CN116685495A

CN116685495A - Method, device and system for determining endurance mileage, electric vehicle and storage medium

Info

Publication number: CN116685495A
Application number: CN202180053715.1A
Authority: CN
Inventors: 林海波; 李宝; 吴凯
Original assignee: Contemporary Amperex Technology Co Ltd
Current assignee: Contemporary Amperex Technology Co Ltd
Priority date: 2021-12-29
Filing date: 2021-12-29
Publication date: 2023-09-01
Also published as: WO2023123094A1

Abstract

The application relates to the technical field of electric automobiles, in particular to a method, a device, a system, an electric automobile and a storage medium for determining a endurance mileage. Then, a historical driving sequence of the electric automobile is obtained, and an effective driving sequence is determined according to the historical driving sequence, wherein the historical driving sequence comprises historical driving information arranged in time sequence, and each piece of historical driving information corresponds to mileage and energy consumption. And determining historical average energy consumption according to the effective driving sequence. And finally, determining the endurance mileage according to the historical average energy consumption, the real-time average energy consumption and the available residual energy. The effective driving sequence is updated according to the continuous accumulation of the historical driving sequence, so that the historical average energy consumption can reflect the average energy consumption performance of the recent electric automobile, and the estimation accuracy of the continuous voyage mileage can be improved.

Description

Method, device and system for determining endurance mileage, electric vehicle and storage medium

Technical Field

The application relates to the technical field of electric automobiles, in particular to a method, a device and a system for determining endurance mileage, an electric automobile and a storage medium.

Background

With the development of green energy, the application of batteries is more and more widespread, and particularly in the field of new energy automobiles which are emerging in recent years, the batteries are used as important energy storage and power supply equipment, for example, for supplying power to electric automobiles. The electric automobile adopts electric energy to solve the problems of energy consumption, greenhouse gas emission and the like brought by the traditional oil truck, and realize energy conservation, emission reduction and environmental protection sustainable development.

The electric automobile has the advantages that the continuous mileage is always a problem that users are contraindicated, in order to reduce the restriction of the development of the electric automobile by the continuous mileage, at present, manufacturers tend to increase the battery capacity and popularizing and building auxiliary equipment such as a charging pile, on one hand, the continuous mileage can be increased as much as possible, and on the other hand, when the continuous mileage is insufficient, the electric automobile can be charged in time. In addition, how to accurately calculate the range of the electric automobile is an important problem affecting the development of the electric automobile, if the range estimated value is very different from the actual range, the user can worry that the current electric quantity cannot ensure that the automobile reaches a destination, so that 'range anxiety feeling' is generated, and the use confidence of the electric automobile is reduced. Therefore, improving the estimation accuracy of the range of the electric vehicle is an important premise for improving the popularization rate of the electric vehicle and promoting the development of the electric vehicle industry.

Disclosure of Invention

In view of the above, the present application provides a method, a system, an electric vehicle and a storage medium for determining a range, which can improve the estimation accuracy of the range.

In a first aspect, the present application provides a method for determining a range, first, energy consumption and a range of an electric vehicle in current driving are obtained, and real-time average energy consumption is determined according to the energy consumption and the range in current driving. Then, a historical driving sequence of the electric automobile is obtained, and an effective driving sequence is determined according to the historical driving sequence, wherein the historical driving sequence comprises historical driving information arranged in time sequence, and each piece of historical driving information corresponds to mileage and energy consumption. And determining historical average energy consumption according to the effective driving sequence. And finally, obtaining available residual energy of a battery on the electric automobile, and determining the endurance mileage of the electric automobile according to the historical average energy consumption, the real-time average energy consumption and the available residual energy.

In the above embodiment of the present application, the effective driving sequence for calculating the historical average energy consumption is determined and updated along with the continuous accumulation of the historical driving sequence, so that the historical average energy consumption can reflect the average energy consumption performance of the recent electric vehicle, and thus, factors such as the weather environment, the actual state of the vehicle, the road state or the change of the driving style can be represented in the historical average energy consumption, and the estimation accuracy of the range can be improved. In addition, the historical average energy consumption and the real-time average energy consumption are combined to reflect the recent average energy consumption condition of the electric automobile, so that the endurance mileage is more accurate.

In a possible implementation manner of the first aspect, the foregoing "determining the valid driving sequence according to the historical driving sequence" includes: and if the accumulated mileage corresponding to the historical driving sequence is greater than or equal to the first mileage threshold value and less than the second mileage threshold value, the historical driving sequence is used as an effective driving sequence.

In the above embodiment of the present application, the first mileage threshold is used to screen out the situation that: the historical average energy consumption is calculated using only these unreliable data as the effective travel sequence when the amount of the previous historical travel information data is too small. Screening with a second mileage threshold excludes the following: when the data amount of the later historical driving information is too large, the historical average energy consumption is calculated by using all the historical driving sequences as effective driving sequences. And only when the accumulated mileage corresponding to the historical driving sequence is greater than or equal to the first mileage threshold and less than the second mileage threshold, the historical driving sequence is directly used as the effective driving sequence, so that the effective driving sequence is relatively reliable, and the historical average energy consumption is relatively reliable and has parametricity.

In a possible implementation manner of the first aspect, the foregoing "determining an effective driving sequence according to a historical driving sequence" further specifically includes: and if the accumulated mileage corresponding to the history running sequence is greater than or equal to the second mileage threshold value, replacing the history running information with the latest history running information in the effective running sequence, and obtaining the updated effective running sequence.

In the above embodiment of the present application, when the accumulated mileage corresponding to the history driving sequence is greater than or equal to the second mileage threshold, the latest history driving information can be continuously updated and incorporated in the effective driving sequence, and the history driving information with the forefront time is replaced, so that the effective driving sequence can reflect the recent driving state of the electric vehicle, and the history average energy consumption can reflect the recent energy consumption performance of the electric vehicle.

In a possible implementation manner of the first aspect, the method further includes: and if the accumulated mileage corresponding to the historical driving sequence is smaller than the first mileage threshold value, determining the historical average energy consumption by adopting a preset standard working condition.

In the embodiment of the application, under the condition of less historical driving information, the historical average energy consumption is determined by adopting the preset standard working condition, so that the historical average energy consumption is suitable for the electric automobile.

In a possible implementation manner of the first aspect, the foregoing "determining the real-time average energy consumption according to the energy consumption and mileage of the current driving" includes: in the current driving, when the current driving mileage reaches the preset mileage each time, the preset mileage and the corresponding energy consumption are adopted to update the real-time average energy consumption.

In the embodiment of the application, when the current driving mileage reaches the preset mileage, the preset mileage and the corresponding energy consumption are adopted to update the real-time average energy consumption, so that the real-time average energy consumption can be updated along with the driving of the electric automobile, and the current driving energy consumption state of the electric automobile can be reflected more.

In one possible implementation manner of the first aspect, the determining the range of the electric vehicle according to the historical average energy consumption, the real-time average energy consumption and the available remaining energy includes: and determining the weighted average energy consumption according to the historical average energy consumption and the real-time average energy consumption. And calculating the endurance mileage according to the weighted average energy consumption and the available residual energy.

In the embodiment of the application, the weighted average energy consumption after the weighted fusion processing is obtained by performing the weighted fusion processing on the historical average energy consumption and the real-time average energy consumption, so that the weighted average energy consumption can reflect the energy consumption state of the historical driving and the energy consumption state of the current driving, and the endurance mileage calculated according to the weighted average energy consumption and the available residual energy is more accurate by considering the result of the historical driving energy consumption state and the current driving energy consumption state.

In a possible implementation manner of the first aspect, the foregoing "determining a weighted average energy consumption according to a historical average energy consumption and a real-time average energy consumption" includes:

calculating weighted average energy consumption by adopting the following formula;

ECa＝α*ECi+(1-α)*ECh；

where ECa is the weighted average energy consumption, α is the weighting coefficient, ECi is the real-time average energy consumption, and ECh is the historical average energy consumption.

