CN109747427B - Method and apparatus for estimating remaining driving ability of electric vehicle when reaching destination - Google Patents

Method and apparatus for estimating remaining driving ability of electric vehicle when reaching destination Download PDF

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CN109747427B
CN109747427B CN201910105214.8A CN201910105214A CN109747427B CN 109747427 B CN109747427 B CN 109747427B CN 201910105214 A CN201910105214 A CN 201910105214A CN 109747427 B CN109747427 B CN 109747427B
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CN109747427A (en
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罗国鹏
李海
葛云飞
刘畅
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Guangzhou Xiaopeng Motors Technology Co Ltd
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Abstract

Embodiments of the present invention relate to a method, an electronic device, and a computer-readable storage medium for estimating a remaining driving ability when an electric vehicle reaches a destination. The electric vehicle transmits navigation subsection driving path information and driving parameters of the vehicle to the server, the server calculates subsection driving energy consumption and accessory energy consumption reaching a destination by using an energy consumption analysis model established by big data analysis, and expected energy consumption information of the vehicle reaching the destination is determined and updated in real time based on the estimated driving energy consumption and accessory energy consumption. In this way, instead of the current vehicle meter dynamically displaying the driving range, the electric vehicle can accurately estimate the remaining driving capability to the destination, thereby assisting the user in reasonably arranging the driving route. In addition, the embodiment of the invention realizes the switching of the estimation modes under the conditions of on-line, off-line and the like according to the change of the communication environment.

Description

Method and apparatus for estimating remaining driving ability of electric vehicle when reaching destination
Technical Field
The present invention relates generally to the field of data processing, and more particularly to a method and apparatus for estimating a remaining driving ability when an electric vehicle reaches a destination.
Background
New energy vehicles such as electric vehicles are becoming the development direction of future vehicles, however, the current new energy transportation facilities and their technologies restrict the driving performance of electric vehicles. The driving ability of the electric vehicle is one of key indexes, and the traveling experience of a user is directly influenced. Under the condition of limited driving capability, the accuracy of driving range calculation influences the judgment of the user on the trip. It is desirable to provide a solution that can accurately and efficiently estimate the remaining driving capability of an electric vehicle to assist a user in more reasonably arranging a travel route.
Disclosure of Invention
In view of the above problems, embodiments of the present invention provide a method, an electronic device, and a computer-readable storage medium for estimating a remaining driving ability of an electric vehicle, which builds an energy consumption analysis model for the electric vehicle through a self-learning process based on big data, and estimates the remaining driving ability to a destination by means of the internet and a navigation technology to recommend driving information to a user.
In a first aspect of the present invention, a method of estimating a remaining driving ability when an electric vehicle reaches a destination is provided. The method comprises the following steps: acquiring running path information and driving parameters of a vehicle; estimating the driving energy consumption and the accessory energy consumption to the destination by using an energy consumption analysis model based on the driving path information and the driving parameters; and determining an expected energy consumption for the vehicle to reach the destination based on the estimated energy consumption for travel and the energy consumption for the accessories.
In certain embodiments, the method further comprises building an energy consumption analysis model, wherein building the energy consumption analysis model comprises: obtaining historical vehicle data of a vehicle, wherein the historical vehicle data comprises a driving mode, an energy recovery mode, a vehicle speed, driving environment information and energy consumption; and establishing a functional relationship between vehicle speed and energy consumption in different environments and vehicle states based on the historical vehicle data, the vehicle states corresponding to different combinations of driving modes and energy recovery modes of the vehicle.
In some embodiments, the historical vehicle data further includes air conditioner setting parameters and air conditioner power, the driving environment information includes ambient temperature, and wherein establishing the energy consumption analysis model further includes establishing a functional relationship between the ambient temperature and the air conditioner power at different air conditioner settings based on the historical vehicle data.
In some embodiments, obtaining the travel path information and the driving parameters of the vehicle includes: acquiring travel path information from a navigation system of a vehicle, the travel path information including path information of at least one path to a destination, the at least one path being divided into a plurality of sections; and obtaining driving parameters transmitted by a control system of the vehicle at a first time interval, the driving parameters including a plurality of: the system comprises the current remaining driving capacity, a driving mode, an energy recovery mode, an air conditioner setting parameter vehicle speed, a direct current voltage conversion related parameter, air conditioner power and driving environment information.
In some embodiments, estimating the energy consumption of the vehicle and the energy consumption of the accessories to the destination comprises: determining a current vehicle state of the vehicle based on the driving parameters; estimating energy consumption for traveling to the destination on a section-by-section basis based on a functional relationship between vehicle speed and energy consumption corresponding to the determined vehicle state; and calculating an accessory energy consumption of the vehicle based on the driving parameters, the accessory energy consumption including at least an air conditioner energy consumption to the destination and other accessory energy consumptions.
In certain embodiments, calculating accessory energy consumption for a vehicle comprises: determining current air conditioner setting parameters of the vehicle based on the driving parameters; estimating air conditioning energy consumption to the destination on a section-by-section basis of the path based on a functional relationship between the ambient temperature and the air conditioning power corresponding to the determined air conditioning setting parameter.
In some embodiments, determining the remaining range capability of the vehicle to reach the destination comprises: and calculating a driving capability reduction coefficient of the vehicle from the destination, wherein the driving capability reduction coefficient represents the ratio of the driving range reduction amount to the destination to the distance to the destination.
In some embodiments, calculating the range degradation factor for the vehicle from the current destination comprises: calculating a current driving ability reduction coefficient k of the vehicle from the destination according to the following formula
Figure BDA0001966569160000021
Wherein, Delta EACPΔ E for estimated air conditioning energy consumption to destinationEXFor estimated energy consumption of other accessories to the destination, ENEDCFor standard condition energy consumption, D is the distance from the current position of the vehicle to the destination along at least one path, N is the number of sections from the current position of the vehicle to the destination on at least one path, DiIs the distance of the i-th section of the N sections, ED_drv_iIs the driving energy consumption of the section i estimated according to the energy consumption analysis model.
