Average energy consumption calculation method and device, electric vehicle and storage medium

Abstract

The invention relates to the technical field of electric vehicles, and discloses an average energy consumption calculation method and device, an electric vehicle and a storage medium, and the method comprises the steps: obtaining battery performance parameters and mileage data of the electric vehicle in real time; calculating first energy consumption of the electric vehicle in a single power-on period and second energy consumption of the electric vehicle in a single charging period based on the battery performance parameters and the driving mileage data; respectively calculating a first weight coefficient corresponding to the first energy consumption and a second weight coefficient corresponding to the second energy consumption according to the battery performance parameter, the driving mileage data and the second energy consumption; and performing weighted calculation on the first energy consumption and the second energy consumption by using the first weight coefficient and the second weight coefficient to obtain the real-time average energy consumption of the electric vehicle. According to the method, the actual influence degree of the energy consumption of the automobile in different running stages and running states can be comprehensively covered, so that the calculation of the real-time average energy consumption is more accurate, and the prediction precision of the endurance mileage and the driving experience of a user are further improved.

Description

Technical Field

[0001] This invention relates to the field of electric vehicle technology, and more specifically to an average energy consumption calculation method, apparatus, electric vehicle, and storage medium. Background Technology

[0002] With the widespread application of electric vehicles, the Battery Management System (BMS), as a key technology to ensure the safe and efficient operation of batteries, is becoming increasingly important. In the electric vehicle field, BMS effectively improves battery performance, extends battery life, and ensures the safety and driving range of electric vehicles by monitoring battery packs in real time, analyzing data, and making intelligent decisions. In practical applications, the calculation of driving range is inseparable from the calculation of the average energy consumption of electric vehicles, and calculating a more accurate average energy consumption has become a pressing technical challenge.

[0003] Currently, the average energy consumption of electric vehicles (EVs) is calculated by dividing the EV's energy consumption by its driving range. However, this method does not consider the driver's driving habits and energy consumption characteristics under different driving conditions. For example, in the initial stage of power-on, driving habits (such as rapid acceleration) can significantly increase the average energy consumption. In cold conditions, the battery may preheat to reach its optimal operating temperature, which can also increase the average energy consumption. Therefore, the average energy consumption may be high in the initial stage of power-on and then decrease slowly. The driving range calculated using such an average energy consumption method may then show a slow increase.

[0004] In summary, traditional methods of calculating average energy consumption ignore the energy consumption characteristics of the driver and various driving conditions, resulting in inaccurate average energy consumption calculations. This further leads to inaccurate predictions of driving range, seriously affecting the user's driving experience. Summary of the Invention

[0005] In view of this, the present invention provides an average energy consumption calculation method, apparatus, electric vehicle and storage medium to solve the problem mentioned in the above-mentioned technical background that the existing vehicle average energy consumption calculation does not take into account the energy consumption characteristics of the driver's state and various driving conditions, resulting in many defects, which seriously affect the accuracy of average energy consumption calculation and the user's driving experience.

[0006] In a first aspect, the present invention provides a method for calculating average energy consumption, the method comprising: Real-time acquisition of battery performance parameters and driving range data of electric vehicles; The first energy consumption of an electric vehicle in a single power-on cycle and the second energy consumption of an electric vehicle in a single charging cycle are calculated based on battery performance parameters and driving mileage data, respectively. The first weighting coefficient corresponding to the first energy consumption and the second weighting coefficient corresponding to the second energy consumption are calculated based on battery performance parameters, driving mileage data and second energy consumption, respectively. The first energy consumption and the second energy consumption are weighted by the first weighting coefficient and the second weighting coefficient to obtain the real-time average energy consumption of the electric vehicle.

[0007] The average energy consumption calculation method of this invention calculates the first energy consumption in a single power-on cycle and the second energy consumption in a single charging cycle based on real-time battery performance parameters and mileage data acquired from electric vehicles. This comprehensively covers the energy consumption of electric vehicles at different operating stages, thus avoiding calculation bias caused by focusing on only a single stage and making the energy consumption calculation more consistent with the energy consumption situation during actual vehicle use. Furthermore, by determining the weight coefficients of the first and second energy consumption based on battery performance parameters, mileage data, and second energy consumption, and then performing a weighted calculation, the method can comprehensively consider the actual impact of energy consumption under different operating conditions, further improving the accuracy of average energy consumption calculation. This provides users with more accurate energy consumption information, has strong versatility and adaptability, meets the energy consumption calculation needs of different users in various usage scenarios, and provides an important basis for the performance evaluation of electric vehicles, helping to improve the accuracy of electric vehicle range prediction and the user driving experience.

[0008] In one optional implementation, the mileage data includes a first cumulative mileage within a single power-on cycle and a preset mileage threshold; the first energy consumption of the electric vehicle within a single power-on cycle is calculated based on battery performance parameters and mileage data, including: Within a single power-on cycle of an electric vehicle, for the first driving process corresponding to the first cumulative mileage, the instantaneous energy consumption during the first driving process is calculated in real time based on battery performance parameters to obtain multiple first instantaneous energy consumptions; all the first instantaneous energy consumptions are accumulated to obtain the first power-on cumulative energy consumption; the first power-on energy consumption is determined based on the ratio of the first power-on cumulative energy consumption to the first cumulative mileage. Within a single power-on cycle of an electric vehicle, for the second driving process corresponding to a preset mileage threshold, the instantaneous energy consumption during the second driving process is calculated in real time based on battery performance parameters to obtain multiple second instantaneous energy consumptions; all second instantaneous energy consumptions are accumulated to obtain the second power-on cumulative energy consumption; the second power-on energy consumption is determined based on the ratio of the second power-on cumulative energy consumption to the preset mileage threshold. The first power-on energy consumption and the second power-on energy consumption are weighted and summed to obtain the first energy consumption of the electric vehicle in a single power-on cycle.

[0009] This invention refines the dimensions of energy consumption calculation. By calculating the energy consumption corresponding to the first cumulative mileage and the preset mileage threshold separately, it considers the differences in energy consumption at different driving mileage stages, which can more accurately reflect the actual energy consumption of the vehicle and avoid the errors caused by a one-size-fits-all approach to energy consumption calculation. Furthermore, the first energy consumption is obtained by weighted summing of the first and second power-on energy consumptions, and corresponding weights are assigned according to the degree of influence of different mileage ranges on the total energy consumption. This comprehensively considers the energy consumption contribution at different driving mileage stages, making the calculation results more scientific and reliable, and more accurately reflecting the actual energy consumption level of electric vehicles in a single power-on cycle. It can not only know the overall energy consumption in a single power-on cycle, but also understand the energy consumption characteristics at different driving stages. It can provide a comprehensive, timely, and accurate understanding of the vehicle's average energy consumption, thereby providing more detailed and accurate data support for electric vehicle design optimization, energy management system improvement, and driving strategy adjustment.

[0010] In one optional implementation, the mileage data includes a second cumulative mileage within a single charging cycle; calculating the second energy consumption of the electric vehicle within a single charging cycle based on battery performance parameters and mileage data includes: Within a single charging cycle of an electric vehicle, for the third driving process corresponding to the second cumulative mileage, the instantaneous energy consumption during the third driving process is calculated in real time based on battery performance parameters to obtain multiple third instantaneous energy consumptions; all third instantaneous energy consumptions are accumulated to obtain the cumulative charging energy consumption. The ratio of cumulative charging energy consumption to the second cumulative mileage is calculated to obtain the second energy consumption of the electric vehicle in a single charging cycle.

