Method, device and equipment for determining energy saving rate based on big data

Abstract

The application provides a method, device and equipment for determining energy-saving rate based on big data. The method comprises the following steps: first, a vehicle sends an electric quantity conversion coefficient query request comprising identification information of the vehicle to a cloud server; the cloud server calculates the electric quantity conversion coefficient after receiving the request and returns the electric quantity conversion coefficient to the vehicle. The vehicle receives the electric quantity conversion coefficient returned by the cloud server, and then calculates the energy-saving rate of the vehicle during the predicted energy management period according to the electric quantity conversion coefficient, the estimated oil-saving rate and the predicted consumption data during the energy management period. The energy-saving rate calculated by the method is not affected by the source of the battery electric quantity, and can reflect the real energy-saving effect.

Description

Technical Field

[0001] This application relates to the field of vehicle information technology, and in particular to a method, apparatus and equipment for determining energy efficiency based on big data. Background Technology

[0002] Plug-in hybrid electric vehicles (PHEVs) can collect traffic information, vehicle speed information, slope information, traffic light information, and distance information of vehicles ahead, and use predictive energy management technology and algorithms to optimize the optimal target battery power consumption curve during the journey to the destination. This optimizes the engine to operate more in the fuel-saving range, achieving the effect of saving fuel and reducing emissions.

[0003] However, the fuel-saving effect of predicted energy management technology is greatly influenced by the vehicle's initial technical parameters, the length of the journey, the vehicle speed, and the amount of battery power. These factors lead to a wide range of variations in the calculated fuel-saving rate, with the variation being particularly affected by the battery's initial charge. Due to their inherent characteristics, PHEV vehicles can initially draw power from the grid or from engine charging. Only when the battery power at the start of the journey comes entirely from the grid can true fuel-saving and emission-reduction effects be achieved.

[0004] Existing technologies, when demonstrating fuel-saving effects, did not consider whether the fuel-saving effect of PHEV vehicles came from the power grid or from the additional charging effect of the engine during the previous trip. Therefore, the actual fuel-saving rate, fuel-saving amount, and other parameters they displayed were distorted and inaccurate values, creating a false impression of energy saving. Summary of the Invention

[0005] This application provides a method, apparatus, and device for determining fuel efficiency based on big data, in order to solve the problem of inaccurate fuel efficiency of PHEV vehicles.

[0006] In a first aspect, embodiments of this application provide a method for determining energy-saving rates based on big data, applied to vehicles, the method comprising:

[0007] Send a power conversion factor query request to the cloud server, the power conversion factor query request including the vehicle's identification information;

[0008] Receive the power conversion factor returned by the cloud server, the power conversion factor being used to represent the proportion of each type of energy in the energy consumed by the vehicle;

[0009] Based on the energy conversion factor, the estimated fuel saving rate, and the consumption data during the predicted energy management period, the energy saving rate of the vehicle during the predicted energy management period is calculated. The consumption data includes the actual amount of fuel consumed during the predicted energy management period and the difference in state of charge (SOC) of the battery before and after the predicted energy management.

[0010] In conjunction with the first aspect, in some embodiments, calculating the energy saving rate of the vehicle during the predicted energy management period based on the energy conversion factor, the estimated fuel saving rate, and the consumption data during the predicted energy management period includes:

[0011] Based on the aforementioned energy conversion factor, the estimated fuel saving rate, the actual fuel consumption during the predicted energy management period, and the difference in battery SOC before and after the predicted energy management, the formula is used: The energy saving rate of the vehicle during the predicted energy management period is calculated;

[0012] The calorific value of fuel required during the absence of predictive energy management is calculated using the estimated fuel saving rate and the actual amount of fuel consumed. The battery charge difference is equal to the rated battery capacity multiplied by the SOC difference of the battery before and after predictive energy management. The actual calorific value of fuel consumed is obtained by multiplying the actual fuel consumed during predictive energy management by the calorific value of gasoline.

[0013] In conjunction with the first aspect, in some embodiments, the method further includes:

[0014] The vehicle's energy efficiency during the predictive energy management period is displayed through the vehicle's infotainment system.

[0015] In conjunction with the first aspect, in some embodiments, before sending the power conversion factor query request to the cloud server, the method further includes:

[0016] After detecting that the predictive energy management function is enabled, it begins to accumulate and record fuel consumption during the predictive energy management period;

[0017] When the predicted energy function is detected to be off, the cumulative fuel consumption during the predicted energy management period is obtained to determine the actual amount of fuel consumed by the vehicle during the predicted energy management period.

[0018] Secondly, embodiments of this application also provide a method for determining energy efficiency based on big data, applied to a cloud server, the method comprising:

[0019] Receive a power conversion factor query request sent by any vehicle, wherein the power conversion factor query request includes the vehicle's identification information;

[0020] Based on the vehicle's identification information, query and obtain the historical energy data corresponding to the vehicle's identification information;

[0021] Determine whether the historical energy data meets the preset energy conversion conditions;

[0022] If the historical energy data meets the energy conversion condition, then the energy conversion factor of the vehicle is calculated based on the historical energy data;

[0023] The electricity conversion factor is sent to the vehicle.

[0024] In conjunction with the second aspect, in some embodiments, the method further includes:

[0025] If the historical energy data does not meet the energy conversion conditions, the vehicle model is determined based on the vehicle's identification information;

[0026] Obtain the energy data of other vehicles corresponding to the model of the vehicle, and calculate the average value of the energy data of the other vehicles;

[0027] The energy conversion factor for the vehicle is calculated based on the average energy data of the other vehicles.