In the embodiment of the application, the historical average energy consumption and the real-time average energy consumption are fused through the formula to obtain the weighted average energy consumption, so that the historical average energy consumption and the real-time average energy consumption can be fused according to complementary weights, and the energy consumption state reflected by the weighted average energy consumption can be more in line with the actual situation through calibrating and adjusting the weighting coefficient.

In a possible implementation manner of the first aspect, the method further includes: and determining the weighting coefficient according to the current driving mode of the electric automobile, the SOC value of the battery on the electric automobile and the ratio of the real-time average energy consumption to the historical average energy consumption of the electric automobile.

In the embodiment of the application, the weighting coefficient alpha is calibrated according to the current driving mode, the SOC value and the ratio of the real-time average energy consumption to the historical average energy consumption, so that the influence caused by unstable working conditions is reduced, the driving habit is considered, the energy consumption state reflected by the weighted average energy consumption is more in line with the actual situation, and the calculated endurance mileage is more accurate.

In one possible implementation manner of the first aspect, a correspondence relationship between a plurality of driving modes and a weighting coefficient map is preset, where the weighting coefficient map includes a correspondence relationship between a weighting coefficient, an SOC value, and a ratio of real-time average energy consumption and historical average energy consumption. The foregoing "determining a weighting coefficient according to a current driving mode of the electric vehicle, an SOC value of a battery on the electric vehicle, and a ratio of real-time average energy consumption to historical average energy consumption of the electric vehicle" specifically includes: and determining a target weighting coefficient map corresponding to the current driving mode according to the current driving mode. And then, according to the SOC value of the battery on the electric vehicle and the ratio of the real-time average energy consumption to the historical average energy consumption of the electric vehicle, searching a corresponding weighting coefficient in a target weighting coefficient map.

In the above embodiment of the present application, the correspondence between the driving modes and the weighting coefficient map is preset, and the weighting coefficient map in each driving mode includes the correspondence between the weighting coefficient, the SOC value, and the ratio of the real-time average energy consumption to the historical average energy consumption, so that the weighting coefficient can be determined according to the driving mode, the SOC value, and the ratio of the real-time average energy consumption to the historical average energy consumption. The determination of the weighting coefficient is more refined and accurate, so that the accuracy of the endurance mileage is improved.

In one possible implementation manner of the first aspect, in the weighting factor map, the weighting factor tends to increase and decrease with an increase in a ratio of the real-time average energy consumption and the historical average energy consumption. In the weighting coefficient map, the weighting coefficient tends to increase with an increase in the SOC value.

In the embodiment of the application, in the weighting coefficient map obtained through trial calibration, the weighting coefficient has a trend of increasing and then decreasing along with the increase of the ratio of the real-time average energy consumption to the historical average energy consumption, and the weighting coefficient has a trend of increasing along with the increase of the SOC value, so that the contribution of the real-time average energy consumption to the weighting average energy consumption can be better distributed through the weighting coefficient, and the influence of the unstable driving working condition on the endurance mileage can be reduced.

In a second aspect, the application provides a device for determining a range, which comprises a real-time average energy consumption determining module, an effective driving sequence determining module, a historical average energy consumption determining module and a range determining module.

The real-time average energy consumption determining module is used for obtaining the current running energy consumption and mileage of the electric automobile and determining the real-time average energy consumption according to the current running energy consumption and mileage. The effective driving sequence determining module is used for obtaining a historical driving sequence of the electric automobile and determining the effective driving sequence according to the historical driving sequence, wherein the historical driving sequence comprises historical driving information arranged in time sequence, and each piece of historical driving information corresponds to mileage and energy consumption. The historical average energy consumption determining module is used for determining historical average energy consumption according to the effective driving sequence. The endurance mileage determining module is used for obtaining available residual energy of a battery on the electric automobile and determining the endurance mileage of the electric automobile according to the historical average energy consumption, the real-time average energy consumption and the available residual energy.

In the above embodiment of the present application, the effective driving sequence for calculating the historical average energy consumption is determined and updated along with the continuous accumulation of the historical driving sequence, so that the historical average energy consumption can reflect the average energy consumption performance of the recent electric vehicle, and thus, factors such as the weather environment, the actual state of the vehicle, the road state or the change of the driving style can be represented in the historical average energy consumption, and the estimation accuracy of the endurance mileage can be improved. In addition, the historical average energy consumption and the real-time average energy consumption are combined to reflect the recent average energy consumption condition of the electric automobile, so that the endurance mileage is more accurate.

In a third aspect, the present application provides a system for determining a range, including a processor and a memory, the processor being communicatively coupled to the memory, the processor being configured to execute program instructions stored in the memory to implement the method for determining a range of any one of the first aspects.

In the above embodiment of the present application, the system for determining a range may implement the method for determining a range in the first aspect, and may improve the accuracy of estimating a range.

In a fourth aspect, the present application provides an electric vehicle, including the system for determining a range of the third aspect.

In the above embodiment of the present application, the electric vehicle has the same technical effects as the above-described system for determining the range, that is, the estimation accuracy of the range can be improved.

In a fifth aspect, the present application provides a computer readable storage medium having stored therein program instructions executable by a processor to implement the method of determining range of any one of the first aspects.

In the above embodiment of the present application, the computer readable storage medium can implement the method for determining the range in the first aspect, and can improve the estimation accuracy of the range.

The foregoing description is only an overview of the present application, and is intended to be implemented in accordance with the teachings of the present application in order that the same may be more clearly understood and to make the same and other objects, features and advantages of the present application more readily apparent.

Drawings

Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the application. Also, like reference numerals are used to designate like parts throughout the accompanying drawings. In the drawings:

FIG. 1 is a schematic view of an application environment of a method for determining a range in some embodiments of the present application;

FIG. 2 is a flow chart of a method for determining a range in some embodiments of the application;

FIG. 3 is a schematic flow chart of a sub-process of step S20 in the method shown in FIG. 2;

FIG. 4 is a schematic diagram of a sliding window screening of an active driving sequence according to some embodiments of the present application;

FIG. 5 is a schematic diagram illustrating calculation of real-time average energy consumption according to some embodiments of the present application;

FIG. 6 is a schematic flow chart of a sub-process of step S40 in the method of FIG. 2;

FIG. 7 is a flow chart of a method for determining a range in some embodiments of the application;

FIG. 8 is a schematic diagram illustrating an apparatus for determining a range according to some embodiments of the present application;

fig. 9 is a schematic structural diagram of a system for determining a range according to some embodiments of the present application.

Detailed Description

Embodiments of the technical scheme of the present application will be described in detail below with reference to the accompanying drawings. The following examples are only for more clearly illustrating the technical aspects of the present application, and thus are merely examples, and are not intended to limit the scope of the present application.

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "comprising" and "having" and any variations thereof in the description of the application and the claims and the description of the drawings above are intended to cover a non-exclusive inclusion.

In the description of embodiments of the present application, the technical terms "first," "second," and the like are used merely to distinguish between different objects and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated, a particular order or a primary or secondary relationship. In the description of the embodiments of the present application, the meaning of "plurality" is two or more unless explicitly defined otherwise.

Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.

In the description of the embodiments of the present application, the term "and/or" is merely an association relationship describing an association object, and indicates that three relationships may exist, for example, a and/or B may indicate: a exists alone, A and B exist together, and B exists alone. In addition, the character "/" herein generally indicates that the front and rear associated objects are an "or" relationship.

In the description of the embodiments of the present application, the term "plurality" means two or more (including two), and similarly, "plural sets" means two or more (including two), and "plural sheets" means two or more (including two).

In the description of the embodiments of the present application, the orientation or positional relationship indicated by the technical terms "center", "longitudinal", "transverse", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc. are based on the orientation or positional relationship shown in the drawings, and are merely for convenience of description and simplification of the description, and do not indicate or imply that the apparatus or element referred to must have a specific orientation, be configured and operated in a specific orientation, and therefore should not be construed as limiting the embodiments of the present application.

