Method and system for calculating driving range of new energy passenger vehicle

Abstract

The invention relates to the technical field of new energy vehicles, in particular to a new energy passenger vehicle driving range calculation method which comprises the steps that battery management system data, navigation and road condition information, driver behavior data, environment data and vehicle electric accessory data of a vehicle are collected; a vehicle movement energy consumption model and an accessory energy consumption model are established, the vehicle movement energy consumption model comprises wind resistance energy consumption, rolling resistance energy consumption and acceleration energy consumption, and the total energy consumption of the vehicle is calculated; estimating the state of charge of the battery by adopting a Kalman filtering algorithm in combination with the capacity attenuation model, and adopting corresponding correction methods according to different intervals of the SOC; and calculating the driving range of the new energy passenger vehicle according to the total energy consumption and the SOC estimation result, training the LSTM neural network according to the historical data of the driver behavior, and outputting a personalized endurance estimation value. According to the technical scheme, the actual driving range of the new energy passenger vehicle can be estimated more accurately.

Description

Technical Field

[0001] This invention relates to the field of new energy vehicle technology, specifically to a method and system for calculating the driving range of a new energy passenger vehicle. Background Technology

[0002] In the field of vehicle engineering, the market share of new energy passenger vehicles is increasing year by year. However, driving range, as one of the key indicators for measuring the performance of new energy passenger vehicles, has always been a focus of consumer attention. Accurately estimating the driving range of new energy passenger vehicles is of vital importance for drivers to plan their trips reasonably, reduce range anxiety, and ensure driving safety.

[0003] Currently, the mainstream algorithms for calculating the driving range of new energy passenger vehicles on the market mainly adopt the WLTC (Worldwide Harmonized Light Vehicles Test Cycle) and the CLTC test cycle algorithm specified in GB / T 18386.1-2021 "Test Methods for Energy Consumption and Driving Range of Electric Vehicles Part 1: Light Vehicles". Both algorithms are performed in a laboratory environment using a chassis dynamometer, according to the strictly regulated test conditions and environment specified in the standards, to obtain the driving range data of new energy passenger vehicles.

[0004] From a macro perspective, the WLTC and CLTC operating cycle algorithms provide a qualitative testing method for evaluating the performance of new energy passenger vehicles, offering guidance for consumers' car purchase decisions. It allows consumers to gain a preliminary understanding of a vehicle's approximate range before purchasing, providing a reference for choosing a model that suits their needs.

[0005] However, in real-world driving scenarios, these two algorithms have revealed numerous problems. First, different drivers have drastically different driving habits. For example, some drivers prefer smooth driving with gentle acceleration and deceleration, while others prefer rapid acceleration and braking, exhibiting a more aggressive driving style. This difference in driving habits leads to a significant deviation between the vehicle's energy consumption during actual driving and that under standard test conditions. Second, the vehicle speed during actual driving often differs from the speeds specified in the WLTC and CLTC test conditions. In real-world driving, vehicle speed is affected by various factors such as traffic flow and road conditions, making it impossible to consistently maintain the speed range set in the standard test conditions.

[0006] In addition, environmental factors also have a significant impact on the driving range of new energy passenger vehicles. Changes in ambient temperature, wind speed, and other conditions will alter the vehicle's energy consumption. For example, in low-temperature environments, battery performance will degrade, leading to a reduction in the vehicle's driving range; while excessive wind speed will increase wind resistance, thereby increasing energy consumption.

[0007] More importantly, in real-world use, the battery energy of new energy passenger vehicles is usually not completely used up. When the battery voltage or charge falls below a certain value, the vehicle will stop driving to protect the battery and ensure basic vehicle operation. However, the WLTC and CLTC cycle algorithms do not fully consider this actual situation, resulting in the calculated driving range often being far greater than the actual driving range of the vehicle.

[0008] Inaccurate range estimations can easily cause range anxiety for drivers of new energy passenger vehicles, especially on highways and other high-speed roads. Worried about running out of power, drivers may frequently search for charging stations, disrupting their original travel plans. More seriously, in extreme cases, the vehicle may run out of power and become unable to continue driving. This not only causes significant inconvenience to the driver but could also lead to traffic accidents, threatening the lives of the driver and passengers. Summary of the Invention

[0009] The purpose of this invention is to propose a method and system for calculating the driving range of new energy passenger vehicles. This technical solution can more accurately estimate the actual driving range of new energy passenger vehicles.

