Vehicle remaining cruising range estimation method, device, storage medium and electronic device

Abstract

The present invention discloses a method, device, storage medium and electronic device for estimating the remaining cruising range of a vehicle. The method comprises: obtaining historical usage data of a target vehicle in at least one driving cycle, wherein the historical usage data includes vehicle navigation information, vehicle driving information, ambient temperature information and average power consumption information corresponding to at least one driving cycle; performing operating condition prediction based on the historical usage data to obtain a target prediction result; calculating a first power consumption value of the target vehicle using the target prediction result, wherein the first power consumption value is used to represent the comprehensive power consumption of the target vehicle; determining the remaining cruising range of the target vehicle according to the first energy value and the first power consumption value, wherein the first energy value is used to represent the battery available energy of the target vehicle in the current state. The present invention solves the technical problem of low accuracy in estimating the remaining cruising range of a vehicle in the related art.

Description

Technical Field

[0001] The present invention relates to the field of vehicle technology, and in particular to a method, device, storage medium and electronic device for estimating the remaining cruising range of a vehicle. Background Art

[0002] The range of electric vehicles is affected by many factors, including driving conditions and temperature. Therefore, it is very important for drivers to accurately display the remaining range. At present, the calculation of the remaining range of vehicles mostly adopts national standard conditions and normal temperature conditions, which is quite different from the actual driving range of users. In addition, in terms of the remaining range display strategy, the remaining range initially displayed when the vehicle is fully charged is generally in accordance with the value advertised by the national standard. As the power is consumed during driving, the remaining range decreases proportionally to the power consumption under the normal temperature national standard conditions. However, the initial value and change value of the remaining range of the vehicle in the above method are quite different from the actual mileage of the user, and cannot reflect the true remaining range.

[0003] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0004] Embodiments of the present invention provide a method, device, storage medium and electronic device for estimating the remaining cruising range of a vehicle, so as to at least solve the technical problem of low accuracy in estimating the remaining cruising range of a vehicle in the related art.

[0005] According to one embodiment of the present invention, a method for estimating a vehicle's remaining cruising range is provided, comprising: obtaining historical usage data of a target vehicle in at least one driving cycle, wherein the historical usage data includes vehicle navigation information, vehicle travel information, ambient temperature information, and average power consumption information corresponding to at least one driving cycle; performing operating condition prediction based on the historical usage data to obtain a target prediction result; calculating a first power consumption value of the target vehicle using the target prediction result, wherein the first power consumption value is used to represent the comprehensive power consumption level of the target vehicle; and determining the remaining cruising range of the target vehicle based on a first energy value and the first power consumption value, wherein the first energy value is used to represent the battery available energy of the target vehicle in the current state.

[0006] Optionally, performing operating condition prediction based on historical usage data to obtain a target prediction result includes: performing operating condition prediction on the historical usage data using a Markov prediction model to obtain a target prediction result.

[0007] Optionally, calculating the first power consumption value of the target vehicle using the target prediction result includes: determining a second power consumption value based on the target prediction result, and determining a third power consumption value based on average power consumption information, wherein the second power consumption value is used to represent the predicted power consumption level of the target vehicle, and the third power consumption value is used to represent the historical power consumption level of the target vehicle; determining the first power consumption value based on the second power consumption value, the third power consumption value and a preset calibration coefficient.

[0008] Optionally, determining the second power consumption value based on the target prediction result includes: determining the instantaneous power of the battery of the target vehicle based on the target prediction result; integrating the instantaneous power of the battery to obtain a second energy value, wherein the second energy value is used to represent the total battery energy consumed by the target vehicle during driving; and determining the second power consumption value using the second energy value and the target driving distance.

[0009] Optionally, determining the instantaneous power of the battery of the target vehicle based on the target prediction result includes: acquiring a plurality of attribute parameters corresponding to the target vehicle; and determining the instantaneous power of the battery based on the plurality of attribute parameters and the target prediction result.

[0010] Optionally, the multiple attribute parameters include: vehicle mass, rolling resistance coefficient, wind resistance coefficient, frontal area, and vehicle rotation conversion coefficient.

[0011] Optionally, the vehicle remaining cruising range estimation method also includes: obtaining a battery state of charge value and a third energy value of the target vehicle, wherein the third energy value is used to represent the available battery energy of the target vehicle when it is fully charged; and determining the first energy value using the battery state of charge value and the third energy value.

[0012] According to one embodiment of the present invention, a vehicle remaining cruising range estimation device is also provided, including: an acquisition module, used to obtain historical usage data of a target vehicle in at least one driving cycle, wherein the historical usage data includes vehicle navigation information, vehicle driving information, ambient temperature information and average power consumption information corresponding to at least one driving cycle; a prediction module, used to perform operating condition prediction based on the historical usage data to obtain a target prediction result, wherein the target prediction result is used to determine the instantaneous power of the battery of the target vehicle; a determination module, used to determine the remaining cruising range of the target vehicle according to the target prediction result.

[0013] Optionally, the prediction module is also used to: use a Markov prediction model to perform operating condition prediction on historical usage data to obtain a target prediction result.

[0014] Optionally, the determination module is also used to: determine a second power consumption value based on the target prediction result, and determine a third power consumption value based on the average power consumption information, wherein the second power consumption value is used to represent the predicted power consumption level of the target vehicle, and the third power consumption value is used to represent the historical power consumption level of the target vehicle; determine the first power consumption value based on the second power consumption value, the third power consumption value and a preset calibration coefficient.

[0015] Optionally, the determination module is also used to: determine the instantaneous power of the battery of the target vehicle based on the target prediction result; integrate the instantaneous power of the battery to obtain a second energy value, wherein the second energy value is used to represent the total battery energy consumed by the target vehicle during driving; and determine the second power consumption value using the second energy value and the target driving distance.

