CN107933317B - Method, device and equipment for estimating remaining driving range and pure electric vehicle - Google Patents

Method, device and equipment for estimating remaining driving range and pure electric vehicle Download PDF

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CN107933317B
CN107933317B CN201710972520.2A CN201710972520A CN107933317B CN 107933317 B CN107933317 B CN 107933317B CN 201710972520 A CN201710972520 A CN 201710972520A CN 107933317 B CN107933317 B CN 107933317B
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power consumption
electric vehicle
average power
pure electric
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CN107933317A (en
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徐清阳
鲁倩倩
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Borgward Automotive China Co Ltd
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L3/00—Electric devices on electrically-propelled vehicles for safety purposes; Monitoring operating variables, e.g. speed, deceleration or energy consumption
    • B60L3/12—Recording operating variables ; Monitoring of operating variables
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L2260/00—Operating Modes
    • B60L2260/40—Control modes
    • B60L2260/50—Control modes by future state prediction
    • B60L2260/52—Control modes by future state prediction drive range estimation, e.g. of estimation of available travel distance
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L2260/00—Operating Modes
    • B60L2260/40—Control modes
    • B60L2260/50—Control modes by future state prediction
    • B60L2260/54—Energy consumption estimation

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  • Transportation (AREA)
  • Mechanical Engineering (AREA)
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Abstract

The invention discloses a method, a device, equipment and a computer readable storage medium for estimating remaining driving range, comprising the following steps of; calculating the measured value of the average power consumption of the pure electric vehicle within a first preset distance; judging the current working condition of the pure electric vehicle; if the current working condition of the pure electric vehicle is driving, optimizing the measured value of the average power consumption by using Kalman filtering so as to obtain the optimal estimated value of the average power consumption within the first preset distance; and estimating the remaining driving range of the pure electric vehicle by using the optimal estimated value of the average power consumption and the remaining electric quantity of the pure electric vehicle. The method, the device, the equipment and the computer storage medium provided by the invention can inhibit the large fluctuation of the estimated value of the remaining driving range. The invention also provides a pure electric vehicle which comprises the equipment for estimating the residual driving range and can estimate the residual driving range more quickly and accurately.