[0028] In conjunction with the second aspect, in some embodiments, calculating the vehicle's energy conversion factor based on the historical energy data includes:

[0029] Formula used: Calculate the energy conversion factor for the vehicle, wherein the historical energy data includes: total electricity charged to the grid and total heat consumed by fuel.

[0030] Thirdly, embodiments of this application also provide an apparatus for determining energy efficiency based on big data, the apparatus comprising:

[0031] The sending module is used to send a power conversion factor query request to the cloud server. The power conversion factor query request includes the identification information of the energy saving rate determination device based on big data.

[0032] The receiving module is used to receive the power conversion factor returned by the cloud server. The power conversion factor is used to represent the proportion of each type of energy in the energy consumed by the energy-saving rate determination device based on big data.

[0033] The calculation module is used to calculate the energy saving rate of the energy saving rate determination device based on big data during the predicted energy management period based on the energy conversion factor, the estimated fuel saving rate, and the consumption data during the predicted energy management period. The consumption data includes the actual amount of fuel consumed during the predicted energy management period and the difference in SOC of the battery before and after the predicted energy management.

[0034] In conjunction with the third aspect, in some embodiments, the computing module is specifically used for:

[0035] Based on the energy conversion factor, the estimated fuel saving rate, the actual fuel consumption during the predicted energy management period, and the difference in battery SOC before and after the predicted energy management, the formula is used: The energy saving rate of the energy saving rate determination device based on big data is calculated during the predicted energy management period;

[0036] The calorific value of fuel required during the absence of predictive energy management is calculated using the estimated fuel saving rate and the actual amount of fuel consumed. The battery charge difference is equal to the rated battery capacity multiplied by the SOC difference of the battery before and after predictive energy management. The actual calorific value of fuel consumed is obtained by multiplying the actual fuel consumed during predictive energy management by the calorific value of gasoline.

[0037] In conjunction with the third aspect, in some embodiments, the apparatus further includes:

[0038] The display module is used to display the energy saving rate of the energy saving rate determination device based on big data during the predicted energy management period through the vehicle interface of the device.

[0039] In conjunction with the third aspect, in some embodiments, before sending the power conversion factor query request to the cloud server, the device further includes:

[0040] The recording module is used to start accumulating and recording fuel consumption during the predictive energy management period after detecting that the predictive energy management function is enabled;

[0041] The consumption module is used to obtain the cumulative fuel consumption during the predictive energy management period to obtain the actual amount of fuel consumed by the vehicle during the predictive energy management period when the predictive energy function is detected to be off.

[0042] Fourthly, embodiments of this application also provide an apparatus for determining energy efficiency based on big data, the apparatus comprising:

[0043] The receiving module is used to receive a power conversion factor query request sent by any vehicle, wherein the power conversion factor query request includes the vehicle's identification information;

[0044] The query module is used to query and obtain the historical energy data corresponding to the vehicle's identification information based on the vehicle's identification information;

[0045] The judgment module is used to determine whether the historical energy data meets the preset energy conversion conditions;

[0046] The calculation module is used to calculate the vehicle's energy conversion factor based on the historical energy data if the historical energy data meets the energy conversion conditions.

[0047] The sending module is used to send the electricity conversion factor to the vehicle.

[0048] In conjunction with the fourth aspect, in some embodiments, the apparatus further includes: a model module and an averaging module.

[0049] The model module is used to determine the vehicle model based on the vehicle's identification information if the historical energy data does not meet the energy conversion conditions.

[0050] The averaging module is used to obtain energy data of other vehicles corresponding to the model of the vehicle, and calculate the average value of the energy data of the other vehicles;

[0051] The calculation module is also used to calculate the vehicle's energy conversion factor based on the average energy data of the other vehicles.

[0052] In conjunction with the fourth aspect, in some embodiments, the computing module is specifically used for:

[0053] Formula used: Calculate the vehicle's energy conversion factor, wherein the historical energy data includes: total electricity charged to the grid and total heat consumed by fuel.

[0054] Fifthly, embodiments of this application also provide a vehicle, including:

[0055] The vehicle body, vehicle controller, memory, and communication interfaces for interacting with other devices;

[0056] The memory stores computer-executed instructions;

[0057] The vehicle controller executes computer execution instructions stored in the memory to implement the method for determining energy saving rate based on big data as described in any of the first aspects.

[0058] Sixthly, embodiments of this application also provide a server, including:

[0059] The processor, the memory communicatively connected to the processor, and the communication interface for interacting with other devices;

[0060] The memory stores computer-executed instructions;

[0061] The processor executes computer execution instructions stored in the memory to implement the method for determining energy efficiency based on big data as described in any of the second aspects.

[0062] In a seventh aspect, embodiments of this application also provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the method for determining energy efficiency based on big data as described in any one of the first or second aspects.

[0063] The method, apparatus, and equipment for determining energy-saving rates based on big data provided in this application involve a vehicle sending a power conversion factor query request, including the vehicle's identification information, to a cloud server. Upon receiving the request, the cloud server determines whether the power conversion conditions are met based on the vehicle's historical energy data and calculates the power conversion factor. The vehicle obtains this power conversion factor and then calculates the energy-saving rate based on the factor, the estimated fuel-saving rate, and the consumption data during the predicted energy management period. This energy-saving rate takes into account the vehicle's historical electricity and fuel consumption data; therefore, the calculated energy-saving effect is not affected by the battery's power source and can accurately represent the fuel-saving and emission-reduction effect. Attached Figure Description

[0064] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0065] Figure 1 This application provides an illustration of the application scenario.