[0011] This invention captures the dynamic changes in energy consumption of a vehicle during a single charging cycle by calculating the instantaneous energy consumption corresponding to the second cumulative mileage within that charging cycle in real time. This allows for a more accurate reflection of actual energy consumption, avoiding errors caused by using fixed parameters to calculate energy consumption. Furthermore, by summing multiple third instantaneous energy consumptions to obtain the cumulative charging energy consumption, the invention comprehensively calculates the total energy consumption of the vehicle throughout the entire charging cycle. This makes the final calculated energy consumption data closer to the true value, providing strong support for accurately assessing the vehicle's energy consumption during the charging cycle. It also provides accurate data input for the vehicle's energy management system, enabling efficient use of vehicle energy, extending the vehicle's driving range, and improving the user experience.

[0012] In one optional implementation, battery performance parameters include a preset BMS control cycle, real-time vehicle discharge current, and battery voltage; the corresponding instantaneous energy consumption is calculated in real-time based on the battery performance parameters, including: The instantaneous energy consumption is obtained by multiplying the preset BMS control cycle, the real-time vehicle discharge current, and the battery voltage.

[0013] The instantaneous energy consumption calculation method designed in this invention can quickly and accurately reflect the energy consumption of a vehicle at every moment, providing a convenient and reliable data source for subsequent average energy consumption analysis of electric vehicles.

[0014] In one optional implementation, the battery performance parameters include the state-of-charge data and battery energy of the battery at the initial moment of a single power-on cycle, and the mileage data includes the first cumulative mileage within a single power-on cycle; a first weighting coefficient corresponding to the first energy consumption and a second weighting coefficient corresponding to the second energy consumption are calculated based on the battery performance parameters, the mileage data, and the second energy consumption, respectively, including: The remaining battery energy is obtained by multiplying the battery's state of charge data at the initial moment of a single power-on cycle by the battery energy. Calculate the first ratio of the current remaining battery energy to the second energy consumption, and divide the first cumulative mileage in a single power-on cycle by the first ratio to obtain the first weighting coefficient corresponding to the first energy consumption; The difference between the preset value and the first weighting coefficient is calculated to obtain the second weighting coefficient corresponding to the second energy consumption.

[0015] This invention calculates the current remaining energy by multiplying the initial state of charge (SBC) at power-on with the battery's energy. This allows the weighting coefficient calculation to be directly linked to the battery's real-time state, dynamically reflecting the differences in energy consumption characteristics at different charge levels. Furthermore, the weights are dynamically adjusted based on the remaining energy, making the average energy consumption calculation more closely aligned with actual driving scenarios and adaptively reflecting the impact of battery status on energy consumption. It also considers the energy consumption differences between the initial power-on period and a period of driving. Specifically, it designs a first weighting coefficient and a second weighting coefficient to balance short-term and long-term energy consumption assessments. When the vehicle frequently starts and stops (short power-on cycle), the first weighting coefficient is lower to avoid misjudgments of range due to short-term high energy consumption fluctuations. Conversely, during long-distance driving (long power-on cycle), the first weighting coefficient is higher, focusing more on the current energy consumption trend, helping to ensure the accuracy of the average energy consumption calculation and further improving the accuracy of range estimation, providing users with more realistic energy consumption feedback.

[0016] In one optional implementation, the first energy consumption and the second energy consumption are weighted using a first weighting coefficient and a second weighting coefficient to obtain the real-time average energy consumption of the electric vehicle, including: Multiply the first weighting coefficient by the first energy consumption to obtain the first value; Multiply the second weighting coefficient by the second energy consumption to obtain the second value; Add the first value to the second value to obtain the real-time average energy consumption of the electric vehicle.

[0017] This invention calculates the real-time average energy consumption of electric vehicles by weighting the first and second energy consumption using a first weighting coefficient and a second weighting coefficient. It achieves a good balance between accuracy, stability, adaptability and computational efficiency, and can adapt to different driving scenarios. This makes the energy consumption calculation more in line with the dynamic characteristics of electric vehicles, and provides users and manufacturers with more valuable decision-making basis.

[0018] In one optional implementation, before weighting the first energy consumption and the second energy consumption using a first weighting coefficient and a second weighting coefficient to obtain the real-time average energy consumption of the electric vehicle, the average energy consumption calculation method further includes: Determine whether the first weighting coefficient and the second weighting coefficient meet the preset value range respectively; If the conditions are met, then the step of weighting the first energy consumption and the second energy consumption using the first weighting coefficient and the second weighting coefficient to obtain the real-time average energy consumption of the electric vehicle is executed. If the conditions are not met, return to the step of obtaining real-time battery performance parameters and driving range data of the electric vehicle.

[0019] This invention significantly improves the reliability and robustness of energy consumption calculation by adding a preset range judgment mechanism for weighting coefficients before weighted calculation. This helps to ensure the accuracy and rationality of average energy consumption calculation, thereby improving the accuracy of range prediction and the user's driving experience.

[0020] In a second aspect, the present invention provides an average energy consumption calculation device, the device comprising: The data acquisition module is used to acquire battery performance parameters and driving range data of electric vehicles in real time. The first calculation module is used to calculate the first energy consumption of the electric vehicle in a single power-on cycle and the second energy consumption of the electric vehicle in a single charging cycle based on battery performance parameters and driving mileage data, respectively. The second calculation module is used to calculate the first weighting coefficient corresponding to the first energy consumption and the second weighting coefficient corresponding to the second energy consumption based on battery performance parameters, driving mileage data and second energy consumption, respectively. The third calculation module is used to perform weighted calculations on the first energy consumption and the second energy consumption using the first weighting coefficient and the second weighting coefficient to obtain the real-time average energy consumption of the electric vehicle.

[0021] The average energy consumption calculation device of this invention can comprehensively cover the energy consumption of electric vehicles in different operating stages, avoiding calculation deviations caused by focusing on only a single stage, and making the energy consumption calculation more consistent with the energy consumption situation during actual vehicle use. Furthermore, it can comprehensively consider the actual impact of energy consumption under different operating conditions, specifically designing weighting coefficients for different energy consumption levels and a weighted calculation process for each energy consumption level, further improving the accuracy of average energy consumption calculation. This provides users with more accurate energy consumption information, has strong versatility and adaptability, meets the energy consumption calculation needs of different users in various usage scenarios, and thus provides an important basis for the performance evaluation of electric vehicles, helping to improve the accuracy of electric vehicle range prediction and the user driving experience.

[0022] Thirdly, the present invention provides an electric vehicle, the electric vehicle including a controller, the controller including a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to perform an average energy consumption calculation method of the first aspect or any corresponding embodiment described above.

[0023] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute an average energy consumption calculation method according to the first aspect or any corresponding embodiment thereof. Attached Figure Description

[0024] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0025] Figure 1 This is a flowchart illustrating the average energy consumption calculation method according to an embodiment of the present invention; Figure 2 This is a flowchart illustrating another method for calculating average energy consumption according to an embodiment of the present invention; Figure 3 This is a diagram illustrating the principle of calculating average energy consumption during vehicle operation. Figure 4 This is a structural block diagram of an average energy consumption calculation device according to an embodiment of the present invention; Figure 5 This is a schematic diagram of the structure of the controller for an electric vehicle according to an embodiment of the present invention. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0027] This invention provides an embodiment of an average energy consumption calculation method. 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.