Description

Method, device and equipment for estimating remaining driving range and pure electric vehicle
Technical Field
The invention relates to the technical field of electric vehicle control, in particular to a method, a device and equipment for estimating remaining driving range, computer readable storage equipment and a pure electric vehicle.
Background
The pure electric vehicle is a transportation tool with a battery as a power source, and the development of the electric vehicle can effectively save a resource instrument and reduce environmental pollution. And judging the running capability of the pure electric vehicle according to the remaining driving range displayed by the instrument of the pure electric vehicle during the driving process of the driver of the pure electric vehicle. The accuracy of the estimation of the remaining driving range is greatly influenced by various factors such as complex driving conditions, variable driving environments, different driving habits of different drivers and the like of the whole pure electric vehicle. The fact that the residual driving range is estimated to be too high can mislead a driver to make wrong judgment on whether the charging of the charging pile needs to be searched in the driving process; the estimation of the residual driving range is too accurate, the residual driving range may jump too fast and too greatly due to the change of a series of factors such as the change of an accelerator, the road resistance, the gradient and the like at each moment, the sense of urgency of a driver is increased, and the reliability of the residual driving range is greatly reduced. Therefore, reasonable estimation of the remaining driving range is to be carried out in a small-amplitude decreasing mode, the estimation can be properly carried out in a high SOC (State of charge battery State of charge), and the estimation is carried out in a conservative mode in a low SOC mode.
In the prior art, the average power consumption of the pure electric vehicle is estimated by adopting a working condition method or an electric quantity integration method. The electric quantity integration method is to integrate the electric quantity consumed by a section of driving mileage of the electric automobile to obtain the electric quantity consumed in the mileage, and the average electric consumption is represented by the ratio of the mileage to the electric quantity consumed in the mileage. The average power consumption represents the power consumption level in the life cycle of a product, and due to the complex running condition of the electric automobile, the average power consumption has a certain up-and-down floating deviation for different driving environments and drivers, wherein the deviation is about +/-10-20%, and if the calculated value of the average power consumption has overlarge jitter, for example: the average power consumption calculated in the last period is 6km/kWh, and the calculated average power consumption becomes 5km/kWh due to more power required by the driver in the current period, so that the residual driving range jumps from 60km to 50km for the electric vehicle with the residual capacity of 10kWh at the updating moment of the average power consumption, and the urgent feeling of the driver is greatly increased.
In the prior art, the selected power consumption integration algorithm estimates the running capacity of the remaining battery power according to the power consumption level in a period of time or mileage, the longer the mileage used in the calculation is, the more the integrated power is, the closer the finally obtained average power consumption is to the average power consumption of the whole vehicle running, but the slower the average power consumption calculation value is converged, the larger the estimated average power consumption fluctuation before reaching the mileage is, and the more severe and inaccurate the fluctuation of the remaining driving mileage during initial running is caused. Therefore, after the average power consumption is obtained, the estimated average power consumption is limited so that the remaining range display value greatly jumps, but the average power consumption cannot be estimated by the calculated value of the average power consumption by using the instantaneous optimization strategy in the prior art, the calculated value of the average power consumption fluctuates greatly in the prior art, and the reliability of the estimated remaining range is low.
From the above, it can be seen that how to suppress the large fluctuation of the remaining range estimation value is a problem to be solved at present.
Disclosure of Invention
The invention aims to provide a method, a device, equipment and a computer readable storage device for estimating remaining driving range, which solve the problem of large amplitude fluctuation of the estimated value of the remaining driving range in the prior art. The invention also provides a pure electric vehicle, and the residual driving range is quickly and accurately estimated by using the equipment for estimating the residual driving range.
To solve the above technical problem, the present invention provides a method for estimating a remaining driving range, comprising: calculating the measured value of the average power consumption of the pure electric vehicle within a first preset distance; judging the current working condition of the pure electric vehicle; if the current working condition of the pure electric vehicle is driving, optimizing the measured value of the average power consumption by using Kalman filtering so as to obtain the optimal estimated value of the average power consumption within the first preset distance; and estimating the remaining driving range of the pure electric vehicle by using the optimal estimated value of the average power consumption and the remaining electric quantity of the pure electric vehicle.
Preferably, the calculating of the measured value of the average power consumption of the pure electric vehicle within the first preset distance includes: calculating the average power consumption measured value of the pure electric vehicle within the first preset distance by utilizing the ratio of the first preset distance to the consumed electric quantity of the pure electric vehicle within the first preset distance; and recalculating the measured value of the average power consumption once every time the pure electric vehicle runs for a second preset distance.
Preferably, the judging the current working condition of the pure electric vehicle includes: and judging the current working condition of the pure electric vehicle by adopting a finite state machine according to a charging signal of a power management system, a vehicle speed signal, a battery current signal, a battery charge state signal, a driver air conditioner request signal and an air conditioner approval signal.
