Electric quantity endurance calculation method and device of new energy vehicle and medium

Abstract

The invention discloses an electric quantity endurance calculation method and device for a new energy vehicle and a medium, and the method comprises the following steps: S1, obtaining vehicle states, and carrying out the vehicle travel division according to different vehicle states; dividing the vehicle journeys in each different vehicle state into a plurality of small journeys at preset time intervals, and collecting vehicle data in each small journey; s2, obtaining the actual consumed electric quantity of the vehicle, and calculating the health degree of the battery according to the actual consumed electric quantity of the vehicle; obtaining the average value of the historical 100-kilometer power consumption of the vehicle travel, and calculating the current 100-kilometer power consumption according to the average value of the historical 100-kilometer power consumption of the vehicle travel and the first, second, third and fourth power consumption impact factors; and S3, according to the current battery electric quantity, the battery health degree and the current power consumption per hundred kilometers of the vehicle, constructing an endurance calculation model, and calculating the current electric quantity endurance value of the new energy vehicle. According to the method, the influence of different working conditions on endurance calculation can be considered, and the vehicle endurance can be accurately calculated.

Description

Technical Field

[0001] The present invention relates to the field of electric vehicle range calculation, and particularly to a method, device and medium for calculating the power range of new energy vehicles. Background Art

[0002] Currently, different models on the market have different range display methods:

[0003] One is to estimate the range based on the average power consumption within the user's recent driving mileage and the remaining battery power. The disadvantage of this method is that the vehicle operating conditions are complex during driving, and the average energy consumption in the past mileage cannot well describe the energy consumption change of the vehicle in the future. For example, if the vehicle was in a low-speed condition in the past mileage, when it changes to a high-speed condition in the future, the predicted driving mileage by the average power consumption method will fluctuate greatly.

[0004] Another is to display the estimated range when the battery is fully charged and calculate the range proportionally according to the battery power. The disadvantage of this method is that in actual use, the actual range is significantly lower than the estimated range, which may mislead users to be "blindly optimistic" and even cause the vehicle to break down due to power exhaustion during driving.

[0005] Therefore, in the prior art, there is a problem that the influence of vehicle operating conditions on vehicle range calculation is ignored, resulting in inaccurate vehicle range calculation and causing range anxiety for users. Summary of the Invention

[0006] The purpose of the present invention is to solve the problem that the influence of vehicle operating conditions on vehicle range calculation is ignored, resulting in inaccurate vehicle range calculation and causing range anxiety for users. The present invention provides a method, system, device and medium for calculating the power range of new energy vehicles, which can achieve accurate calculation of the power range of new energy vehicles.

[0007] To solve the above technical problems, an embodiment of the present invention discloses a method for calculating the power range of a new energy vehicle, including the following steps:

[0008] S1: Obtain the vehicle state and divide the vehicle journey according to different vehicle states; wherein, the vehicle journey in each different vehicle state is divided into several small journeys at a predetermined time interval, and vehicle data in each small journey is collected; wherein

[0009] Obtain the hard acceleration behavior data of the current small journey, and determine the first power consumption influence factor according to the hard acceleration behavior data of the current small journey;

[0010] Obtain the idling behavior data of the current small journey, and determine the second power consumption influence factor according to the idling behavior data of the current small journey;

[0011] Obtain data for different speed intervals of the current short trip, and determine the third power consumption impact factor based on the speed interval data of the current short trip;

[0012] Obtain the air conditioner turn-on data of the current short trip, and determine the fourth power consumption impact factor based on the air conditioner turn-on data of the current short trip;

[0013] S2: Obtain the actual power consumption of the vehicle, and calculate the battery health based on the actual power consumption of the vehicle; and

[0014] Obtain the average value of the historical power consumption per 100 kilometers of the vehicle trip, and calculate the current power consumption per 100 kilometers based on the average value of the historical power consumption per 100 kilometers of the vehicle trip and the first, second, third, and fourth power consumption impact factors;

[0015] S3: Construct a cruising range calculation model based on the current battery power, battery health, and current power consumption per 100 kilometers of the vehicle, and calculate and determine the current power cruising range value of the new energy vehicle.

[0016] With the above technical solutions, by dividing the vehicle trips according to different vehicle states respectively; among them, the vehicle trips in each different vehicle state are divided into several short trips at a predetermined time interval, and the vehicle data in each short trip is collected, the working condition data of the vehicle in different states can be accurately collected, providing accurate data for the calculation of the vehicle's power cruising range; further, by obtaining the actual power consumption of the vehicle and calculating the battery health based on the actual power consumption of the vehicle; and, by obtaining the average value of the historical power consumption per 100 kilometers of the vehicle trip and calculating the current power consumption per 100 kilometers based on the average value of the historical power consumption per 100 kilometers of the vehicle trip and each impact factor, calculating the battery health and the current power consumption per 100 kilometers can provide accurate data for the calculation of the vehicle's cruising range that is updated in real time. By refining the vehicle state and merging fragmented trips, considering and processing the parking conditions and driving conditions that do not belong to the driving conditions but affect the power consumption according to the driving scenario, user behavior, and possible signal mutations, etc., the cruising range of the vehicle can be calculated more accurately.

[0017] An embodiment of the present invention discloses a method for calculating the power cruising range of a new energy vehicle, including: in step S1, the vehicle states include: driving state, charging state, parking state, and wake-up state; among them, the vehicle states are divided according to the driver's behavior and the vehicle power-on state; and, according to the driver's behavior and driving scenario, fragmented integration is performed on each vehicle state.

[0018] With the above technical solutions, by dividing according to the driver's behavior and the vehicle power-on state; and, by performing fragmented integration on each vehicle state according to the driver's behavior and driving scenario, dividing the vehicle states can ensure data collection of the vehicle in different states, facilitating more accurate calculation of the vehicle's cruising range.

[0019] An embodiment of the present invention discloses a method for calculating the power endurance of a new energy vehicle, including: in step S2, calculating the battery health based on the actual power consumption of the vehicle, including:

[0020] Taking the time interval between two adjacent charges of the vehicle as a cycle, obtaining the battery power data within each cycle in multiple cycles, comparing the actual power consumption of the vehicle with the power consumption value calculated theoretically using the battery discharge state, and obtaining the power achievement rate of the vehicle; obtaining the power achievement rates of each cycle in multiple cycles and calculating the average value as the battery health.

[0021] By adopting the above technical solution, taking the time interval between two adjacent charges of the vehicle as a cycle, obtaining the battery power data within each cycle in multiple cycles, comparing the actual power consumption of the vehicle with the power consumption value calculated theoretically using the battery discharge state, and obtaining the power achievement rate of the vehicle; obtaining the power achievement rates of each cycle in multiple cycles and calculating the average value as the battery health, it can ensure the accurate calculation of the vehicle battery health, taking the average value of the battery health in multiple cycles, providing the most accurate battery health for the vehicle endurance calculation, and ensuring the accuracy of the vehicle endurance calculation.