[0028] This embodiment provides an average energy consumption calculation method, applicable to controllers in hybrid electric vehicles, such as microcontrollers and microprocessors. Figure 1 This is a flowchart illustrating the average energy consumption calculation method according to an embodiment of the present invention, as shown below. Figure 1 As shown, the process includes the following steps: Step S101: Obtain the battery performance parameters and driving range data of the electric vehicle in real time.

[0029] It should be noted that the specific content and acquisition method of the battery performance parameters and driving range data in this embodiment can be adaptively adjusted according to actual needs. For example, the battery performance parameters include discharge current (i.e., the current output when the battery discharges, reflecting the battery's load capacity) and voltage, which can be obtained through the battery management system (i.e., BMS).

[0030] It is important to note that the mileage data in this embodiment describes the mileage recorded by the vehicle under actual driving conditions, such as the mileage data corresponding to a single power-on cycle and a single charging cycle. This data needs to be adapted to the actual mileage recording method of the electric vehicle (such as cumulative mileage, total mileage, or single mileage). For example, for the total cumulative mileage of a single power-on cycle, firstly, the initial mileage of the electric vehicle is determined (i.e., the total cumulative mileage at the beginning of the power-on cycle, which includes all historical mileage of the vehicle). This is the cumulative mileage recorded by the dashboard or in-vehicle system (i.e., the total distance traveled by the vehicle from the factory to the current time up to the moment of power-on). This value is stored in real-time by the vehicle's odometer (or electronic control unit) and is not affected by the vehicle's power-on or power-off operations; it only increases with the actual driving of the vehicle. Then, the initial mileage is used to determine the corresponding mileage data for a single power-on cycle. That is, the vehicle reads the initial mileage each time it is powered on and records the ending mileage when it is powered off; the difference between the two is the current mileage.

[0031] Step S102: Calculate the first energy consumption of the electric vehicle in a single power-on cycle and the second energy consumption of the electric vehicle in a single charging cycle based on battery performance parameters and driving mileage data, respectively.

[0032] It should be noted that, in this embodiment, a single power-on cycle refers to the complete process from when the vehicle is connected to high voltage and enters a drivable state until the vehicle completes a series of driving, parking, and other operations and finally disconnects from high voltage; a single charging cycle refers to the time period from when the vehicle completes one charging cycle and disconnects the charging equipment until the next charging cycle begins, which may include at least one single power-on cycle.

[0033] Step S103: Calculate the first weighting coefficient corresponding to the first energy consumption and the second weighting coefficient corresponding to the second energy consumption based on the battery performance parameters, driving mileage data, and second energy consumption.

[0034] In this embodiment, the first weighting coefficient and the second weighting coefficient are dynamic adjustment coefficients related to driving mileage. This can effectively avoid a common drawback in traditional energy consumption calculation methods, namely, that in the initial stage of power-on, since the vehicle has just started and the various systems have not yet entered a stable working state, the average energy consumption calculated at this time is often too high. As the driving mileage continues to accumulate, the various systems enter a highly efficient and stable operating state, and the average energy consumption also decreases and tends to stabilize. This fluctuation in average energy consumption can lead to misjudgment of the vehicle's range and affect the user's driving experience. However, through the aforementioned weighting coefficients in this embodiment, a more accurate calculation of average energy consumption can be achieved. This ensures that the calculation of average energy consumption is closer to the actual operating state of the vehicle during different driving stages, especially during the transition from the initial stage to the stable period. Specifically, the first weighting coefficient is relatively small at the initial power-on stage, resulting in a smaller proportion of the first energy consumption in subsequent energy consumption calculations. This allows the focus to be on the impact of the energy consumption corresponding to the charging cycle on the subsequent real-time average energy consumption of the vehicle, avoiding the impact of large differences in energy consumption at the initial power-on stage. As the driving mileage accumulates, the proportion of the first energy consumption in subsequent energy consumption calculations increases accordingly, thereby ensuring the authenticity and accuracy of the calculated average energy consumption. Therefore, using the average energy consumption calculation method of this embodiment, a more accurate average energy consumption can be obtained. Using this to predict the vehicle's driving range not only improves the accuracy of driving range prediction and provides users with a reliable driving range reference, but also greatly enhances the user's driving experience.

[0035] Step S104: The first energy consumption and the second energy consumption are weighted and calculated using the first weighting coefficient and the second weighting coefficient to obtain the real-time average energy consumption of the electric vehicle.

[0036] The average energy consumption calculation method of this invention calculates the first energy consumption in a single power-on cycle and the second energy consumption in a single charging cycle based on real-time battery performance parameters and mileage data acquired from electric vehicles. This comprehensively covers the energy consumption of electric vehicles at different operating stages, thus avoiding calculation errors caused by focusing on only a single stage and making the energy consumption calculation more consistent with the actual energy consumption during vehicle use. Furthermore, the method determines the weighting coefficients of the first and second energy consumption based on battery performance parameters, mileage data, and the second energy consumption, and then performs a weighted calculation. This allows for a comprehensive consideration of the actual impact of energy consumption under different operating conditions, further improving the accuracy of the average energy consumption calculation and providing users with more precise energy consumption information. Simultaneously, it can comprehensively consider various influencing factors of the vehicle. Whether the vehicle is driving on urban roads with frequent starts and stops or on long-distance highways, it can accurately calculate energy consumption based on actual battery performance and driving conditions. This method has strong versatility and adaptability, meeting the energy consumption calculation needs of different users in various usage scenarios. Therefore, it provides an important basis for the performance evaluation of electric vehicles, helping to improve the accuracy of electric vehicle range prediction and user experience.

[0037] This embodiment provides a method for calculating average energy consumption. Figure 2 This is a flowchart illustrating another average energy consumption calculation method according to an embodiment of the present invention, as shown below. Figure 2 As shown, the process includes the following steps: Step S201: Acquire real-time battery performance parameters and driving range data of the electric vehicle. For details, please refer to [link to relevant documentation]. Figure 1 Step S101 of the illustrated embodiment will not be described again here.

[0038] Step S202: Calculate the first energy consumption of the electric vehicle in a single power-on cycle and the second energy consumption of the electric vehicle in a single charging cycle based on battery performance parameters and driving mileage data, respectively.

[0039] It should be noted that in this embodiment, the first energy consumption represents the average energy consumption of the electric vehicle during the driving process after high-voltage power-on. The corresponding real-time collected mileage data includes the first cumulative mileage within a single power-on cycle and a preset mileage threshold. The first cumulative mileage represents the total mileage driven during the entire power-on cycle, aiming to monitor the overall energy consumption of the vehicle. The preset mileage threshold represents a specific mileage stage of interest to the user, and its specific value can be adaptively adjusted according to actual needs, such as a mileage of 1 kilometer, 2 kilometers, or other mileage, aiming to monitor the energy consumption of the vehicle at different mileage stages. Therefore, the calculation of the first energy consumption of the electric vehicle within a single power-on cycle based on battery performance parameters and mileage data in step S202 includes: Step a1: Within a single power-on cycle of the electric vehicle, for the first driving process corresponding to the first cumulative mileage, calculate the instantaneous energy consumption in the first driving process in real time based on the battery performance parameters to obtain multiple first instantaneous energy consumptions; accumulate all the first instantaneous energy consumptions to obtain the first power-on cumulative energy consumption; determine the first power-on energy consumption based on the ratio of the first power-on cumulative energy consumption to the first cumulative mileage.