[0010] To achieve the above objectives, in a first aspect, the present invention provides a method for calculating the driving range of a new energy passenger vehicle, comprising: Collect data from the vehicle's battery management system, navigation and road condition information, driver behavior data, environmental data, and vehicle electrical accessory data; Establish vehicle motion energy consumption model and accessory energy consumption model. The vehicle motion energy consumption model includes wind resistance energy consumption. Rolling resistance energy consumption Accelerated energy consumption The formula for calculating the total energy consumption of a vehicle is as follows:

[0011] The state of charge (SOC) of the battery is estimated by using a Kalman filter algorithm combined with a capacity decay model, and corresponding correction methods are applied according to different SOC ranges. The steps involve calculating the driving range of new energy passenger vehicles based on the total energy consumption and SOC estimation results, training an LSTM neural network based on historical driver behavior data, and outputting a personalized driving range estimate.

[0012] Beneficial effects of the basic solution: Traditional range calculations often rely solely on battery data or basic vehicle condition, leading to significant discrepancies between the calculated range and actual range. This solution collects multi-dimensional data, including battery management system data, navigation and road condition information, driver behavior data, environmental data, and vehicle electrical accessory data, achieving comprehensive coverage of "vehicle's own condition, external influencing factors, and human intervention factors." Traditional methods often simplify total vehicle energy consumption to driving energy consumption, ignoring the energy consumption of accessories such as air conditioning, lights, and onboard equipment. This results in a lower calculated total energy consumption than the actual value, thus overestimating the driving range. In this solution, the vehicle motion energy consumption model subdivides core driving energy consumption, breaking it down into wind resistance energy consumption, rolling resistance energy consumption, and acceleration energy consumption. These are the main sources of energy consumption during vehicle driving, and each is directly related to vehicle speed, road conditions, and driving operations, and can be dynamically calculated based on real-time data. The accessory energy consumption model calculates non-driving energy consumption separately, modeling the energy consumption of accessories such as air conditioning, seat heating / ventilation, and in-vehicle entertainment systems. It accurately quantifies their energy consumption percentage using accessory power usage data, ensuring no omissions in the total energy consumption calculation. The combination of these two models makes the total energy consumption calculation more closely reflect real-world driving scenarios, reducing the estimation error of driving range.

[0013] This solution uses a Kalman filter algorithm to correct measurement errors in real time. During vehicle operation, battery voltage, current, and other measurement data are easily affected by sensor accuracy and electromagnetic interference, leading to instantaneous fluctuations in State of Charge (SOC). The Kalman filter, through a prediction-update loop, combines the battery model with real-time measurements to dynamically smooth errors, ensuring the real-time performance and stability of SOC estimation. To adapt to battery aging, traditional methods often assume a constant battery capacity. However, as usage years increase, the actual battery capacity decreases. If the State of Charge (SOC) is still estimated based on the initial capacity, the displayed remaining battery level will be higher than the actual level, resulting in falsely advertised range. Capacity degradation models can adjust the actual battery capacity in real time based on historical data such as battery cycle count and operating temperature, ensuring the long-term accuracy of SOC estimation. Furthermore, they improve accuracy across specific scenarios. Different SOC ranges have different battery characteristics; for example, the voltage drops faster in the low SOC range. Targeted adjustments avoid localized deviations caused by a uniform algorithm across the entire range.

[0014] In addition, the LSTM neural network in this solution is good at processing time-series data (such as the driver's historical acceleration / braking frequency, average vehicle speed, etc.). By training on a driver's long-term driving behavior data, it can learn the unique driving style-energy consumption mapping relationship, and the final output of personalized range estimate can better match the actual driving habits of a specific driver.

[0015] As a feasible preferred embodiment, the data acquisition step includes: Battery management system data acquisition includes single-cell voltage sampling, charge / discharge current integration, and temperature compensation; Navigation and traffic information collection, including the driver determining the driving destination, expected driving route, driving time and traffic congestion situation through the in-vehicle navigation system before departure, and correcting the route energy consumption by combining real-time congestion and construction information obtained from roadside units; Driver behavior data collection includes average driving speed, braking frequency, and energy recovery rate for each speed-limited road section; Environmental data acquisition, including data collection from vehicle-mounted sensors and cloud-based weather data collection; Data collection on vehicle electrical accessories determines the historical and current power consumption of electrical accessories in new energy vehicles.