[0066] Figure 2 A flowchart illustrating an embodiment of the method for determining energy efficiency based on big data provided in this application;

[0067] Figure 3 A flowchart illustrating Embodiment 2 of the method for determining energy saving rate based on big data provided in this application;

[0068] Figure 4 A flowchart illustrating Embodiment 3 of the method for determining energy efficiency based on big data provided in this application;

[0069] Figure 5 A flowchart illustrating Embodiment 4 of the method for determining energy efficiency based on big data provided in this application;

[0070] Figure 6 A flowchart illustrating Embodiment 5 of the method for determining energy efficiency based on big data provided in this application;

[0071] Figure 7 A schematic diagram of the structure of an embodiment of a device for determining energy efficiency based on big data provided in this application;

[0072] Figure 8A schematic diagram of the structure of a second embodiment of a device for determining energy efficiency based on big data provided in this application;

[0073] Figure 9 A structural schematic diagram of a vehicle provided in this application;

[0074] Figure 10 This is a schematic diagram of the structure of a server provided in this application.

[0075] The accompanying drawings have illustrated specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to specific embodiments. Detailed Implementation

[0076] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0077] First, let me explain the terms used in this application:

[0078] Predictive energy management, in hybrid vehicles, refers to predicting future vehicle operating conditions based on a fuel-minimizing strategy, analyzing changes in future energy demand, and formulating corresponding dynamic adjustment strategies. Based on vehicle-to-everything (V2X) communication technology, predictive energy management collects vehicle operating status and traffic information as input variables for the predictive model, outputting an energy allocation strategy for vehicle operation to improve fuel economy and the balance of the battery's state of charge (SOC).

[0079] Estimated fuel saving rate: refers to the fuel saving rate calculated by a big data model based on the energy distribution strategy output by the predictive energy management technology of a vehicle. It represents the proportion of fuel saved compared to the amount of fuel consumed without predictive energy management.

[0080] Plug-in hybrid electric vehicles (PHEVs) can collect traffic information, vehicle speed, slope information, traffic light information, and distance information from vehicles ahead. Using predictive energy management technology and algorithms, they optimize the battery consumption curve to determine the optimal path to the destination, thus optimizing engine operation within the fuel-efficient range and achieving fuel savings and emission reductions. When displaying the energy-saving effects of these technologies, the display typically shows the cumulative fuel savings in grams or liters when the fuel-saving technology is activated; or it may convert the fuel savings into increased driving range; or it may convert the fuel savings into CO2 reductions; or it may directly display the average fuel consumption value.

[0081] However, the fuel-saving effect of predicted energy management technology is greatly influenced by the vehicle's initial technical parameters, the length of the journey, and the vehicle speed. The fuel-saving rate varies considerably, especially the battery charge at the start of the journey. Due to their inherent characteristics, PHEV vehicles can initially draw power from the grid or from engine charging. Only when the battery charge at the start of the journey comes entirely from the grid can true fuel saving and emission reduction be achieved. For example, during a short, low-speed journey, only a portion of the battery charge may be consumed. In this case, the predicted fuel-saving rate of predicted energy management technology might be as high as 100%. However, some of this consumed energy comes from the additional charging energy generated by the engine, resulting in a discrepancy between the calculated fuel-saving rate and the actual rate. Therefore, existing technologies, when demonstrating fuel-saving effects, do not consider whether the fuel saving effect of PHEV vehicles comes from the grid or from the additional charging effect of the engine during the previous journey. Consequently, the displayed actual fuel-saving rate, fuel-saving amount, and other parameters are distorted and inaccurate values, creating a false impression of energy saving.

[0082] In view of the above problems, this application provides a method, apparatus, and device for determining energy saving rate based on big data. During vehicle operation, fuel savings from battery power come from two sources: charging from the power grid and charging from the engine. To accurately describe the energy-saving effect, it is necessary to distinguish between these two sources of battery power. Based on the driver's historical charging habits and historical electricity and fuel consumption data, a power conversion factor is calculated. This factor represents the proportion of each type of energy in the vehicle's energy consumption. Using this factor, the proportion of fuel and electricity used in the battery power can be deduced, thus distinguishing the sources of battery power and enabling a more accurate calculation of fuel-saving effects.

[0083] Figure 1 The application scenario diagram provided in this application is as follows: Figure 1As shown, the method of this application is applicable to PHEV vehicles, displaying the energy-saving and emission-reduction effects after the vehicle has finished driving. The system architecture of this application includes a PHEV vehicle, an in-vehicle infotainment system, and a cloud server. The PHEV vehicle is used for energy-consuming driving; the in-vehicle infotainment system records the energy consumed during the trip and calculates the energy-saving rate; the cloud server records the cumulative energy consumed by the vehicle, calculates the estimated fuel-saving rate and the amount of charging power obtained during the predicted energy management period, and can calculate the energy conversion factor.

[0084] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0085] Figure 2 A flowchart illustrating an embodiment of the method for determining energy efficiency based on big data provided in this application is shown below. Figure 2 As shown, this method is applied to vehicles and specifically includes the following steps:

[0086] S101. Send a power conversion factor query request to the cloud server. The power conversion factor query request includes the vehicle's identification information.

[0087] In this step, to accurately calculate the energy saving rate, the impact of different energy sources in the battery charge needs to be considered. This impact can be eliminated by using a charge conversion factor that represents the proportion of energy sources in the battery charge. The cloud server can calculate this charge conversion factor based on the vehicle's historical energy data. Therefore, when the energy saving rate needs to be calculated, the vehicle sends a charge conversion factor query request to the cloud server. This query request includes the vehicle's identification information, such as the Vehicle Identification Number (VIN), which allows the cloud server to uniquely identify the vehicle.

