption can be predicted more accurately.

[0062] Optionally, the vehicle's energy consumption is predicted based on interval duration, battery status data, historical battery status data, power status data, and historical power status data to obtain prediction results, including: determining the remaining charge of the battery in at least one preset time period based on interval duration, battery status data, and historical battery status data; determining the remaining energy of the power unit in at least one preset time period based on interval duration, power status data, and historical power status data; and predicting the vehicle's remaining energy consumption in at least one preset time period based on the remaining charge and remaining energy in at least one preset time period.

[0063] In one optional embodiment, the power consumption of the battery during a historical time period is determined based on battery state data and historical battery state data. The power consumption rate of the battery is obtained by interval duration, and the remaining power of the battery in at least one preset time period is predicted based on the power consumption rate. The energy consumption of the power device during a historical time period is determined based on power state data and historical power state data. The energy consumption rate of the power battery is obtained by interval duration, and the remaining power of the battery in at least one preset time period is predicted based on the energy consumption rate.

[0064] By using the energy consumption rate and power consumption rate, the remaining power and energy for at least one preset time period can be calculated using the battery status data and power status data at the current time, thus obtaining the remaining energy consumption for at least one preset time period. This makes energy consumption prediction more accurate.

[0065] For example, when the vehicle type is an electric vehicle, the remaining energy calculation result is the remaining charge of the power battery; when the vehicle type is a gasoline vehicle, the remaining energy calculation result is the remaining fuel. Based on the remaining charge and remaining energy over a preset time period, the vehicle's remaining energy consumption for that preset time period is predicted.

[0066] Optionally, determining the remaining battery capacity in at least one preset time period based on the interval duration, battery status data, and historical battery status data includes: determining the current remaining battery capacity and historical remaining battery capacity based on the battery status data and historical battery status data, respectively; determining the difference between the current remaining battery capacity and the historical remaining battery capacity; determining the battery capacity consumption rate based on the capacity difference and the interval duration; and determining the remaining battery capacity in at least one preset time period based on the capacity consumption rate and the current remaining battery capacity.

[0067] In one optional embodiment, the current remaining power and historical remaining power of the battery are determined based on battery status data and historical battery status data. The difference between the current remaining power and the historical remaining power is calculated to obtain the power difference value between the current remaining power and the historical remaining power. The power consumption rate of the battery is determined by the ratio of the power difference value to the interval duration. The remaining power of the battery in a preset time period is determined based on the power consumption rate and the current remaining power.

[0068] This application obtains the battery power difference by subtracting the battery state data from the historical battery state data, and determines the battery power consumption rate by the ratio of the power difference to the interval duration. The method for determining the battery power consumption rate provided by this application is simple and easy to implement, and is applicable to different types of vehicles.

[0069] Optionally, determining the remaining energy of the power unit in at least one preset time period based on the interval duration, power status data, and historical power status data includes: determining the current remaining energy and historical remaining energy of the power unit based on the power status data and historical power status data, respectively; determining the energy difference between the current remaining energy and the historical remaining energy; determining the energy consumption rate of the power unit based on the energy difference and the interval duration; and determining the remaining energy of the power unit in at least one preset time period based on the energy consumption rate and the current remaining energy.

[0070] In one optional embodiment, the current remaining energy and historical remaining energy of the power device are determined based on the power state data and historical power state data. The difference between the current remaining energy and historical remaining energy of the power battery is obtained. The energy consumption rate of the power device is determined by the ratio of the energy difference to the interval duration. The remaining energy of the power device in a preset time period is determined based on the energy consumption rate and the current remaining energy.

[0071] This application obtains the energy difference by subtracting the power state data from the historical power state data, and determines the energy consumption rate of the power unit by the ratio of the energy difference to the interval duration. The method for determining the energy consumption rate of the power unit provided in this application is simple and easy to implement, and is applicable to different types of vehicles.

[0072] Optionally, the method further includes: in response to receiving a communication connection instruction between the first terminal device and the vehicle, pushing the prediction result to the first terminal device.

[0073] The aforementioned first terminal device is a terminal device that displays the prediction results, and can be a mobile phone, tablet, computer, or other terminal.

[0074] In one optional embodiment, upon receiving an instruction to connect the mobile phone and the vehicle's cellular communication network, the prediction results are sent to the mobile phone so that the user can remotely view the predicted vehicle energy consumption.

[0075] In another alternative embodiment, upon receiving an instruction to establish a cellular network communication connection between the computer and the vehicle, the prediction results are sent to the computer so that the user can remotely view the predicted vehicle energy consumption.