[0022] An embodiment of the present invention discloses a method for calculating the power endurance of a new energy vehicle, including: in step S1, determining the first power consumption influencing factor according to the following method: counting whether there is an emergency acceleration behavior in the current short trip, using the power consumption per 100 kilometers of the trips without emergency acceleration behavior in the historical data as a benchmark, comparing the power consumption per 100 kilometers of the trips with emergency acceleration behavior in the historical data, and calculating the first power consumption influencing factor according to the power consumption per 100 kilometers of the trips without emergency acceleration behavior in the historical data and the power consumption per 100 kilometers of the trips with emergency acceleration behavior in the historical data.

[0023] By adopting the above technical solution, by obtaining the power consumption per 100 kilometers in the recent 50 km and obtaining the power consumption per 100 kilometers of each short trip in the recent 50 km and calculating the average value as the current power consumption per 100 kilometers, it can ensure the real-time update of the current power consumption per 100 kilometers, making the accuracy of the endurance calculation model higher.

[0024] An embodiment of the present invention discloses a method for calculating the power endurance of a new energy vehicle, including: in step S1, determining the first power consumption influencing factor according to the following method: counting whether there is an emergency acceleration behavior in the current short trip, using the power consumption per 100 kilometers of the trips without emergency acceleration behavior in the historical data as a benchmark, comparing the power consumption per 100 kilometers of the trips with emergency acceleration behavior in the historical data, and calculating the first power consumption influencing factor according to the power consumption per 100 kilometers of the trips without emergency acceleration behavior in the historical data and the power consumption per 100 kilometers of the trips with emergency acceleration behavior in the historical data.

[0025] Adopting the above technical solution, by counting whether there is an emergency acceleration behavior within the current small driving range, and using the electricity consumption per 100 kilometers of the driving ranges without emergency acceleration behavior in historical data as a benchmark, comparing the electricity consumption per 100 kilometers of the driving ranges with emergency acceleration behavior in historical data to obtain the first electricity consumption impact factor, the accuracy of the calculation of the first electricity consumption impact factor can be ensured. By comparing the emergency acceleration behavior with the situation without emergency acceleration behavior to obtain the first electricity consumption impact factor, the calculation of the electricity consumption impact factor of the vehicle within the current small driving range can be guaranteed to be accurate. Further, the calculation of the vehicle's cruising range is made accurate.

[0026] The embodiment of the present invention discloses a method for calculating the electricity-based cruising range of a new energy vehicle, including: in step S1, determining the second electricity consumption impact factor according to the following method: establishing an idle non-linear model based on historical data to calculate the change trend of the electricity consumption per 100 kilometers under different proportions of idle working conditions duration, and counting the proportion of the idle working conditions in the current small driving range; using the electricity consumption per 100 kilometers of the non-idle working condition driving ranges in historical data as a benchmark, comparing the electricity consumption per 100 kilometers in the range of the proportion of idle working conditions in historical data, and calculating the second electricity consumption impact factor according to the electricity consumption per 100 kilometers in the range of the proportion of idle working conditions in historical data and the electricity consumption per 100 kilometers of the non-idle working condition driving ranges in historical data.

[0027] Adopting the above technical solution, by establishing an idle non-linear model based on historical data to calculate the change trend of the electricity consumption per 100 kilometers under different proportions of idle working conditions duration, and counting the proportion of the idle working conditions in the current small driving range; using the electricity consumption per 100 kilometers of the non-idle working condition driving ranges in historical data as a benchmark, comparing the electricity consumption per 100 kilometers in the range of the proportion of idle working conditions in historical data to obtain the second electricity consumption impact factor, the calculation of the electricity consumption impact factor of the vehicle within the current small driving range can be guaranteed to be accurate. Further, the calculation of the vehicle's cruising range is made accurate.

[0028] The embodiment of the present invention discloses a method for calculating the electricity-based cruising range of a new energy vehicle, including: in step S1, determining the third electricity consumption impact factor according to the following method:

[0029] Counting the average speed of several small driving ranges and dividing them into at least three speed intervals, and respectively counting the electricity consumption per 100 kilometers corresponding to each speed interval; establishing a speed non-linear model based on historical data to calculate the change trend of the electricity consumption per 100 kilometers within each speed interval, and obtaining the speed interval of the current small driving range to obtain the electricity consumption per 100 kilometers of the speed interval of the current small driving range; using the electricity consumption per 100 kilometers of the most economical speed interval among at least three speed intervals in historical data as a benchmark, comparing the electricity consumption per 100 kilometers of the speed interval of the current small driving range, and calculating the third electricity consumption impact factor according to the electricity consumption per 100 kilometers of the speed interval of the current small driving range and the electricity consumption per 100 kilometers of the most economical speed interval among at least three speed intervals in historical data.

[0030] With the above technical solution, by taking the power consumption per 100 kilometers in the most economical speed range among at least three speed ranges in historical data as a benchmark and comparing the power consumption per 100 kilometers in the speed range of the current short trip, the third power consumption impact factor can be obtained, which can ensure the accurate calculation of the power consumption impact factor within the current short trip of the vehicle. Further, the vehicle endurance calculation is made accurate.

[0031] An embodiment of the present invention discloses a method for calculating the power endurance of a new energy vehicle, including: in step S1, determining the fourth power consumption impact factor according to the following method: establishing an air-conditioning non-linear model based on historical data to calculate the change trend of the power consumption per 100 kilometers under different air-conditioning on-time ratios, and counting the air-conditioning on-time ratio of the current short trip to obtain the power consumption per 100 kilometers in the air-conditioning on-time ratio range of the current short trip; taking the power consumption per 100 kilometers of the non-air-conditioning-on trips in the historical data as a benchmark, comparing the power consumption per 100 kilometers in the air-conditioning on-time ratio range of the current short trip, and calculating the fourth power consumption impact factor according to the power consumption per 100 kilometers of the non-air-conditioning-on trips in the historical data and the power consumption per 100 kilometers in the air-conditioning on-time ratio range of the current short trip.

[0032] With the above technical solution, by taking the power consumption per 100 kilometers of the non-air-conditioning-on trips in the historical data as a benchmark and comparing the power consumption per 100 kilometers in the air-conditioning on-time ratio range of the current short trip, the fourth power consumption impact factor can be obtained, which can ensure the accurate calculation of the power consumption impact factor within the current short trip of the vehicle. Further, the vehicle endurance calculation is made accurate.

[0033] An embodiment of the present invention discloses a method for calculating the power endurance of a new energy vehicle, including: after step S3, further including:

[0034] S4: The vehicle-mounted terminal of the new energy vehicle sends the collected vehicle data, as well as the vehicle's idle non-linear model, speed non-linear model, and air-conditioning non-linear model and related data to the big data platform; the big data platform trains the models based on the collected multiple idle non-linear models, multiple speed non-linear models, and multiple air-conditioning non-linear models and related data, and pushes the trained new models to the corresponding vehicle-mounted terminals according to the vehicle identification code. The vehicle-mounted terminal calculates the endurance mileage according to the currently collected data and the battery health, and sends the newly collected data to the big data platform again to form a closed-loop model.