In the description of the embodiments of the present application, unless explicitly specified and limited otherwise, the terms "mounted," "connected," "secured" and the like should be construed broadly and may be, for example, fixedly connected, detachably connected, or integrally formed; or may be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communicated with the inside of two elements or the interaction relationship of the two elements. The specific meaning of the above terms in the embodiments of the present application will be understood by those of ordinary skill in the art according to specific circumstances.

The endurance mileage refers to the driving mileage that the battery can provide for the electric automobile in the current state of charge. It can be understood that as the battery power is consumed, for example, the electric automobile runs or uses the air conditioner, the sound equipment and other peripherals, the endurance mileage is gradually reduced without charging. If the user can not charge the battery before the range is cleared, the electric automobile can not be started, and if the user can find the charging pile to charge the battery or find the power exchange station to exchange the battery with insufficient power before the range is cleared, the range can be correspondingly increased, and when the battery is fully charged, the range also correspondingly reaches the maximum value.

In the process of using the electric automobile, the matched equipment such as the charging pile or the power exchange station can not be used at any time, and the user needs to reserve a certain mileage, so that the electric automobile can travel to the nearby charging pile or the nearby power exchange station to charge the battery. Therefore, it is necessary to accurately calculate the range of the electric vehicle, so that a user can conveniently plan the journey and the charging item according to the range, and the half electric vehicle is prevented from suddenly stopping to start. It can be appreciated that if the estimated range value is very different from the actual range, the user may worry about the fact that the existing electric quantity cannot ensure that the vehicle reaches the charging pile or the power exchange station, and a "range anxiety feeling" is generated. If the user charges for a long time with sufficient mileage due to 'mileage anxiety', the driving efficiency is greatly reduced, and the travel is affected.

The inventor notes that at present, the average energy consumption is generally estimated based on the standard working condition average energy consumption of the endurance test, and whether the air conditioner starts to perform real-time calculation under different working conditions or not is considered, the influence of the vehicle speed is considered, and the like, so that the calculation is complicated due to more consideration factors. In addition, when the vehicle controller cannot receive the related information, the endurance mileage cannot be accurately calculated, for example, whether an air conditioner is started or not is judged on some vehicle types, and the vehicle controller cannot recognize whether the air conditioner is started or not, so that the accuracy of the endurance mileage is affected. In addition, the technical schemes cannot distinguish personal driving habits, for example, some people have good driving habits, good driving road conditions and low actual average energy consumption, but vehicles with the same electric quantity and different electric quantity are always displayed with the same driving mileage by utilizing the existing schemes, the driving habits are considered, the driving mileage is dynamically displayed, and the driving mileage displayed by the vehicles with good driving habits and low energy consumption is longer than the driving mileage displayed by using the test standard working condition energy consumption, and is shorter. If the real-time average energy consumption is used for calculating the mileage, the mileage obtained by calculating the mileage can also fluctuate greatly, which causes trouble to the driver.

Based on the above consideration, the inventor of the present application has found through research that a method for determining a range is provided, by updating the historical driving mileage and energy consumption, iteratively calculating the historical average energy consumption, and then determining the range by combining the updated historical average energy consumption and the updated real-time average energy consumption. The historical average energy consumption after iterative updating can reflect the average energy consumption performance of the recent electric automobile by updating the historical driving mileage and the energy consumption, so that factors such as climate change, vehicle performance reduction or driving style change can be reflected in the historical average energy consumption after iterative updating, and the estimation accuracy of the cruising mileage can be improved.

Specifically, the current running energy consumption and mileage of the electric automobile are obtained, and the real-time average energy consumption is determined according to the current running energy consumption and mileage. And acquiring a historical driving sequence of the electric automobile, and determining an effective driving sequence according to the historical driving sequence, wherein the historical driving sequence comprises historical driving information arranged in time sequence, and each piece of historical driving information corresponds to mileage and energy consumption. And determining historical average energy consumption according to the effective driving sequence. And finally, acquiring available residual energy of a battery on the electric automobile, and determining the endurance mileage of the electric automobile according to the historical average energy consumption, the real-time average energy consumption and the available residual energy.

The effective driving sequence for calculating the historical average energy consumption is determined and updated along with the continuous accumulation of the historical driving sequence, so that the historical average energy consumption can reflect the average energy consumption performance of the recent electric automobile, and factors such as weather environment, actual state of the vehicle, road state or driving style change can be embodied in the historical average energy consumption, and the estimation accuracy of the endurance mileage can be improved. In addition, the historical average energy consumption and the real-time average energy consumption are combined to reflect the recent average energy consumption condition of the electric automobile, so that the endurance mileage is more accurate.

The method for determining the endurance mileage provided by the embodiment of the application can be applied to a domain controller or a whole vehicle controller of an electric vehicle, and is executed by the domain controller or the whole vehicle controller to calculate the endurance mileage in real time.

It will be appreciated that the program or instructions corresponding to the method for determining the range may be stored in a memory communicatively connected to a controller (a domain controller or a whole vehicle controller) or stored in a memory of the controller itself, and as shown in fig. 1, the controller (the domain controller or the whole vehicle controller) is also communicatively connected to a Battery Management System (BMS) of a battery, a tachograph of an electric vehicle, and an instrument panel.

Among them, a Battery Management System (BMS) is a protection and management unit designed specifically for a battery by applying a control algorithm for a power source to a driving load such as an engine, measurement of electrical characteristics such as current, voltage, etc., charge and discharge control, voltage balance control, evaluation monitoring of state of charge (SOC) and available remaining energy, etc., abnormality monitoring, etc. The driving recorder is a digital electronic recording device which records and stores driving speed, time and mileage of the electric vehicle and other state information related to the driving of the vehicle, and can realize data output through an interface. The instrument panel corresponds to an interactive interface of the electric automobile and can display state data of the electric automobile.

Therefore, the controller can acquire available residual energy obtained through monitoring by the battery management system, the historical driving energy consumption and mileage recorded by the driving recorder and the current driving energy consumption and mileage, call and execute a program or instruction corresponding to the method for determining the driving mileage so as to realize the method for determining the driving mileage, acquire the driving mileage of the electric automobile, and send the driving mileage to the instrument panel for display. Therefore, the user can plan the journey according to the updated endurance mileage displayed on the instrument panel in real time.

Referring to fig. 2, fig. 2 is a flowchart of a method for determining a range according to an embodiment of the present application, and the method S100 includes, but is not limited to, the following steps:

s10: the method comprises the steps of obtaining the current running energy consumption and mileage of the electric automobile, and determining the real-time average energy consumption according to the current running energy consumption and mileage.

The current driving refers to the driving of the electric automobile during the driving process. For example, when an electric vehicle lands, a tachograph on the electric vehicle records and stores the speed, time, mileage, and energy consumption of each trip. For example, the tachograph records data of 100 completed runs, and when the user starts the electric vehicle 101 times, the running 101 times before starting to end the running from the power-on may be referred to as the current running. The current mileage is accumulated with the distance traveled (or the length of the road traveled). The current running energy consumption is the battery energy consumed by the current running, and it can be understood that the current running energy consumption is accumulated along with the running distance.

In some calculations, the mileage may be equal to an integrated value of the speed of the electric vehicle over time. In some calculations, the energy consumption may be equal to an integrated value of the discharge power of the battery over time. In some calculation modes, the energy consumption and mileage corresponding to a driving period in the current driving can be taken, and the real-time average energy consumption can be determined. For example, the energy consumption Q corresponding to the driving period (t 1, t 2) is taken in the current driving _(t1,t2) Corresponding mileage S _(t1,t2) Then the real-time average energy consumption can be Q _(t1,t2) /S _(t1,t2) . Wherein, (t 1, t 2) can be selected according to the actual situation.

It can be understood that if the user turns on the peripheral devices such as the air conditioner or the sound device during the current driving, or walks on the climbing road section, the corresponding energy consumption is larger, namely the same mileage, more energy consumption is needed, and therefore, the real-time average energy consumption is relatively larger.