[0016] Optionally, the acquisition module is further used to: acquire multiple attribute parameters corresponding to the target vehicle; the determination module is further used to: determine the instantaneous power of the battery based on the multiple attribute parameters and the target prediction result.

[0017] Optionally, the multiple attribute parameters include: vehicle mass, rolling resistance coefficient, wind resistance coefficient, frontal area, and vehicle rotation conversion coefficient.

[0018] Optionally, the acquisition module is also used to: obtain the battery state of charge value and a third energy value of the target vehicle, wherein the third energy value is used to represent the available battery energy of the target vehicle when it is fully charged; the determination module is also used to: determine the first energy value using the battery state of charge value and the third energy value.

[0019] According to one embodiment of the present invention, a non-volatile storage medium is further provided, in which a computer program is stored, wherein the computer program is configured to execute any of the above-mentioned methods for estimating the remaining cruising range of a vehicle when running.

[0020] According to one embodiment of the present invention, there is also provided an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute any of the above-mentioned methods for estimating the remaining cruising range of a vehicle.

[0021] In an embodiment of the present invention, a method is adopted in which historical usage data of a target vehicle in at least one driving cycle is obtained, and an operating condition prediction is performed based on the historical usage data to obtain a target prediction result. By using the target prediction result to calculate a first power consumption value of the target vehicle and determining the remaining cruising range of the target vehicle based on the first energy value and the first power consumption value, the purpose of accurately estimating the remaining cruising range of the vehicle is achieved, thereby achieving the technical effect of improving the accuracy of the estimation of the remaining cruising range of the vehicle, and further solving the technical problem of low accuracy in estimating the remaining cruising range of the vehicle in the related art. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0023] Figure 1 is a flow chart of a method for estimating remaining cruising range of a vehicle according to one embodiment of the present invention;

[0024] Figure 2 is a schematic diagram of a vehicle remaining cruising range estimation system according to one embodiment of the present invention;

[0025] Figure 3 is a flow chart of another method for estimating the remaining cruising range of a vehicle according to one embodiment of the present invention;

[0026] Figure 4 The present invention is a structural block diagram of a vehicle remaining cruising range estimation device according to one embodiment of the present invention. DETAILED DESCRIPTION

[0027] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0028] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0029] According to an embodiment of the present invention, a method embodiment of a vehicle remaining cruising range estimation method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0030] The method embodiment can be executed in an electronic device or a similar computing device including a memory and a processor. Taking running on a vehicle terminal as an example, the vehicle terminal may include one or more processors (the processor may include but is not limited to a central processing unit (CPU), a graphics processing unit (GPU), a digital signal processing (DSP) chip, a microcontroller unit (MCU), a programmable logic device (Field Programmable Gate Array, FPGA), a neural network processor (Neural-network Processor Unit, NPU), a tensor processor (Tensor Processing Unit, TPU), an artificial intelligence (Artificial Intelligence, AI) type processor, etc.) and a memory for storing data. Optionally, the above-mentioned vehicle terminal may also include a transmission device, an input and output device, and a display device for communication functions. It can be understood by those of ordinary skill in the art that the above-mentioned structural description is only for illustration and does not limit the structure of the above-mentioned vehicle terminal. For example, the vehicle terminal may also include more or fewer components than the above-mentioned structural description, or have a configuration different from the above-mentioned structural description.

[0031] The memory can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the vehicle remaining cruising range estimation method in the embodiment of the present invention. The processor executes various functional applications and data processing by running the computer program stored in the memory, that is, the above-mentioned vehicle remaining cruising range estimation method is realized. The memory may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory may further include a memory remotely arranged relative to the processor, and these remote memories can be connected to the mobile terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0032] The transmission device is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the mobile terminal. In one example, the transmission device includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device can be a radio frequency (Radio Frequency, referred to as RF) module, which is used to communicate with the Internet wirelessly.

[0033] The display device may be, for example, a touch screen type liquid crystal display (LCD) and a touch display (also referred to as a "touch screen" or "touch display screen"). The liquid crystal display may enable a user to interact with a user interface of a mobile terminal. In some embodiments, the mobile terminal has a graphical user interface (GUI), and a user may interact with the GUI by finger contacts and / or gestures on a touch-sensitive surface, wherein the human-computer interaction functions here may optionally include the following interactions: creating web pages, drawing, word processing, making electronic documents, games, video conferencing, instant messaging, sending and receiving emails, call interfaces, playing digital videos, playing digital music and / or web browsing, etc. The executable instructions for executing the above human-computer interaction functions are configured / stored in a computer program product or readable storage medium executable by one or more processors.

[0034] Figure 1 is a flow chart of a method for estimating the remaining cruising range of a vehicle according to one embodiment of the present invention. Figure 1 As shown, the method comprises the following steps:

[0035] Step S10, obtaining historical usage data of the target vehicle in at least one driving cycle, wherein the historical usage data includes vehicle navigation information, vehicle driving information, ambient temperature information, and average power consumption information corresponding to at least one driving cycle;

[0036] In the above step S10, a driving cycle refers to the driving information of the target vehicle within a period of time. For example, the remaining mileage display mode in the instrument is divided into four modes: "urban", "suburban", "highway" and "comprehensive". The user sets the "urban" mode, then a driving cycle refers to the driving information of the target vehicle from the start to the end of the current "urban" mode. The above vehicle navigation information is used to record the vehicle starting position, vehicle end position and total distance of each driving cycle, where the vehicle starting position is recorded as An, the vehicle end position is recorded as Bn, and the total distance of each driving cycle is recorded as S totalnThe above vehicle driving information refers to the average vehicle speed and average acceleration in each driving cycle, where the average vehicle speed is recorded as The average vehicle acceleration is denoted by The above ambient temperature information refers to the average ambient temperature in each driving cycle, denoted as The above average power consumption information refers to the average power consumption in each driving cycle, recorded as EC avgn .