[0088] S102. Receive the power conversion factor returned by the cloud server. The power conversion factor is used to represent the proportion of each type of energy in the energy consumed by the vehicle.

[0089] In this step, after the vehicle sends a request for a battery conversion factor, the cloud server calculates the battery conversion factor, and the vehicle receives the battery conversion factor returned by the cloud server. The cloud server calculates the battery conversion factor based on the vehicle's historical charging and fuel consumption habits, i.e., the total historical charging and fuel consumption. This battery conversion factor represents the proportion of electricity and fuel consumption in the vehicle's energy consumption. The vehicle uses this battery conversion factor to calculate its energy efficiency.

[0090] S103. Based on the energy conversion factor, the estimated fuel saving rate and the consumption data during the predicted energy management period, the energy saving rate of the vehicle during the predicted energy management period is calculated. The consumption data includes the actual amount of fuel consumed during the predicted energy management period and the difference in state of charge (SOC) of the battery before and after the predicted energy management.

[0091] In this step, predictive energy management technology, based on vehicle-to-everything (V2X) communication technology and a fuel consumption minimization strategy, predicts future vehicle driving conditions, analyzes changes in future energy demand, and formulates corresponding driving strategies. During predictive energy management, information such as traffic conditions, vehicle speed, gradient, and distance to other vehicles on the route is collected. Predictive energy management technology and algorithms are used to optimize engine operation within the fuel-efficient range. At the end of the predictive energy management function, the vehicle uploads information such as speed, battery SOC, actual fuel consumption, and engine speed during the predicted energy management period to a cloud server. The cloud server inputs this information into a big data model to output the estimated fuel-saving rate for the vehicle's driving process and sends the estimated fuel-saving rate back to the vehicle. The estimated fuel-saving rate represents the proportion of fuel saved compared to the fuel consumed without predictive energy management.

[0092] In one specific implementation, predictive energy management can be enabled via in-vehicle navigation, utilizing information from the navigation system for predictive energy management. In this solution, there are no restrictions on how predictive energy management is enabled or on the big data model-driven calculation method for estimating fuel savings.

[0093] The vehicle records consumption data during the predictive energy management period, including the actual amount of fuel consumed and the difference in battery SOC before and after predictive energy management. Based on the estimated fuel saving rate, the actual amount of fuel consumed, and the calorific value of the fuel, the vehicle can calculate the calorific value of the fuel required without predictive energy management. Then, based on the energy conversion factor received from the cloud server and the consumption data during the predictive energy management period, the vehicle can calculate the energy saving rate during the predictive energy management period.

[0094] In one specific implementation, the fuel-saving rate is estimated based on the energy conversion factor, the actual fuel consumption during the predicted energy management period, and the difference in battery SOC before and after the predicted energy management, using the following formula:

[0095] The energy saving rate of the vehicle during the predicted energy management period is calculated;

[0096] The calorific value of fuel required without predictive energy management can be calculated from the estimated fuel-saving rate and the actual amount of fuel consumed. Specifically, in one calculation method, the calorific value of fuel required without predictive energy management is equal to the actual amount of fuel consumed with predictive energy management multiplied by (1 + estimated fuel-saving rate) multiplied by the calorific value of a unit of gasoline; in another calculation method, the calorific value of fuel required without predictive energy management is equal to the actual amount of fuel consumed divided by (1 - estimated fuel-saving rate) multiplied by the calorific value of a unit of gasoline. In this embodiment, the calculation method for the calorific value of fuel required without predictive energy management is not limited.

[0097] The battery charge difference equals the rated battery capacity multiplied by the difference in SOC of the battery before and after predicted energy management. The actual fuel calorific value consumed is obtained by multiplying the actual amount of fuel consumed during the predicted energy management period by the unit gasoline calorific value. The historical average engine thermal efficiency is a given fixed value.

[0098] The energy-saving rate calculation formula subtracts a portion from the numerator, representing the actual fuel calorific value consumed plus the charging power and the electricity converted from fuel consumed by the engine. The denominator represents the actual energy consumed as a percentage of the fuel calorific value required without predictive energy management. This energy-saving rate calculation method fully considers the energy source of the battery power used during driving, rather than simply attributing all battery power to grid charging. A portion of the battery power is obtained from engine fuel, making the energy-saving effect more consistent with reality.

[0099] The energy-saving rate determination method based on big data provided in this embodiment involves obtaining an energy conversion factor from the cloud based on historical charging and fuel consumption data after the predicted energy management period ends. This energy conversion factor is then used to calculate the energy-saving rate based on the estimated fuel-saving rate obtained through a big data model and the driving consumption data during the predicted energy management period. This solution introduces an energy conversion factor based on the vehicle's historical charging and fuel consumption habits to calculate the energy-saving rate, eliminating the influence of the battery's power source on the energy-saving effect and making the energy-saving results more accurate.

[0100] Figure 3 A flowchart illustrating Embodiment Two of the method for determining energy efficiency based on big data provided in this application is shown below. Figure 3 As shown, based on the above embodiment, after step 103, the method further includes the following steps:

[0101] S201. Display the vehicle's energy saving rate during the predictive energy management period through the vehicle's infotainment system interface.