[0076] In another alternative embodiment, the mobile phone and the vehicle can be connected via Bluetooth. After receiving the instruction to connect the mobile phone and the vehicle via Bluetooth, the prediction results are sent to the mobile phone so that the user can remotely view the predicted results of the vehicle's energy consumption.

[0077] By sending the prediction results to the terminal device, users can be promptly reminded to check the prediction results, thus improving the user experience.

[0078] Optionally, the method further includes: when the vehicle is of a preset type, in response to receiving a remote start command for the vehicle, performing energy consumption prediction on the vehicle based on power state data and historical power state data, and obtaining the prediction result.

[0079] In another optional embodiment, when the vehicle is an electric vehicle, in response to the remote start command of the electric vehicle, the energy consumption of the vehicle is predicted based on the power state data after the electric vehicle is started and recharged and the historical power state data, and the prediction result is obtained.

[0080] Since electric vehicles require recharging when remotely started, it is necessary to predict vehicle energy consumption using power status data after recharging and historical power status data, so that vehicle energy consumption can be accurately predicted even when recharging is required.

[0081] Example 2

[0082] According to another aspect of the present invention, a vehicle energy consumption prediction device is also provided. This device can execute the vehicle energy consumption prediction method of the above embodiments. The specific implementation method and preferred application scenarios are the same as those of the above embodiments, and will not be described in detail here.

[0083] Figure 2 This is a schematic diagram of an optional vehicle energy consumption prediction device according to an embodiment of the present invention, as shown below. Figure 2 As shown, the device includes: a data acquisition module 20, which is used to acquire battery status data of the vehicle's battery and power status data of the vehicle's power unit during the vehicle's communication cycle, and generate a data upload command; a wake-up module 22, which is used to wake up at least one processing unit associated with the battery and power unit on the vehicle based on the data upload command; a retrieval module 24, which is used to retrieve historical battery status data of the battery and historical power status data of the power unit using at least one processing unit, wherein the historical battery status data represents the battery status data acquired at a historical acquisition time point, and the historical power status data represents the power status data acquired at a historical acquisition time point; and a prediction module 26, which is used to predict the energy consumption of the vehicle based on the battery status data, historical battery status data, power status data, and historical power status data, and obtain a prediction result, wherein the prediction result represents the remaining energy consumption of the vehicle for at least one preset time period after the current moment.

[0084] Optionally, the wake-up module 22 includes: an acquisition unit for acquiring the vehicle type of the vehicle; a determination unit for determining a plurality of preset processing units contained in the electronic control unit on the vehicle; and a wake-up unit for waking up at least one processing unit associated with the battery and power unit from the plurality of preset processing units based on the vehicle type.

[0085] Optionally, the prediction module 26 includes: a time unit, which is used to acquire the current acquisition time point of battery status data and power status data; an interval unit, which is used to determine the interval between the current acquisition time point and the historical acquisition time point; and a prediction unit, which is used to predict the energy consumption of the vehicle based on the interval, battery status data, historical battery status data, power status data and historical power status data, and obtain the prediction result.

[0086] Optionally, the prediction unit is also used to determine the remaining charge of the battery in at least one preset time period based on the interval duration, battery status data and historical battery status data; to determine the remaining energy of the power unit in at least one preset time period based on the interval duration, power status data and historical power status data; and to predict the vehicle based on the remaining charge and remaining energy in at least one preset time period to obtain the remaining energy consumption in at least one preset time period.

[0087] Optionally, the prediction unit is further configured to determine the current remaining capacity and historical remaining capacity of the battery based on battery status data and historical battery status data, respectively; determine the difference between the current remaining capacity and the historical remaining capacity; determine the battery's power consumption rate based on the power difference and the interval duration; and determine the remaining capacity of the battery in at least one preset time period based on the power consumption rate and the current remaining capacity.

[0088] Optionally, the prediction unit is further configured to determine the current remaining energy and historical remaining energy of the power unit based on the power state data and historical power state data respectively; determine the energy difference between the current remaining energy and the historical remaining energy; determine the energy consumption rate of the power unit based on the energy difference and the interval duration; and determine the remaining energy of the power unit in at least one preset time period based on the energy consumption rate and the current remaining energy.

[0089] Optionally, the device further includes a push module, which, in response to receiving a communication connection instruction between the first terminal device and the vehicle, pushes the prediction result to the first terminal device.

[0090] Optionally, the device is also used to predict the energy consumption of the vehicle based on power state data and historical power state data in response to receiving a remote start command for the vehicle when the vehicle is of a preset type, and to obtain the prediction result.