[0035] With the above technical solution, by sending the collected data from the vehicle-mounted terminal to the big data platform, the big data platform trains multiple models after receiving the data, pushes the trained models to the corresponding vehicle-mounted terminals according to the vehicle identification code, the vehicle-mounted terminal calculates the vehicle endurance based on the data, and pushes the newly collected data to the big data platform, which can ensure that the vehicle endurance calculation model is continuously optimized after being trained by the big data platform, and accurate endurance calculation can be obtained to ensure the real-time update of the vehicle endurance calculation.

[0036] Moreover, when calculating the remaining driving range on the big data platform, factors such as the degree of battery attenuation and the vehicle's historical driving conditions (historical electricity consumption per 100 kilometers), real-time driving conditions (speed distribution), driving habits (idle conditions, hard acceleration behavior), and in-vehicle environment (proportion of air conditioner operation time) that affect power consumption are comprehensively considered, improving the accuracy of the remaining driving range estimation.

[0037] Another embodiment of the present invention also discloses an electronic device, including: a processor, and a memory communicatively connected to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement the above-mentioned driving range calculation method for new energy vehicles.

[0038] Another embodiment of the present invention also discloses a computer-readable storage medium storing computer-executable instructions, which are used to implement the above-mentioned driving range calculation method for new energy vehicles when executed by a processor.

[0039] The beneficial effects of the present invention are as follows: By collecting trip data in different vehicle states and dividing the vehicle trip into small trips at a predetermined time interval, it can ensure that the collected vehicle data is more accurate and updated in real time. Considering the influence of different driving conditions on the vehicle driving range calculation, the power consumption impact factors corresponding to different driving conditions are calculated respectively, and the current electricity consumption per 100 kilometers is calculated using the average value of the historical electricity consumption per 100 kilometers and the power consumption impact factors corresponding to each driving condition. Further, according to the battery health, the current electricity consumption per 100 kilometers, and the current remaining battery power, a driving range calculation model is constructed, which can calculate the vehicle's power driving range more accurately. By refining the vehicle state and combining fragmented trips, parking conditions that do not belong to driving conditions but affect power consumption are considered and processed together with driving conditions according to the driving scenario, user behavior, and possible signal mutations. The model and data are continuously optimized through the big data platform, comprehensively considering factors such as the degree of battery attenuation and the vehicle's historical driving conditions (historical electricity consumption per 50 kilometers), real-time driving conditions (speed distribution), driving habits (idle conditions, hard acceleration behavior), and in-vehicle environment (proportion of air conditioner operation time), making the vehicle's power driving range calculation more accurate and ensuring the real-time update of vehicle data. Description of the Drawings

[0040] Figure 1 is a flowchart of the driving range calculation steps provided by an embodiment of the present invention;

[0041] Figure 2 is a flowchart of the driving range calculation provided by an embodiment of the present invention;

[0042] Figure 3 is a flowchart provided by an embodiment of the present invention;

[0043] Figure 4 This is a schematic structural diagram of an electronic device provided by an embodiment of the present invention.

[0044] Description of the reference numerals:

[0045] 121, transceiver; 122, processor; 123, memory. Specific embodiments

[0046] To make the objectives, technical solutions, and advantages of the present invention clearer, the following will further describe in detail the embodiments of the present invention with reference to the accompanying drawings.

[0047] Embodiment 1:

[0048] Based on the problem in the prior art that the vehicle driving conditions are ignored, which affects the vehicle's endurance calculation and leads to inaccurate vehicle endurance calculation, the present invention proposes a method for calculating the power endurance of a new energy vehicle, referring to Figure 1 , including the following steps:

[0049] S1: Obtain the vehicle state, and divide the vehicle itinerary according to different vehicle states respectively; among them, divide the vehicle itinerary in each different vehicle state into several small itineraries at a predetermined time interval, and collect the vehicle data in each small itinerary; among them

[0050] Obtain the rapid acceleration behavior data of the current small itinerary, and determine the first power consumption influence factor according to the rapid acceleration behavior data of the current small itinerary;

[0051] Obtain the idling behavior data of the current small itinerary, and determine the second power consumption influence factor according to the idling behavior data of the current small itinerary;

[0052] Obtain the data of different speed intervals of the current small itinerary, and determine the third power consumption influence factor according to the speed interval data of the current small itinerary;

[0053] Obtain the air conditioner turning-on data of the current small itinerary, and determine the fourth power consumption influence factor according to the air conditioner turning-on data of the current small itinerary;

[0054] S2: Obtain the actual power consumption of the vehicle, and calculate the battery health according to the actual power consumption of the vehicle; and

[0055] Obtain the average value of the historical power consumption per 100 kilometers of the vehicle itinerary, and calculate the current power consumption per 100 kilometers according to the average value of the historical power consumption per 100 kilometers of the vehicle itinerary and the first, second, third, and fourth power consumption influence factors;

[0056] S3: Construct a endurance calculation model according to the current battery power, battery health, and current power consumption per 100 kilometers of the vehicle, and calculate and determine the current power endurance value of the new energy vehicle.

[0057] For example, in a specific embodiment, the vehicle trips in different states are divided into multiple 5-minute sub-trips, and vehicle data is collected. For example, during a one-hour vehicle driving process, the vehicle trip is divided into 12 5-minute sub-trips, and different trips or working conditions should at least include: hard acceleration behavior working condition, idling behavior working condition, different speed range working conditions, air conditioner on duration working condition. By collecting the above data, the corresponding first, second, third, and fourth power consumption impact factors are calculated; further, the battery health of the vehicle and the current power consumption per 100 kilometers are calculated; furthermore, a vehicle endurance calculation model is constructed using the above data; for example, the following model is constructed:

[0058] Electric endurance mileage = remaining power / power consumption

[0059] =(current battery power * battery health) / (a * b * c * d * E history50 )

[0060] Where: a is the first power consumption impact factor, b is the second power consumption impact factor, c is the third power consumption impact factor, d is the fourth power consumption impact factor, and E history50 is the historical power consumption per 100 kilometers.

[0061] For example, in a specific embodiment, a is the power consumption impact factor of hard acceleration behavior, b is the power consumption impact factor of idling behavior, c is the power consumption impact factor of different speed ranges, d is the power consumption impact factor of air conditioner on duration, and E history50 is the historical power consumption per 100 kilometers; a endurance calculation model is constructed using the above data. Further, in the embodiment, the corresponding power consumption impact factors can be increased according to the increase of vehicle working conditions to calculate the endurance model, such as the power consumption impact factor corresponding to the proportion duration of hard braking working condition, the power consumption impact factor corresponding to the on duration of in-vehicle intelligent terminal, the power consumption impact factor corresponding to the headlight on working condition, etc. Furthermore, each power consumption impact factor in the above embodiment is the power consumption impact factor within the current sub-trip, that is, corresponding to the working condition of the current sub-trip; as above, the vehicle trip is divided into multiple sub-trips, the vehicle data within each sub-trip is collected to obtain the power consumption impact factors corresponding to different working conditions, and the endurance of each sub-trip is calculated, considering the impact of different working conditions on endurance calculation.