[0102] In this step, after calculating the energy-saving rate based on the vehicle's historical charging and fuel consumption habits, the energy-saving rate needs to be displayed to the user. The vehicle's infotainment interface displays the vehicle's energy-saving rate during the predicted energy management period. Users can select customized energy-saving data types on the infotainment interface, such as the percentage of fuel saved, the number of grams of fuel saved, and the amount of carbon dioxide saved.

[0103] In this embodiment, by displaying the calculated energy-saving rate in a personalized manner on the vehicle interface, users can quickly obtain the fuel-saving effect during the predicted energy management period, resulting in a better display effect.

[0104] Figure 4 A flowchart illustrating Embodiment 3 of the method for determining energy efficiency based on big data provided in this application is shown below. Figure 4 As shown, based on the above embodiment, before sending the power conversion factor query request to the cloud server in step 101, the method further includes the following steps:

[0105] S301. After detecting that the predictive energy management function is enabled, start accumulating and recording fuel consumption during the predictive energy management period.

[0106] In this step, while driving, the vehicle user activates the Predictive Energy Management (REM) function for energy-efficient driving. Once the vehicle detects that REM is activated, it records the fuel consumption during the REM period and the battery SOC value when REM is activated. Specifically, the user can activate REM by starting navigation and deactivate it when navigation ends or exits.

[0107] S302. When the predictive energy function is detected to be off, the cumulative fuel consumption during the predictive energy management period is obtained to obtain the actual amount of fuel consumed by the vehicle during the predictive energy management period.

[0108] In this step, when the vehicle detects that the predictive energy function is off, it obtains the cumulative fuel consumption recorded during the predictive energy management period and the battery SOC value when the predictive energy function is off. This yields the actual amount of fuel consumed by the vehicle during the predictive energy management period and the difference in battery SOC before and after predictive energy management, which are used to calculate the actual calorific value of the fuel consumed and the difference in battery charge.

[0109] In this embodiment, by recording the cumulative fuel consumption and battery SOC value during the predicted energy management period, the actual amount of fuel consumed and the difference between battery SOC during this period can be obtained, providing actual data for calculating the energy saving rate.

[0110] Figure 5 The flowchart of Embodiment 4 of the method for determining energy saving rate based on big data provided in this application is shown below. Figure 5As shown, this method is applied to a cloud server and specifically includes the following steps:

[0111] S401. Receive a power conversion factor query request sent by any vehicle. The power conversion factor query request includes the vehicle's identification information.

[0112] In this step, the cloud server has the capability to calculate the energy conversion factor based on historical energy data. When any vehicle needs this energy conversion factor, it sends a query request to the cloud. The cloud server receives the query request, which includes the vehicle's identification information, specifically the vehicle's VIN code, IP address, and MAC address. The cloud server determines the vehicle's identity and model based on the VIN code, and then queries the vehicle's energy data from historical data based on this identity information. The IP address and MAC address are used to return the energy conversion factor to the uniquely identified vehicle.

[0113] The cloud server also receives information uploaded by the vehicle during the predicted energy management period, such as vehicle speed, battery SOC value, actual fuel consumption, and engine speed. The cloud server inputs this information into a big data model to output the estimated fuel saving rate during the vehicle's driving process.

[0114] S402. Based on the vehicle's identification information, query and obtain the historical energy data corresponding to the vehicle's identification information.

[0115] In this step, after receiving the query request, the cloud server identifies the vehicle based on its identification information and then queries the vehicle's historical energy data in the cloud. Historical energy data consists of the vehicle's total energy consumption via grid charging, total calorific value of fuel consumed, and total distance traveled, all uploaded by the vehicle and including its identification information.

[0116] Specifically, the total grid charging energy in the historical energy data of the cloud server is uploaded in real time by the vehicle during each charging process, while the total calorific value of fuel consumption is the fuel consumption uploaded in real time by the vehicle during driving. The cloud server calculates this based on the cumulative fuel consumption and the calorific value per unit of fuel. The total distance traveled by the vehicle is uploaded in real time by the vehicle.

[0117] S403. Determine whether the historical energy data meets the preset energy conversion conditions.

[0118] In this step, after retrieving the historical energy data based on the vehicle's equipment identification, calculations are performed based on this historical energy data. To prevent calculation errors caused by insufficient historical data, it is necessary to determine whether the historical energy data meets the preset energy conversion conditions.

[0119] Specifically, the conversion conditions can be set in the following ways:

[0120] The first method requires the vehicle to have traveled 1,000 kilometers; the second method requires the vehicle to have consumed more than 120 liters of fuel; the third method requires the vehicle to have traveled 1,000 kilometers and consumed more than 120 liters of fuel.

[0121] This embodiment does not limit the specific method of calculating the electricity consumption.

[0122] S404. If the historical energy data meets the energy conversion conditions, then calculate the vehicle's energy conversion factor based on the historical energy data.

[0123] In this step, to prevent calculation errors caused by insufficient historical data, historical energy data is compared with preset energy conversion conditions.

[0124] If the historical energy data meets the energy conversion conditions, then the energy conversion factor is calculated based on the vehicle's historical data.

[0125] If the historical energy data does not meet the energy conversion conditions, the cloud server determines the vehicle model through the vehicle identification information, obtains the historical energy data of all vehicles of that model on the cloud server, and calculates the energy conversion factor based on the historical energy data of all models.

[0126] In one specific implementation, the vehicle's energy conversion factor is calculated based on historical energy data, including:

[0127] Formula used: Calculate the vehicle's energy conversion factor, where historical energy data includes: total electricity charged to the grid and total heat consumed by fuel.