[0016] As a feasible and preferred solution, the formula for wind resistance energy consumption is as follows:

[0017]

[0018] in, This is the drag coefficient. For vehicle speed, For wind speed, the forward wind speed is taken as a positive value, and when facing a headwind, it is added to the vehicle speed v. The windward surface area of ​​the vehicle. Air density, based on real-time air pressure Corrected for temperature T. is the gas constant of air; Rolling resistance energy consumption, the formula is as follows:

[0019] in, The rolling resistance coefficient is... For vehicle curb weight, It is the acceleration due to gravity. For vehicle speed; Accelerating energy consumption, the formula is as follows:

[0020] Where 'a' represents real-time acceleration.

[0021] As a feasible and preferred option, the vehicle motion energy consumption model also includes gradient gravity compensation energy consumption, as shown in the following formula:

[0022] in, The slope is indicated by short-term slope measurements taken in real time by the IMU and long-term slope measurements taken from pre-loaded elevation data on the navigation map.

[0023] As a feasible and preferred approach, the SOC estimation based on the state-space model has the following state equations:

[0024]

[0025]

[0026] Where SOC(k) represents the state of charge at time k. This represents the battery terminal current at time k. Indicates time compensation; The observation equation is as follows:

[0027]

[0028] The Kalman gain update formula is as follows:

[0029] Where R is the voltage measurement noise variance;

[0030] in.

[0031] As a feasible and preferred solution, the formula for compensating for the number of iterations is as follows:

[0032] Q_now is updated every 50 cycles via full charge calibration.

[0033] As a feasible and preferred option, the power correction method includes: When SOC < 20% or > 95%, the OCV - SOC lookup table method is used, and correction is made in conjunction with the detection of the voltage slope at the end of charging and discharging. During fast charging, a dynamic internal resistance analysis method is used to adjust the charging current in real time.

[0034] As a feasible preferred solution, it also includes a range display and risk warning procedure, including dynamic range display and mandatory reminder to recharge when the real-time range is less than 1.2 times the destination distance.

[0035] As a feasible and preferred solution, it also includes a range display and risk warning procedure, as well as a graphical interface on the dashboard to intuitively display the impact of different environmental factors on the driving range.

[0036] Secondly, this application also provides a new energy passenger vehicle range calculation system, which utilizes the aforementioned new energy passenger vehicle range calculation method, including a data acquisition layer for real-time acquisition of BMS battery level, navigation route, ambient temperature, and accessory power consumption; an energy consumption modeling layer for constructing a physical model of vehicle motion energy consumption and accessory energy consumption; an AI prediction layer for training an LSTM neural network using historical driver behavior data to output a personalized range estimate; and an interaction layer for providing real-time range display and refueling suggestions. Attached Figure Description

[0037] Figure 1 This is a logical schematic diagram of a method for calculating the driving range of a new energy passenger vehicle according to the present invention.

[0038] Figure 2 This is a schematic diagram of the electronic device structure according to an embodiment of the present invention.

[0039] Reference numerals: Electronic device 500, processor 501, communication interface 502, memory 503, bus 504. Detailed Implementation

[0040] To make the technical solution and advantages of this application clearer, the technical solution of the present invention will be further described in detail below with reference to the accompanying drawings. It is understood that the specific embodiments described herein are only some embodiments of the present invention, and are only used to explain this application, not to limit it. It should be noted that the technical features or combinations of technical features described in the following embodiments should not be considered isolated; they can be combined with each other to achieve better technical effects. The same reference numerals appearing in the accompanying drawings of the following embodiments represent the same features or components, and can be applied to different embodiments.

[0041] Furthermore, unless otherwise defined, the technical or scientific terms used in this invention description shall have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains.

[0042] The present invention will now be described in further detail with reference to the accompanying drawings.

[0043] Reference Figure 1 A new energy passenger vehicle range calculation system includes a data acquisition layer for real-time acquisition of BMS power, navigation route, ambient temperature, accessory power consumption, etc.; an energy consumption modeling layer for constructing physical models of vehicle motion energy consumption (wind resistance / rolling resistance / acceleration) and accessory energy consumption (air conditioning / lighting); an AI prediction layer for training an LSTM neural network using historical driver behavior data to output personalized range estimates; and an interaction layer for providing real-time range display and refueling suggestions.