[0062] By establishing an endurance calculation model, using the battery health, each impact factor, and the current remaining power of the vehicle to calculate the vehicle endurance, considering the power consumption impact of different working conditions during the vehicle trips in different states. By calculating the vehicle endurance through this model, accurate vehicle endurance can be obtained.

[0063] Reference Figure 2, during a vehicle trip, data is collected at the vehicle end, the trip is divided under different vehicle states, and the trips are merged. The vehicle trip is divided into small trips at 5-minute time intervals, and the power consumption impact factors of sharp acceleration conditions, idle conditions, speed distribution, and air-conditioning on-time are calculated. The battery health is calculated based on the remaining battery power. The current per-100-kilometer power consumption is calculated based on the average value of the historical per-100-kilometer power consumption and each power consumption impact factor. Finally, the power endurance value of the vehicle is calculated based on the current battery power, battery health, and current per-100-kilometer power consumption of the vehicle.

[0064] In the above embodiment, the purpose of dividing the vehicle small trips at a predetermined time interval and dividing the trips under different vehicle states is to make the data collected under various vehicle conditions more accurate, so that the calculation of the vehicle's endurance can be more accurate. Further, the predetermined time interval in the above embodiment is generally in minutes, for example, 5 minutes, 10 minutes, and the predetermined time interval can be dynamically adjusted according to the driver's driving habits. For example, for vehicles that often drive long distances, the predetermined time interval can be set to 20 minutes or longer; for vehicles that often drive short distances, such as household vehicles for commuting and grocery shopping, the time interval can be set to 5 minutes. Specifically, the predetermined time interval is not a fixed value but can be changed according to the driving habits.

[0065] In addition, in the above embodiment, the vehicle data can be collected by the in-vehicle computer through the CAN bus from the built-in ECU and from sensing devices such as vehicle radars and cameras. The method of obtaining vehicle data is not specifically limited.

[0066] It should be noted that the current per-100-kilometer power consumption is substantially calculated based on the data corresponding to the current small trip (including the average value of the historical per-100-kilometer power consumption, the first, second, third, and fourth power consumption impact factors). And this current per-100-kilometer power consumption represents the per-100-kilometer power consumption within a period of time (such as several seconds to several minutes) after the end of the current small trip.

[0067] By dividing the vehicle trips separately according to different vehicle states; among them, the vehicle trips in each different vehicle state are divided into several small trips at a predetermined time interval, and the vehicle data in each small trip is collected, so that the working condition data of the vehicle under different states can be accurately collected, providing accurate data for the calculation of the vehicle's power endurance. Further, by obtaining the actual power consumption of the vehicle and calculating the battery health based on the actual power consumption of the vehicle; and obtaining the average value of the historical per-100-kilometer power consumption of the vehicle trip and calculating the current per-100-kilometer power consumption based on the average value of the historical per-100-kilometer power consumption of the vehicle trip and the power consumption impact factors, calculating the battery health and the current per-100-kilometer power consumption can calculate the vehicle's endurance more accurately.

[0068] Further, the method for calculating the power endurance of a new energy vehicle provided by the embodiment of the present invention further includes:

[0069] In step S1, the vehicle states include: driving state, charging state, parking state, and wake-up state; among them, the vehicle states are divided according to the driver's behavior and the vehicle power-on state; and,

[0070] Fragmented integration is performed on each vehicle state according to the driver's behavior and the vehicle usage scenario.

[0071] For example, in a specific embodiment, the vehicle state is refined according to the process of the driver getting on and off the vehicle and the signal values such as the battery state, in-vehicle computer state, and powertrain state collected by the Tbox. The vehicle state is divided into a driving state (at this time, the vehicle battery is powered on, the in-vehicle computer works normally, and the powertrain works), a charging state (at this time, the vehicle battery is in a charging state, the in-vehicle computer does not work, and the powertrain does not work), a parking state (waiting for someone, getting off midway, resting in the car, etc.) (at this time, the vehicle battery is powered on, the in-vehicle computer works, and the powertrain does not work), an active wake-up state of the vehicle, and a passive wake-up state of the vehicle (the active wake-up and passive wake-up states of the vehicle refer to the wake-up methods of the network management (AUTOSAR) in the in-vehicle computer); further, according to the vehicle usage scenario (such as scenarios where passengers get off temporarily, open and close the door, and get on and off multiple times) and the driver's behavior and possible signal mutations, etc., the fragmented trips are classified and merged to reduce the complete vehicle user usage scenario being divided into multiple segments due to special fragmented trips such as the vehicle user getting off temporarily, repeatedly closing the door, getting on and off multiple times, and abnormal charging. In the above specific embodiment, the merging rule is: the parking segment and the vehicle wake-up segment adjacent to the driving segment are merged into the driving segment, and the parking segment and the vehicle wake-up segment adjacent to the charging segment are merged into the charging segment.

[0072] For example, in the above embodiment, fragmented integration refers to integrating the behaviors of the vehicle user getting on and off temporarily, opening and closing the door, getting on and off multiple times, and having a short abnormal charging during the vehicle trip. The temporary getting on and off or temporary parking in the driving segment can be merged into the driving segment, and the short abnormal charging and the parking segment adjacent to the charging segment are merged into the charging segment. Those skilled in the art should understand that fragmented integration can also be achieved by integrating adjacent front and rear segments, etc.

[0073] By dividing according to the driver's behavior and the vehicle power-on state; and, performing fragmented integration on each vehicle state according to the driver's behavior and the vehicle usage scenario, the vehicle state can be divided, which can ensure data collection of the vehicle in different states and facilitate more accurate calculation of the vehicle's endurance.

[0074] The embodiment of the present invention provides a method for calculating the power endurance of a new energy vehicle, including:

[0075] In step S2, calculating the battery health based on the actual power consumption of the vehicle includes:

[0076] Taking the time interval between two adjacent charges of the vehicle as a cycle, obtaining the battery power data within each cycle in multiple cycles, comparing the actual power consumption of the vehicle with the power consumption value calculated theoretically using the battery discharge state, and obtaining the power achievement rate of the vehicle; obtaining the power achievement rates of each cycle in multiple cycles and calculating the average value as the battery health.