[0128] The energy conversion factor calculated using the formula is the sum of the total heat consumed by fuel and the total heat consumed by charging from the grid. This formula represents the proportion of energy consumed by the vehicle from fuel to its total energy consumption, or the proportion of energy consumed by the vehicle from either fuel or charging. This energy conversion factor can be used to distinguish the proportion of battery power consumed from charging and fuel, thus leading to a more accurate calculation of energy savings.

[0129] S405: Send the electricity conversion factor to the vehicle.

[0130] In this step, the cloud server calculates the energy conversion factor and the estimated fuel saving rate, and sends the energy conversion factor to the vehicle based on the vehicle identification information for the calculation of the vehicle's energy saving rate.

[0131] This embodiment provides a method for determining energy-saving rates based on big data. After receiving a request from a vehicle to query the energy conversion factor, the cloud server queries the vehicle's historical energy data to determine whether the historical energy data meets the energy conversion conditions. If the conditions are met, the cloud server calculates the energy conversion factor based on the historical energy data and sends the calculated energy conversion factor to the vehicle. This solution considers vehicle charging habits and calculates the energy conversion factor based on the vehicle's historical energy data, which can distinguish the proportion of each type of energy consumed, making the calculated energy-saving rate more consistent with reality.

[0132] Figure 6 A flowchart illustrating Embodiment 5 of the method for determining energy efficiency based on big data provided in this application is shown below. Figure 6 As shown, based on Example 4, the method further includes the following steps:

[0133] S501. If the historical energy data does not meet the conditions for energy conversion, the vehicle model shall be determined based on the vehicle's identification information.

[0134] In this step, the cloud server compares historical energy data with preset energy conversion conditions. If the historical energy data does not meet the energy conversion conditions, the cloud server determines the vehicle model based on the vehicle identification information. Specifically, the vehicle identification information includes the VIN code; by identifying the VIN code, the vehicle model information can be determined.

[0135] S502. Obtain the energy data of other vehicles corresponding to the vehicle model, and calculate the average value of the energy data of other vehicles.

[0136] In this step, when the vehicle's historical energy data does not meet the energy conversion conditions, the cloud server obtains the historical energy data of all vehicles of that model from the cloud server, and calculates the average historical energy data of that model based on the historical energy data of all vehicles of that model. This average historical energy data is used as the historical energy data for calculating the energy saving rate of that vehicle.

[0137] S503. Calculate the vehicle's energy conversion factor based on the average energy data of other vehicles.

[0138] In this step, if the vehicle's historical energy data does not meet the energy conversion conditions, the vehicle's energy conversion factor is calculated based on the calculated average historical energy data.

[0139] This embodiment provides a method for determining energy-saving rates based on big data. When historical energy data for a vehicle does not meet the requirements for energy conversion, an energy conversion factor is calculated based on the average historical energy data of all vehicles of that model. This approach avoids significant deviations in energy-saving rates due to insufficient historical data.

[0140] Figure 7 A schematic diagram of an embodiment of a device for determining energy efficiency based on big data provided in this application is shown below. Figure 7 As shown, the device 200 includes:

[0141] The sending module 211 is used to send a power conversion factor query request to the cloud server, wherein the power conversion factor query request includes the identification information of the energy saving rate determination device based on big data;

[0142] The receiving module 212 is used to receive the power conversion coefficient returned by the cloud server. The power conversion coefficient is used to represent the proportion of each type of energy in the energy consumed by the energy-saving rate determination device based on big data.

[0143] The calculation module 213 is used to calculate the energy saving rate of the energy saving rate determination device based on big data during the predicted energy management period based on the energy conversion factor, the estimated fuel saving rate and the consumption data during the predicted energy management period. The consumption data includes the actual amount of fuel consumed during the predicted energy management period and the difference in SOC of the battery before and after the predicted energy management.

[0144] Optionally, the calculation module 213 is specifically used for:

[0145] Based on the aforementioned energy conversion factor, the estimated fuel saving rate, the actual fuel consumption during the predicted energy management period, and the difference in battery SOC before and after the predicted energy management, the formula is used: The energy saving rate of the energy saving rate determination device based on big data is calculated during the predicted energy management period;

[0146] The calorific value of fuel required during the absence of predictive energy management is calculated using the estimated fuel saving rate and the actual amount of fuel consumed. The battery charge difference is equal to the rated battery capacity multiplied by the SOC difference of the battery before and after predictive energy management. The actual calorific value of fuel consumed is obtained by multiplying the actual fuel consumed during predictive energy management by the calorific value of gasoline.

[0147] Optionally, the device further includes:

[0148] Display module 214 is used to display the energy saving rate of the energy saving rate determination device based on big data during the predicted energy management period through the vehicle interface of the device.

[0149] Optionally, before sending the power conversion factor query request to the cloud server, the device further includes:

[0150] The recording module 215 is used to start accumulating and recording fuel consumption during the predictive energy management period after detecting that the predictive energy management function is enabled.

[0151] The consumption module 216 is used to obtain the cumulative fuel consumption during the predictive energy management period to obtain the actual amount of fuel consumed by the vehicle during the predictive energy management period when the predictive energy function is detected to be off.

[0152] The energy-saving rate determination device based on big data provided in this application embodiment can execute the energy-saving rate determination method based on big data applied to the vehicle side in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described again here.

[0153] Figure 8 A schematic diagram of a second embodiment of a device for determining energy efficiency based on big data provided in this application is shown below. Figure 8 As shown, the device 300 includes:

[0154] The receiving module 311 is used to receive a power conversion factor query request sent by any vehicle, wherein the power conversion factor query request includes the vehicle's identification information;

[0155] The query module 312 is used to query and obtain the historical energy data corresponding to the vehicle's identification information based on the vehicle's identification information.