[0044] Reference Figure 1A method for calculating the driving range of a new energy passenger vehicle includes the following steps.

[0045] Step S100, data acquisition, including: Step S101, Battery Management System (BMS) data acquisition, including: Individual cell voltage sampling is performed using a 24-bit Σ-Δ ADC (such as TI's ADS1256), achieving a sampling accuracy of ±0.5mV and covering a flat voltage range of 2.5V to 3.6V. Individual cell voltages are sampled every 5ms (for a battery module consisting of n cells, each cell has an independent sampling line), and the raw voltage data is transmitted in real-time to the BMS main control unit via the SPI bus. For example, in a battery module composed of multiple individual cells, each cell is equipped with an independent voltage sampling line to ensure accurate acquisition of the voltage value of each individual cell.

[0046] The charging and discharging current is integrated using a bidirectional Hall sensor (such as LEM's CASR 50-NP), with a range of ±200A and an error of less than 0.1% FS. This is combined with real-time integration using a 16-bit ADC to accurately calculate the battery's charging and discharging current. When the vehicle is charging, the current sensor records the magnitude and direction of the charging current; when the vehicle is driving and discharging, it records the magnitude and direction of the discharging current. The change in battery capacity is calculated by integrating the current over time, as shown in the following formula:

[0047] Where I(t) is the current value at time t (including direction sign), and the integration time interval is 100ms (balancing computational efficiency and real-time performance).

[0048] Temperature compensation is achieved by placing an NTC thermistor (such as MF52AT) next to each battery cell and polling the battery temperature using a multiplexer (MAX4617) (a single module supports simultaneous monitoring of 32 measurement points). Because the internal resistance of the battery changes with temperature, the temperature compensation circuit can compensate for the battery's internal resistance based on the collected temperature data, thereby improving the accuracy of power estimation.

[0049] The BMS uses a lookup table to obtain the internal resistance correction factor k(T) at the current temperature T (in °C). The corrected internal resistance is then calculated. R' as follows: R' = R 0× k(T) in, R 0 represents the internal resistance at the nominal temperature of 25°C; this is used to adjust the polarization loss term in the SOC estimation.

[0050] Step S102, navigation and traffic information collection, includes: before departure, the driver determines the driving destination, expected driving route, driving time, and traffic congestion situation through the vehicle's built-in navigation system. Simultaneously, real-time congestion and construction information is obtained from roadside units (RSUs) to correct route energy consumption. When construction congestion occurs on a section of the navigation route, the RSU transmits this information to the vehicle's navigation system in real time, and the system adjusts the energy consumption estimate for that section based on the congestion situation (length of the congested section and estimated travel time).

[0051] Step S103: Long-term collection of driver behavior data, including average driving speed, braking frequency, and energy recovery rate in various speed-limited road sections.

[0052] Specifically, real-time vehicle speed is obtained through a GPS module (such as u-blox ZED-F9P, with a positioning accuracy of ±2m), and combined with the speed limit signs on the navigation map, the actual average speed of the driver in each speed limit zone is calculated (e.g., a driver often maintains 105km / h in a 120km / h speed limit zone).

[0053] Braking frequency is measured by a brake pedal pressure sensor (range 0-100 bar, accuracy ±0.5%FS) that collects pressure values ​​every 100ms. When the pressure exceeds a threshold (e.g., 5 bar), it is considered a valid braking action. The number of braking actions per unit distance is counted.

[0054] Energy recovery rate: The motor controller records the energy feedback power during regenerative braking and, combined with braking energy loss, calculates the energy recovery efficiency η: η = recovered energy / total braking energy.

[0055] Initially, insufficient data may affect accuracy, but as driving time increases and the amount of data gradually becomes richer, the accuracy will become higher and higher.

[0056] Step S104, Environmental data collection, including: Step S104-1: Vehicle-mounted sensor data acquisition. The vehicle-mounted sensors include an NTC temperature sensor, a wind speed and direction sensor, an atmospheric pressure sensor, an inertial measurement unit (IMU), and a GPS / BeiDou module.

[0057] Specifically, NTC temperature sensors are used to measure the temperature of the battery pack and the ambient temperature, positioned between battery modules and at the vehicle's air intake, with an accuracy requirement of ±0.5℃ (-30 ~ 80℃). For example, placing multiple NTC temperature sensors between battery modules can monitor the temperature at different locations within the battery modules in real time, preventing localized overheating.