[0077] For example, in a specific embodiment, taking the time data between two charges of the vehicle as a cycle, comparing the actual power consumption of the vehicle with the power calculated theoretically by converting the SOC (state of charge of the battery), obtaining the power achievement rate of the user, and taking the average value of the power achievement rates of each charging cycle as the battery health SOH. Further, the specific method of converting the power by SOC (state of charge of the battery) is the ampere-hour integration method (also called the current integration method or the coulomb counting method). That is, when the battery is charged and discharged, the SOC is estimated by accumulating the charged and discharged power. Furthermore, the power calculated by the above method is the theoretical power. In this embodiment, the actual power consumption is calculated and compared with the theoretical power consumption to obtain the power achievement rate. The average power achievement rate of multiple cycles can be used as the battery health. In this embodiment, the measurement of the actual power consumption of the vehicle can be measured through the charging pile data or the sensors in the vehicle head unit.

[0078] By taking the time interval between two adjacent charges of the vehicle as a cycle, obtaining the battery power data within each cycle in multiple cycles, comparing the actual power consumption of the vehicle with the power consumption value calculated theoretically using the battery discharge state, and obtaining the power achievement rate of the vehicle; obtaining the power achievement rates of each cycle in multiple cycles and calculating the average value as the battery health, the calculation of the vehicle battery health can be ensured to be accurate. Taking the average value of the battery health of multiple cycles provides the most accurate battery health for the vehicle range calculation and ensures the accuracy of the vehicle range calculation.

[0079] An embodiment of the present invention provides a method for calculating the power range of a new energy vehicle, including:

[0080] In step S2, calculating the current per 100 km power consumption according to the average value of the historical per 100 km power consumption of the vehicle's journey and the first, second, third, and fourth power consumption influencing factors includes:

[0081] Obtaining the per 100 km power consumption in the recent 50 km, and obtaining the per 100 km power consumption of each small journey in the per 100 km power consumption in the recent 50 km and calculating the average value as the current per 100 km power consumption.

[0082] In this embodiment, the power consumption per 100 km for the nearest 50 km is the power consumption per 100 km for the nearest 50 km in the previous trip, and this value is continuously updated. It can be understood that in other specific embodiments, the power consumption per 100 km for the nearest 50 km can also be adjusted to the power consumption per 100 km for 25 km or 75 km according to the length of the trip.

[0083] For example, if the vehicle is used for long-distance transportation or passenger carrying and needs to drive long distances frequently, the above data can be adjusted to 100 km or higher to collect the power consumption per 100 km of the vehicle; if the vehicle is often used for short distances, the data can be appropriately reduced to collect and calculate the power consumption per 100 km. In the above embodiments, obtaining the power consumption per 100 km can be based on the data displayed on the in-vehicle computer, and the in-vehicle computer can update and calculate the power consumption per 100 km in real time.

[0084] By obtaining the power consumption per 100 km for the nearest 50 km, and obtaining the power consumption per 100 km for each small trip within the power consumption per 100 km for the nearest 50 km and calculating the average value as the current power consumption per 100 km, it is possible to ensure the real-time update of the current power consumption per 100 km, making the accuracy of the endurance calculation model higher.

[0085] An embodiment of the present invention provides a method for calculating the power endurance of a new energy vehicle, including:

[0086] In step S1, the first power consumption influencing factor is determined according to the following method:

[0087] Statistically determine whether there is an emergency acceleration behavior within the current small trip, and use the power consumption per 100 km of the trips without emergency acceleration behavior in the historical data as a benchmark, compare the power consumption per 100 km of the trips with emergency acceleration behavior in the historical data, and calculate the first power consumption influencing factor based on the power consumption per 100 km of the trips without emergency acceleration behavior in the historical data and the power consumption per 100 km of the trips with emergency acceleration behavior in the historical data. Specifically, the first power consumption factor is calculated by the following formula:

[0088]

[0089] Where:

[0090] a is the first power consumption influencing factor;

[0091] E(acc_tag = 0) is the power consumption per 100 km of the trips without emergency acceleration behavior in the historical data;

[0092] E(acc_tag = 1) is the power consumption per 100 km of the trips with emergency acceleration behavior in the historical data.

[0093] For example, in a specific embodiment, the definition of a rapid acceleration behavior is as follows: the acceleration pedal is greater than a certain threshold and the acceleration exceeds the normal range (acceleration > 0.3G). If it occurs, acc_tag = 1; if it does not occur, acc_tag = 0. Further, it is statistically determined whether a rapid acceleration behavior occurs during a 5-minute journey. Taking the power consumption per 100 kilometers of the journey without a rapid acceleration behavior as a benchmark, the power consumption per 100 kilometers of the journey with a rapid acceleration behavior is calculated by comparing it, and thus the power consumption per 100 kilometers of the rapid acceleration behavior is obtained. Furthermore, the division time of the journey in the above embodiment can be different and can be flexibly adjusted according to the actual situation.

[0094] In the above embodiment, the rapid acceleration behavior of the vehicle can be detected through vehicle sensors or the ECU on the vehicle head unit. By comparing the power consumption per 100 kilometers of the obtained rapid acceleration behavior with that of the journey without a rapid acceleration behavior, the power consumption impact factor is obtained. All the above data can be calculated and obtained by the vehicle head unit. Using the above method to calculate the impact factor can ensure that the impact coefficient can be used to calculate the vehicle endurance model whether the vehicle has a rapid acceleration behavior or not, taking into account the impact of the rapid acceleration behavior on the vehicle endurance calculation.

[0095] By statistically determining whether a rapid acceleration behavior occurs within the current short journey and using the power consumption per 100 kilometers of the journey without a rapid acceleration behavior in the historical data as a benchmark, and comparing the power consumption per 100 kilometers of the journey with a rapid acceleration behavior in the historical data to obtain the first power consumption impact factor, the accuracy of the calculation of the first power consumption impact factor can be ensured. By comparing the rapid acceleration behavior with the absence of a rapid acceleration behavior to obtain the first power consumption impact factor, the calculation of the power consumption impact factor of the vehicle within the current short journey can be ensured to be accurate, and further, the vehicle endurance calculation is accurate.

[0096] An embodiment of the present invention provides a method for calculating the power endurance of a new energy vehicle, including:

[0097] In step S1, the second power consumption impact factor is determined according to the following method:

[0098] An idle non-linear model is established based on historical data to calculate the change trend of the power consumption per 100 kilometers under different proportions of idle working condition durations, and the proportion of the idle working condition of the current short journey is statistically determined; taking the power consumption per 100 kilometers of the non-idle working condition journey in the historical data as a benchmark, comparing the power consumption per 100 kilometers in the idle working condition proportion interval in the historical data, and calculating the second power consumption impact factor according to the power consumption per 100 kilometers in the idle working condition proportion interval in the historical data and the power consumption per 100 kilometers of the non-idle working condition journey in the historical data. Specifically, the second power consumption factor is calculated by the following formula:

[0099]

[0100] Where:

[0101] b is the second power consumption impact factor;

[0102] E 2 is the power consumption per 100 kilometers in the idle condition proportion interval in historical data;

[0103] E(Idle%=0) is the power consumption per 100 kilometers for non-idle condition trips in historical data.