S20: and acquiring a historical driving sequence of the electric automobile, and determining an effective driving sequence according to the historical driving sequence, wherein the historical driving sequence comprises historical driving information arranged in time sequence, and each piece of historical driving information corresponds to mileage and energy consumption.

Based on the above-mentioned electric automobile when falling to the ground, the travel recorder on the electric automobile can record and store the speed, time, mileage and energy consumption of each travel, and thus, the completed travel can be referred to as historical travel. For example, the tachograph may record 100 completed runs stored as historical runs. The history travel sequence includes history travel information arranged in chronological order. It will be appreciated that the time sequence here is the time sequence of each drive of the user.

The history running information refers to the data (speed, time, mileage, and energy consumption) of the completed running, and the running recorder records the stored data of 100 completed running. In some embodiments, the historical driving information includes mileage and energy consumption. Thus, the history running information of each time is recorded from the time of the electric automobile landing, and the history running information is arranged in time sequence to form a history running sequence [ (Q) ₁ ,S ₁ ),(Q ₂ ,S ₂ ),(Q ₃ ,S ₃ ),......,(Q _i ,S _i )]Wherein Q is _i Represents the energy consumption of the ith driving, S _i The i-th mileage is represented, i being the chronological index. The historical driving information in the historical driving sequence is updated and increased continuously along with the increase of the driving times of the electric automobile.

It can be understood that, as the historical driving information in the historical driving sequence increases, the historical driving information before the time cannot reflect the recent driving state of the electric vehicle. If a part of the historical driving information, which is in front of the time in the historical driving sequence, is used for calculating the historical average energy consumption, the historical average energy consumption can hardly reflect the recent average energy consumption performance of the electric automobile. For example, in one case, the user a often travels on a small area of urban flat road after buying the electric vehicle, the average energy consumption is small, and 200 pieces of history travel information are accumulated in the history travel sequence after using the electric vehicle for a period of time, i.e., the history travel sequence includes [ (Q) ₁ ,S ₁ ),(Q ₂ ,S ₂ ),(Q ₃ ,S ₃ ),......,(Q ₂₀₀ ,S100)]. Then, the user A turns the electric automobile to the user B for use, and the user B drives the electric automobile frequently to drive in mountainous and hilly areas, frequently climbs a slope, the average energy consumption is larger, after the user B uses the electric automobile for a period of time, the history driving sequence is accumulated to 300 from 200 history driving information before the user B takes the hand, namely, the history driving sequence comprises [ (Q) ₁ ,S ₁ ),(Q ₂ ,S ₂ ),(Q ₃ ,S ₃ ),......,(Q ₂₀₀ ,S100)，......,(Q ₃₀₀ ,S ₃₀₀ )]. It can be understood that the first 200 pieces of historical driving information cannot reflect the driving habit of the user B, which is equivalent to invalid data, and if the first 200 pieces of historical driving information are adopted to calculate the historical average energy consumption, the historical average energy consumption is smaller than the current driving situation of the user B, so that the estimated endurance mileage is larger, half-road power loss of the electric automobile is easy to occur, and the electric automobile cannot be kept running to the destination. Therefore, in order to ensure accurate rationality of the historical average energy consumption, an effective driving sequence is determined from the historical driving sequences. For example, for user B, the active travel sequence may include the last 100 pieces of historical travel information in the historical travel sequence, i.e., the active travel sequence includes [ (Q) ₂₀₀ ,S100),......,(Q ₃₀₀ ,S ₃₀₀ )]。

Similarly, in some cases, for example, the user a often turns on the air conditioner in summer and does not turn on the air conditioner in other seasons, so that the energy consumption is larger in the historical driving information generated in summer, the historical driving information generated in summer is adopted to calculate the historical average energy consumption in autumn, the calculated historical average energy consumption is larger, the energy consumption condition in autumn cannot be reflected, and the cruising mileage can be too conservative.

In some cases, as the battery ages, the historical average energy consumption will also become larger and smaller if the historical average energy consumption of the aging program is calculated by using the historical driving information of the aging degree smaller, the historical average energy consumption will be smaller, and the endurance mileage will be larger and too credible.

Therefore, according to the historical driving sequence, the effective driving sequence is determined, so that the effective driving sequence can reflect the recent driving state of the electric automobile.

S30: and determining historical average energy consumption according to the effective driving sequence.

For example, the historical average energy consumption may be the accumulation of energy consumption corresponding to each historical driving information in the effective driving sequence divided by the accumulation of mileage.

The effective driving sequence is continuously redetermined along with the driving of the electric automobile when the historical average energy consumption is calculated each time, so that the effective driving sequence can reflect the recent use state of the electric automobile, the historical average energy consumption can better reflect the recent driving condition (such as driving style, climate environment or vehicle state) of the electric automobile, and the improvement of the accuracy of the endurance mileage is facilitated.

S40: and acquiring available residual energy of a battery on the electric automobile, and determining the endurance mileage of the electric automobile according to the historical average energy consumption, the real-time average energy consumption and the available residual energy.

In some calculation modes, the endurance mileage can be equal to a weighted value of available remaining energy divided by historical average energy consumption and real-time average energy consumption, so that the recent average energy consumption condition of the electric automobile is reflected by combining the historical average energy consumption and the real-time average energy consumption, and the endurance mileage is more accurate.

In addition, in this embodiment, the effective driving sequence for calculating the historical average energy consumption is determined and updated along with the continuous accumulation of the historical driving sequence, so that the historical average energy consumption can reflect the average energy consumption performance of the recent electric automobile, and thus, factors such as the climate environment, the actual state of the vehicle, the road state or the change of the driving style can be embodied in the historical average energy consumption, and the estimation accuracy of the endurance mileage can be improved.

Optionally, referring to fig. 3, the step S20 specifically includes:

s21: and if the accumulated mileage corresponding to the historical driving sequence is greater than or equal to the first mileage threshold value and less than the second mileage threshold value, the historical driving sequence is used as an effective driving sequence.

The accumulated mileage corresponding to the history running sequence is the sum of the mileage of each history running information in the history running sequence. Here, the first mileage threshold is a mileage threshold lower limit for determining that the history travel information in the history travel sequence is valid, and the second mileage threshold is a mileage threshold upper limit for determining that the history travel information in the history travel sequence is valid. It can be understood that when the electric automobile starts to be used when just falling to the ground, the driving times are less, the historical driving information can be the driving data when the user tries to run and experiences in the selling process, the driving habit and the driving state are difficult to reflect, the historical average energy consumption is calculated by only using the unreliable data as an effective driving sequence, the historical average energy consumption is inaccurate, and the recent and real energy consumption condition of the electric automobile cannot be reflected. Thus, the first mileage threshold is employed to screen out the situation: the historical average energy consumption is calculated using only these unreliable data as the effective travel sequence when the amount of the previous historical travel information data is too small.

As the driving times are more and more, the accumulated mileage is larger and larger, the recent driving state of the electric automobile cannot be reflected by the historical driving information before the time point, and if a part of the historical driving information before the time in the historical driving sequence is adopted to calculate the historical average energy consumption, the historical average energy consumption can hardly reflect the recent average energy consumption performance of the electric automobile.

For example, in the above example of use by user A to turn an electric vehicle to user B, the historical driving sequence includes [ (Q) ₁ ,S ₁ ),(Q ₂ ,S ₂ ),(Q ₃ ,S ₃ ),......,(Q ₂₀₀ ,S100)，......,(Q ₃₀₀ ,S ₃₀₀ )]Wherein the former section [ (Q) ₁ ,S ₁ ),(Q ₂ ,S ₂ ),(Q ₃ ,S ₃ ),......,(Q ₂₀₀ ,S100)]Is generated by the driving of the user A on the urban flat road, the latter section [ (Q) ₂₀₀ ,S100)，......,(Q ₃₀₀ ,S ₃₀₀ )]Is generated by the driving of the user B on the hilly road in the mountain area, [ (Q) ₁ ,S ₁ ),(Q ₂ ,S ₂ ),(Q ₃ ,S ₃ ),......,(Q ₂₀₀ ,S100)]If the historical average energy consumption is calculated by adopting the method, the historical average energy consumption is smaller than the current driving situation of the user B, the estimated endurance mileage is larger, the transition is confidence, the half-road power loss of the electric automobile is easy to occur, and the electric automobile cannot be kept running to a destination.