[0040] Step a2: Within a single power-on cycle of the electric vehicle, for the second driving process corresponding to the preset mileage threshold, calculate the instantaneous energy consumption in the second driving process in real time based on the battery performance parameters to obtain multiple second instantaneous energy consumptions; accumulate all the second instantaneous energy consumptions to obtain the second power-on cumulative energy consumption; determine the second power-on energy consumption based on the ratio of the second power-on cumulative energy consumption to the preset mileage threshold.

[0041] Step a3: The first power-on energy consumption and the second power-on energy consumption are weighted and summed to obtain the first energy consumption of the electric vehicle in a single power-on cycle.

[0042] This invention refines the dimensions of energy consumption calculation. By calculating the energy consumption corresponding to the first cumulative mileage and the preset mileage threshold separately, it considers the differences in energy consumption at different driving mileage stages, which can more accurately reflect the actual energy consumption of the vehicle and avoid the errors caused by a one-size-fits-all approach to energy consumption calculation. Furthermore, the first energy consumption is obtained by weighted summation of the first power-on energy consumption and the second power-on energy consumption. Appropriate weights are assigned according to the degree of influence of different mileage ranges on the total energy consumption, which can comprehensively consider the energy consumption contribution at different driving mileage stages, making the calculation results more scientific and reliable, and more accurately reflecting the actual energy consumption level of electric vehicles in a single power-on cycle. It can not only know the overall energy consumption in a single power-on cycle, but also understand the energy consumption characteristics at different driving stages. It can provide a comprehensive, timely, and accurate understanding of the vehicle's average energy consumption, thereby providing more detailed and accurate data support for electric vehicle design optimization, energy management system improvement, and driving strategy adjustment.

[0043] It should be noted that in this embodiment, the second energy consumption represents the average energy consumption of the electric vehicle during the driving process within a single charging cycle, and the corresponding real-time collected driving mileage data includes the second cumulative mileage within a single charging cycle; wherein, the second cumulative mileage represents the total driving mileage of the vehicle throughout the entire charging cycle. Therefore, the calculation of the second energy consumption of the electric vehicle within a single charging cycle based on battery performance parameters and driving mileage data in step S202 above includes: Step b1: Within a single charging cycle of the electric vehicle, for the third driving process corresponding to the second cumulative mileage, calculate the instantaneous energy consumption in the third driving process in real time based on the battery performance parameters to obtain multiple third instantaneous energy consumptions.

[0044] Step b2: Accumulate all the third instantaneous energy consumption to obtain the cumulative charging energy consumption.

[0045] Step b3: Calculate the ratio of cumulative charging energy consumption to the second cumulative mileage to obtain the second energy consumption of the electric vehicle in a single charging cycle.

[0046] In this embodiment of the invention, by calculating the instantaneous energy consumption corresponding to the second cumulative mileage within a single charging cycle in real time, the dynamic changes in energy consumption of the vehicle during the driving process of that charging cycle can be captured, thereby more accurately reflecting the actual energy consumption situation and avoiding the errors caused by using fixed parameters to calculate energy consumption. Furthermore, by accumulating multiple third instantaneous energy consumptions to obtain the cumulative charging energy consumption, the total energy consumption of the vehicle throughout the entire charging cycle can be comprehensively counted, making the final calculated energy consumption data closer to the real value. This provides strong support for accurately assessing the energy consumption of the vehicle during the charging cycle, and further provides accurate data input for the vehicle's energy management system to achieve efficient utilization of vehicle energy, extend the vehicle's driving range, and improve the user experience.

[0047] It should be noted that the battery performance parameters used in calculating the first energy consumption of an electric vehicle in a single power-on cycle and the second energy consumption in a single charging cycle include the preset BMS control cycle, real-time vehicle discharge current, and battery voltage. The preset BMS control cycle is the operating cycle of the BMS controller, referring to the time interval between the BMS software or hardware executing a complete set of control logic (such as data acquisition, state calculation, strategy output, etc.), usually measured in milliseconds (e.g., 10ms, 50ms, etc.). It is a key parameter for the real-time performance and reliability design of the BMS, directly affecting the performance and safety of the battery system. It is important to note that the specific value of the preset BMS control cycle needs to be determined by comprehensively considering functional requirements, hardware capabilities, safety standards, algorithm characteristics, and adaptability to actual testing and verification. For example, cycle levels can be divided according to functional priority; based on the real-time performance and safety criticality of the functions, BMS functions can be divided into high, medium, and low priorities, with different cycle ranges set with reference to industry experience; or the maximum allowable cycle can be calculated based on hardware and communication capabilities. This is only an example for illustration.

[0048] In this embodiment, the instantaneous energy consumption calculated in real time based on battery performance parameters in steps a1 and b1 includes multiplying the preset BMS control cycle, the real-time vehicle discharge current, and the battery voltage to obtain the corresponding instantaneous energy consumption. Specifically, the instantaneous energy consumption calculation method designed in this embodiment can quickly and accurately reflect the energy consumption of the vehicle at each moment, providing a convenient and reliable data source for subsequent average energy consumption analysis of electric vehicles.

[0049] Step S203: Calculate the first weighting coefficient corresponding to the first energy consumption and the second weighting coefficient corresponding to the second energy consumption based on the battery performance parameters, driving mileage data, and second energy consumption.

[0050] In this embodiment, battery performance parameters include the state of charge (SOC) data and battery energy of the battery at the initial moment of a single power-on cycle, and the mileage data includes the first cumulative mileage within a single power-on cycle. It should be noted that the SOC data at the initial moment of a single power-on cycle represents the remaining battery capacity (SOC) at the initial power-on of the single power-on cycle, specifically obtained through conventional estimation methods in the art, such as the open-circuit voltage method and the coulomb counting method; battery energy represents the total amount of electrical energy stored in the battery, directly reflecting the electrical energy the battery can provide (such as the range of an electric vehicle and the power supply duration of energy storage devices), and is one of the core indicators of battery performance. Specifically, it is determined by the battery's nominal voltage (referring to the standard operating voltage during battery design (e.g., the nominal voltage of lithium batteries is mostly 3.7V / string, while electric vehicle battery packs may be 300V~400V, designed to reflect the potential difference of the battery under rated conditions) and rated capacity (referring to the amount of charge the battery can release under rated conditions (such as specific temperature and discharge current)).

[0051] Specifically, step S203 includes: Step S2031: Multiply the battery state-of-charge data and battery energy at the initial moment of a single power-on cycle of the electric vehicle to obtain the current remaining battery energy.

[0052] Step S2032: Calculate the first ratio of the current remaining battery energy to the second energy consumption, and divide the first cumulative mileage in a single power-on cycle by the first ratio to obtain the first weighting coefficient corresponding to the first energy consumption.

[0053] Step S2033: Calculate the difference between the preset value and the first weighting coefficient to obtain the second weighting coefficient corresponding to the second energy consumption.

[0054] In this embodiment, the specific value of the preset value is adaptively adjusted according to actual needs. For example, the preset value is 1, which is only used as an example.