[0104] For example, in a specific embodiment, the proportion of idle conditions in a 5-minute short trip is statistically analyzed (in this embodiment, the idle condition is the vehicle condition where the speed is equal to 0 during vehicle driving), a non-linear model framework is established to estimate the change trend of power consumption per 100 kilometers under different proportions of idle condition durations, and three indicators, namely the fitting coefficient (R2), root mean square error (RMSE), and sum of squared residuals (SSE), are used to select the optimal model E 2 = f(Idle%). Based on the power consumption per 100 kilometers for non-idle condition trips, the influence coefficient of the idle condition proportion is obtained by comparing the power consumption per 100 kilometers in the idle condition proportion interval; further, in this specific embodiment, the optimal model is the generalized additive model: the spline model is fitted by automatically selecting knots. In the non-linear regression model, the accuracy of selecting the most suitable fitting model is the same as that of the linear model, and the root mean square variance (RMSE), sum of squared residuals (SSE), and R square (fitting coefficient R2) are used. RMSE represents the model prediction error, that is, the average difference between the observed result value and the predicted result value. R2 represents the squared correlation between the observed and predicted result values. The best model is the one with the lowest RMSE, the lowest SSE, and the highest R2. The division time of the trips in the above embodiment can be different and can be flexibly adjusted according to the actual situation.

[0105] By establishing an idle non-linear model based on historical data to calculate the change trend of power consumption per 100 kilometers under different proportions of idle condition durations, and statistically analyzing the proportion of idle conditions in the current short trip; based on the power consumption per 100 kilometers for non-idle condition trips in historical data, comparing the power consumption per 100 kilometers in the idle condition proportion interval in historical data, the second power consumption influence factor can be obtained, which can ensure the accurate calculation of the power consumption influence factor within the current short trip of the vehicle, and further make the vehicle endurance calculation accurate.

[0106] An embodiment of the present invention provides a method for calculating the power endurance of a new energy vehicle, including:

[0107] In step S1, the third power consumption influence factor is determined according to the following method:

[0108] Statistically analyze the average speed of several short trips and divide them into at least three speed intervals, and respectively statistically analyze the power consumption per 100 kilometers corresponding to each speed interval; establish a speed non-linear model based on historical data to calculate the change trend of power consumption per 100 kilometers within each speed interval, and obtain the speed interval of the current short trip to obtain the power consumption per 100 kilometers of the speed interval of the current short trip;

[0109] Based on the electricity consumption per 100 kilometers in the most economical speed range among at least three speed ranges in historical data, compare the electricity consumption per 100 kilometers in the speed range of the current short trip, and calculate the third electricity consumption impact factor according to the electricity consumption per 100 kilometers in the speed range of the current short trip and the electricity consumption per 100 kilometers in the most economical speed range among at least three speed ranges in historical data. Specifically, the third electricity consumption factor is calculated by the following formula:

[0110]

[0111] Wherein:

[0112] c is the third electricity consumption impact factor;

[0113] E 3 is the electricity consumption per 100 kilometers in the speed range of the current short trip;

[0114] E(Veco) is the electricity consumption per 100 kilometers in the most economical speed range among at least three speed ranges in historical data.

[0115] For example, in a specific embodiment, the average speed of a 5-minute trip is divided into four ranges: V1 (0 - 20 km / h), V2 (20 - 40 km / h), V3 (40 - 60 km / h), V4 (60 - 80 km / h), V5 (above 80 km / h), and the corresponding electricity consumption per 100 kilometers is statistically analyzed. A non-linear model framework is established to estimate the change trend of electricity consumption per 100 kilometers in different speed ranges, and three indicators, namely the fitting coefficient (R2), root mean square error (RMSE), and sum of squared residuals (SSE), are used to select the optimal model E 3 = f(V i), i = 1, 2, 3, 4. Based on the electricity consumption per 100 kilometers in the most economical speed range (set as V eco), the electricity consumption impact factor is obtained by comparing the electricity consumption per 100 kilometers in different speed ranges; further, the speed range can be divided into four, six or three segments (low-speed segment, medium-speed segment, high-speed segment) according to speed, and the different speed ranges can be adjusted according to different situations. For example, if the vehicle driver often drives at high speed, the upper limit of the low-speed range can be set higher, such as 30 - 50 km / h for the low-speed segment; if the vehicle driver often drives in the urban area, the upper limit of the vehicle speed range can be set lower; furthermore, in this specific embodiment, the optimal model is the generalized additive model: the spline model is fitted by automatically selecting knots. In the non-linear regression model, the accuracy of selecting the most suitable fitting model is the same as that of the linear model, and the root mean square error (RMSE), sum of squared residuals (SSE) and R-squared (R2) are used. RMSE represents the model prediction error, that is, the average difference between the observed result value and the predicted result value. R2 represents the squared correlation between the observed and predicted result values. The best model is the one with the lowest RMSE, the lowest SSE and the highest R2. In the above embodiments, the division time of the journey can be different and can be flexibly adjusted according to the actual situation.

[0116] By taking the electricity consumption per 100 kilometers in the most economical speed range among at least three speed ranges in historical data as a benchmark, and comparing the electricity consumption per 100 kilometers in the speed range of the current small journey, the third electricity consumption impact factor can be obtained, which can ensure the accurate calculation of the electricity consumption impact factor within the current small journey of the vehicle, and further, make the vehicle endurance calculation accurate.

[0117] An embodiment of the present invention provides a method for calculating the electricity endurance of a new energy vehicle, including:

[0118] In step S1, the fourth electricity consumption impact factor is determined according to the following method:

[0119] An air-conditioning non-linear model is established based on historical data to calculate the change trend of the electricity consumption per 100 kilometers under different proportions of air-conditioning on-time, and the proportion of air-conditioning on-time in the current small journey is counted to obtain the electricity consumption per 100 kilometers in the proportion range of air-conditioning on-time in the current small journey;

[0120] Taking the electricity consumption per 100 kilometers in the non-air-conditioning-on journey in historical data as a benchmark, comparing the electricity consumption per 100 kilometers in the proportion range of air-conditioning on-time in the current small journey, and calculating the fourth electricity consumption impact factor according to the electricity consumption per 100 kilometers in the non-air-conditioning-on journey in historical data and the electricity consumption per 100 kilometers in the proportion range of air-conditioning on-time in the current small journey. Specifically, the fourth electricity consumption factor is calculated by the following formula:

[0121]

[0122] Where:

[0123] d is the fourth power consumption impact factor

[0124] E(AC% = 0) is the power consumption per 100 kilometers for non-air-conditioning trips in historical data;

[0125] E 4 is the power consumption per 100 kilometers for the proportion interval of the air-conditioning on-time in the current short trip.