Thus, the second mileage threshold is employed to screen out the situation: when the data amount of the later historical driving information is too large, the historical average energy consumption is calculated by using all the historical driving sequences as effective driving sequences. Therefore, when the accumulated mileage corresponding to the historical driving sequence is larger than or equal to the first mileage threshold value and smaller than the second mileage threshold value, the historical driving sequence is directly used as the effective driving sequence.

It is understood that the first mileage threshold value and the second mileage threshold value may be empirical values of those skilled in the art, for example, the first mileage threshold value may be 500km, and the second mileage threshold value may be 5000km.

In this embodiment, a first mileage threshold is employed to screen out the cases: the historical average energy consumption is calculated using only these unreliable data as the effective travel sequence when the amount of the previous historical travel information data is too small. Screening with a second mileage threshold excludes the following: when the data amount of the later historical driving information is too large, the historical average energy consumption is calculated by using all the historical driving sequences as effective driving sequences. And only when the accumulated mileage corresponding to the historical driving sequence is greater than or equal to the first mileage threshold and less than the second mileage threshold, the historical driving sequence is directly used as the effective driving sequence, so that the effective driving sequence is relatively reliable, and the historical average energy consumption is relatively reliable and has parametricity.

Optionally, referring to fig. 3, the step S20 specifically further includes:

s22: and if the accumulated mileage corresponding to the history running sequence is greater than or equal to the second mileage threshold value, replacing the history running information with the latest history running information in the effective running sequence, and obtaining the updated effective running sequence.

Based on the above, the second mileage threshold is an upper mileage threshold limit for determining that the history running information in the history running sequence is valid. Screening with a second mileage threshold excludes the following: when the data amount of the later historical driving information is too large, the historical average energy consumption is calculated by using all the historical driving sequences as effective driving sequences.

For example, the first mileage threshold value is 500km, the second mileage threshold value is 5000km, and the history travel sequence is taken as the effective travel sequence when the accumulated mileage corresponding to the history travel sequence is greater than or equal to 500km and less than 5000 km. It can be understood that, since the history running information in the history running sequence is automatically updated and increased along with the driving use of the electric vehicle, each time the running is completed, the latest history running information is added in the history running sequence, and the effective running sequence is correspondingly updated and the latest history running information is added.

If the accumulated mileage corresponding to the history running sequence is 4900km after the i-1 th running is completed, the effective running sequence is the history running sequence [ (Q) ₁ ,S ₁ ),(Q ₂ ,S ₂ ),(Q ₃ ,S ₃ ),...,(Q _i-1 ,S _i-1 )]. If 150km is travelled next (i-th time), the accumulated mileage of the history travel sequence is 5050km, which is greater than the second mileage threshold value 5000km, and the effective travel sequence [ (Q) before update is replaced with the i-th time history travel information (Qi, si) ₁ ,S ₁ ),(Q ₂ ,S ₂ ),(Q ₃ ,S ₃ ),...,(Q _i-1 ,S _i-1 )]Historical travel information (Q) of the 1 st travel with the forefront time ₁ ,S ₁ ) Obtaining updated effective driving sequence [ (Q) ₂ ,S ₂ ),(Q ₃ ,S ₃ ),...,(Q _i ,S _i )]。

If 200km is travelled by the (i+1) th time, the accumulated mileage of the history travel sequence is greater than the second mileage threshold value 5000km, so that the (Q) th time of the history travel information (i+1) _i+1 ,S _i+1 ) Replacement of the pre-update active travel sequence [ (Q) ₂ ,S ₂ ),(Q ₃ ,S ₃ ),...,(Q _i ,S _i )]Historical travel information of the 2 nd time of the most forward time (Q ₂ ,S ₂ ) Obtaining updated effective driving sequence [ (Q) ₃ ,S ₃ ),...,(Q _i ,S _i ),(Q _i+1 ,S _i+1 )]。

It can be understood that when the ith travel is completed and the accumulated mileage of the history travel sequence is greater than the second mileage threshold in the subsequent travel, the subsequent effective travel sequence is updated by replacing the history travel information with the newly added history travel information with the most previous history travel information in the effective travel sequence before the update, so as to obtain the updated effective travel sequence. As shown in fig. 4, similar to "sliding window screening", as the history travel information increases, the history travel information (mileage and energy consumption) of "the time point most forward" is iteratively removed, and the latest generated history travel information is increased, resulting in an updated effective travel sequence.

By the method, when the accumulated mileage corresponding to the history driving sequence is greater than or equal to the second mileage threshold value, the latest history driving information can be continuously updated and brought into the effective driving sequence, and the history driving information with the forefront time is replaced, so that the effective driving sequence can reflect the recent driving state of the electric automobile, and the history average energy consumption can reflect the recent energy consumption performance of the electric automobile.

Optionally, referring to fig. 3 again, the step S20 specifically further includes:

s23: and if the accumulated mileage corresponding to the historical driving sequence is smaller than the first mileage threshold value, determining the historical average energy consumption by adopting a preset standard working condition.

Based on the above knowledge, the first mileage threshold is a mileage threshold lower limit for determining that the history running information in the history running sequence is valid, and the condition is screened and excluded by adopting the first mileage threshold: the historical average energy consumption is calculated using only these unreliable data as the effective travel sequence when the amount of the previous historical travel information data is too small.

When the accumulated mileage corresponding to the history driving sequence is smaller than the first mileage threshold value, the running times of the electric vehicle are smaller, at the moment, the electric vehicle is equivalent to a new vehicle, the history driving information is smaller, the driving habit and the driving habit condition of the vehicle are difficult to reflect, the statistics is not provided, and the electric vehicle belongs to unreliable data.

And under the condition of less historical driving information, determining historical average energy consumption by adopting a preset standard working condition, wherein the preset standard working condition can be a Chinese automobile driving working condition or a NEDC working condition.

For example, the method adopts 'Chinese automobile driving working condition', and calculates according to the specified working condition, so that the calculated historical average energy consumption is more suitable for vehicles driving in China.

In this embodiment, when the historical driving information is less, the historical average energy consumption is determined by adopting the preset standard working condition, so that the historical average energy consumption is suitable for the electric automobile.

According to some embodiments of the application, optionally, the step S10 specifically further includes:

in the current driving, when the current driving mileage reaches the preset mileage each time, the preset mileage and the corresponding energy consumption are adopted to update the real-time average energy consumption.

Here, the electric vehicle is in a driving state before the current driving is not ended, and the current driving mileage may be equal to an integrated value of a vehicle speed of the electric vehicle with respect to time. The energy consumption of the current running may be equal to an integrated value of the discharge power of the battery with respect to time. With the increase of the driving time, the current driving mileage is also increased, and the energy consumption is also increased.

In order to update the real-time average energy consumption, when the mileage reaches a preset mileage (for example, 3 km) each time, a new real-time average energy consumption is calculated by using the preset mileage and the corresponding energy consumption. The "corresponding energy consumption" refers to the energy consumption spent for driving the preset mileage. The real-time average energy consumption is equal to the energy consumption spent traveling the preset mileage divided by the preset mileage. It is understood that the person skilled in the art may set the preset mileage according to the actual situation, for example, the preset mileage is 1km,2km,3km,4km, 5km, etc.

As shown in fig. 5, if the preset mileage is 3km, after the electric vehicle starts, each time the electric vehicle runs 3km, the new real-time average energy consumption is calculated by dividing the energy consumption spent in the 3km by 3km, so that the real-time average energy consumption can be updated along with the running of the electric vehicle, and the current running energy consumption state of the electric vehicle can be reflected more.