[0037] Specifically, historical usage data of the target vehicle in at least one driving cycle is obtained. Table 1 records the historical usage data of the target vehicle in the driving cycle. As shown in Table 1, the historical usage data of n driving cycles are recorded, wherein the usage data of each driving cycle includes vehicle navigation information, vehicle driving information, ambient temperature information and average power consumption information.

[0038] Step S12, performing operating condition prediction based on historical usage data to obtain a target prediction result;

[0039] Specifically, the operating condition prediction is performed based on the historical usage data to obtain the predicted speed value of the target vehicle.

[0040] Table 1 Historical usage data of target vehicle driving cycle

[0041]

[0042] Step S14, calculating a first power consumption value of the target vehicle using the target prediction result, wherein the first power consumption value is used to represent the comprehensive power consumption level of the target vehicle;

[0043] In the above step S14, the first power consumption value refers to the comprehensive power consumption of the target vehicle, which is recorded as EC_combined.

[0044] Step S16, determining the remaining cruising range of the target vehicle according to the first energy value and the first power consumption value, wherein the first energy value is used to represent the available battery energy of the target vehicle in the current state.

[0045] In the above step S16, the first energy value refers to the available energy of the battery pack of the current target vehicle, which is recorded as E_avail_now. The remaining cruising range of the above target vehicle refers to the mileage that can be continued to travel, which is usually displayed on the vehicle's dashboard or vehicle information system, recorded as S_avail_pre.

[0046] Specifically, the remaining cruising range of the target vehicle is calculated based on the first energy value and the first power consumption value. The specific calculation process is shown in expression (1).

[0047] S_avail_pre=E_avail_now / EC_combined (1)

[0048] Based on the above steps S10 to S16, the historical usage data of the target vehicle in at least one driving cycle is obtained, and the operating condition prediction is performed based on the historical usage data to obtain a target prediction result. By using the target prediction result to calculate the first power consumption value of the target vehicle and determining the remaining cruising range of the target vehicle according to the first energy value and the first power consumption value, the purpose of accurately estimating the remaining cruising range of the vehicle is achieved, thereby achieving the technical effect of improving the accuracy of the estimation of the remaining cruising range of the vehicle, and further solving the technical problem of low accuracy in estimating the remaining cruising range of the vehicle in the related art.

[0049] Optionally, in the above step S12, operating condition prediction is performed based on historical usage data to obtain a target prediction result including:

[0050] Step S121, using the Markov prediction model to perform operating condition prediction on the historical usage data to obtain a target prediction result.

[0051] In the above step S121 , the Markov prediction model can predict the predicted speed value of the target vehicle based on the historical usage data in the target vehicle driving cycle.

[0052] Specifically, first, the state transition probability matrix is ​​calculated, where the probability of the target vehicle's speed changing from the current speed state to the speed state at the next moment is the state transition probability, and the i-th row and j-th column element of the state transition probability matrix is ​​denoted as P i,j The target vehicle's speed is discretized into finite values ​​using the nearest neighbor method, as shown in expression (2). The target vehicle's speed is divided into several possible states, denoted as U i .

[0053] v s ∈{v1,v2,…,v N}(2)

[0054] Where N represents the number of discrete speeds of the target vehicle. For example, the target vehicle's speed is divided into 100 possible states, and the target vehicle's speed discrete interval is 5 km / h. The target vehicle's speed states are numbered and recorded as U k The target vehicle's speed is determined by the current speed state U i The vehicle speed state at the next moment is U j The probability of state transition probability P i,j , that is, at the current moment, the target vehicle's speed is v i At the next moment, the speed of the vehicle is v jThe probability of is calculated as shown in expression (3).

[0055] P i,j =P(v(k+1)=v j |v(k)=v i ) (3)

[0056] Among them, k represents the current moment, and k+1 is the next moment of the current moment.

[0057] Furthermore, P i,j The value can be obtained by maximum likelihood estimation, as shown in expression (4).

[0058]

[0059] Among them, F i,j Indicates that the target vehicle's speed changes from v i Transfer to v j The number of times, F i Indicates that the target vehicle's speed changes from v i The total number of transfers, i,j=0,1,…,N.

[0060] Calculate the transition probability and number of times from the current driving speed of the target vehicle to the next driving speed, and combine each state probability value to generate a Markov transition probability matrix P, as shown in expression (5).

[0061]

[0062] Secondly, determine the Markov prediction model for the target vehicle speed state probability. In this process, assuming that there are n independent target vehicle speed states, the initial speed state vector of the target vehicle is as shown in expression (6).

[0063] S(0)=[S1(0),S2(0),…,S m (0),…S n (0)] (6)

[0064] Among them, S m (0) represents the initial probability of the driving speed state of the mth target vehicle. After k steps of state transition, the probability of the driving speed state of the mth target vehicle is S m (k), therefore, the speed state vector of the target vehicle after k-step state transition is as shown in expression (7).

[0065] S(k)=[S1(k),S2(k),…,S m (k),…S n (k)] (7)

[0066] Among them, S m (k) represents the probability that the target vehicle is in the mth driving speed state at time k.

[0067] From the above, it can be obtained that the Markov prediction model of the target vehicle's driving speed state probability is shown in expression (8).

[0068]

[0069] Finally, based on the Markov prediction model and the solution of the state transfer matrix, the target prediction result, that is, the predicted speed value of the target vehicle, is obtained, where the predicted speed value of the target vehicle is shown in expression (9).