[0126] For example, in a specific embodiment, the proportion of the air-conditioning on-time in a 5-minute short trip is statistically analyzed, and a non-linear model framework is established to estimate the change trend of the power consumption per 100 kilometers under different proportions of the air-conditioning on-time. Three indicators, namely the fitting coefficient (R2), root mean square error (RMSE), and sum of squared residuals (SSE), are used to select the optimal model E 4 = f(AC%). Based on the power consumption per 100 kilometers for non-air-conditioning trips in historical data, the power consumption impact factor is obtained by comparing the power consumption per 100 kilometers for the proportion interval of the air-conditioning on-time. Further, in this specific embodiment, the optimal model is the generalized additive model: the spline model is fitted by automatically selecting knots. In the non-linear regression model, the accuracy of selecting the most suitable fitting model is the same as that of the linear model, and the root mean square variance (RMSE), sum of squared residuals (SSE), and R square (R2) are used. RMSE represents the model prediction error, that is, the average difference between the observed result value and the predicted result value. R2 represents the squared correlation between the observed and predicted result values. The best model is the one with the lowest RMSE, the lowest SSE, and the highest R2. The division time of the trips in the above embodiments can be different and can be flexibly adjusted according to the actual situation.

[0127] By taking the power consumption per 100 kilometers for non-air-conditioning trips in historical data as a benchmark and comparing the power consumption per 100 kilometers for the proportion interval of the air-conditioning on-time in the current short trip to obtain the fourth power consumption impact factor, it can ensure the accurate calculation of the power consumption impact factor within the current short trip of the vehicle. Further, it makes the vehicle endurance calculation accurate.

[0128] It should be noted that the historical data used for calculating the first, second, third, and fourth power consumption impact factors are all the data corresponding to the current short trip.

[0129] An embodiment of the present invention provides a method for calculating the power endurance of a new energy vehicle, referring to Figure 3 , including:

[0130] After step S3, it further includes:

[0131] S4: The vehicle-mounted terminal of the new energy vehicle sends the collected vehicle data, as well as the vehicle's idle non-linear model, speed non-linear model, and air-conditioning non-linear model and related data to the big data platform;

[0132] The big data platform conducts model training based on multiple idle non - linear models, multiple speed non - linear models, multiple air - conditioner non - linear models and related data collected. The trained new models are pushed to the corresponding in - vehicle terminals according to the vehicle identification number. The in - vehicle terminal calculates the cruising range based on the currently collected data and the battery health, and then sends the newly collected data to the big data platform again, forming a closed - loop model.

[0133] For example, in a specific embodiment, the big data analysis platform trains model parameters such as battery attenuation degree, and the proportion of rapid acceleration, idle time, speed distribution, and air - conditioner operation time affecting power consumption according to vehicle historical data. Further, the big data platform regularly pushes the trained models and the remaining battery power to the corresponding in - vehicle terminals according to the vehicle identification number (VIN). The in - vehicle terminal estimates the cruising range based on the power - consumption - affecting data and the remaining battery power collected at the current moment. The data collected by the in - vehicle terminal enters the big data platform again, and the big data platform optimizes the model according to the newly collected vehicle data. The optimized model is pushed to the in - vehicle terminal again, forming an effective closed - loop, achieving one model for one vehicle.

[0134] By sending the data collected by the in - vehicle terminal to the big data platform, the big data platform conducts training on multiple models after receiving the data, pushes the trained models to the corresponding in - vehicle terminals according to the vehicle identification number, the in - vehicle terminal calculates the vehicle cruising range based on the data, and pushes the newly collected data to the big data platform again. It can ensure that the vehicle's cruising - range calculation model is continuously optimized after being trained by the big data platform, accurate cruising - range calculation can be obtained, and the real - time update of the vehicle's cruising - range calculation is guaranteed.

[0135] This embodiment divides the vehicle status and divides the trip into trips in different statuses, refines the vehicle status and merges fragmented trips, and considers and processes parking conditions and driving conditions that are not driving conditions but affect power consumption according to vehicle usage scenarios, user behaviors, and possible signal mutations. The model and data are continuously optimized through the big data platform, and the battery attenuation degree and the vehicle's historical conditions (historical 50km power consumption), real-time conditions (speed distribution), driving habits (idling conditions, sudden acceleration behaviors), vehicle environment (percentage of air conditioning on time) and other factors that affect power consumption are comprehensively considered, so that the vehicle's battery life calculation is more accurate and real-time updating of vehicle data is guaranteed. The trip is divided into small trips at predetermined time intervals, and the vehicle data in each small trip is obtained. The power consumption influencing factors corresponding to each working condition are calculated, and the battery health and the current power consumption per 100 kilometers are calculated. The above data are used to calculate the vehicle's battery life. The impact of various working conditions on the vehicle's battery life during driving is fully considered, and the vehicle's battery life in each small trip is calculated. The big data platform is linked with the vehicle computer to make the data more accurate and the model more stable. This method of calculating vehicle battery life can calculate the most accurate vehicle battery life in real time, which can reduce the inaccurate battery life calculation caused by inaccurate battery life calculation during driving. For example, when a vehicle is driving on a highway, the vehicle trip is divided into small trips at intervals of 15 minutes, and the data of the vehicle in the small trip is collected, including: sudden acceleration behavior, idling behavior, speed range, air conditioning on time, power consumption per 100 kilometers, and battery health; the remaining power of the vehicle is calculated by using the endurance calculation model of this embodiment through the collected data, that is, the real-time power endurance can be obtained within a small trip of 15 minutes, which can remind the driver whether charging is needed, and avoid the possibility that inaccurate endurance calculation may cause the driver to misjudge the charging time, affecting the vehicle driving and the driver's judgment.

[0136] Example 2:

[0137] The embodiment of the present invention provides a method for calculating the battery life of a new energy vehicle, referring to Figure 4 , comprising: an electronic device, comprising: a processor, and a memory connected to the processor in communication;

[0138] Memory stores computer-executable instructions;

[0139] The processor executes the computer execution instructions stored in the memory to implement the above-mentioned method for calculating the battery life of new energy vehicles.

[0140] Figure 4 is a schematic diagram of the structure of the electronic device provided in the embodiment of the present application. Figure 4 As shown in , the electronic device may include: a transceiver 121, a processor 122, and a memory 123. ​

[0141] The processor 122 executes computer-executable instructions stored in the memory, enabling the processor 122 to execute the solutions in the above embodiments. The processor 122 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0142] The memory 123 is connected to the processor 122 via the system bus and completes communication therebetween. The memory 123 is used to store computer program instructions.

[0143] The transceiver 121 can be used to obtain the task to be run and the configuration information of the task to be run.

[0144] Embodiment 3:

[0145] An embodiment of the present invention provides a computer-readable storage medium. Computer-executable instructions are stored in the computer-readable storage medium. When the computer-executable instructions are executed by a processor, they are used to implement the above-mentioned method for calculating the power endurance of a new energy vehicle.

[0146] Although the present invention has been illustrated and described by referring to some preferred embodiments of the present invention, those of ordinary skill in the art should understand that the above content is a further detailed description of the present invention in combination with specific embodiments, and it cannot be determined that the specific implementation of the present invention is limited only to these descriptions. Those skilled in the art can make various changes in form and detail, including making several simple deductions or substitutions, without departing from the spirit and scope of the present invention.