In this embodiment, when the current driving mileage reaches the preset mileage, the preset mileage and the corresponding energy consumption are adopted to update the real-time average energy consumption, so that the real-time average energy consumption can be updated along with the driving of the electric automobile, and the current driving energy consumption state of the electric automobile can be reflected more.

Optionally, referring to fig. 6, the step S40 specifically further includes:

s41: and determining the weighted average energy consumption according to the historical average energy consumption and the real-time average energy consumption.

S42: and calculating the endurance mileage according to the weighted average energy consumption and the available residual energy.

And performing weighted fusion processing on the historical average energy consumption and the real-time average energy consumption to obtain weighted average energy consumption after the weighted fusion processing. Therefore, the weighted average energy consumption can reflect the energy consumption state of the historical running and the energy consumption state of the current running.

Therefore, the cruising mileage is calculated based on the weighted average energy consumption and the available residual energy, so that the cruising mileage is more accurate. In some embodiments, the available remaining energy divided by the weighted average energy consumption may be used to obtain the range.

In this embodiment, the weighted average energy consumption after the weighted fusion processing is obtained by performing the weighted fusion processing on the historical average energy consumption and the real-time average energy consumption, so that the weighted average energy consumption can reflect the energy consumption state of the historical driving and the energy consumption state of the current driving, and the cruising mileage calculated according to the weighted average energy consumption and the available residual energy is a result of considering the historical driving energy consumption state and the current driving energy consumption state, which is more accurate.

According to some embodiments of the application, optionally, the step S41 specifically includes:

ECa＝α*ECi+(1-α)*ECh；

The weighting coefficient alpha can be calibrated by a person skilled in the art according to the actual test run situation. The greater the weighting coefficient α, the greater the degree of consideration of the real-time average energy consumption, and the lesser the degree of consideration of the historical average energy consumption.

In this embodiment, the historical average energy consumption and the real-time average energy consumption are fused through the above formula to obtain the weighted average energy consumption, so that the historical average energy consumption and the real-time average energy consumption can be fused according to complementary weights, and the energy consumption state reflected by the weighted average energy consumption can be more in line with the actual situation through calibrating and adjusting the weighting coefficient.

According to some embodiments of the application, optionally, the method S100 further comprises:

s50: and determining a weighting coefficient according to the current driving mode of the electric automobile, the SOC value of the battery on the electric automobile and the ratio of the real-time average energy consumption to the historical average energy consumption of the electric automobile.

The driving mode is to make adjustment and intervention on the hardware according to the set operation logic and program mode by means of electric control hardware and various sensors (speed, rotating speed, vehicle body posture and the like), so that different driving experiences are created. The driving mode may include a sport mode, an economy mode, a normal mode, etc., in which the electric power engine may provide 100% of the kinetic energy, in which the electric power engine may provide 80% of the kinetic energy, and in which the electric power engine may provide 70% of the kinetic energy.

The SOC value of the battery is the state of charge of the battery, reflecting the remaining capacity, and indicates that the battery is completely discharged when the SOC is 0 and that the battery is completely charged when the SOC is 1. The change in the SOC value of the battery may reflect driving habits, e.g., an increase in the SOC value may be when the battery has just been charged. The ratio of the real-time average energy consumption to the historical average energy consumption is affected by the stability of the working condition, such as sudden acceleration, the ratio of the real-time average energy consumption to the historical average energy consumption is increased, sudden deceleration, and the ratio of the real-time average energy consumption to the historical average energy consumption is reduced.

In this embodiment, the weighting coefficient α is calibrated according to three factors, that is, the current driving mode, the SOC value, and the ratio of the real-time average energy consumption to the historical average energy consumption, so that the influence caused by the unstable working condition is reduced, the driving habit is considered, the energy consumption state reflected by the weighted average energy consumption is more in line with the actual situation, and the calculated range is more accurate.

According to some embodiments of the present application, optionally, a correspondence relationship between a plurality of driving modes and a weighting coefficient map including a correspondence relationship between a weighting coefficient, an SOC value, and a ratio of real-time average energy consumption and historical average energy consumption is preset.

For example, the motion pattern corresponds to the weighting factor map 1#, the normal pattern corresponds to the weighting factor map 2#, and the economy pattern corresponds to the weighting factor map 3#, which includes, for each weighting factor map, a correspondence between the weighting factor α, the SOC value, and ECi/ECh (ratio of real-time average energy consumption and historical average energy consumption). It is understood that the correspondence between α, SOC value and ECi/ECh in the weighting factor map in each driving mode may be obtained by statistics of actual test run by those skilled in the art.

The step S50 specifically includes:

s51: and determining a target weighting coefficient map corresponding to the current driving mode according to the current driving mode.

S52: and according to the SOC value of the battery on the electric vehicle and the ratio of the real-time average energy consumption to the historical average energy consumption of the electric vehicle, searching a corresponding weighting coefficient in the target weighting coefficient map.

The current driving mode is a driving mode corresponding to the current running. After the current driving mode is acquired, a target weighting factor map corresponding to the current driving mode can be determined. It is to be understood that the weighting factor map corresponding to the current driving mode is referred to as a target weighting factor map for illustrative purposes only.

After the target weighting coefficient map is determined, based on the corresponding relation among the weighting coefficient alpha, the SOC value and the ECi/ECh included in the target weighting coefficient map, the corresponding weighting coefficient can be found out from the target weighting coefficient map according to the SOC value and the ECi/ECh of the battery on the electric automobile. The SOC value of the battery on the electric automobile can be obtained from a battery management system.

In this embodiment, the correspondence between the driving modes and the weighting coefficient map is preset, and the weighting coefficient map in each driving mode includes the correspondence between the weighting coefficient, the SOC value, and the ratio of the real-time average energy consumption to the historical average energy consumption, so that the weighting coefficient can be determined according to the driving mode, the SOC value, and the ratio of the real-time average energy consumption to the historical average energy consumption. The determination of the weighting coefficient is more refined and accurate, so that the accuracy of the endurance mileage is improved.

According to some embodiments of the application, optionally, the weighting factor map exhibits the same trend regardless of driving mode. Specifically, in the weighting coefficient map, the weighting coefficient tends to increase and decrease with an increase in the ratio of the real-time average energy consumption and the historical average energy consumption. In the weighting coefficient map, the weighting coefficient tends to increase with an increase in the SOC value.

As shown in table 1 below, table 1 is a data table of a weighting factor map in the normal mode.

TABLE 1

As shown in table 1 above, when the real-time average energy consumption/historical average energy consumption (ECi/ECh) is less than 1, the weighting coefficient α tends to increase as the real-time average energy consumption/historical average energy consumption (ECi/ECh) increases; when the real-time average power consumption/history average power consumption (ECi/ECh) is smaller than 1, the weighting coefficient α tends to decrease as the real-time average power consumption/history average power consumption (ECi/ECh) increases. Because, when the real-time average energy consumption/historical average energy consumption (ECi/ECh) is larger or smaller, the unstable fluctuation of the current driving working condition is indicated to be large, and the weighting coefficient alpha is reduced, so that the contribution of the real-time average energy consumption to the weighted average energy consumption is reduced, and the influence of the unstable driving working condition on the endurance mileage can be reduced.

It can also be seen from table 1 that the weighting coefficient tends to increase with increasing SOC value. It can be understood that when the SOC value of the battery increases, the driver may drive the vehicle again after charging the vehicle for a period of time by the charging post after completing one driving, and this time may be the driving habit of the original driver or other persons, and the driving habits of different persons are different, so that the difference between the real-time average energy consumption and the historical average energy consumption is large.

In this embodiment, in the weighting coefficient map obtained through test calibration, the weighting coefficient has a trend of increasing and then decreasing with increasing of the ratio of the real-time average energy consumption and the historical average energy consumption, and the weighting coefficient has a trend of increasing with increasing of the SOC value, so that contribution of the real-time average energy consumption to the weighting average energy consumption can be better distributed through the weighting coefficient, and influence of unstable driving conditions on the endurance mileage can be reduced.