[0070] v(k)=[(U k -1)+r k ]d (9)

[0071] Where v(k) represents the speed of the target vehicle at time k, U k represents the target vehicle's driving speed state at time k, d represents the target vehicle's driving speed state division length, r k Represents a random number uniformly distributed at time k.

[0072] Based on the above step S121, the Markov prediction model is used to predict the operating conditions of the historical usage data to obtain the target prediction result, thereby achieving the purpose of accurately estimating the remaining cruising range of the vehicle, thereby achieving the technical effect of improving the accuracy of the remaining cruising range estimation of the vehicle.

[0073] Optionally, in the above step S14, calculating the first power consumption value of the target vehicle using the target prediction result includes:

[0074] Step S141, determining a second power consumption value based on the target prediction result, and determining a third power consumption value based on the average power consumption information, wherein the second power consumption value is used to represent the predicted power consumption level of the target vehicle, and the third power consumption value is used to represent the historical power consumption level of the target vehicle;

[0075] In the above step S141, the second power consumption value refers to the predicted power consumption of the target vehicle, which is recorded as EC_after. The above third power consumption value refers to the average power consumption of the last driving cycle, which is recorded as EC_history.

[0076] Step S142, determining the first power consumption value based on the second power consumption value, the third power consumption value and a preset calibration coefficient.

[0077] Specifically, the specific calculation process of determining the first power consumption value based on the second power consumption value, the third power consumption value and the preset calibration coefficient is shown in expression (10).

[0078] EC_combined=EC_history*η+EC_after*(1-η) (10)

[0079] Among them, η is calibrated according to the performance of the target vehicle, and the value range of η is 0<η<1.

[0080] Based on the above steps S141 to S142, the second power consumption value is determined based on the target prediction result, and the third power consumption value is determined based on the average power consumption information; the first power consumption value is determined based on the second power consumption value, the third power consumption value and the preset calibration coefficient, and the power consumption of the target vehicle is comprehensively considered, thereby further improving the accuracy of the vehicle's remaining cruising range estimation.

[0081] Optionally, in the above step S141, determining the second power consumption value based on the target prediction result includes:

[0082] Step S1411, determining the instantaneous power of the battery of the target vehicle based on the target prediction result;

[0083] Specifically, the power of the battery terminal is calculated at each moment according to the predicted speed value of the target vehicle, for example, the power of the battery terminal is updated every 0.1 s.

[0084] Step S1412, integrating the instantaneous power of the battery to obtain a second energy value, wherein the second energy value is used to represent the total battery energy consumed by the target vehicle during driving;

[0085] Step S1413: determine a second power consumption value using the second energy value and the target driving distance.

[0086] Specifically, the total battery energy consumed by the target vehicle during driving can be calculated by integrating the instantaneous power of the battery over time, that is, the second energy value, recorded as E_total. The above-mentioned target driving distance refers to the total distance traveled by the target vehicle, which can be determined based on Table 1. The second power consumption value EC_after is calculated based on the second energy value and the target driving distance, and the specific process is shown in Expression (11).

[0087] EC_after=E_total / S_totaln (11)

[0088] Based on the above steps S1411 to S1413, the instantaneous power of the battery of the target vehicle is determined based on the target prediction result; the instantaneous power of the battery is integrated to obtain a second energy value; the second power consumption value is determined using the second energy value and the target driving distance. By integrating the instantaneous power of the battery, the total battery energy consumed by the target vehicle during driving can be obtained. The second power consumption value is determined in combination with the target driving distance, and the remaining range of the target vehicle can be more accurately estimated.

[0089] Optionally, in the above step S1411, determining the instantaneous battery power of the target vehicle based on the target prediction result includes:

[0090] Step S1411A, obtaining multiple attribute parameters corresponding to the target vehicle;

[0091] Step S1411B: determining the instantaneous power of the battery based on multiple attribute parameters and the target prediction result.

[0092] Specifically, the specific calculation process of determining the battery instantaneous power based on multiple attribute parameters of the target vehicle and the target prediction result is shown in expression (12).

[0093]

[0094] Where m represents the weight of the target vehicle and g represents the acceleration due to gravity, which is usually 9.8 m / s 2 , f represents the rolling resistance coefficient, Cd represents the wind resistance coefficient, A represents the frontal area of ​​the target vehicle, v(i) represents the predicted speed value of the target vehicle at time i, δ represents the total vehicle rotation conversion coefficient of the target vehicle, and a(i) represents the acceleration of the target vehicle at time i.

[0095] Based on the above steps S1411A to S1411B, multiple attribute parameters corresponding to the target vehicle are obtained; the battery instantaneous power is determined based on the multiple attribute parameters and the target prediction results, which can more comprehensively calculate the battery instantaneous power and help improve the accuracy of the calculation of the remaining cruising range of the target vehicle.

[0096] Optionally, the multiple attribute parameters include: vehicle mass, rolling resistance coefficient, wind resistance coefficient, frontal area, and vehicle rotation conversion coefficient.

[0097] Specifically, the mass m of the target vehicle represents the weight of the target vehicle.

[0098] The rolling resistance coefficient f of the target vehicle is an important parameter that describes the rolling resistance encountered by the target vehicle when driving on a flat road. It is a physical quantity that represents the ratio of the rolling resistance encountered by the target vehicle during driving to its gravity.

[0099] The magnitude of the above rolling resistance coefficient depends on factors such as the weight of the target vehicle, the type of tire, the tire pressure, and the roughness of the road surface.

[0100] The drag coefficient Cd of the target vehicle refers to the magnitude of the air resistance encountered by the target vehicle when driving, and is usually used to describe the aerodynamic performance of the target vehicle when driving.