[0156] The judgment module 313 is used to determine whether the historical energy data meets the preset energy conversion conditions;

[0157] The calculation module 314 is used to calculate the vehicle's energy conversion factor based on the historical energy data if the historical energy data meets the energy conversion conditions.

[0158] The sending module 315 is used to send the electricity conversion factor to the vehicle.

[0159] Optionally, the device further includes: a model module 316 and an averaging module 317;

[0160] The model module 316 is used to determine the model of the vehicle based on the vehicle's identification information if the historical energy data does not meet the energy conversion conditions.

[0161] The averaging module 317 is used to obtain energy data of other vehicles corresponding to the model of the vehicle, and calculate the average value of the energy data of the other vehicles;

[0162] The calculation module 314 is also used to calculate the energy conversion factor of the vehicle based on the average energy data of the other vehicles.

[0163] Optionally, the calculation module 314 is specifically used for:

[0164] Formula used: Calculate the vehicle's energy conversion factor, wherein the historical energy data includes: total electricity charged to the grid and total heat consumed by fuel.

[0165] The energy-saving rate determination device based on big data provided in this application embodiment can execute the energy-saving rate determination method based on big data applied to the cloud server side in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described again here.

[0166] Figure 9 A structural schematic diagram of a vehicle provided in this application, such as Figure 9 As shown, the vehicle includes:

[0167] The vehicle body 411, the vehicle controller 412, the memory 413, and the communication interface 414 for interacting with other devices;

[0168] The memory 413 stores computer-executed instructions;

[0169] The vehicle controller 412 executes the computer execution instructions stored in the memory 413 to implement the technical solution on the vehicle side as described in any of the aforementioned method embodiments.

[0170] Figure 10 A schematic diagram of the structure of a server provided in this application is shown below. Figure 10 As shown, the server includes:

[0171] The processor 511, the memory 512 communicatively connected to the processor, and the communication interface 513 for interacting with other devices;

[0172] The memory 512 stores computer-executed instructions;

[0173] The processor 511 executes the computer execution instructions stored in the memory 512 to implement the server-side technical solution as described in any of the foregoing method embodiments.

[0174] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the method for determining energy efficiency based on big data as described in the foregoing embodiments.

[0175] In the specific implementation of the aforementioned server, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this application can be directly manifested as execution by a hardware processor, or execution by a combination of hardware and software modules within the processor.

[0176] All or part of the steps in the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a readable memory. When the program is executed, it performs the steps of the above method embodiments; and the aforementioned memory (storage medium) includes: read-only memory (ROM), RAM, flash memory, hard disk, solid-state drive, magnetic tape, floppy disk, optical disk, and any combination thereof.

[0177] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the claims.

[0178] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A method for determining energy-saving rate based on big data, characterized in that, Applied to vehicles, the method includes: Send a power conversion factor query request to the cloud server, the power conversion factor query request including the vehicle's identification information; Receive the power conversion factor returned by the cloud server, the power conversion factor being used to represent the proportion of the energy consumed by the vehicle in fuel to the total energy of the vehicle; Based on the energy conversion factor, the estimated fuel saving rate, and the consumption data during the predicted energy management period, the energy saving rate of the vehicle during the predicted energy management period is calculated. The consumption data includes the actual amount of fuel consumed during the predicted energy management period and the difference in state of charge (SOC) of the battery before and after the predicted energy management. The estimated fuel saving rate represents the proportion of fuel that can be saved to the amount of fuel consumed without the predicted energy management period.

2. The method according to claim 1, characterized in that, The step of calculating the vehicle's energy saving rate during the predicted energy management period based on the energy conversion factor, the estimated fuel saving rate, and the consumption data during the predicted energy management period includes: Based on the energy conversion factor, the estimated fuel saving rate, the actual fuel consumption during the predicted energy management period, and the difference in battery SOC before and after the predicted energy management, the formula is used: The energy saving rate of the vehicle during the predicted energy management period is calculated; The calorific value of fuel required during the absence of predictive energy management is calculated using the estimated fuel saving rate and the actual amount of fuel consumed. The battery charge difference is equal to the rated battery capacity multiplied by the SOC difference of the battery before and after predictive energy management. The actual calorific value of fuel consumed is obtained by multiplying the actual fuel consumed during predictive energy management by the calorific value of gasoline.

3. The method according to claim 1, characterized in that, The method further includes: The vehicle's energy efficiency during the predictive energy management period is displayed through the vehicle's infotainment system.

4. The method according to any one of claims 1 to 3, characterized in that, Before sending the power conversion factor query request to the cloud server, the method further includes: After detecting that the predictive energy management function is enabled, it begins to accumulate and record fuel consumption during the predictive energy management period; When the predicted energy function is detected to be off, the cumulative fuel consumption during the predicted energy management period is obtained to determine the actual amount of fuel consumed by the vehicle during the predicted energy management period.