[0055] In this embodiment of the invention, the current remaining energy is obtained by multiplying the initial state of charge (SBC) upon power-on with the battery energy. This allows the weighting coefficient calculation to be directly linked to the real-time battery state, thereby dynamically reflecting the differences in energy consumption characteristics of the battery at different charge levels. Furthermore, the weights are dynamically adjusted based on the remaining energy, making the average energy consumption calculation more consistent with actual driving scenarios and adaptively reflecting the impact of battery state on energy consumption. Simultaneously, the energy consumption differences between the initial power-on period and a certain driving period are considered. Specifically, a first weighting coefficient and a second weighting coefficient are designed to balance short-term and long-term energy consumption assessments. When the vehicle frequently starts and stops (short power-on cycle), the first weighting coefficient is lower to avoid misjudgments of range due to short-term high energy consumption fluctuations. Conversely, during long-distance driving (long power-on cycle), the first weighting coefficient is higher, focusing more on the current energy consumption trend, which helps ensure the accuracy of the average energy consumption calculation and further improves the accuracy of range estimation, providing users with more realistic energy consumption feedback.

[0056] Step S204: The first energy consumption and the second energy consumption are weighted and calculated using the first weighting coefficient and the second weighting coefficient to obtain the real-time average energy consumption of the electric vehicle.

[0057] Specifically, step S204 includes: Step S2041: Multiply the first weighting coefficient by the first energy consumption to obtain the first value.

[0058] Step S2042: Multiply the second weighting coefficient by the second energy consumption to obtain the second value.

[0059] Step S2043: Add the first value to the first value to obtain the real-time average energy consumption of the electric vehicle.

[0060] In this embodiment of the invention, the first energy consumption and the second energy consumption are weighted and calculated using a first weighting coefficient and a second weighting coefficient to obtain the real-time average energy consumption of electric vehicles. This achieves a good balance between accuracy, stability, adaptability and computational efficiency, and can adapt to different vehicle driving scenarios. This makes the energy consumption calculation more in line with the dynamic characteristics of electric vehicles, and provides users and manufacturers with more valuable decision-making basis.

[0061] It should be noted that this embodiment also sets preset value ranges for the first weighting coefficient and the second weighting coefficient, and verifies whether the weighting coefficients obtained from actual calculations are within the corresponding preset value ranges to ensure the accuracy and rationality of the weighting coefficients, thereby improving the accuracy of subsequent calculations of vehicle average energy consumption. Therefore, before using the first weighting coefficient and the second weighting coefficient to perform weighted calculations on the first energy consumption and the second energy consumption to obtain the real-time average energy consumption of the electric vehicle, the average energy consumption calculation method in this embodiment further includes: Step c1: Determine whether the first weight coefficient and the second weight coefficient meet the preset value range.

[0062] In this embodiment, the specific value of the preset range is adaptively adjusted according to actual needs, such as the preset range being [0,1].

[0063] Step c2: If satisfied, then perform the step of weighting the first energy consumption and the second energy consumption using the first weighting coefficient and the second weighting coefficient to obtain the real-time average energy consumption of the electric vehicle.

[0064] If step c3 is not satisfied, return to the step of obtaining the battery performance parameters and driving range data of the electric vehicle in real time.

[0065] In this embodiment of the invention, by adding a preset range judgment mechanism for weighting coefficients before weighted calculation, the reliability and robustness of energy consumption calculation can be significantly improved, which helps to ensure the accuracy and rationality of average energy consumption calculation, thereby improving the accuracy of range prediction and the user driving experience.

[0066] In one specific embodiment, since traditional average energy consumption calculation methods lead to inaccurate range predictions, this embodiment proposes a more accurate and comprehensive average energy consumption calculation method—an innovative BMS average energy consumption calculation method. Specifically, by comprehensively considering different forms of average energy consumption characteristics, it achieves precise calculation of the average energy consumption of the battery pack (also known as the battery), thereby improving the accuracy of electric vehicle range predictions, enhancing user experience, and providing strong support for the widespread application of electric vehicles. Figure 3 This is a diagram illustrating the principle of calculating average energy consumption during vehicle operation. As shown in the diagram, the specific calculation process includes: Step 1: Obtain basic vehicle information, including current, voltage, and mileage.

[0067] In this embodiment, step 1 belongs to the data processing stage, and specifically includes: a) The BMS collects key parameters in real time, including the vehicle's discharge current I (I>0, this current is based on the positive current value monitored during the vehicle's power-consuming driving phase. Because the system may execute an energy recovery mechanism during the vehicle's operating cycle, the current flow direction is opposite to that during normal power-consuming driving. The energy consumption data calculated in this embodiment is based only on the positive current value monitored during the vehicle's power-consuming driving phase to ensure the accuracy and standardization of energy consumption assessment), battery pack voltage U, and the vehicle's real-time mileage D (obtained through the instrument panel), where the BMS calculation period is T (e.g., T is 0.01s).

[0068] b) Store the cumulative energy consumption E2 and the mileage D2 calculated within a single charging cycle. Note that the cumulative energy consumption E2 and the mileage D2 calculated within a single charging cycle need to be added to the BMS's storage for subsequent data use.

[0069] c) The battery pack energy is denoted as E, and its specific value is determined by multiplying the battery pack's nominal voltage by its rated capacity. For example, for a certain new energy vehicle, the battery pack energy E is 282 kWh.

[0070] d) Cumulative energy consumption is calculated by adding the instantaneous energy consumption (i.e., U×I×T) within each time period.

[0071] e) At the initial power-on of a single power-on cycle, the remaining battery charge, i.e., the State of Charge (SOC), is denoted as SOC0. For example, for a certain new energy vehicle, at the initial power-on of a single power-on cycle, the remaining SOC of the battery is 90%, and this is denoted as SOC0.

[0072] Step 2: Obtain the instantaneous energy consumption at a certain moment based on the current, voltage, and controller calculation cycle.

[0073] In this embodiment, the instantaneous energy consumption is calculated as described above, i.e., instantaneous energy consumption = U × I × T.

[0074] Step 3: In a single power-on cycle, the average energy consumption is calculated using two strategies and then weighted to obtain the average energy consumption for a single power-on cycle: Strategy 1 accumulates from the initial power-on period and calculates it in stages with mileage; Strategy 2 calculates it within a specific mileage segment.

[0075] In this embodiment, step 3 belongs to the average energy consumption calculation stage for a single power-on cycle. That is, once the vehicle is powered on and starts driving, the cumulative energy consumption 1 of the whole vehicle is calculated by the cumulative energy consumption calculation module 1. Every time the vehicle's mileage increases by a preset mileage threshold (which specifically covers driving 1 km, 2 km, or 5 km, etc., to ensure the diversity of data collection and the comprehensiveness of analysis), the average energy consumption for a single power-on cycle is calculated once using the cumulative energy consumption 1 and the mileage 1. The average energy consumption for a single power-on cycle is reset to zero and recalculated when the vehicle is powered on and driven again. It should be noted that the mileage 1 in a single power-on cycle is the mileage accumulated from the beginning of this power-on, and is reset to zero and re-accumulated when the vehicle is powered on and driven again.

[0076] It should be noted that in this embodiment, the cumulative energy consumption calculation module 1 calculates energy consumption as follows: within a single power-on cycle, the BMS collects voltage and current in real time, and combines this with the operating cycle of the BMS controller to calculate the instantaneous energy consumption through multiplication. Subsequently, for a specific driving segment, these instantaneous energy consumption values ​​are accumulated to obtain the cumulative energy consumption value for that driving segment.