In some embodiments, the estimated air conditioning energy consumption to reach the destination is calculated according to the following equation:
Figure BDA0001966569160000022
wherein, PiFor the air-conditioning power of section i estimated from the energy consumption analysis model, TiIs the travel time of the i-th section.
In some embodiments, the estimated energy consumption of other accessories to reach the destination is calculated according to the following equation:
Figure BDA0001966569160000031
wherein, PEXiOther accessory power for section i, TiIs the travel time of the i-th section.
In certain embodiments, the method further comprises: and updating the energy consumption analysis model based on the acquired driving parameters.
In certain embodiments, the method further comprises: selecting different driving parameter combinations according to the component state combinations of the vehicle, wherein the component states of the vehicle comprise a vehicle speed, an air conditioner state and a direct current voltage conversion component state; calculating the range of the driving ability reduction coefficient under the condition of different driving parameter combinations; and transmitting the range of the drivability reduction factor to the vehicle.
In certain embodiments, determining the expected energy consumption of the vehicle to reach the destination further comprises: calculating an expected energy consumption of the vehicle to reach the destination based on the cruising ability reduction coefficient; and transmitting the expected energy consumption to the vehicle such that the vehicle determines a remaining range capability to reach the destination.
In certain embodiments, the method further comprises: the ride capacity degradation factor is transmitted to the vehicle for the vehicle to calculate a remaining ride capacity to reach the destination.
In a second aspect of the present invention, a method of estimating a residual driving capability of an electric vehicle is provided. The method comprises the following steps: transmitting the driving path information and the driving parameters of the vehicle to a server; receiving information about a current expected energy consumption of a vehicle from a destination, the information estimated by a server based on an energy consumption analysis model that analyzes historical vehicle data for the vehicle; and calculating the remaining driving capability of the vehicle based on the received information.
In some embodiments, the information includes a range degradation factor k of the vehicle from the destination, the range degradation factor k characterizing a ratio of a range degradation to the destination to a distance to the destination, and wherein calculating the remaining range of the vehicle comprises: calculating the remaining driving ability d according to the following formula
d＝d0-k*D
Wherein d is0For the current remaining range, D is the distance of the vehicle from the destination along at least one path currently.
In certain embodiments, the method further comprises: detecting the current communication condition of the vehicle and the server; calculating an expected energy consumption to reach the destination based on an average energy consumption of the vehicle within a predetermined distance from the current location in response to the communication condition indicating that the vehicle is in an offline state; and determining a remaining range to the destination based on the current remaining range and the expected energy consumption of the vehicle.
In certain embodiments, the method further comprises: acquiring the range of the driving ability reduction coefficient k under the condition of different driving parameter combinations from a server, wherein the different driving parameter combinations are selected according to the component state combinations of the vehicle, and the component states of the vehicle comprise the vehicle speed, the air conditioner state and the direct current voltage conversion component state; and determining a cruising ability reduction coefficient k of the vehicle from the destination based on a range of the cruising ability reduction coefficient k in response to the communication condition indicating that the communication with the server is interrupted; and calculating the remaining driving range d according to the following formula
d＝d0-k*D
Wherein d is0For the current remaining range, D is the current distance of the vehicle from the destination along the at least one route.
In certain embodiments, the method further comprises: outputting a prompt message based on the calculated remaining driving capability of the vehicle, the prompt message including at least one of: recommended routes, recommended driving modes, recommended power consumption component settings.
In a third aspect of the present invention, there is provided an electronic apparatus comprising: a processor; and a memory storing instructions that, when executed by the processor, cause the apparatus to perform the methods described according to the first and second aspects of the invention.
In a fourth aspect of the invention, there is provided a computer readable storage medium storing machine readable instructions which, when executed by a machine, cause the machine to perform the method described in accordance with the first and second aspects of the invention.
The embodiment of the invention provides a method for establishing a driving energy consumption analysis model and an accessory energy consumption analysis model for each electric vehicle through a self-learning module based on big data, and estimating the energy consumption service condition of the electric vehicle as accurately as possible; and on-line correction of the estimation method is realized simultaneously based on a big data analysis fitting algorithm. Further, the estimation manner can be switched between online and offline in consideration of a change in communication environment. The scheme of dynamically displaying the driving range of the current vehicle instrument is replaced, the calculation and display of a navigation system are combined, the residual driving capacity of the destination is accurately estimated in the navigation mode, and therefore the user is assisted in reasonably arranging a travel route.
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FIG. 1 shows a schematic block diagram of a system for estimating the remaining range capacity of an electric vehicle according to one embodiment of the present invention;
FIG. 2 illustrates a schematic block diagram of a process for a server to predict a remaining range based on big data, according to one embodiment of the present invention;
FIG. 3 illustrates a schematic view of a fitted curve between average vehicle speed and average energy consumption for driving according to an embodiment of the present invention;
FIG. 4 shows a schematic diagram of a fitted curve between air conditioning power and ambient temperature according to one embodiment of the present invention;
FIG. 5 illustrates a flow diagram of a method of estimating the electric vehicle's remaining range capability according to one embodiment of the present invention;
FIG. 6 illustrates a flow chart of a method of estimating the electric vehicle's remaining range capability according to another embodiment of the present invention; and
FIG. 7 illustrates a block diagram of an electronic device suitable for implementing embodiments of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to specific embodiments and the accompanying drawings. Those skilled in the art will appreciate that the present invention is not limited to the drawings and the following examples.
As used herein, the term "include" and its various variants are to be understood as open-ended terms, which mean "including, but not limited to. The term "based on" may be understood as "based at least in part on". The term "one embodiment" may be understood as "at least one embodiment". The term "another embodiment" may be understood as "at least one other embodiment".
As described above, the driving capability of the electric vehicle is always a focus of attention of the user, and the calculation accuracy of the remaining driving capability affects the judgment of the user's trip, which is one of the key technologies applied to the electric vehicle. With the development of internet technology, the integration of the internet and new energy vehicles becomes the development trend of future automobiles. The embodiment of the invention aims to provide a method for efficiently and accurately estimating the residual driving capability of a destination by means of the Internet technology so as to give reasonable travel suggestions and energy consumption reduction reminding to users.