[0058] The wind speed and direction sensor uses an ultrasonic type to measure real-time wind speed and direction, with a wind speed accuracy of ±0.3 m / s. By measuring wind speed and direction, the drag effect of wind on vehicle movement can be calculated.

[0059] An atmospheric pressure sensor, using a BMP388 barometer (I2C interface), measures altitude and air pressure with an accuracy of ±0.5 hPa. Combining altitude data with air density calculations allows for more accurate calculations, thus affecting wind resistance calculations.

[0060] The inertial measurement unit (IMU), employing a 6-axis MEMS (BMI088) combined with Kalman filtering, measures the vehicle's pitch and roll angles, with a slope angle accuracy of ±0.1°. The IMU allows for real-time acquisition of the vehicle's attitude information, which is crucial for calculating the vehicle's energy consumption while driving on inclines.

[0061] GPS / BeiDou module: Employs dual-frequency positioning (such as u-blox ZED-F9P) to acquire altitude and latitude / longitude information, with an altitude error of less than 2 meters. This precise positioning information can be combined with navigation maps to obtain more accurate road and terrain information.

[0062] Step S104-2: Cloud-based weather data acquisition. Weather data, such as temperature changes and wind speed forecasts for the next 2 hours, is received from the cloud via a 5G / V2X module. The domain controller of the onboard computing unit deploys an environmental compensation algorithm to process sensor data in real time, achieving a 10ms response time. Cloud-based weather data (such as the China Meteorological Administration API) is updated every 5 seconds via the network. Combining weather forecasts and historical data, environmental changes for the next 30 minutes are predicted and weighted accordingly. For short-term (<5 minutes), onboard sensor data is the primary source (weight 0.8); for long-term (>15 minutes), cloud-based forecasts are integrated (weight 0.6).

[0063] The summary table is shown in Table 1. Table 1. Onboard Environmental Data Sensor Network

[0064] Step S104, vehicle electrical accessory data collection, including: Determine the historical and current power consumption of electrical accessories in new energy vehicles, such as exterior lights, interior ambient lighting, vehicle audio, air conditioning, cameras, and radar. For example, calculate the power consumption of accessories by separately measuring the power of the air conditioner in different operating modes (PTC heating ≈ 7kW vs. heat pump ≈ 3kW) and the power of accessories such as the entertainment system.

[0065] Step S200, energy consumption modeling, including vehicle motion energy consumption modeling and accessory energy consumption modeling, the formula is as follows:

[0066] Specifically, it includes the following: Step S201, vehicle motion energy consumption modeling, including: Wind resistance energy consumption, the formula is as follows:

[0067]

[0068] in, This is the drag coefficient. For vehicle speed, For wind speed, the forward wind speed is taken as a positive value, and when facing a headwind, it is added to the vehicle speed v. The windward surface area of ​​the vehicle. Air density, based on real-time air pressure (BMP388 measurement) and temperature T (NTC sensor measurement) correction, Let be the gas constant of air.

[0069] Rolling resistance energy consumption, the formula is as follows:

[0070] in, This is the rolling resistance coefficient (generally related to the road surface material). For vehicle curb weight, It is the acceleration due to gravity. The speed is the vehicle speed.

[0071] The energy consumption during acceleration (acceleration a>0) is calculated using the following formula:

[0072] Where 'a' represents real-time acceleration.

[0073] Gravity compensation for slope is calculated using the following formula:

[0074] in, The slope is indicated by short-term slope measurements taken in real time by the IMU and long-term slope measurements taken from pre-loaded elevation data on the navigation map.

[0075] Step S202 involves accessory energy consumption modeling, which involves metering vehicle electrical accessories such as air conditioning, lighting, and entertainment systems. For example, the power consumption of the air conditioner varies depending on the temperature setting and heating / cooling mode; the PTC heating power is approximately 7kW, and the heat pump heating power is approximately 3kW. By monitoring the operating status and power of vehicle accessories in real time, accessory energy consumption is obtained.

[0076] Step S300, SOC estimation and correction method: SOC (State of Charge) is the core parameter for calculating driving range. This embodiment uses a Kalman filter algorithm combined with a capacity decay model to achieve high-precision estimation, including the following: Step S301, SOC estimation based on the state-space model, the state equations are as follows:

[0077]

[0078]

[0079] Where SOC(k) represents the state of charge at time k. This represents the battery terminal current at time k. This indicates time compensation.