[0077] In this embodiment, there are two calculation strategies for the average energy consumption per power-on cycle, specifically including: 1. Strategy 1: Start the energy consumption accumulation process at the beginning of vehicle power-on to obtain cumulative energy consumption 1. Then, whenever the vehicle's mileage increases to reach a preset mileage threshold, divide the cumulative energy consumption 1 by the total mileage driven after this power-on to obtain the average energy consumption of a single power-on cycle.

[0078] 2. Strategy Two: Only calculate the average energy consumption of the vehicle within the most recent range that meets a preset mileage threshold.

[0079] It should be noted that while Strategy 1 calculates the average energy consumption per power-on cycle, reflecting the vehicle's energy consumption throughout the entire power-on cycle, including stages such as starting, acceleration, constant speed, and deceleration, providing more consistent data, it doesn't offer timely insights into the vehicle's current energy consumption. Strategy 2, on the other hand, reflects the vehicle's energy consumption status more promptly, analyzing the energy consumption characteristics at different driving stages more accurately, but the calculated energy consumption data is too fragmented, making it difficult to form a comprehensive energy consumption analysis. Therefore, this embodiment uses a weighted average of the average energy consumption calculated by the two strategies. If the timeliness of Strategy 2 is emphasized, the weight of the Strategy 2 calculation result can be appropriately increased; if the comprehensiveness of Strategy 1 is emphasized, the weight of the Strategy 1 calculation result can be appropriately increased; if both timeliness and comprehensiveness are to be balanced, the weights can be allocated relatively evenly, for example, 50% each; if timeliness is considered crucial for user decision-making or dynamic vehicle management, then the weight of the Strategy 2 result can be appropriately increased.

[0080] In one specific embodiment, the average energy consumption calculation process for a single power-on cycle includes: a) After the vehicle is connected to the high voltage, the instantaneous energy consumption is calculated by the cumulative energy consumption calculation module 1.

[0081] b) After the vehicle is connected to high voltage, the initial mileage D01 is obtained. After the vehicle starts driving, the mileage D1 = D - D01 of the vehicle in a single power-on cycle is calculated.

[0082] c) Calculation of average energy consumption per power-on cycle, specifically including: i. Strategy 1: Instantaneous energy consumption accumulation process is performed at the initial stage of power-on to obtain the cumulative energy consumption E11 (that is, the summation of instantaneous energy consumption calculated at each moment). When the vehicle travels a preset mileage threshold, the average energy consumption of a single power-on cycle is calculated as A11=E11 / D1.

[0083] ii. Strategy 2: When the vehicle's mileage increment reaches the preset mileage threshold, all instantaneous energy consumption within this preset mileage threshold is accumulated to obtain the cumulative energy consumption E12, and the average energy consumption per power-on cycle is A12 = E12 / preset mileage threshold.

[0084] iii. The calculated average energy consumption per power-on cycle is A1 = X × A11 + (1-X) × A12. Where X is a weighting coefficient. To prioritize the real-time performance of the average energy consumption, the weight of X can be reduced; to prioritize the comprehensiveness of the average energy consumption, the weight of X can be increased. If both comprehensiveness and real-time performance are desired, X can be set to 0.5 (i.e., while maintaining both timeliness and comprehensiveness in the calculation of average energy consumption per power-on cycle, the calculated average energy consumption per power-on cycle is A1 = 0.5 × A11 + 0.5 × A12). It should be explained that A11 in this embodiment is suitable for scenarios involving understanding the energy consumption trend and level throughout the vehicle's entire driving cycle, such as assessing the total energy consumption of a vehicle during a trip. A12 can more accurately analyze the energy consumption characteristics of the vehicle at different driving stages. For example, in congested road conditions, frequent start-stop cycles result in significant energy consumption variations, and A12 can more accurately reflect the energy consumption situation at this stage.

[0085] Step 4: Calculate the average energy consumption per charging cycle based on the energy consumption between two charging operations and the vehicle's mileage.

[0086] In this embodiment, step 4 belongs to the average energy consumption calculation stage for a single charging cycle. That is, after the vehicle starts driving after each charging is completed (a single charging cycle refers to the period from when the vehicle completes a charge and disconnects the charging equipment until the next charging begins. During this period, the vehicle may experience multiple driving, parking, and power consumption (i.e., multiple single power-on cycles), but until the next charging begins, they are all considered as the same charging cycle), the cumulative energy consumption 2 of the whole vehicle is calculated by the cumulative energy consumption calculation module 2. When the vehicle travels an additional preset mileage threshold (see the previous text for relevant content, which will not be repeated here), the average energy consumption of a single charging cycle is obtained by using the cumulative energy consumption 2 and the mileage 2. The average energy consumption of a single charging cycle is recalculated until the next charging is completed.

[0087] It should be noted that in this embodiment, the cumulative energy consumption calculation module 2 calculates energy consumption as follows: using the cumulative energy consumption calculation module 1 as the basic unit, it is responsible for calculating the cumulative energy consumption from the beginning of power-on in each independent power-on cycle. Then, using storage and accumulation functions, the cumulative energy consumption of multiple consecutive power-on cycles is accumulated to obtain the cumulative energy consumption consumed by the vehicle in a single charging cycle. It should be noted that the driving mileage 2 in a single charging cycle is the mileage accumulated from the end of this charging cycle, and is reset to zero and re-accumulated after the second charging cycle.

[0088] In one specific embodiment, the average energy consumption calculation process for a single charging cycle includes: a) After the vehicle finishes charging, the cumulative energy consumption E2 for a single charging cycle is calculated using the cumulative energy consumption calculation module 2.

[0089] b) After the vehicle is fully charged, the initial mileage D02 is obtained and stored. After the vehicle starts driving, the mileage of the vehicle in a single charging cycle is obtained as D2 = D - D02.

[0090] c) For each preset mileage threshold distance traveled by the vehicle, the average energy consumption of a single charging cycle, A2=E2 / D2, will be calculated.

[0091] It should be noted that after the vehicle finishes charging, the average energy consumption A2 for a single charging cycle is reset to zero and recalculated. The cumulative energy consumption E2 for a single charging cycle is calculated by the cumulative energy consumption calculation module 2. E2 needs to be stored to prevent it from being lost when the vehicle is powered off during a single power-on cycle. The average energy consumption A2 = E2 / D2 for a single charging cycle is calculated and updated every time the vehicle travels 1 kilometer (or 2 kilometers, 5 kilometers, etc.).

[0092] Step 5: Calculate the vehicle's average energy consumption by weighting the average energy consumption of a single power-on cycle and the average energy consumption of a single charging cycle.

[0093] In this embodiment, step 5 belongs to the weighted calculation stage, that is, the weighting coefficient K = driving mileage 1 / (remaining battery energy / average energy consumption per charging cycle); real-time average energy consumption = average energy consumption per power-on cycle × K + average energy consumption per charging cycle × (1-K).

[0094] In this embodiment, the remaining battery energy refers to the remaining energy of the vehicle battery at the initial power-on of a single power-on cycle. Dividing the remaining battery energy by the average energy consumption per charging cycle helps predict the remaining mileage at the beginning of a single power-on cycle. Subsequently, as the vehicle travels, a weighting coefficient K is obtained based on the mileage already traveled, where K ranges from [0,1]. As the mileage increases, the weighting coefficient K shows an increasing trend, indicating that the proportion of the average energy consumption per power-on cycle to the total energy consumption gradually increases within each power-on cycle. This phenomenon makes the calculated average energy consumption value more closely reflect the actual energy consumption under current driving conditions, thereby improving the accuracy and relevance of energy consumption assessment.