Herein, the remaining driving capability refers to a capability of the electric vehicle capable of continuing driving under typical conditions, and may be characterized as a remaining driving range under typical conditions or a remaining battery capacity, etc. The typical condition may be a driving test condition standard NEDC standard condition.
Embodiments of the present invention are further described below with reference to the accompanying drawings. Fig. 1 shows a schematic block diagram of a system 100 for estimating the remaining driving capability of an electric vehicle according to an embodiment of the invention. As shown, the system 100 includes a plurality of electric vehicles 110a, 110b, 110c (collectively electric vehicles 110) that include a travel control system 111 and a navigation system 112. The electric vehicle 110 communicates with the server 120 in the big data platform through the network 130. The network 130 may include a wireless network that enables communication using any suitable communication technology, such as a cellular technology based network, a WiMAX network, and so forth.
According to the embodiment of the present invention, the server 120 cooperates with the electric vehicle 110 to estimate the remaining driving ability of the electric vehicle 110 to reach the destination in real time by using the advantage of big data, and the accurately calculated remaining driving ability is displayed by the navigation system 112 instead of the driving range dynamically displayed by the existing vehicle meter. In this way, the user is provided with timely and accurate driving capability information.
The travel control system 111 of the electric vehicle 110 may include various vehicle components connected in a Controller Area Network (CAN) through which the vehicle components interact information. Such information includes motor rotation speed, motor torque, vehicle speed, direct current-direct current (DC-DC) converter input side voltage and current, air conditioning state or power, vehicle running environment information (such as region, air temperature, etc.), and the like.
The electric vehicle 110 has a networking function, and a network communication module (not shown) thereof CAN transmit CAN network information and other data as vehicle data or driving parameters to the server 120 through the network 130, and CAN also receive information from the server 120. Here, the other data may include a current remaining driving ability, a driving mode, an energy recovery mode, and the like, and the server 120 estimates the remaining driving ability of the electric vehicle 110 in real time according to the driving parameters. It is understood that the above listed CAN network information and other data are only examples, and the present invention is not limited to these information or data, which may be fused with each other.
The navigation system 112 of the electric vehicle 110 is adapted to the electric vehicle 110, which may include a display screen, may estimate the sectional path information from the current position to the destination according to the traffic flow in addition to providing a navigation function, and may also calculate the remaining driving ability of the electric vehicle 110 in cooperation with the server 120. According to an embodiment of the present invention, the navigation system 112 transmits the navigation path information to the server 120 through the network communication module of the vehicle via the network 130 so that the server 120 estimates the remaining driving ability of the electric vehicle 110 in real time according to the driving parameters.
The server 120 is part of a large data platform in the system 100 that communicates with a plurality of electric vehicles 110 in the system 100. By means of the computing power and big data analysis capability of the server 120, information specific to the driving parameters of each electric vehicle 110 can be provided, including information related to the current remaining driving capability of each electric vehicle 110, and can be pushed to a vehicle large-screen display system and a user mobile phone APP in real time.
It is to be appreciated that system 100 is merely illustrative and that other components of electric vehicle 110 and server 120 are not specifically shown and described to facilitate describing embodiments of the present invention so as to not unnecessarily obscure aspects of the embodiments of the present invention. Further, there may be any number of electric vehicles for system 100, and the invention is not limited in this respect.
FIG. 2 shows a schematic block diagram of a process 200 for a server to estimate a remaining range based on big data, according to one embodiment of the invention. As shown, the travel control system 111 of the electric vehicle 110 uploads vehicle data to the server 120. In one embodiment, the vehicle data includes: current remaining driving capability, driving mode, energy recovery mode, motor speed, motor torque, vehicle speed, direct current-to-direct current (DC-DC) converter input side voltage and current, air conditioning power, and the like. The electric vehicle 110 transmits the vehicle data as vehicle history data at certain time intervals for the server 120 to analyze the energy consumption of the electric vehicle 110.
The server 120 includes a self-learning module 221 that analyzes historical vehicle speed versus energy consumption for different vehicle conditions based on vehicle historical data and modifies the energy consumption analysis model based on energy consumption information for other vehicles passing through the same segment of the travel path. In one embodiment, the vehicle state includes at least different combinations of driving modes and energy consumption recovery modes, the vehicle driving modes may include, for example, an Economy (ECO) mode or a normal mode configured by the electric vehicle, the energy consumption recovery modes may include different energy recovery strengths configured by the electric vehicle, and the like.
According to the embodiment of the invention, the pearson correlation coefficients of the average vehicle speed and the average energy consumption of the electric vehicle 110 in different scenes are respectively calculated, so that the correlation is more than 0.95, namely, the correlation is extremely strong, and therefore, the relation between the electricity consumption per hundred kilometers and the vehicle speed can be fitted by using a function.
In one embodiment, the server 120 analyzes the vehicle based on the historical data uploaded by the electric vehicle 110 using the following function (1)
y＝f(x)＝∑aixi (1)
And fitting the discrete points of the average vehicle speed and the average energy consumption. And (4) searching the best matched curve by utilizing a linear algebraic least square method through adjusting the coefficient of the highest order term. The higher the highest term, the closer the resulting curve is to discrete data. To avoid overfitting, the fitted curve needs to be compatible with the test set of discrete points simultaneously. Proved by practice, when the highest-order item is x6The error of the test set is minimal.
The self-learning module 221 obtains a series of fitted curves of 'power consumption per hundred kilometers and vehicle speed' in different vehicle states according to historical vehicle data of the electric vehicle 110 in different vehicle states, and forms an initial driving energy consumption analysis model. As an example, fig. 3 shows a fitted curve between the average vehicle speed and the average traveling energy consumption in a vehicle state, which may correspond to a state of the electric vehicle 110 in the eco-drive mode and the high energy recovery intensity mode.
In the present embodiment, the history vehicle data further includes traveling environment information such as a vehicle traveling region, weather conditions, air temperature, and the like. The self-learning module 221 establishes a functional relationship between vehicle speed and energy consumption in different environments and vehicle states according to historical vehicle data.