[0091] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0092] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0093] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0094] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0095] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0096] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0097] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method of energy consumption prediction of a vehicle, characterized by, The method comprises the following steps: collecting battery state data of a battery and power state data of a power device on a vehicle during a communication period of the vehicle, and generating a data uploading instruction; awakening at least one processing unit associated with the battery and the power device on the vehicle based on the data uploading instruction; calling historical battery state data of the battery and historical power state data of the power device by using the at least one processing unit, wherein the historical battery state data is used to represent battery state data collected at a historical collection time point, and the historical power state data is used to represent power state data collected at the historical collection time point; acquiring a current collection time point of the battery state data and the power state data; determining an interval length between the current collection time point and the historical collection time point; determining a current remaining power of the battery and a historical remaining power of the battery respectively according to the battery state data and the historical battery state data; determining a power difference value between the current remaining power and the historical remaining power; determining a power consumption rate of the battery based on the power difference value and the interval length; determining a remaining power of the battery in at least one preset time period based on the power consumption rate and the current remaining power; determining a current remaining energy of the power device and a historical remaining energy of the power device respectively according to the power state data and the historical power state data; determining an energy difference value between the current remaining energy and the historical remaining energy; determining an energy consumption rate of the power device based on the energy difference value and the interval length; determining a remaining energy of the power device in the at least one preset time period based on the energy consumption rate and the current remaining energy, wherein the remaining energy is a remaining oil amount; predicting the vehicle based on the remaining power in the at least one preset time period and the remaining energy in the at least one preset time period to obtain a remaining energy consumption in the at least one preset time period, and obtaining a prediction result based on the remaining energy consumption, wherein the prediction result is used to represent a remaining energy consumption of the vehicle in at least one preset time period after a current time.

2. The energy consumption prediction method of a vehicle according to claim 1, characterized by, The method further comprises the following steps: acquiring a vehicle type of the vehicle; determining a plurality of preset processing units contained in an electronic control unit on the vehicle; awakening the at least one processing unit associated with the battery and the power device from the plurality of preset processing units based on the vehicle type.

3. The energy consumption prediction method of a vehicle according to claim 1, characterized by, The method further comprises the following steps: in response to receiving a communication connection instruction of a first terminal device and the vehicle, pushing the prediction result to the first terminal device.

4. The energy consumption prediction method of a vehicle according to claim 1, characterized by, The method further comprises the following steps: in a case where the vehicle is of a preset type, in response to receiving a remote start instruction of the vehicle, performing energy consumption prediction on the vehicle based on the power state data and the historical power state data to obtain the prediction result.

5. An energy consumption prediction device of a vehicle, characterized by, The method comprises the following steps: The collection module is configured to collect battery state data of a battery and power state data of a power device on the vehicle in a communication period of the vehicle, and generate a data uploading instruction; The wake-up module is configured to wake up at least one processing unit associated with the battery and the power device on the vehicle based on the data uploading instruction; The retrieval module is configured to retrieve historical battery state data of the battery and historical power state data of the power device by using the at least one processing unit, wherein the historical battery state data is used to represent battery state data collected at a historical collection time point, and the historical power state data is used to represent power state data collected at the historical collection time point; The prediction module is configured to obtain a current collection time point of the battery state data and the power state data; determine an interval length between the current collection time point and the historical collection time point; determine a current remaining power of the battery and a historical remaining power of the battery according to the battery state data and the historical battery state data, respectively; determine a power difference between the current remaining power and the historical remaining power; determine a power consumption rate of the battery based on the power difference and the interval length; determine a remaining power of the battery in the at least one preset time period based on the power consumption rate and the current remaining power; determine a current remaining energy of the power device and a historical remaining energy of the power device according to the power state data and the historical power state data, respectively; determine an energy difference between the current remaining energy and the historical remaining energy; determine an energy consumption rate of the power device based on the energy difference and the interval length; determine a remaining energy of the power device in the at least one preset time period based on the energy consumption rate and the current remaining energy, wherein the remaining energy is a remaining oil amount; predict the vehicle based on the remaining power in the at least one preset time period and the remaining energy in the at least one preset time period to obtain a remaining energy consumption in the at least one preset time period, and obtain a prediction result based on the remaining energy consumption, wherein the prediction result is used to represent a remaining energy consumption of the vehicle in at least one preset time period after a current time.

6. An electronic device, comprising: comprise: a memory storing an executable program; a processor configured to run the program, wherein the program performs the method of any one of claims 1 to 4 when running.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium comprises a stored executable program, wherein the executable program controls the device where the storage medium is located to perform the method of any one of claims 1 to 4 when running.

8. A computer program product, characterised in that, comprise a computer program, which, when executed by a processor, implements the method of any one of claims 1 to 4.

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