[0095] In one specific embodiment, the weighted calculation process includes: a) Weighting coefficient K = D1 / (SOC0×E / A2); (0≤K≤1). Note that SOC0×E represents the remaining battery energy. For example, for a certain new energy vehicle mentioned above, the weighting coefficient K = D1 / (0.9×282 / A2), which is only used as an example.

[0096] b) Real-time average energy consumption A = A1 × K + A2 × (1 - K). It should be noted that the energy consumption per power-on cycle in this embodiment reflects the comprehensive energy consumption of the vehicle during a single power-on driving process, such as different energy consumption situations during urban congestion or highway driving; the average energy consumption per charging cycle reflects energy consumption over a longer period; the weighted sum of the two avoids the limitations of a single calculation. That is, as the vehicle travels, the mileage increases, causing the weighting coefficient to increase, and the proportion of the average energy consumption per power-on cycle in the real-time average energy consumption calculation increases, further highlighting the energy consumption during the current driving phase. The calculated real-time average energy consumption is more consistent with the actual situation and can improve the accuracy of the remaining mileage calculation.

[0097] In summary, the average energy consumption calculation method of this invention uses a weighted approach to calculate the real-time average energy consumption value based on the average energy consumption of a single charging cycle and the average energy consumption of a single power-on cycle calculated by two strategies. This enables a more accurate calculation of average energy consumption, ensuring that the average energy consumption calculation is closer to the actual operating state of the vehicle during different driving stages, especially during the transition from the initial stage to the stable period. This provides users with a reliable range reference and enhances the user's driving experience.

[0098] This embodiment also provides an average energy consumption calculation device for implementing the above embodiments and preferred embodiments; details already described will not be repeated. As used below, a "module" can be a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0099] This invention provides an average energy consumption calculation device, such as... Figure 4 As shown, the device includes: The data acquisition module 401 is used to acquire the battery performance parameters and driving mileage data of electric vehicles in real time.

[0100] The first calculation module 402 is used to calculate the first energy consumption of the electric vehicle in a single power-on cycle and the second energy consumption of the electric vehicle in a single charging cycle based on battery performance parameters and driving mileage data, respectively.

[0101] The second calculation module 403 is used to calculate the first weighting coefficient corresponding to the first energy consumption and the second weighting coefficient corresponding to the second energy consumption based on the battery performance parameters, driving mileage data and the second energy consumption.

[0102] The third calculation module 404 is used to perform weighted calculations on the first energy consumption and the second energy consumption using the first weighting coefficient and the second weighting coefficient to obtain the real-time average energy consumption of the electric vehicle.

[0103] In some optional implementations, the first calculation module 402 includes: a power-on energy consumption calculation submodule, a charging energy consumption calculation submodule, and an instantaneous energy consumption calculation submodule; wherein, the power-on energy consumption calculation submodule is used to calculate the instantaneous energy consumption in real time during the first driving process corresponding to the first cumulative mileage within a single power-on cycle of the electric vehicle, based on battery performance parameters, to obtain multiple first instantaneous energy consumptions; to accumulate all the first instantaneous energy consumptions to obtain a first power-on cumulative energy consumption; to determine the first power-on energy consumption based on the ratio of the first power-on cumulative energy consumption to the first cumulative mileage; and to calculate the instantaneous energy consumption in real time during the second driving process corresponding to the preset mileage threshold within a single power-on cycle of the electric vehicle, based on battery performance parameters, to obtain multiple second instantaneous energy consumptions; and to accumulate all the second instantaneous energy consumptions. The system calculates the energy consumption of the electric vehicle in a single charging cycle by: obtaining the second cumulative energy consumption upon power-on; determining the second energy consumption upon power-on based on the ratio of the second cumulative energy consumption upon power-on to a preset mileage threshold; weighting and summing the first energy consumption upon power-on to the second energy consumption upon power-on to obtain the first energy consumption of the electric vehicle in a single charging cycle; calculating the instantaneous energy consumption during the third driving process corresponding to the second cumulative mileage within a single charging cycle of the electric vehicle, based on battery performance parameters, to obtain multiple third instantaneous energy consumptions; summing all the third instantaneous energy consumptions to obtain the cumulative energy consumption upon power-on; calculating the ratio of the cumulative energy consumption upon power-on to the second cumulative mileage to obtain the second energy consumption of the electric vehicle in a single charging cycle; and multiplying the preset BMS control cycle, the real-time vehicle discharge current, and the battery voltage to obtain the corresponding instantaneous energy consumption.

[0104] In some optional implementations, the second calculation module 403 includes: a first coefficient calculation submodule, a second coefficient calculation submodule, and a third coefficient calculation submodule; wherein, the first coefficient calculation submodule is used to multiply the battery's state of charge data and battery energy at the initial moment of a single power-on cycle to obtain the current remaining battery energy; the second coefficient calculation submodule is used to calculate a first ratio of the current remaining battery energy to the second energy consumption, and divide the first cumulative mileage within a single power-on cycle by the first ratio to obtain a first weighting coefficient corresponding to the first energy consumption; the third coefficient calculation submodule is used to calculate the difference between a preset value and the first weighting coefficient to obtain a second weighting coefficient corresponding to the second energy consumption.

[0105] In some optional implementations, the third calculation module 404 includes: a first weighted calculation submodule, a second weighted calculation submodule, and a third weighted calculation submodule; wherein, the first weighted calculation submodule is used to multiply a first weight coefficient by a first energy consumption to obtain a first value; the second weighted calculation submodule is used to multiply a second weight coefficient by a second energy consumption to obtain a second value; and the third weighted calculation submodule is used to add the first value to the first value to obtain the real-time average energy consumption of the electric vehicle.

[0106] In some optional embodiments, the device further includes: a coefficient verification module, used to determine whether the first weighting coefficient and the second weighting coefficient meet the preset value range; if they meet the range, the step of weighting the first energy consumption and the second energy consumption using the first weighting coefficient and the second weighting coefficient to obtain the real-time average energy consumption of the electric vehicle is executed; if they do not meet the range, the step of obtaining the battery performance parameters and driving mileage data of the electric vehicle in real time is returned.

[0107] Further functional descriptions of the above modules are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0108] In this embodiment, the average energy consumption calculation device is presented in the form of a functional unit. Here, a unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0109] The average energy consumption calculation device of this invention comprehensively covers the energy consumption of electric vehicles at different operating stages, avoiding calculation deviations caused by focusing on only a single stage. This makes the energy consumption calculation more consistent with the actual energy consumption during vehicle use. Furthermore, it comprehensively considers the actual impact of energy consumption under different operating conditions, and accordingly designs different weighting coefficients for energy consumption and weighted calculation processes for each energy consumption, further improving the accuracy of average energy consumption calculation. This provides users with more accurate energy consumption information, has strong versatility and adaptability, and greatly meets the energy consumption calculation needs of different users in various usage scenarios. It further provides an important basis for the performance evaluation of electric vehicles, helping to improve the accuracy of electric vehicle range prediction and the user driving experience.