As shown in the figure, the abscissa represents the vehicle speed, the ordinate represents the power consumption per kilometer of the vehicle, and the curves C1 and C2 represent the relationship between the vehicle speed and the power consumption of the vehicle in different situations under the vehicle state and the environment, respectively. Wherein C1 represents the energy consumption situation under the standard working condition NEDC scene, and C2 represents the driving energy consumption curve under the uniform speed simulation scene. It is understood that the curve shown in fig. 3 is only an illustration, and in practical applications, the server 120 constructs a series of driving energy consumption fitting curves for the electric vehicle 110 according to different environmental conditions and vehicle states.
The server 120 also analyzes the relationship between the ambient temperature and the air conditioning power at different air conditioning settings based on the vehicle history data. These air conditioning settings include at least temperature, air volume, internal/external circulation modes, etc. The self-learning module 221 of the server 120 may also fit the discrete points using the function (1), and by controlling the coefficient of the highest order term, find the most matched curve using a linear algebraic least squares method, obtain a series of fitted curves of "air conditioner power and ambient temperature" under different air conditioner settings, and form an initial air conditioner power analysis model.
As an example, fig. 4 shows a fitted curve between air conditioning power and ambient temperature at a certain air conditioning setting, which may correspond to a certain temperature, air volume and whether the inner and outer cycles are turned on for the setting. As shown in the figure, the abscissa represents the ambient temperature, the ordinate represents the air conditioner power, and the curve represents the relationship between the ambient temperature and the air conditioner power under the air conditioner setting.
It is understood that the curve shown in fig. 4 is merely illustrative, and in practical applications, the server 120 constructs a series of air conditioner power fitting curves for the electric vehicle 110 according to different air conditioner settings. In addition, server 120 may also analyze other accessory energy consumption based on vehicle historical data to build other accessory energy consumption analysis models.
The server 120 further updates the historical database according to the vehicle data uploaded by the electric vehicle 110 in real time, and optimizes and updates the driving energy consumption model and the air conditioner power analysis model through the self-learning module 221 of the server 120.
Since the server 120 is able to learn data information about each vehicle in the entire network of driven vehicles, in another embodiment of the present invention, the server 120 modifies the energy consumption analysis model by obtaining energy consumption information about other vehicles passing through the same section of the travel path. Specifically, the server 120 acquires energy consumption information of other vehicles passing through the same section on the travel path, and compares the established energy consumption analysis model with energy consumption analysis models of other vehicles to calculate the energy consumption correction parameter. In this way, the server 120 can estimate the remaining driving capability to the destination based on the energy consumption correction parameter and the estimated electric vehicle 110 driving energy consumption and accessory energy consumption.
In one embodiment, the navigation system 112 uploads the route information to the server 120, and the server 120 calculates the driving energy consumption of the electric vehicle 110 and the accessory energy consumption according to the energy consumption analysis model and provides the energy consumption information to the electric vehicle 110, so that the remaining driving continuation capacity after the destination is reached can be calculated. The specific process will be described in detail below in conjunction with fig. 5.
Fig. 5 shows a flow diagram of a method 500 of estimating a residual drivability of an electric vehicle according to one embodiment of the invention, the method 500 may be implemented at the server 120.
At 510, driving path information and driving parameters of the vehicle are obtained. In one embodiment, the navigation system 112 of the electric vehicle 110 identifies and recommends to the user at least one route to the destination based on the starting location and destination set by the user. According to an embodiment of the present invention, the navigation system 112 divides each path into a plurality of sections. The navigation system 112 may decompose each route into multiple segments based on current road conditions, charging facility location, town geographic location, etc. of each route to facilitate more accurate estimation of the expected energy consumption of the electric vehicle 110.
Specifically, for each route, the navigation system 112 decomposes the distance to the destination into a plurality of trips, each trip distance being noted as Di. And calculating the passing time Ti of each section of the journey according to the road condition and the congestion degree of each section of the journey, and estimating the distance D to the destination and the total passing time T on the basis. The navigation system 120 uploads the travel route information to the server 120, and the server 120 acquires the navigation route information of the electric vehicle 110.
The electric vehicle 110 also transmits vehicle CAN network information and other data as driving parameters to the server 120, whereby the server 120 acquires driving information of the electric vehicle 110. These driving parameters include: current remaining driving capability, driving mode, energy recovery mode, motor speed, motor torque, vehicle speed, direct current-to-direct current (DC-DC) converter input side voltage and current, air conditioning power, and the like.
In one embodiment, the electric vehicle 110 uploads the data in real time at a certain frequency, for example, the uploading frequency is not lower than 1 time/second, without affecting the normal functions and driving safety of the vehicle. In this way, the server 120 may obtain information updates in a timely manner, thereby maintaining information synchronization with the electric vehicle 110.
At 520, energy consumption of the vehicle and energy consumption of the accessories to the destination are estimated using the energy consumption analysis model based on the travel path information and the driving parameters. According to an embodiment of the present invention, the server 120 decomposes the calculation of the vehicle energy consumption into the driving energy consumption and other accessory energy consumption, such as air conditioner energy consumption, direct current voltage conversion energy consumption, and the like.
As described above, the server 120 establishes the driving energy consumption analysis model and the accessory energy consumption analysis model including the air conditioner power analysis model through the self-learning module 221 based on the vehicle history data of the electric vehicle 110 and the vehicle data updated in time. In one embodiment, the server 120 may take a curve fitted with historical data of the electric vehicle 110 as the initial calculated value.
The server 120 calculates the average vehicle speed of each route segment for each route based on the route information uploaded by the navigation system 112. From the vehicle data uploaded by the electric vehicle 110, the server 120 determines the current vehicle state and estimates the remaining driving ability to the destination on a segment-by-segment basis based on the aforementioned driving energy consumption fitting curve corresponding to the current vehicle state.