[0080] The observation equation is as follows:

[0081]

[0082] The Kalman gain update formula is as follows:

[0083] Where R is the voltage measurement noise variance.

[0084]

[0085] The capacity degradation model considers the impact of battery cycle count on capacity. The cycle count compensation formula is as follows:

[0086] Q_now is updated every 50 cycles via full charge calibration.

[0087] Step S302, power correction, as shown in Table 2, when SOC < 20% or > 95%, the OCV - SOC lookup method is used, combined with the voltage slope detection at the end of charging and discharging for correction. The resting criterion is that the current < 0.05C and the duration > 30 minutes, triggering the OCV - SOC lookup (calibration data must include three sets of curves at 5℃ / 25℃ / 45℃). At the end of charging (SOC > 95%), Δ... V / Δ t Slope detection and correction of full charge capacity.

[0088] During fast charging, dynamic internal resistance analysis (DVA) is used to adjust the charging current in real time. By monitoring the changes in the battery's internal resistance during fast charging, the charging strategy is optimized to improve charging efficiency and ensure battery safety.

[0089] Table 2. Algorithm-aided correction methods

[0090] Step S400: Train an LSTM neural network using historical driver behavior data to output a personalized range estimate; the historical data includes the driver's navigation route gradient / speed limit, historical rapid acceleration frequency, energy recovery efficiency, and other data as input.

[0091] Step S500, battery life display and risk warning, includes: Step S501, Dynamic Range Display, provides "Typical" / "Ideal" range dual modes, such as "Headwind: -12km" and "Low Temperature: -8%". The impact of different environmental factors on the driving range is intuitively displayed on the instrument panel through a graphical interface.

[0092] Step S502: When the real-time range is less than 1.2 times the destination distance, a mandatory reminder to recharge is issued. For example, if the destination distance is 100km, and the real-time range is less than 120km, the system will forcibly remind the driver to recharge via sound, pop-up window, or other means.

[0093] This application also provides an electronic device 500 that utilizes the aforementioned method for calculating the driving range of a new energy passenger vehicle. The device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps of the aforementioned method for calculating the driving range of a new energy passenger vehicle. In this application embodiment, the processor is the control center of the computer method; it can be a physical machine processor or a virtual machine processor.

[0094] Reference Figure 2 The electronic device 500 includes at least one processor 501, at least one communication interface 502, at least one memory 503, and at least one bus 504. The bus 504 is used for communication between these components, the communication interface 502 is used for signaling or data communication with other node devices, and the memory 503 stores machine-readable instructions executable by the processor 501. When the electronic device 500 is running, the processor 501 communicates with the memory 503 via the bus 504. When the machine-readable instructions are invoked by the processor 501, they execute the steps of the above-described method for calculating the driving range of a new energy passenger vehicle.

[0095] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor of an electronic device, can implement the steps of a method for calculating the driving range of a new energy passenger vehicle as described above.

[0096] Those skilled in the art will understand that implementing all or part of the process in a method for calculating the driving range of a new energy passenger vehicle can be accomplished by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium. When executed, the program can include the processes of various embodiments of the method for calculating the driving range of a new energy passenger vehicle. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0097] The above content is merely an embodiment of the present invention. Commonly known structures and characteristics of the solutions are not described in detail here. Those skilled in the art are aware of all common technical knowledge in the field prior to the application date or priority date, are aware of all prior art in that field, and have the ability to apply conventional experimental methods prior to that date. Those skilled in the art can improve and implement this solution based on the guidance provided in this application and their own capabilities. Some typical well-known structures or systems should not be obstacles for those skilled in the art to implement this application. It should be noted that those skilled in the art can make several modifications and improvements without departing from the structure of the present invention. These should also be considered within the scope of protection of the present invention, and will not affect the effectiveness of the implementation of the present invention or the practicality of the patent. The scope of protection claimed in this application should be determined by the content of its claims, and the specific embodiments described in the specification can be used to interpret the content of the claims.