5. A method for determining energy-saving rate based on big data, characterized in that, Applied to a cloud server, the method includes: Receive a power conversion factor query request sent by any vehicle, wherein the power conversion factor query request includes the vehicle's identification information; Based on the vehicle's identification information, query and obtain the historical energy data corresponding to the vehicle's identification information; Determine whether the historical energy data meets the preset energy conversion conditions; If the historical energy data meets the energy conversion condition, then the energy conversion factor of the vehicle is calculated based on the historical energy data; The energy conversion factor is sent to the vehicle; the vehicle is used to calculate the energy saving rate during the predicted energy management period based on the energy conversion factor, the estimated fuel saving rate, and the consumption data during the predicted energy management period. The consumption data includes the actual amount of fuel consumed during the predicted energy management period and the difference in state of charge (SOC) of the battery before and after the predicted energy management. The estimated fuel saving rate represents the proportion of fuel that can be saved to the amount of fuel consumed during the period without predicted energy management.

6. The method according to claim 5, characterized in that, The method further includes: If the historical energy data does not meet the energy conversion conditions, the vehicle model is determined based on the vehicle's identification information; Obtain the energy data of other vehicles corresponding to the model of the vehicle, and calculate the average value of the energy data of the other vehicles; The energy conversion factor for the vehicle is calculated based on the average energy data of the other vehicles.

7. The method according to claim 5, characterized in that, The calculation of the vehicle's energy conversion factor based on the historical energy data includes: Formula used: Calculate the energy conversion factor for the vehicle, wherein the historical energy data includes: total electricity charged to the grid and total heat consumed by fuel.

8. A device for determining energy efficiency based on big data, characterized in that, The device includes: The sending module is used to send a power conversion factor query request to the cloud server. The power conversion factor query request includes the identification information of the energy saving rate determination device based on big data. A receiving module is used to receive the power conversion factor returned by the cloud server, wherein the power conversion factor is used to represent the proportion of the energy consumed by the vehicle to the total energy of the vehicle; The calculation module is used to calculate the energy saving rate of the vehicle during the predicted energy management period based on the energy conversion factor, the estimated fuel saving rate, and the consumption data during the predicted energy management period. The consumption data includes the actual amount of fuel consumed during the predicted energy management period and the difference in SOC of the battery before and after the predicted energy management. The estimated fuel saving rate represents the proportion of fuel that can be saved to the amount of fuel consumed without the predicted energy management period.

9. The apparatus according to claim 8, characterized in that, The calculation module is specifically used for: Based on the aforementioned energy conversion factor, the estimated fuel saving rate, the actual fuel consumption during the predicted energy management period, and the difference in battery SOC before and after the predicted energy management, the formula is used: The energy saving rate of the vehicle during the predicted energy management period is calculated; The calorific value of fuel required during the absence of predictive energy management is calculated using the estimated fuel saving rate and the actual amount of fuel consumed. The battery charge difference is equal to the rated battery capacity multiplied by the SOC difference of the battery before and after predictive energy management. The actual calorific value of fuel consumed is obtained by multiplying the actual fuel consumed during predictive energy management by the calorific value of gasoline.

10. The apparatus according to claim 8, characterized in that, The device further includes: The display module is used to display the energy saving rate of the vehicle during the predictive energy management period through the vehicle's in-vehicle interface.

11. The apparatus according to any one of claims 8 to 10, characterized in that, Before sending the power conversion factor query request to the cloud server, the device further includes: The recording module is used to start accumulating and recording fuel consumption during the predictive energy management period after detecting that the predictive energy management function is enabled; The consumption module is used to obtain the cumulative fuel consumption during the predictive energy management period to obtain the actual amount of fuel consumed by the vehicle during the predictive energy management period when the predictive energy function is detected to be off.

12. A device for determining energy efficiency based on big data, characterized in that, The device includes: The receiving module is used to receive a power conversion factor query request sent by any vehicle, wherein the power conversion factor query request includes the vehicle's identification information; The query module is used to query and obtain the historical energy data corresponding to the vehicle's identification information based on the vehicle's identification information; The judgment module is used to determine whether the historical energy data meets the preset energy conversion conditions; The calculation module is used to calculate the vehicle's energy conversion factor based on the historical energy data if the historical energy data meets the energy conversion conditions. A sending module is used to send the energy conversion factor to the vehicle; the vehicle is used to calculate the energy saving rate of the vehicle during the predicted energy management period based on the energy conversion factor, the estimated fuel saving rate, and the consumption data during the predicted energy management period. The consumption data includes the actual amount of fuel consumed during the predicted energy management period and the difference in state of charge (SOC) of the battery before and after the predicted energy management; the estimated fuel saving rate represents the proportion of fuel that can be saved to the amount of fuel consumed during the period without predicted energy management.

13. The apparatus according to claim 12, characterized in that, The device also includes: a model module and an average module. The model module is used to determine the vehicle model based on the vehicle's identification information if the historical energy data does not meet the energy conversion conditions. The averaging module is used to obtain energy data of other vehicles corresponding to the model of the vehicle, and calculate the average value of the energy data of the other vehicles; The calculation module is also used to calculate the vehicle's energy conversion factor based on the average energy data of the other vehicles.

14. The apparatus according to claim 12, characterized in that, The calculation module is specifically used for: Formula used: Calculate the vehicle's energy conversion factor, wherein the historical energy data includes: total electricity charged to the grid and total heat consumed by fuel.

15. A vehicle, characterized in that, include: The vehicle body, vehicle controller, memory, and communication interfaces for interacting with other devices; The memory stores computer-executed instructions; The vehicle controller executes computer execution instructions stored in the memory to implement the method for determining energy saving rate based on big data as described in any one of claims 1 to 4.

16. A server, characterized in that, include: The processor, the memory communicatively connected to the processor, and the communication interface for interacting with other devices; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method for determining energy efficiency based on big data as described in any one of claims 5 to 7.

17. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method for determining energy efficiency based on big data as described in any one of claims 1 to 7.

Images