On the other hand, the server 120 estimates accessory power consumption, such as air conditioning power consumption, dc voltage conversion power consumption, and the like, for each path. In one embodiment, the server 120 may estimate air conditioner energy consumption to reach the destination based on an air conditioner power analysis model according to the vehicle state information and the navigation path information uploaded by the driving control system 111; and estimating the direct-current voltage conversion energy consumption of the destination according to the current direct-current voltage conversion average power.
Next, at 530, an expected energy consumption for the vehicle to reach the destination is determined based on the estimated energy consumption for the vehicle and the energy consumption for the accessories. In one embodiment, the expected energy consumption of the electric vehicle 110 to reach the destination is implemented by calculating a driving capability reduction coefficient k of the electric vehicle 110 from the destination, and according to the correction coefficient k, the server 120 or the electric vehicle 110 can calculate the expected energy consumption to reach the destination, and further obtain the remaining driving capability of the electric vehicle 110.
In the present embodiment, the driving ability degradation coefficient k represents a ratio of the estimated driving range degradation value to the distance to the end point, which can be calculated according to the following formula (2):
Figure BDA0001966569160000091
wherein, Delta EACPΔ E for estimated air conditioning energy consumption to destinationEXFor estimated energy consumption of other accessories to the destination, ENEDCEnergy consumption is standard working condition.
In the above equation (2), D is the distance from the current position of the electric vehicle 110 to the destination along the current path in question, N is the number of sections from the current position of the electric vehicle 110 to the destination on the current path, and DiIs the distance of the i-th section of the N sections, ED_drv_iIs the energy consumption of the section i.
It should be understood that the segmental energy consumption E for drivingD_drv_iCan be obtained from the fitted curve of energy consumption for driving as described above, which is related to the vehicle state, as described above, where the vehicle state includes at least the driving mode and the energy recovery mode. In the initial navigation stage, N is the total number of segments of the route, and the current position is the initial position.
According to the air conditioner power fitting curve described above, the server 120 estimates the air conditioner energy consumption of each section of each path, and then the air conditioner energy consumption of the corresponding path can be obtained according to the following formula (3).
Figure BDA0001966569160000092
Where N is the number of segments from the current location of the electric vehicle 110 to the destination on the current route, PiIs the air conditioning power of the i-th section of the N sections, TiIs the travel time of the i-th section. Therefore, the air conditioner energy consumption of each path reaching the destination can be estimated.
In another embodiment, the energy consumption of other accessories to the destination may also be estimated for the currently discussed path segment based on historical data of the electric vehicle 110, and further the energy consumption of other accessories for the corresponding path may be obtained according to equation (4) below.
Figure BDA0001966569160000093
Wherein, PEXiOther accessory power for the ith sector. In one embodiment, Δ E in the above formula (2)ACPAnd Δ EDCIt can also be calculated as the following formula (5):
Figure BDA0001966569160000101
where T is the total transit time of the currently discussed path, P0Is the average power of the air conditioner, PEX0Average power for other accessories.
According to the embodiment of the invention, the server 120 updates the algorithm curve fitted by each electric vehicle 110 in real time within a certain time according to different driving habits and driving modes of each electric vehicle 110. Further, the value of the driving ability degradation coefficient k is updated according to the current driving position of the electric vehicle 110 and the real-time driving parameters. This update may be performed for each route to the destination, thereby enabling an online correction of the driving energy consumption estimation method.
Next, the server 120 or the electric vehicle 110 may calculate the expected consumed energy to reach the destination, and thus the remaining driving capability of the electric vehicle 110. In one embodiment, the remaining range to the destination may be calculated according to equation (6) below:
d＝d0-k*D (6)
where d is the remaining driving range of the electric vehicle 110 to the destination, d0The current remaining range of the electric vehicle 110, D is the current distance of the electric vehicle 110 from the destination along the current path in question, k x D is the expected mileage consumed to reach the destination.
It is understood that the server 120 may push the calculated expected consumed mileage to the destination or the remaining driving ability to the electric vehicle 110 or the user's electronic device APP, etc., so that the user is informed of the estimated driving ability situation. In order to save communication resources and improve communication efficiency, the server 120 may transmit the correction coefficient k to the electric vehicle 110, and the remaining driving capability may be calculated by the navigation system 112, the driving control system 111, or the like.
Further, in one embodiment of the present invention, there may be an interruption in the communication between the electric vehicle 110 and the server 120 in consideration of the actual communication conditions. The server 120 selects different driving parameter combinations according to the component state combinations uploaded when the electric vehicle 110 is online, and calculates the range of the driving range degradation coefficient k under the conditions of the different driving parameter combinations.
The component states of the vehicle may include a vehicle speed, an air conditioning state, a direct-current voltage conversion component state, and the like, thereby obtaining a discrete range of the cruising ability reduction coefficient k in different driving scenes. The server 120 transmits the range of the cruising ability reduction coefficient k to the electric vehicle 110. In this way, even if the electric vehicle 110 cannot communicate with the server 120 at some time, it can estimate the remaining driving ability based on the discrete range of the driving ability degradation coefficient k.
Fig. 6 illustrates a flow diagram of a method 600 of estimating the electric vehicle's remaining range capability, the method 600 may be implemented at the electric vehicle 110, according to one embodiment of the invention.
At 610, the driving path information and driving parameters of the vehicle are transmitted to the server. As described above, the electric vehicle 110 transmits the segmented navigation path information of the navigation system 110 and various driving parameters to the server 120.
At 620, information is received regarding a current expected energy consumption of the vehicle from a destination, the information estimated by the server based on an energy consumption analysis model that analyzes historical vehicle data for the vehicle. In one embodiment, the information includes a current ride capacity degradation factor k of the vehicle from the destination, or the information may be an expected energy consumption of the vehicle from the destination.
Next, at 630, based on the received information, a remaining driving capability of the vehicle is determined. In one embodiment, the electric vehicle 110 may calculate the remaining driving capability of the vehicle according to equation (6) above.
According to an embodiment of the present invention, the electric vehicle 110 may detect its current communication condition with the server 120 at certain time intervals. When the communication condition is good, the electric vehicle 110 may obtain information updated in real time from the server 120 to calculate the remaining driving capability on line.