Referring to fig. 7, a method for determining a range according to some embodiments of the present application includes the steps of:

(1) Calculating historical average energy consumption: the following 3 cases are determined;

a) And if the accumulated mileage corresponding to the historical driving sequence is less than the first mileage threshold value of 500km, determining the historical average energy consumption by adopting China automobile driving condition.

b) And if the accumulated mileage corresponding to the historical driving sequence is greater than or equal to the first mileage threshold 500km and less than the second mileage threshold 5000km, taking the historical driving sequence as an effective driving sequence, wherein the historical average energy consumption is the accumulated energy consumption of the effective driving sequence divided by the accumulated mileage.

c) And if the accumulated mileage corresponding to the history running sequence is greater than or equal to the second mileage threshold value, replacing the history running information with the latest history running information in the effective running sequence, and obtaining the updated effective running sequence. The historical average energy consumption is the accumulated energy consumption of the updated active travel sequence divided by the accumulated mileage.

(2) Calculating real-time average energy consumption: in the current driving, when the mileage reaches 3km each time, the energy consumption spent for driving the 3km is divided by 3km, so as to obtain updated real-time average energy consumption.

(3) Calculating weighted average energy consumption: calculating weighted average energy consumption by adopting the following formula;

ECa＝α*ECi+(1-α)*ECh；

(4) And calculating the endurance mileage, wherein the endurance mileage is equal to the ratio of the available residual energy to the weighted average energy consumption.

In this embodiment, the historical average energy consumption is determined according to 3 situations of the driving condition of the electric vehicle, where the effective driving sequence for calculating the historical average energy consumption is determined and updated along with the continuous accumulation of the historical driving sequence, so that the historical average energy consumption can reflect the average energy consumption performance of the recent electric vehicle, and thus factors such as the weather environment, the actual state of the vehicle, the road state or the change of driving style can be reflected in the historical average energy consumption, and the estimation accuracy of the range can be improved.

Specifically, under the condition that the historical driving information is less, the historical average energy consumption is determined by adopting a preset standard working condition, so that the historical average energy consumption is suitable for the electric automobile. And only when the accumulated mileage corresponding to the historical driving sequence is greater than or equal to the first mileage threshold and less than the second mileage threshold, the historical driving sequence is directly used as the effective driving sequence, so that the effective driving sequence is relatively reliable, and the historical average energy consumption is relatively reliable and has parametricity. When the accumulated mileage corresponding to the history running sequence is greater than or equal to the second mileage threshold, the latest history running information can be continuously updated and brought into the effective running sequence, and the history running information with the forefront time is replaced, so that the effective running sequence can reflect the recent running state of the electric automobile, and the historical average energy consumption can reflect the recent energy consumption performance of the electric automobile.

And finally, the historical average energy consumption and the real-time average energy consumption are combined to reflect the recent average energy consumption condition of the electric automobile, so that the endurance mileage is more accurate.

Referring to fig. 8, the present application further provides an apparatus 200 for determining a range according to some embodiments of the present application, which includes a real-time average energy consumption determining module 201, an effective driving sequence determining module 202, a historical average energy consumption determining module 203, and a range determining module 204.

The real-time average energy consumption determining module 201 is configured to obtain current running energy consumption and mileage of the electric vehicle, and determine real-time average energy consumption according to the current running energy consumption and mileage.

The effective driving sequence determining module 202 is configured to obtain a historical driving sequence of the electric vehicle, and determine the effective driving sequence according to the historical driving sequence, where the historical driving sequence includes historical driving information arranged in time sequence, and each piece of historical driving information corresponds to mileage and energy consumption.

The historical average energy consumption determining module 203 is configured to determine historical average energy consumption according to the effective driving sequence.

The endurance mileage determining module 204 is configured to obtain available remaining energy of a battery on the electric vehicle, and determine an endurance mileage of the electric vehicle according to the historical average energy consumption, the real-time average energy consumption, and the available remaining energy.

In the above apparatus 200, the effective driving sequence for calculating the historical average energy consumption is determined and updated along with the continuous accumulation of the historical driving sequence, so that the historical average energy consumption can reflect the average energy consumption performance of the recent electric vehicle, and thus, factors such as the climate environment, the actual state of the vehicle, the road state or the change of the driving style of the driver can be reflected in the historical average energy consumption, and the estimation accuracy of the endurance mileage can be improved. In addition, the historical average energy consumption and the real-time average energy consumption are combined to reflect the recent average energy consumption condition of the electric automobile, so that the endurance mileage is more accurate.

According to some embodiments of the present application, the effective driving sequence determining module 202 is specifically configured to: and if the accumulated mileage corresponding to the historical driving sequence is greater than or equal to the first mileage threshold value and less than the second mileage threshold value, the historical driving sequence is used as an effective driving sequence.

According to some embodiments of the present application, the effective driving sequence determining module 202 is further specifically configured to: and if the accumulated mileage corresponding to the history running sequence is greater than or equal to the second mileage threshold value, replacing the history running information with the latest history running information in the effective running sequence, and obtaining the updated effective running sequence.

In this embodiment, by the above manner, when the accumulated mileage corresponding to the history driving sequence is greater than or equal to the second mileage threshold, the latest history driving information can be continuously updated and incorporated in the effective driving sequence, and the history driving information with the forefront time is replaced, so that the effective driving sequence can reflect the recent driving state of the electric automobile, and the history average energy consumption can reflect the recent energy consumption performance of the electric automobile.

According to some embodiments of the present application, the above-mentioned historical average energy consumption determining module 203 is further specifically configured to: and if the accumulated mileage corresponding to the historical driving sequence is smaller than the first mileage threshold value, determining the historical average energy consumption by adopting a preset standard working condition.

According to some embodiments of the present application, the real-time average energy consumption determining module 201 is further specifically configured to: in the current driving, when the current driving mileage reaches the preset mileage each time, the preset mileage and the corresponding energy consumption are adopted to update the real-time average energy consumption.

According to some embodiments of the present application, the range determining module 204 further includes a weighted average energy consumption determining unit and a range determining unit (not shown), where the weighted average energy consumption determining unit is specifically configured to determine the weighted average energy consumption according to the historical average energy consumption and the real-time average energy consumption. The endurance mileage determining unit is specifically configured to calculate the endurance mileage according to the weighted average energy consumption and the available remaining energy.

According to some embodiments of the application, the weighted average energy consumption determination unit is further specifically configured to: calculating weighted average energy consumption by adopting the following formula;

ECa＝α*ECi+(1-α)*ECh；

According to some embodiments of the present application, the apparatus 200 for determining a range further includes a weighting coefficient determining module (not shown), which is specifically configured to determine the weighting coefficient according to a current driving mode of the electric vehicle, an SOC value of a battery on the electric vehicle, and a ratio of a real-time average energy consumption to a historical average energy consumption of the electric vehicle.

According to some embodiments of the present application, a correspondence relationship between a plurality of driving modes and a weighting coefficient map including a correspondence relationship between a weighting coefficient, an SOC value, and a ratio of real-time average energy consumption and historical average energy consumption is preset. The weighting coefficient determining module is specifically configured to: and determining a target weighting coefficient map corresponding to the current driving mode according to the current driving mode. And then, according to the SOC value of the battery on the electric vehicle and the ratio of the real-time average energy consumption to the historical average energy consumption of the electric vehicle, searching a corresponding weighting coefficient in a target weighting coefficient map.

According to some embodiments of the application, in the weighting factor map, the weighting factor tends to increase and decrease with increasing ratio of the real-time average energy consumption and the historical average energy consumption. In the weighting coefficient map, the weighting coefficient tends to increase with an increase in the SOC value.

Referring to fig. 9, the present application further provides a system 300 for determining a range according to some embodiments of the present application, including a processor and a memory, where the processor is communicatively connected to the memory, and the processor is configured to execute program instructions stored in the memory to implement the method for determining a range according to the foregoing embodiments.