[0101] The frontal area A of the target vehicle refers to the total area of ​​the front of the target vehicle that interacts with the air during driving. The frontal area is usually used to calculate the air resistance that the target vehicle is subjected to when driving at high speed. The size of the frontal area will affect the air resistance that the target vehicle is subjected to when driving at high speed, and further affect the fuel economy and power performance of the target vehicle.

[0102] The vehicle's total rotation conversion coefficient δ refers to the ratio of the torque required for the target vehicle's additional rotation to the target vehicle's total mass when the target vehicle rotates during driving. The vehicle's total rotation conversion coefficient can be used to evaluate the vehicle's stability and handling performance.

[0103] Optionally, the vehicle remaining cruising range estimation method further includes:

[0104] Step S17, obtaining a battery state of charge value and a third energy value of the target vehicle, wherein the third energy value is used to represent the available energy of the battery of the target vehicle in a fully charged state;

[0105] In the above step S17, the battery state of charge value of the target vehicle refers to the electric energy stored in the battery reported by the current battery management system (BMS) in the target vehicle, which is recorded as SOC_now. The above third energy value refers to the available energy of the battery pack of the target vehicle when it is fully charged, which is recorded as E_avail_total.

[0106] Step S18: determining the first energy value by using the battery state of charge value and the third energy value.

[0107] Specifically, the first energy value is calculated based on the battery state of charge value and the third energy value. The specific calculation process is shown in expression (13).

[0108] E_avail_now=E_avail_total*SOC_now / 100 (13)

[0109] Furthermore, first, the user sets the remaining cruising range display mode in the instrument, which is specifically divided into four modes: "urban", "suburban", "highway" and "comprehensive". After the user determines the remaining cruising range display mode, the instrument sends the remaining cruising range display mode to the vehicle control unit (VCU). If the cruising range display mode selected by the user is the same as the previous driving cycle, the cruising range display initial value is calculated based on the first energy value E_avail_now and the third power consumption value EC_history; if the cruising range display mode selected by the user is different from the previous driving cycle, the cruising range display initial value is calculated based on the percentage of the electric energy stored in the battery currently displayed by the instrument to the total capacity, where the cruising range display initial value is recorded as S_initial.

[0110] Specifically, when the cruising range display mode selected by the user is the same as that of the previous driving cycle, the third energy value E_avail_total at different temperatures is tested experimentally according to the above four modes in the early stage, and the data obtained are shown in Table 2.

[0111] Table 2 The third energy values ​​at different temperatures in four modes

[0112] 35℃ 25℃ 15℃ 5℃ -5℃ -15℃ -25℃ -35 Urban area E1 E2 E3 E4 E5 E6 E7 E8 suburb E9 E10 E11 E12 E13 E14 E15 E16 high speed E17 E18 E19 E20 E21 E22 E23 E24 comprehensive E25 E26 E27 E28 E29 E30 E31 E32

[0113] According to the average power consumption EC_history of the last driving cycle recorded by the VCU, the initial value of the cruising range is calculated. The specific calculation process is shown in expression (14).

[0114] S_initial=E_avail_now / EC_history (14)

[0115] Specifically, when the cruising range display mode selected by the user is different from that of the previous driving cycle, the vehicle power consumption corresponding to different temperatures of the target vehicle under the above four working conditions is determined by means of experiments or simulations, which is recorded as EC, as shown in Table 3.

[0116] Table 3 Vehicle power consumption at different temperatures in four modes

[0117] 35℃ 25℃ 15℃ 5℃ -5℃ -15℃ -25℃ -35 Urban area EC1 EC2 EC3 EC4 EC5 EC6 EC7 EC8 suburb EC9 EC10 EC11 EC12 EC13 EC14 EC15 EC16 high speed EC17 EC18 EC19 EC20 EC21 EC22 EC23 EC24 comprehensive EC25 EC26 EC27 EC28 EC39 EC30 EC31 EC32

[0118] From Tables 2 and 3 above, the vehicle range corresponding to different temperatures under four working conditions can be obtained, that is, the range of the target vehicle when fully charged, denoted as S_initial, as shown in Table 4, where the calculation process of the vehicle range is shown in Expression (15).

[0119] S_initial=E_avail_total / EC (15)

[0120] Table 4 Vehicle cruising range under four working conditions and different temperatures

[0121] 35℃ 25℃ 15℃ 5℃ -5℃ -15℃ -25℃ -35 Urban area S1 S2 S3 S4 S5 S6 S7 S8 suburb S9 S10 S11 S12 S13 S14 S15 S16 high speed S17 S18 S19 S20 S21 S22 S23 S24 comprehensive S25 S26 S27 S28 S30 S31 S32 S33

[0122] Specifically, after the target vehicle is started, the initial value S_initial of the target vehicle's range when fully charged is determined by querying Table 4 according to the remaining range display mode and ambient temperature selected by the user. If the ambient temperature is not in the above Table 4, the initial value S_initial_1 of the target vehicle's range when fully charged is determined by linear interpolation. For example, if the user selects an urban operating condition and the ambient temperature is 10°C, the calculation process of S_initial_1 of the target vehicle is shown in Expression (16). Based on the percentage of the total capacity of the battery that has been stored in the current instrument display and S_initial_1, the initial range of the target vehicle is determined. The specific calculation process is shown in Expression (17), where the percentage of the total capacity of the battery that has been stored in the current instrument display is recorded as SOC_initial.

[0123] S_initial_1=(S3+S4) / 2 (16)

[0124] S_initial=(SOC_initial*SOC_initial_1) / 100) (17)

[0125] Based on the above steps S17 to S18, the battery state of charge value and the third energy value of the target vehicle are obtained, and the first energy value is determined using the battery state of charge value and the third energy value, thereby achieving the purpose of accurately estimating the remaining cruising range of the vehicle, thereby achieving the technical effect of improving the accuracy of the remaining cruising range estimation of the vehicle, and further solving the technical problem of low accuracy of the remaining cruising range estimation of the vehicle in the related technology.