[0110] This invention also provides an electric vehicle, which includes a controller. The controller in this embodiment is an electric vehicle controller, used for powering on / off and waking up its subordinate sub-controllers and network nodes, and each of its power supply interfaces can collect the real-time output current. Other controllers with the above functions are also applicable.

[0111] Figure 5 This is a schematic diagram of the structure of the controller provided in an optional embodiment of the present invention, as shown below. Figure 5 As shown, the controller includes one or more processors 10, memory 20, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other via different buses and can be mounted on a common motherboard or otherwise as required. The processors can process instructions executed within the controller, including instructions stored in or on memory to display graphical information of a GUI on an external input / output system (such as a display device coupled to the interface). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple controllers can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 5 Take a processor 10 as an example.

[0112] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GDA), or any combination thereof.

[0113] The memory 20 stores instructions executable by at least one processor 10 to cause at least one processor 10 to perform the method shown in the above embodiments.

[0114] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the controller. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, and these remote memories may be connected to the controller via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0115] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0116] The controller also includes a communication interface 30 for the main control chip to communicate with other devices or communication networks.

[0117] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor main control chips, or programmable hardware include storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the methods shown in the above embodiments are implemented.

[0118] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. An average energy consumption calculation method characterized by, The method comprises: real-time acquisition of battery performance parameters and driving mileage data of an electric vehicle; based on the battery performance parameters and the driving mileage data, calculation of first energy consumption of the electric vehicle in a single power-on period and calculation of second energy consumption of the electric vehicle in a single charging period; based on the battery performance parameters, the driving mileage data and the second energy consumption, calculation of a first weight coefficient corresponding to the first energy consumption and a second weight coefficient corresponding to the second energy consumption; weighted calculation of the first energy consumption and the second energy consumption by using the first weight coefficient and the second weight coefficient to obtain real-time average energy consumption of the electric vehicle.

2. The average energy consumption calculation method according to claim 1, characterized in that, The driving mileage data comprises a first cumulative mileage in a single power-on period and a preset mileage threshold; based on the battery performance parameters and the driving mileage data, calculation of first energy consumption of the electric vehicle in a single power-on period comprises: in a single power-on period of the electric vehicle, for a first driving process corresponding to the first cumulative mileage, real-time calculation of instantaneous energy consumption in the first driving process based on the battery performance parameters to obtain a plurality of first instantaneous energy consumptions; accumulation of all the first instantaneous energy consumptions to obtain first power-on cumulative energy consumption; determination of first power-on energy consumption based on a ratio of the first power-on cumulative energy consumption to the first cumulative mileage; in a single power-on period of the electric vehicle, for a second driving process corresponding to the preset mileage threshold, real-time calculation of instantaneous energy consumption in the second driving process based on the battery performance parameters to obtain a plurality of second instantaneous energy consumptions; accumulation of all the second instantaneous energy consumptions to obtain second power-on cumulative energy consumption; determination of second power-on energy consumption based on a ratio of the second power-on cumulative energy consumption to the preset mileage threshold; weighted summation of the first power-on energy consumption and the second power-on energy consumption to obtain the first energy consumption of the electric vehicle in a single power-on period.

3. The average energy consumption calculation method according to claim 1, characterized in that, The driving mileage data comprises a second cumulative mileage in a single charging period; based on the battery performance parameters and the driving mileage data, calculation of second energy consumption of the electric vehicle in a single charging period comprises: in a single charging period of the electric vehicle, for a third driving process corresponding to the second cumulative mileage, real-time calculation of instantaneous energy consumption in the third driving process based on the battery performance parameters to obtain a plurality of third instantaneous energy consumptions; accumulation of all the third instantaneous energy consumptions to obtain charging cumulative energy consumption; calculation of a ratio of the charging cumulative energy consumption to the second cumulative mileage to obtain the second energy consumption of the electric vehicle in a single charging period.

4. The average energy consumption calculation method according to any one of claims 2 to 3, characterized in that, The battery performance parameters comprise a preset BMS control period, real-time vehicle discharge current and battery voltage; the real-time calculation of corresponding instantaneous energy consumption based on the battery performance parameters comprises: multiplication of the preset BMS control period, the real-time vehicle discharge current and the battery voltage to obtain the corresponding instantaneous energy consumption.

5. The average energy consumption calculation method according to claim 1, characterized in that, The battery performance parameter includes the state of charge data and the battery energy of the battery corresponding to the initial moment of a single power-on cycle of the electric vehicle, and the driving mileage data includes a first cumulative mileage in a single power-on cycle; The first weight coefficient corresponding to the first energy consumption and the second weight coefficient corresponding to the second energy consumption are calculated according to the battery performance parameter, the driving mileage data and the second energy consumption, including: The state of charge data and the battery energy of the battery corresponding to the initial moment of a single power-on cycle of the electric vehicle are multiplied to obtain the current battery residual energy; A first ratio of the current battery residual energy to the second energy consumption is calculated, and the first cumulative mileage in a single power-on cycle is divided by the first ratio to obtain the first weight coefficient corresponding to the first energy consumption; A difference between a preset value and the first weight coefficient is calculated to obtain the second weight coefficient corresponding to the second energy consumption.

6. The average energy consumption calculation method according to claim 1 or 5, characterized by, The first weight coefficient and the second weight coefficient are used to perform weighted calculation on the first energy consumption and the second energy consumption to obtain the real-time average energy consumption of the electric vehicle, including: The first weight coefficient is multiplied by the first energy consumption to obtain a first value; The second weight coefficient is multiplied by the second energy consumption to obtain a second value; The first value and the second value are added to obtain the real-time average energy consumption of the electric vehicle.

7. The average energy consumption calculation method according to claim 6, characterized in that, Before the first weight coefficient and the second weight coefficient are used to perform weighted calculation on the first energy consumption and the second energy consumption to obtain the real-time average energy consumption of the electric vehicle, the method further includes: It is judged whether the first weight coefficient and the second weight coefficient satisfy a preset value range, respectively; If yes, the step of using the first weight coefficient and the second weight coefficient to perform weighted calculation on the first energy consumption and the second energy consumption to obtain the real-time average energy consumption of the electric vehicle is executed; If no, the step of real-time acquisition of the battery performance parameter and the driving mileage data of the electric vehicle is returned.

8. An average energy consumption calculation apparatus characterized by comprising: The device includes: A data acquisition module is configured to acquire the battery performance parameter and the driving mileage data of the electric vehicle in real time; A first calculation module is configured to calculate the first energy consumption of the electric vehicle in a single power-on cycle and the second energy consumption of the electric vehicle in a single charging cycle based on the battery performance parameter and the driving mileage data, respectively; A second calculation module is configured to calculate the first weight coefficient corresponding to the first energy consumption and the second weight coefficient corresponding to the second energy consumption according to the battery performance parameter, the driving mileage data and the second energy consumption, respectively; A third calculation module is configured to use the first weight coefficient and the second weight coefficient to perform weighted calculation on the first energy consumption and the second energy consumption to obtain the real-time average energy consumption of the electric vehicle.

9. An electric vehicle, characterized by The electric vehicle comprises a controller, the controller comprises a memory and a processor, the memory and the processor are connected in communication with each other, the memory stores computer instructions, and the processor executes the computer instructions to execute the average energy consumption calculation method in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer instructions, and the computer instructions are used to make the computer execute the average energy consumption calculation method in any one of claims 1 to 7.

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