Claims

1. A method for calculating the driving range of a new energy passenger vehicle, characterized in that, include: Collect data from the vehicle's battery management system, navigation and road condition information, driver behavior data, environmental data, and vehicle electrical accessory data; Establish vehicle motion energy consumption model and accessory energy consumption model. The vehicle motion energy consumption model includes wind resistance energy consumption. Rolling resistance energy consumption Accelerated energy consumption The formula for calculating the total energy consumption of a vehicle is as follows: The state of charge (SOC) of the battery is estimated by using a Kalman filter algorithm combined with a capacity decay model, and corresponding correction methods are applied according to different SOC ranges. The steps involve calculating the driving range of new energy passenger vehicles based on the total energy consumption and SOC estimation results, training an LSTM neural network based on historical driver behavior data, and outputting a personalized driving range estimate.

2. The method for calculating the driving range of a new energy passenger vehicle according to claim 1, characterized in that: The data acquisition steps include: Battery management system data acquisition includes single-cell voltage sampling, charge / discharge current integration, and temperature compensation; Navigation and traffic information collection, including the driver determining the driving destination, expected driving route, driving time and traffic congestion situation through the in-vehicle navigation system before departure, and correcting the route energy consumption by combining real-time congestion and construction information obtained from roadside units; Driver behavior data collection includes average driving speed, braking frequency, and energy recovery rate for each speed-limited road section; Environmental data acquisition, including data collection from vehicle-mounted sensors and cloud-based weather data collection; Data collection on vehicle electrical accessories determines the historical and current power consumption of electrical accessories in new energy vehicles.

3. The method for calculating the driving range of a new energy passenger vehicle according to claim 1, characterized in that, Wind resistance energy consumption, the formula is as follows: in, This is the drag coefficient. For vehicle speed, For wind speed, the forward wind speed is taken as a positive value, and when facing a headwind, it is added to the vehicle speed v. The windward surface area of ​​the vehicle. Air density, based on real-time air pressure Corrected for temperature T, is the gas constant of air; Rolling resistance energy consumption, the formula is as follows: in, The rolling resistance coefficient is... For vehicle curb weight, It is the acceleration due to gravity. For vehicle speed; Accelerating energy consumption, the formula is as follows: Where 'a' represents real-time acceleration.

4. The method for calculating the driving range of a new energy passenger vehicle according to claim 3, characterized in that, The vehicle motion energy consumption model also includes gradient gravity compensation energy consumption, as shown in the following formula: in, The slope is indicated by short-term slope measurements taken in real time by the IMU and long-term slope measurements taken from pre-loaded elevation data on the navigation map.

5. The method for calculating the driving range of a new energy passenger vehicle according to claim 2, characterized in that, SOC estimation based on the state-space model, the state equation is as follows: Where SOC(k) represents the state of charge at time k. This represents the battery terminal current at time k. Indicates time compensation; The observation equation is as follows: The Kalman gain update formula is as follows: Where R is the voltage measurement noise variance; in.

6. The method for calculating the driving range of a new energy passenger vehicle according to claim 5, characterized in that, The formula for compensating for the number of iterations is as follows: Q_now is updated every 50 cycles via full charge calibration.

7. The method for calculating the driving range of a new energy passenger vehicle according to claim 1, characterized in that, Battery correction methods include: When SOC < 20% or > 95%, the OCV - SOC lookup table method is used, and correction is made in conjunction with the detection of the voltage slope at the end of charging and discharging. During fast charging, a dynamic internal resistance analysis method is used to adjust the charging current in real time.

8. The method for calculating the driving range of a new energy passenger vehicle according to claim 1, characterized in that, It also includes battery life display and risk warning steps, including dynamic battery life display and mandatory reminder to recharge when the real-time battery life is less than 1.2 times the destination distance.

9. The method for calculating the driving range of a new energy passenger vehicle according to claim 2, characterized in that, It also includes a range display and risk warning function, as well as a graphical interface on the dashboard that visually displays the impact of different environmental factors on the driving range.

10. A system for calculating the driving range of a new energy passenger vehicle, characterized in that, The method for calculating the driving range of a new energy passenger vehicle as described in claims 1-9 includes a data acquisition layer for real-time acquisition of BMS power, navigation path, ambient temperature, and accessory power consumption; an energy consumption modeling layer for constructing a physical model of vehicle motion energy consumption and accessory energy consumption; an AI prediction layer for training an LSTM neural network using historical driver behavior data to output a personalized driving range estimate; and an interaction layer for providing real-time driving range display and refueling suggestions.

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