Claims

1. A method for calculating the battery life of a new energy vehicle, characterized in that: The following steps are involved: S1: Acquire the vehicle state, and divide the vehicle trip according to different vehicle states; wherein the vehicle trip under each different vehicle state is divided into a number of small trips at predetermined time intervals, and the vehicle data in each small trip is collected; wherein Acquire the sudden acceleration behavior data of the current short stroke, and determine the first power consumption influencing factor according to the sudden acceleration behavior data of the current short stroke; Acquiring idle behavior data of the current short trip, and determining a second power consumption influencing factor according to the idle behavior data of the current short trip; Acquire different speed interval data of the current small stroke, and determine a third power consumption influencing factor according to the speed interval data of the current small stroke; Acquiring the air conditioning start-up data of the current short trip, and determining a fourth power consumption influencing factor according to the air conditioning start-up data of the current short trip; S2: obtaining actual power consumption of the vehicle, and calculating the battery health according to the actual power consumption of the vehicle; and Obtaining an average value of the historical power consumption per 100 kilometers of the vehicle's travel, and calculating the current power consumption per 100 kilometers based on the average value of the historical power consumption per 100 kilometers of the vehicle's travel and the first, second, third, and fourth power consumption influencing factors; S3: constructing a cruising range calculation model according to the current battery power of the vehicle, the battery health, and the current power consumption per 100 kilometers, and calculating and determining the current power cruising range value of the new energy vehicle.

2. The method for calculating the battery life of a new energy vehicle according to claim 1, characterized in that: In step S1, the vehicle state includes: driving state, charging state, parking state, and awake state; wherein the vehicle state is divided according to the driver's behavior and the vehicle power-on state; and, According to the driver's behavior and vehicle usage scenario, each vehicle status is integrated in a fragmented manner.

3. The method for calculating the battery life of a new energy vehicle according to claim 1, characterized in that: In step S2, calculating the battery health according to the actual power consumption of the vehicle includes: Taking the time interval between two adjacent charges of the vehicle as a cycle, obtaining the battery power data in each of the multiple cycles, comparing the actual power consumption of the vehicle with the power consumption value theoretically calculated using the battery discharge state, to obtain the power achievement rate of the vehicle; obtaining the power achievement rate of each cycle in the multiple cycles and calculating the average value as the battery health.

4. The method for calculating the battery life of a new energy vehicle according to claim 1, characterized in that: In the step S2, the current power consumption per 100 kilometers is calculated according to the average value of the historical power consumption per 100 kilometers of the vehicle travel and the first, second, third and fourth power consumption influencing factors, including: The power consumption per 100 kilometers in the latest 50 km is obtained, and the power consumption per 100 kilometers of each of the short trips in the power consumption per 100 kilometers in the latest 50 km is obtained and the average value is calculated as the current power consumption per 100 kilometers.

5. The method for calculating the battery life of a new energy vehicle according to claim 1, wherein in step S1, the first power consumption influencing factor is determined according to the following method: Statistics are collected on whether sudden acceleration occurs in the current short trip, and the electricity consumption per 100 kilometers of a trip without sudden acceleration in historical data is used as a benchmark to compare the electricity consumption per 100 kilometers of a trip with sudden acceleration in historical data, and the first electricity consumption influencing factor is calculated based on the electricity consumption per 100 kilometers of a trip without sudden acceleration in historical data and the electricity consumption per 100 kilometers of a trip with sudden acceleration in historical data.

6. The method for calculating the battery life of a new energy vehicle according to claim 1, wherein in step S1, the second power consumption influencing factor is determined according to the following method: Based on the historical data, an idle nonlinear model is established to calculate the changing trend of the electricity consumption per 100 kilometers under different idle condition time proportions, and the idle condition proportion of the current short trip is counted; taking the electricity consumption per 100 kilometers of non-idle condition trips in the historical data as a benchmark, the electricity consumption per 100 kilometers in the idle condition proportion interval in the historical data is compared, and the second electricity consumption influencing factor is calculated based on the electricity consumption per 100 kilometers in the idle condition proportion interval in the historical data and the electricity consumption per 100 kilometers of non-idle condition trips in the historical data.

7. The method for calculating the battery life of a new energy vehicle according to claim 1, wherein in step S1, the third power consumption influencing factor is determined according to the following method: The average speeds of the several short trips are counted and divided into at least three speed intervals, and the power consumption per 100 kilometers corresponding to each speed interval is counted respectively; a speed nonlinear model is established according to historical data to calculate the change trend of the power consumption per 100 kilometers in each speed interval, and the speed interval of the current short trip is obtained to obtain the power consumption per 100 kilometers in the speed interval of the current short trip; Taking the electricity consumption per 100 kilometers in the most economical speed range among at least three speed ranges in the historical data as a benchmark, the electricity consumption per 100 kilometers in the speed range of the current short trip is compared, and the third electricity consumption influencing factor is calculated based on the electricity consumption per 100 kilometers in the speed range of the current short trip and the electricity consumption per 100 kilometers in the most economical speed range among at least three speed ranges in the historical data.

8. The method for calculating the battery life of a new energy vehicle according to claim 1, wherein in step S1, the fourth power consumption influencing factor is determined according to the following method: Based on historical data, a nonlinear air-conditioning model is established to calculate the changing trend of the power consumption per 100 kilometers under different air-conditioning on-time ratios, and the air-conditioning on-time ratio of the current short trip is counted to obtain the power consumption per 100 kilometers in the air-conditioning on-time ratio interval of the current short trip; Taking the electricity consumption per 100 kilometers of non-air-conditioning trips in historical data as a benchmark, compare the electricity consumption per 100 kilometers in the interval of the air-conditioning on time ratio of the current short trip, and calculate the fourth electricity consumption influencing factor based on the electricity consumption per 100 kilometers of non-air-conditioning trips in historical data and the electricity consumption per 100 kilometers in the interval of the air-conditioning on time ratio of the current short trip.

9. The method for calculating the battery life of a new energy vehicle according to claim 1, characterized in that: After step S3, the method further includes: S4: The vehicle computer terminal of the new energy vehicle sends the collected vehicle data, the idle nonlinear model, the speed nonlinear model, the air conditioning nonlinear model and related data of the vehicle to the big data platform; The big data platform performs model training based on the collected multiple idle nonlinear models, multiple speed nonlinear models, multiple air-conditioning nonlinear models and related data, and pushes the trained new model to the corresponding vehicle terminal according to the vehicle identification code. The vehicle terminal calculates the cruising range based on the currently collected data and the battery health, and sends the newly collected data to the big data platform again to form a closed-loop model.

10. An electronic device, characterized in that: include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the new energy vehicle endurance calculation method as described in any one of claims 1-9.

11. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the new energy vehicle range calculation method as described in any one of claims 1 to 9.

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