In one embodiment, the electric vehicle 110 stores a range of the cruising ability reduction coefficient k for different combinations of driving parameters obtained from the server. When the electric vehicle 110 detects the interruption of the communication with the server 120, the electric vehicle 110 determines the vehicle's current cruising ability decreasing coefficient k from the destination according to the range of the cruising ability decreasing coefficient k, and estimates the remaining cruising ability to the destination.
In another embodiment, when the electric vehicle 110 detects that the electric vehicle 110 cannot connect to the server 120, is offline for a long time, and cannot call the k value according to the current driving condition, the electric vehicle 110 estimates the energy consumption according to the driving condition thereof. The expected mileage consumed to reach the destination may be calculated based on the average energy consumption of the vehicle within a predetermined distance of the electric vehicle 110 from the current location.
As an example, the remaining driving capability may be calculated according to the following equation (7) with reference to the near 1 km average power consumption:
Figure BDA0001966569160000111
wherein E isrThe average power consumption is nearly 1 kilometer.
After the electric vehicle 110 calculates the remaining driving capability, it may be corrected according to actual conditions. If the remaining driving ability is insufficient to reach the destination, the electric vehicle 110 audibly, visually, or tactilely alerts the user, provides the user with a recommended route, a recommended driving mode, a recommended power consumption component setting, and the like. Prompting the user to reasonably arrange a trip range or charge in time, and giving suggestions for reducing energy consumption such as closing an air conditioner and the like.
It will be appreciated that the operation of the electric vehicle 110 described above may be coordinated by the navigation system 112 and the travel control system 110 or other vehicle operating systems. For example, the navigation system 112 transmits the remaining driving ability to the destination to the travel control system 111, and the travel control system 111 updates the driving parameters and controls the driving behavior of the electric vehicle 110; the travel control system 111 transmits the travel energy consumption of the near distance to the navigation system 112 so that it calculates the remaining cruising power and the like. Any suitable way of implementing the above-described scheme is feasible and the invention is not limited in this respect.
According to the scheme for estimating the residual driving capability of the electric vehicle, provided by the embodiment of the invention, energy consumption calculation is decomposed into driving energy consumption, accessory energy consumption and the like, and accurate estimation is carried out on finer granularity; and on the basis of a big data analysis fitting algorithm and a self-learning model, the online correction of the estimation method is realized. Further, the estimation method can be switched between online and offline by means of transferring energy consumption information or the like in consideration of a change in communication environment. The scheme of dynamically displaying the driving range of the current vehicle instrument is replaced, the calculation and display of a navigation system are combined, the residual driving capacity of the destination is accurately estimated in the navigation mode, and therefore the user is assisted in reasonably arranging a travel route.
Fig. 7 illustrates a block diagram of an electronic device 700 suitable for implementing embodiments of the present invention. The device 700 may be used to implement a server 120 or a portion of an electric vehicle 110. As shown, the device 700 includes a processor 710. Processor 710 controls the operation and functions of device 700. For example, in some embodiments, processor 710 may perform various operations by way of instructions 730 stored in memory 720 coupled thereto. The memory 720 may be of any suitable type suitable to the local technical environment and may be implemented using any suitable data storage technology, including but not limited to semiconductor-based memory devices, magnetic memory devices and systems, optical memory devices and systems. Although only one memory unit is shown in FIG. 7, there may be multiple physically distinct memory units within device 700.
The processor 710 may be of any suitable type suitable to the local technical environment, and may include, but is not limited to, one or more of general purpose computers, special purpose computers, microcontrollers, digital signal controllers (DSPs), and controller-based multi-core controller architectures. The device 700 may also include multiple processors 710.
When device 700 is acting as, or part of, server 120, processor 710, when executing instructions 730, causes device 700 to perform actions to implement method 500 described above with reference to fig. 1-5. According to an embodiment of the invention, the actions include:
when device 700 acts as server 120, processor 710, when executing instructions 730, causes device 700 to perform actions to implement method 200 described above with reference to fig. 1-4. According to an embodiment of the invention, the actions include: acquiring running path information and driving parameters of a vehicle; estimating the driving energy consumption and the accessory energy consumption to the destination by using an energy consumption analysis model based on the driving path information and the driving parameters; and determining an expected energy consumption for the vehicle to reach the destination based on the estimated energy consumption for travel and the estimated energy consumption for the accessories; the remaining range to the destination is estimated based on the current remaining range and the expected energy consumption.
In certain embodiments, the acts further comprise establishing an energy consumption analysis model, wherein establishing an energy consumption analysis model comprises: obtaining historical vehicle data of a vehicle, wherein the historical vehicle data comprises a driving mode, an energy recovery mode, a vehicle speed, driving environment information and energy consumption; and establishing a functional relationship between vehicle speed and energy consumption in different environments and vehicle states based on the historical vehicle data, the vehicle states corresponding to different combinations of driving modes and energy recovery modes of the vehicle.
In some embodiments, the historical vehicle data further includes air conditioner setting parameters and air conditioner power, the driving environment information includes ambient temperature, and wherein establishing the energy consumption analysis model further includes establishing a functional relationship between the ambient temperature and the air conditioner power at different air conditioner settings based on the historical vehicle data.
In some embodiments, obtaining the travel path information and the driving parameters of the vehicle includes: acquiring travel path information from a navigation system of a vehicle, the travel path information including path information of at least one path to a destination, the at least one path being divided into a plurality of sections; and obtaining driving parameters transmitted by a control system of the vehicle at a first time interval, the driving parameters including a plurality of: the system comprises the current remaining driving capacity, a driving mode, an energy recovery mode, an air conditioner setting parameter vehicle speed, a direct current voltage conversion related parameter, air conditioner power and driving environment information.
In some embodiments, the actions further include: acquiring energy consumption information of other vehicles passing through the same section on a driving path; calculating energy consumption correction parameters by comparing the established energy consumption analysis model with energy consumption analysis models of other vehicles; and estimating the remaining driving ability to the destination based on the energy consumption correction parameter and the estimated driving energy consumption and the accessory energy consumption.