[0126] Figure 2 is a schematic diagram of a vehicle remaining cruising range estimation system according to one embodiment of the present invention. Figure 2As shown, the system includes an in-vehicle navigation system, an air conditioning controller, a BMS, an instrument, a cloud server, and a VCU. The in-vehicle navigation system sends information such as the estimated driving time from the starting point to the end point, the distance from the starting point to the end point, and the average speed of the traffic ahead to the VCU. The air conditioning controller sends information such as the air conditioning on / off status, the average power of the air conditioning system, and the ambient temperature to the VCU. The BMS sends information such as the battery temperature, the battery charge and discharge state (State of Charge, SOC), and the battery instantaneous charge and discharge power to the VCU. The instrument sends the display mode of the remaining cruising range of the target vehicle to the VCU. The cloud server continuously stores the driving data of the target vehicle and sends the historical driving big data to the VCU. The VCU calculates the remaining cruising range of the target vehicle based on various information.

[0127] Figure 3 is a flow chart of another method for estimating the remaining cruising range of a vehicle according to one embodiment of the present invention; Figure 3 As shown, the method comprises the following steps:

[0128] Step S301, obtaining historical usage data of a target vehicle in at least one driving cycle;

[0129] Step S302, using the Markov prediction model to perform operating condition prediction on the historical usage data to obtain a target prediction result;

[0130] Step S303, obtaining multiple attribute parameters corresponding to the target vehicle;

[0131] Step S304, determining the instantaneous power of the battery based on the multiple attribute parameters and the target prediction result;

[0132] Step S305, integrating the instantaneous power of the battery to obtain a second energy value;

[0133] Step S306, determining a second power consumption value using the second energy value and the target driving distance, and determining a third power consumption value based on the average power consumption information;

[0134] Step S307, determining a third power consumption value based on the average power consumption information;

[0135] Step S308, determining the first power consumption value based on the second power consumption value, the third power consumption value and a preset calibration coefficient;

[0136] Step S309, obtaining the battery state of charge value and the third energy value of the target vehicle;

[0137] Step S310, determining a first energy value by using the battery state of charge value and the third energy value;

[0138] Step S311, determining the remaining cruising range of the target vehicle according to the first energy value and the first power consumption value.

[0139] In the above-mentioned vehicle remaining cruising range estimation method, the historical usage data of the target vehicle in at least one driving cycle is obtained, and the operating condition prediction is performed based on the historical usage data to obtain the target prediction result. By using the target prediction result to calculate the first power consumption value of the target vehicle and determining the remaining cruising range of the target vehicle based on the first energy value and the first power consumption value, the purpose of accurately estimating the remaining cruising range of the vehicle is achieved, thereby achieving the technical effect of improving the accuracy of the vehicle's remaining cruising range estimation, and further solving the technical problem of low accuracy in estimating the vehicle's remaining cruising range in the related art.

[0140] The following is an example to introduce the workflow of the above vehicle remaining cruising range estimation method in detail:

[0141] First, the user sets the remaining mileage display mode in the instrument, which is divided into four modes: "Urban", "Suburban", "Highway" and "Comprehensive". After the user determines the remaining mileage display mode, the instrument sends the remaining mileage display mode to the VCU.

[0142] Secondly, the initial value of the remaining range display is determined based on information such as the ambient temperature and the battery pack temperature, recorded as S_initial. If the range display mode selected by the user is the same as the previous driving cycle, S_initial is calculated based on E_avail_now and EC_history; if the range display mode selected by the user is different from the previous driving cycle, S_initial is calculated based on the percentage of the total capacity of the battery stored in the current instrument display.

[0143] Specifically, when the mileage display mode selected by the user is the same as the previous driving cycle, the E_avail_total at different temperatures is tested experimentally according to the above four modes in the early stage, and the data obtained are shown in Table 2. According to the average power consumption EC_history of the last driving cycle recorded by the VCU, the initial value of the mileage display is calculated, and the specific calculation process is shown in expression (14).

[0144] Specifically, when the range display mode selected by the user is different from that of the previous driving cycle, the EC corresponding to the different temperatures of the target vehicle under the above four working conditions is determined by means of experiments or simulations, as shown in Table 3. From Tables 2 and 3 above, the S_initial corresponding to the different temperatures under the four working conditions can be obtained, as shown in Table 4, where the calculation process of the vehicle's range is shown in Expression (15). After the target vehicle is started, the initial value S_initial of the range of the target vehicle under full power is determined by querying Table 4 according to the remaining range display mode and ambient temperature selected by the user. If the ambient temperature is not in Table 4 above, the initial value S_initial_1 of the range of the target vehicle under full power is determined by linear interpolation. For example, if the user selects the urban working condition and the ambient temperature is 10°C, the calculation process of S_initial_1 of the target vehicle is shown in Expression (16). Based on the percentage of the electric energy stored in the battery to the total capacity and S_initial_1 displayed by the current instrument, the initial cruising range of the target vehicle is determined. The specific calculation process is shown in expression (17).

[0145] Then, the Markov prediction model is used to predict the working condition of the historical usage data to obtain the target prediction result. Specifically, the vehicle navigation system records and stores the starting position and the end position of the target vehicle corresponding to each driving cycle, the VCU records and stores the average speed, the total distance traveled, and the average acceleration of the target vehicle in each driving cycle, and the air conditioning controller stores the average ambient temperature data of each driving cycle. The above-mentioned stored historical usage data of the target vehicle is shown in Table 1. After each driving cycle, the vehicle navigation system, the air conditioning controller and the VCU upload the above-mentioned historical usage data to the cloud server for storage. Using the data in Table 1, the state transition probability matrix is ​​calculated, as shown in expressions (2)-(5); the Markov prediction model for the speed state probability of the target vehicle is determined, as shown in expressions (6)-(8); based on the solution of the Markov prediction model and the state transition matrix, the target prediction result, that is, the predicted speed value of the target vehicle, is obtained, as shown in expression (9).