Memory 301 may include, among other things, read only memory and random access memory, and provides instructions and data to the processor. A portion of the memory 301 may also include non-volatile random access memory (non-volatile random accedd memory, NVRAM). The memory 301 stores operating instructions, executable modules or data structures, or a subset thereof, or an extended set thereof.

The processor 302 may be an integrated circuit chip with signal processing capabilities. In implementation, the steps of the method for determining the range may be performed by an integrated logic circuit of hardware or instructions in software form in the processor 302. The processor 302 may be a general purpose processor, a digital signal processor (diginal signal processing, DSP), a microprocessor or microcontroller, and may further include an application specific integrated circuit (application specific integrated circuit, ASIC), a field-programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components. The processor can realize the method for determining the endurance mileage.

In some embodiments, the processor 302 and the memory 301 in the system 300 for determining the range may be a domain controller or a whole vehicle controller of the electric vehicle, that is, the domain controller or the whole vehicle controller executes program instructions to implement the foregoing method for determining the range, and calculate the range in real time.

It is appreciated that in some embodiments, the system 300 further includes a dashboard 303, and the processor 302 and memory 301 are communicatively coupled to the dashboard 303, a Battery Management System (BMS) of a battery, and a tachograph (not shown) of an electric vehicle, respectively.

Wherein the battery management system provides parameters regarding the battery, such as energy consumption, available remaining capacity or SOC value, etc. The tachograph records and stores parameters related to each trip, such as speed, time, mileage, etc. The dashboard 303 can display status data of the electric vehicle.

Thus, the processor 302 may obtain the available remaining energy monitored by the battery management system, the historical driving energy consumption and mileage recorded by the driving recorder and the current driving energy consumption and mileage, execute the program or instruction corresponding to the method for determining the driving mileage in the memory 301, so as to implement the method for determining the driving mileage, obtain the driving mileage of the electric automobile, and send the driving mileage to the instrument panel 303 for display. Thus, the user can plan the trip according to the updated range in real time displayed on the dashboard 303.

In this embodiment, the system for determining the range can implement the method for determining the range in the foregoing embodiment, and has the same technical effects as the method for determining the range in the foregoing embodiment, that is, the estimation accuracy of the range can be improved.

According to some embodiments of the present application, the present application further provides an electric vehicle, including the aforementioned system for determining a range.

In this embodiment, the electric vehicle has the same technical effects as the aforementioned system for determining the range, i.e., the estimation accuracy of the range can be improved.

According to some embodiments of the present application, a computer readable storage medium is provided, in which program instructions are stored, the program instructions may be executed by a processor to implement the processes of the foregoing method embodiments for determining a range, and the same technical effects may be achieved, so that repetition is avoided and redundant description is omitted.

The computer readable storage medium may include a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic or optical disk, and the like.

Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present application, and not for limiting the same; although the application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the application, and are intended to be included within the scope of the appended claims and description. In particular, the technical features mentioned in the respective embodiments may be combined in any manner as long as there is no structural conflict. The present application is not limited to the specific embodiments disclosed herein, but encompasses all technical solutions falling within the scope of the claims.

Claims

A method of determining a range, comprising:

acquiring the current running energy consumption and mileage of an electric automobile, and determining real-time average energy consumption according to the current running energy consumption and mileage;

acquiring a historical driving sequence of the electric automobile, and determining an effective driving sequence according to the historical driving sequence, wherein the historical driving sequence comprises historical driving information arranged in time sequence, and each piece of historical driving information corresponds to mileage and energy consumption;

according to the effective driving sequence, determining historical average energy consumption;

and acquiring available residual energy of a battery on the electric automobile, and determining the endurance mileage of the electric automobile according to the historical average energy consumption, the real-time average energy consumption and the available residual energy.
The method of claim 1, wherein said determining an effective travel sequence from said historical travel sequence comprises:

and if the accumulated mileage corresponding to the historical driving sequence is greater than or equal to a first mileage threshold value and less than a second mileage threshold value, the historical driving sequence is used as the effective driving sequence.
The method of claim 2, wherein said determining an effective travel sequence from said historical travel sequence further comprises:

And if the accumulated mileage corresponding to the history running sequence is greater than or equal to the second mileage threshold value, replacing the history running information with the latest history running information in the effective running sequence, so as to obtain an updated effective running sequence.
A method according to any one of claims 1-3, characterized in that the method further comprises:

and if the accumulated mileage corresponding to the historical driving sequence is smaller than the first mileage threshold, determining the historical average energy consumption by adopting a preset standard working condition.
The method according to any one of claims 1-4, wherein said determining a real-time average energy consumption from said current driving energy consumption and mileage comprises:

and in the current driving, when the current driving mileage reaches a preset mileage each time, updating the real-time average energy consumption by adopting the preset mileage and the corresponding energy consumption.
The method of claim 5, wherein the determining the range of the electric vehicle based on the historical average energy consumption, the real-time average energy consumption, and the available remaining energy comprises:

determining weighted average energy consumption according to the historical average energy consumption and the real-time average energy consumption;

And calculating the endurance mileage according to the weighted average energy consumption and the available residual energy.
The method of claim 6, wherein said determining a weighted average energy consumption from said historical average energy consumption and said real-time average energy consumption comprises:

calculating the weighted average energy consumption by adopting the following formula;

ECa＝α*ECi+(1-α)*ECh；

where ECa is the weighted average energy consumption, α is a weighting coefficient, ECi is the real-time average energy consumption, and ECh is the historical average energy consumption.
The method of claim 7, wherein the method further comprises:

and determining the weighting coefficient according to the current driving mode of the electric automobile, the SOC value of the battery on the electric automobile and the ratio of the real-time average energy consumption to the historical average energy consumption of the electric automobile.
The method according to claim 8, wherein correspondence between a plurality of driving modes and a weighting coefficient map including correspondence between weighting coefficients, SOC values, and ratios of real-time average energy consumption and historical average energy consumption is preset;

the determining the weighting coefficient according to the current driving mode of the electric automobile, the SOC value of the battery on the electric automobile, and the ratio of the real-time average energy consumption to the historical average energy consumption of the electric automobile includes:

Determining a target weighting coefficient map corresponding to the current driving mode according to the current driving mode;

and searching a corresponding weighting coefficient in the target weighting coefficient map according to the SOC value of the battery on the electric automobile and the ratio of the real-time average energy consumption to the historical average energy consumption of the electric automobile.
The method of claim 9, wherein the step of determining the position of the substrate comprises,

in the weighting coefficient map, the weighting coefficient tends to increase and decrease with the increase of the ratio of the real-time average energy consumption and the historical average energy consumption;

in the weighting coefficient map, the weighting coefficient tends to increase with an increase in the SOC value.
An apparatus for determining a range, comprising:

the real-time average energy consumption determining module is used for acquiring the current running energy consumption and mileage of the electric automobile and determining the real-time average energy consumption according to the current running energy consumption and mileage;

the effective driving sequence determining module is used for acquiring a historical driving sequence of the electric automobile and determining the effective driving sequence according to the historical driving sequence, wherein the historical driving sequence comprises historical driving information arranged in time sequence, and each piece of historical driving information corresponds to mileage and energy consumption;

The historical average energy consumption determining module is used for determining historical average energy consumption according to the effective driving sequence;

and the endurance mileage determining module is used for acquiring available residual energy of the battery on the electric automobile and determining the endurance mileage of the electric automobile according to the historical average energy consumption, the real-time average energy consumption and the available residual energy.
A system for determining range comprising a processor and a memory, the processor being communicatively coupled to the memory, the processor being configured to execute program instructions stored in the memory to implement the method for determining range of any of claims 1-10.
An electric vehicle comprising the system for determining range of claim 12.
A computer readable storage medium having stored therein program instructions executable by a processor to implement the method of determining range as claimed in any one of claims 1-10.