In some embodiments, estimating the energy consumption of the vehicle and the energy consumption of the accessories to the destination comprises: determining a current vehicle state of the vehicle based on the driving parameters; estimating energy consumption for traveling to the destination on a section-by-section basis based on a functional relationship between vehicle speed and energy consumption corresponding to the determined vehicle state; and calculating an accessory energy consumption of the vehicle based on the driving parameters, the accessory energy consumption including at least an air conditioner energy consumption to the destination and other accessory energy consumptions.
In certain embodiments, calculating accessory energy consumption for a vehicle comprises: determining current air conditioner setting parameters of the vehicle based on the driving parameters; estimating air conditioning energy consumption to the destination on a section-by-section basis of the path based on a functional relationship between the ambient temperature and the air conditioning power corresponding to the determined air conditioning setting parameter.
In some embodiments, determining the remaining range capability of the vehicle to reach the destination comprises: and calculating a driving capability reduction coefficient of the vehicle from the destination, wherein the driving capability reduction coefficient represents the ratio of the driving range reduction amount to the destination to the distance to the destination.
In some embodiments, calculating the range degradation factor for the vehicle from the current destination comprises: calculating a current driving ability reduction coefficient k of the vehicle from the destination according to the following formula
Figure BDA0001966569160000131
Wherein, Delta EACPΔ E for estimated air conditioning energy consumption to destinationEXFor estimated energy consumption of other accessories to the destination, ENEDCFor standard condition energy consumption, D is the distance from the current position of the vehicle to the destination along at least one path, N is the number of sections from the current position of the vehicle to the destination on at least one path, DiIs the distance of the i-th section of the N sections, ED_drv_iIs the driving energy consumption of the section i estimated according to the energy consumption analysis model.
In some embodiments, the estimated air conditioning energy consumption to reach the destination is calculated according to the following equation:
Figure BDA0001966569160000132
wherein, PiFor the air-conditioning power of section i estimated from the energy consumption analysis model, TiIs the travel time of the i-th section.
In some embodiments, the estimated energy consumption of other accessories to reach the destination is calculated according to the following equation:
Figure BDA0001966569160000133
wherein the content of the first and second substances,PEXiother accessory power for section i, TiIs the travel time of the i-th section.
In some embodiments, the actions further include: and updating the energy consumption analysis model based on the acquired driving parameters. In some embodiments, the actions further include: selecting different driving parameter combinations according to the component state combinations of the vehicle, wherein the component states of the vehicle comprise a vehicle speed, an air conditioner state and a direct current voltage conversion component state; calculating the range of the driving ability reduction coefficient under the condition of different driving parameter combinations; and transmitting the range of the drivability reduction factor to the vehicle.
In certain embodiments, determining the expected energy consumption of the vehicle to reach the destination further comprises: calculating an expected energy consumption of the vehicle to reach the destination based on the cruising ability reduction coefficient; and transmitting the expected energy consumption to the vehicle such that the vehicle determines a remaining range capability to reach the destination.
In some embodiments, the actions further include: the ride capacity degradation factor is transmitted to the vehicle for the vehicle to calculate a remaining ride capacity to reach the destination.
When device 700 is acting as part of electric vehicle 110, processor 710, when executing instructions 730, causes device 700 to perform actions to implement method 600 described above with reference to fig. 1-6. According to an embodiment of the invention, the actions include: transmitting the driving path information and the driving parameters of the vehicle to a server; receiving information about a current expected energy consumption of a vehicle from a destination, the information estimated by a server based on an energy consumption analysis model that analyzes historical vehicle data for the vehicle; and calculating the remaining driving capability of the vehicle based on the received information.
In some embodiments, the information includes a range degradation factor k of the vehicle from the destination, the range degradation factor k characterizing a ratio of a range degradation to the destination to a distance to the destination, and wherein calculating the remaining range of the vehicle comprises: calculating the remaining driving ability d according to the following formula
d＝d0-k*D
Wherein d is0Continue driving for the current remainderCapability, D, is the distance the vehicle is currently along at least one path from the destination.
In some embodiments, the actions further include: detecting the current communication condition of the vehicle and the server; calculating an expected energy consumption to reach the destination based on an average energy consumption of the vehicle within a predetermined distance from the current location in response to the communication condition indicating that the vehicle is in an offline state; and determining a remaining range to the destination based on the current remaining range and the expected energy consumption of the vehicle.
In some embodiments, the actions further include: acquiring the range of the driving ability reduction coefficient k under the condition of different driving parameter combinations from a server, wherein the different driving parameter combinations are selected according to the component state combinations of the vehicle, and the component states of the vehicle comprise the vehicle speed, the air conditioner state and the direct current voltage conversion component state; and determining a cruising ability reduction coefficient k of the vehicle from the destination based on a range of the cruising ability reduction coefficient k in response to the communication condition indicating that the communication with the server is interrupted; and calculating the remaining driving range d according to the following formula
d＝d0-k*D
Wherein d is0For the current remaining range, D is the current distance of the vehicle from the destination along the at least one route.
In some embodiments, the actions further include: outputting a prompt message based on the calculated remaining driving capability of the vehicle, the prompt message including at least one of: recommended routes, recommended driving modes, recommended power consumption component settings.
Embodiments of the present invention also provide a computer readable storage medium having stored thereon machine-readable instructions which, when executed by a machine, cause the machine to perform a method described in accordance with the present invention.
Those of skill in the art will understand that the logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be viewed as implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The embodiments of the present invention have been described above. However, the present invention is not limited to the above embodiment. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (13)

1. A method of estimating a remaining driving ability when an electric vehicle reaches a destination, characterized by comprising:
acquiring running path information and driving parameters of a vehicle;
estimating the driving energy consumption and the accessory energy consumption to the destination by using an energy consumption analysis model based on the driving path information and the driving parameters; and
determining an expected energy consumption of the vehicle to reach the destination based on the estimated travel energy consumption and the estimated accessory energy consumption, wherein determining the expected energy consumption of the vehicle to reach the destination comprises:
calculating a driving capability reduction coefficient of the vehicle from the destination at present, wherein the driving capability reduction coefficient represents the ratio of the driving range reduction amount to the destination to the distance to the destination; and
transmitting the ride capacity reduction factor to the vehicle for the vehicle to calculate a remaining ride capacity to a destination; or calculating an expected mileage consumed by the vehicle to the destination based on the driving capability reduction coefficient, and transmitting the expected mileage consumed to the vehicle so that the vehicle determines the remaining driving capability to the destination.