[0146] Finally, the target prediction result is used to calculate the first power consumption value EC_combined of the target vehicle, and the remaining cruising range S_avail_pre of the target vehicle is determined based on the first energy value E_avail_now and the first power consumption value, as shown in expression (1). In this process, according to the predicted speed value of the target vehicle, the power of the battery terminal at each moment is calculated, and the instantaneous power of the battery is integrated to obtain the second energy value E_total. The second power consumption value EC_after is determined using E_total and the target driving distance, as shown in expression (11), wherein the instantaneous power calculation of the battery of the target vehicle is shown in expression (12); EC_combined is determined based on the second power consumption value EC_after, the third power consumption value EC_history and the preset calibration coefficient, and the specific calculation process is shown in expression (10); the battery state of charge value SOC_now and the third energy value E_avail_total of the target vehicle are obtained, and E_avail_now is determined based on SOC_now and E_avail_total, as shown in expression (13).

[0147] Based on the above steps S301 to S310, the historical usage data of the target vehicle in at least one driving cycle is obtained, and the operating condition prediction is performed based on the historical usage data to obtain a target prediction result. By using the target prediction result to calculate the first power consumption value of the target vehicle and determining the remaining cruising range of the target vehicle according to the first energy value and the first power consumption value, the purpose of accurately estimating the remaining cruising range of the vehicle is achieved, thereby achieving the technical effect of improving the accuracy of the estimation of the remaining cruising range of the vehicle, and further solving the technical problem of low accuracy in estimating the remaining cruising range of the vehicle in the related art.

[0148] Figure 4 is a structural block diagram of a vehicle remaining cruising range estimation device according to one embodiment of the present invention; Figure 4 As shown, the device comprises:

[0149] An acquisition module 401 is used to acquire historical usage data of a target vehicle in at least one driving cycle, wherein the historical usage data includes vehicle navigation information, vehicle driving information, ambient temperature information, and average power consumption information corresponding to at least one driving cycle;

[0150] A prediction module 402 is used to perform operating condition prediction based on historical usage data to obtain a target prediction result, wherein the target prediction result is used to determine the instantaneous power of the battery of the target vehicle;

[0151] The determination module 403 is used to determine the remaining cruising range of the target vehicle according to the target prediction result.

[0152] Optionally, the prediction module 402 is further used to: use the Markov prediction model to perform operating condition prediction on the historical usage data to obtain a target prediction result.

[0153] Optionally, the determination module 403 is also used to: determine a second power consumption value based on the target prediction result, and determine a third power consumption value based on the average power consumption information, wherein the second power consumption value is used to represent the predicted power consumption level of the target vehicle, and the third power consumption value is used to represent the historical power consumption level of the target vehicle; determine the first power consumption value based on the second power consumption value, the third power consumption value and a preset calibration coefficient.

[0154] Optionally, the determination module 403 is also used to: determine the instantaneous power of the battery of the target vehicle based on the target prediction result; integrate the instantaneous power of the battery to obtain a second energy value, wherein the second energy value is used to represent the total battery energy consumed by the target vehicle during driving; and determine the second power consumption value using the second energy value and the target driving distance.

[0155] Optionally, the acquisition module 401 is further used to: acquire multiple attribute parameters corresponding to the target vehicle; the determination module 403 is further used to: determine the instantaneous power of the battery based on the multiple attribute parameters and the target prediction result.

[0156] Optionally, the multiple attribute parameters include: vehicle mass, rolling resistance coefficient, wind resistance coefficient, frontal area, and vehicle rotation conversion coefficient.

[0157] Optionally, the acquisition module 401 is also used to: obtain the battery state of charge value and a third energy value of the target vehicle, wherein the third energy value is used to represent the available battery energy of the target vehicle when it is fully charged; the determination module 403 is also used to: determine the first energy value using the battery state of charge value and the third energy value.

[0158] It should be noted that the above modules can be implemented by software or hardware. For the latter, it can be implemented in the following ways, but not limited to: the above modules are all located in the same processor; or the above modules are located in different processors in any combination.

[0159] According to one embodiment of the present invention, a non-volatile storage medium is further provided, in which a computer program is stored, wherein the computer program is configured to execute any of the above-mentioned methods for estimating the remaining cruising range of a vehicle when running.

[0160] Optionally, in this embodiment, the storage medium may be configured to store a computer program for performing the following steps:

[0161] S1, obtaining historical usage data of a target vehicle in at least one driving cycle, wherein the historical usage data includes vehicle navigation information, vehicle driving information, ambient temperature information, and average power consumption information corresponding to at least one driving cycle;

[0162] S2, predicting the working condition based on historical usage data to obtain the target prediction result;

[0163] S3, calculating a first power consumption value of the target vehicle using the target prediction result, wherein the first power consumption value is used to represent a comprehensive power consumption degree of the target vehicle;

[0164] S4, determining the remaining cruising range of the target vehicle according to the first energy value and the first power consumption value, wherein the first energy value is used to represent the battery available energy of the target vehicle in the current state.

[0165] Optionally, in this embodiment, the above-mentioned storage medium may include but is not limited to: a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk, and other media that can store computer programs.

[0166] According to one embodiment of the present invention, there is also provided an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute any of the above-mentioned methods for estimating the remaining cruising range of a vehicle.

[0167] Optionally, in this embodiment, the processor may be configured to perform the following steps through a computer program:

[0168] S1, obtaining historical usage data of a target vehicle in at least one driving cycle, wherein the historical usage data includes vehicle navigation information, vehicle driving information, ambient temperature information, and average power consumption information corresponding to at least one driving cycle;

[0169] S2, predicting the working condition based on historical usage data to obtain the target prediction result;

[0170] S3, calculating a first power consumption value of the target vehicle using the target prediction result, wherein the first power consumption value is used to represent a comprehensive power consumption degree of the target vehicle;

[0171] S4, determining the remaining cruising range of the target vehicle according to the first energy value and the first power consumption value, wherein the first energy value is used to represent the battery available energy of the target vehicle in the current state.

[0172] Optionally, the specific examples in this embodiment may refer to the examples described in the above embodiments and optional implementation modes, and this embodiment will not be described in detail here.

[0173] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0174] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0175] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0176] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0177] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0178] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0179] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for estimating the remaining cruising range of a vehicle, characterized in that: include: Acquire historical usage data of the target vehicle in at least one driving cycle, wherein the historical usage data includes vehicle navigation information, vehicle driving information, ambient temperature information, and average power consumption information corresponding to the at least one driving cycle; Performing operating condition prediction based on the historical usage data to obtain a target prediction result, wherein the target prediction result is used to represent a predicted speed value of the target vehicle; Calculating a first power consumption value of the target vehicle using the target prediction result, wherein the first power consumption value is used to represent a comprehensive power consumption degree of the target vehicle; Determining the remaining cruising range of the target vehicle according to a first energy value and the first power consumption value, wherein the first energy value is used to represent the available energy of the battery of the target vehicle in a current state; Wherein, calculating the first power consumption value of the target vehicle using the target prediction result includes: determining a second power consumption value based on the target prediction result, and determining a third power consumption value based on the average power consumption information, wherein the second power consumption value is used to represent the predicted power consumption level of the target vehicle, and the third power consumption value is used to represent the historical power consumption level of the target vehicle; determining the first power consumption value based on the second power consumption value, the third power consumption value and a preset calibration coefficient, wherein the first power consumption value is represented by the following formula: EC_combined=EC_history*n+EC_after*(1-n) Among them, EC_combined represents the first power consumption value, EC_history represents the third power consumption value, EC_after represents the second power consumption value, η represents a preset calibration coefficient, η is calibrated according to the performance of the target vehicle, and the value range of η is 0<η<1.

2. The method for estimating the remaining cruising range of a vehicle according to claim 1, characterized in that: Performing operating condition prediction based on the historical usage data to obtain the target prediction result includes: The Markov prediction model is used to perform operating condition prediction on the historical usage data to obtain the target prediction result.

3. The method for estimating the remaining cruising range of a vehicle according to claim 1, characterized in that: Determining the second power consumption value based on the target prediction result includes: Determining the instantaneous power of the battery of the target vehicle based on the target prediction result; Integrating the instantaneous power of the battery to obtain a second energy value, wherein the second energy value is used to represent the total battery energy consumed by the target vehicle during driving; The second power consumption value is determined using the second energy value and the target driving distance.

4. The method for estimating the remaining cruising range of a vehicle according to claim 3, characterized in that: Determining the battery instantaneous power of the target vehicle based on the target prediction result includes: Acquire multiple attribute parameters corresponding to the target vehicle; The battery instantaneous power is determined based on the plurality of attribute parameters and the target prediction result.

5. The method for estimating the remaining cruising range of a vehicle according to claim 4, characterized in that: The multiple attribute parameters include: vehicle mass, rolling resistance coefficient, wind resistance coefficient, frontal area, and vehicle rotation conversion coefficient.

6. The method for estimating the remaining cruising range of a vehicle according to claim 1, characterized in that: The method further comprises: Acquire a battery state of charge value and a third energy value of the target vehicle, wherein the third energy value is used to represent the available energy of the battery of the target vehicle in a fully charged state; The first energy value is determined using the battery state of charge value and the third energy value.

7. A vehicle remaining cruising range estimation device, characterized in that: include: An acquisition module, configured to acquire historical usage data of a target vehicle in at least one driving cycle, wherein the historical usage data includes vehicle navigation information, vehicle driving information, ambient temperature information, and average power consumption information corresponding to the at least one driving cycle; A prediction module, used for performing working condition prediction based on the historical usage data to obtain a target prediction result, wherein the target prediction result is used to represent a predicted speed value of the target vehicle; A determination module, configured to calculate a first power consumption value of the target vehicle using the target prediction result, wherein the first power consumption value is used to represent a comprehensive power consumption degree of the target vehicle; The determination module is further used to: determine the remaining cruising range of the target vehicle according to the first energy value and the first power consumption value, wherein the first energy value is used to represent the battery available energy of the target vehicle in the current state; The determination module is further used to: determine a second power consumption value based on the target prediction result, and determine a third power consumption value based on the average power consumption information, wherein the second power consumption value is used to represent the predicted power consumption of the target vehicle, and the third power consumption value is used to represent the historical power consumption of the target vehicle; determine the first power consumption value based on the second power consumption value, the third power consumption value and a preset calibration coefficient, wherein the first power consumption value is represented by the following formula: EC_combined=EC_history*n+EC_after*(1-n) Among them, EC_combined represents the first power consumption value, EC_history represents the third power consumption value, EC_after represents the second power consumption value, η represents a preset calibration coefficient, η is calibrated according to the performance of the target vehicle, and the value range of η is 0<η<1.

8. A non-volatile storage medium, characterized in that: The storage medium stores a computer program, wherein the computer program is configured to execute the vehicle remaining cruising range estimation method described in any one of claims 1 to 6 when running.

9. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to run the computer program to execute the vehicle remaining cruising range estimation method described in any one of claims 1 to 6.

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