2. The method of claim 1, further comprising building the energy consumption analysis model, wherein building the energy consumption analysis model comprises:
obtaining historical vehicle data of the vehicle, wherein the historical vehicle data comprises a driving mode, an energy recovery mode, a vehicle speed, driving environment information and energy consumption; and
based on the historical vehicle data, a functional relationship between vehicle speed and energy consumption is established for different environments and vehicle states corresponding to different combinations of driving modes and energy recovery modes of the vehicle.
3. The method of claim 2, wherein obtaining the driving path information and the driving parameters of the vehicle comprises:
acquiring the travel path information from a navigation system of the vehicle, the travel path information including path information of at least one path to a destination, the at least one path being divided into a plurality of sections; and
obtaining driving parameters transmitted by a control system of the vehicle at a first time interval, the driving parameters including a plurality of: the system comprises the current remaining driving capacity, a driving mode, an energy recovery mode, air conditioner setting parameters, a vehicle speed, direct current voltage conversion related parameters, air conditioner power and driving environment information.
4. The method of claim 3, wherein estimating the energy consumption of the vehicle and the energy consumption of the accessories to the destination comprises:
determining a current vehicle state of the vehicle based on the driving parameters; and
estimating energy consumption for traveling to the destination on a section-by-section basis of the route based on a functional relationship between vehicle speed and energy consumption corresponding to the determined vehicle state.
5. The method of claim 3, wherein the historical vehicle data further includes air conditioner setting parameters and air conditioner power, the driving environment information includes ambient temperature, and wherein building the energy consumption analysis model further comprises:
establishing a functional relation between the ambient temperature and the air conditioner power under different air conditioner settings based on the historical vehicle data;
and wherein calculating the accessory energy consumption of the vehicle comprises:
determining current air conditioner setting parameters of the vehicle based on the driving parameters; and
estimating air conditioner energy consumption to the destination on a section-by-section basis of the path based on a functional relationship between the ambient temperature and the air conditioner power corresponding to the determined air conditioner setting parameter.
6. The method of claim 3, wherein calculating the current ride capacity degradation factor of the vehicle from the destination comprises:
calculating a current driving ability reduction coefficient k of the vehicle from the destination according to the following formula
Figure FDA0002761430270000021
Wherein, Delta EACPΔ E for estimated air conditioning energy consumption to destinationEXFor estimated energy consumption of other accessories to the destination, ENEDCD is the distance between the current position of the vehicle and the destination along the at least one path, N is the number of sections from the current position of the vehicle to the destination on the at least one path, and D is the energy consumption of the standard working conditioniIs the distance of the i-th section of the N sections, ED_drv_iThe energy consumption of the section i is estimated according to the energy consumption analysis model.
7. The method of claim 6, wherein the estimated air conditioning energy consumption to the destination is calculated according to the following equation:
Figure FDA0002761430270000022
and the estimated energy consumption of other accessories to reach the destination is calculated according to the following formula:
Figure FDA0002761430270000023
wherein, PiFor the air-conditioning power of section i, estimated from the energy consumption analysis model, PEXiOther accessory power for section i, TiIs the travel time of the i-th section.
8. The method of claim 4, further comprising:
selecting different driving parameter combinations according to component state combinations of the vehicle, wherein the component states of the vehicle comprise a vehicle speed, an air conditioner setting state and a direct current voltage conversion component state;
calculating the range of the driving ability reduction coefficient under the condition of different driving parameter combinations, wherein the driving ability reduction coefficient represents the ratio of the driving range reduction amount to the destination to the distance to the destination; and
transmitting the range of the drivability reduction factor to the vehicle.
9. A method of estimating a remaining driving capability of an electric vehicle, comprising:
transmitting the driving path information and the driving parameters of the vehicle to a server;
receiving information about a current expected energy consumption of the vehicle from a destination, the information about the current expected energy consumption of the vehicle from the destination being estimated by the server based on an energy consumption analysis model obtained by analyzing historical vehicle data of the vehicle, the information about the current expected energy consumption of the vehicle from the destination comprising a current driving range degradation coefficient k of the vehicle from the destination, the driving range degradation coefficient k characterizing a ratio of a driving range degradation to the destination to a distance to the destination; and
determining a remaining range of the vehicle based on the received information regarding the expected energy consumption of the vehicle from the current destination, the remaining range being a remaining range d calculated according to
d＝d0-k*D，
Wherein d is0And D is the distance from the vehicle to the destination along at least one path currently.
10. The method of claim 9, further comprising:
detecting the current communication condition of the vehicle and the server;
in response to the communication condition indicating that the vehicle is offline,
calculating an expected energy consumption to reach the destination based on an average energy consumption of the vehicle within a predetermined distance from the current location; and
determining a remaining range to a destination based on the current remaining range of the vehicle and the expected energy consumption.
11. The method of claim 10, further comprising:
acquiring the range of the driving ability reduction coefficient k under the condition of different driving parameter combinations from the server, wherein the different driving parameter combinations are selected according to the component state combinations of the vehicle, and the component states of the vehicle comprise the vehicle speed, the air conditioner state and the direct current voltage conversion component state; and
in response to the communication condition indicating a communication interruption with the server,
and determining the current driving ability reduction coefficient k of the vehicle from the destination based on the range of the driving ability reduction coefficient k.
12. An electronic device, comprising:
a processor; and
a memory storing instructions that, when executed by the processor, cause the apparatus to perform the method of any of claims 1-11.
13. A computer readable storage medium having stored thereon machine readable instructions which, when executed by the machine, cause the machine to perform the method of any one of claims 1-11.
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