Preferably, the judging the current working condition of the pure electric vehicle includes: judging whether the state of charge of the battery of the pure electric vehicle is lower than a preset threshold value or not, and if so, judging that the remaining driving range is zero; if not, judging whether the pure electric vehicle is in a charging state; if the pure electric vehicle is in a charging state, determining that the average power consumption is a fixed value, and if the pure electric vehicle is in a non-charging state, determining that the pure electric vehicle is in a standby working condition; when the pure electric vehicle is in a standby working condition, judging whether the pure electric vehicle is in a charging state and/or the battery charge state is lower than a preset threshold value, and if not, driving the pure electric vehicle to run under the working condition.
Preferably, if the current working condition of the pure electric vehicle is driving, optimizing the measured value of the average power consumption by using kalman filtering so as to obtain the optimal estimated value of the average power consumption within the first preset distance includes: judging whether the measured value of the average power consumption of the pure electric vehicle at the time t within a first preset distance changes or not; if yes, obtaining a predicted value of the average power consumption of the pure electric vehicle at the t moment within the first preset distance according to the average power consumption of the pure electric vehicle at the t-1 moment within the first preset distance; obtaining the prediction covariance of the predicted value according to the covariance of the average power consumption at the t-1 moment; acquiring a Kalman filtering coefficient at the t moment according to the prediction covariance so as to calculate the covariance of the optimal estimated value of the average power consumption at the t moment; and obtaining the optimal estimated value of the average power consumption at the t moment according to the Kalman filtering coefficient at the t moment, the predicted value of the average power consumption at the t moment and the measured value of the average power consumption at the t moment.
The present invention also provides an apparatus for estimating a remaining driving range, comprising:
the measuring module is used for calculating the measured value of the average power consumption of the pure electric vehicle within a first preset distance;
the judging module is used for judging the current working condition of the pure electric vehicle;
the optimization module is used for optimizing the measured value of the average power consumption by using Kalman filtering when the current working condition of the pure electric vehicle is driving so as to obtain the optimal estimated value of the average power consumption within the first preset distance;
and the estimation module is used for estimating the remaining driving range of the pure electric vehicle by utilizing the optimal estimated value of the average power consumption and the remaining electric quantity of the pure electric vehicle.
Preferably, the optimization module is specifically configured to: judging whether the measured value of the average power consumption of the pure electric vehicle at the time t within a first preset distance changes or not; if yes, obtaining a predicted value of the average power consumption of the pure electric vehicle at the t moment within the first preset distance according to the average power consumption of the pure electric vehicle at the t-1 moment within the first preset distance; obtaining the prediction covariance of the predicted value according to the covariance of the average power consumption at the t-1 moment; acquiring a Kalman filtering coefficient at the t moment according to the prediction covariance so as to calculate the covariance of the optimal estimated value of the average power consumption at the t moment; and obtaining the optimal estimated value of the average power consumption at the t moment according to the Kalman filtering coefficient at the t moment, the predicted value of the average power consumption at the t moment and the measured value of the average power consumption at the t moment.
The present invention also provides an apparatus for estimating a remaining driving range, comprising: a memory for storing a computer program; a processor for implementing the steps of one of the above-described methods of estimating a remaining driving range when executing the computer program.
The present invention also provides a computer-readable storage medium for estimating a remaining range, having a computer program stored thereon, which, when being executed by a processor, carries out the above-mentioned steps of a method of estimating a remaining range.
The invention also provides a pure electric vehicle which comprises the equipment for estimating the residual driving range.
The method, the device, the equipment and the computer readable storage medium for estimating the remaining driving range provided by the invention are used for calculating the measured value of the average power consumption of the pure electric vehicle; when the current working condition of the pure electric vehicle is driving, optimizing the measured value of the average power consumption by using Kalman filtering to obtain the optimal estimated value of the average power consumption of the pure electric vehicle; and estimating the remaining driving range of the pure electric vehicle by using the optimal estimated value of the average power consumption and the remaining electric quantity of the pure electric vehicle. According to the method, the device, the equipment and the computer readable storage medium, the running working conditions of the pure electric vehicle are considered, the updating conditions of the average power consumption of the pure electric vehicle are different under different working conditions, when the pure electric vehicle is driven to run, the Kalman filtering is utilized to optimize the measured value of the average power consumption of the pure electric vehicle, the large-amplitude fluctuation of the average power consumption is restrained, and further, the large-amplitude fluctuation of the estimated remaining driving range is restrained, so that the remaining driving range of the pure electric vehicle can be accurately estimated.
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In order to more clearly illustrate the embodiments or technical solutions of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained based on these drawings without creative efforts.
FIG. 1 is a flow chart of a first embodiment of a method for estimating remaining driving range according to the present invention;
FIG. 2 is a flow chart of pure electric vehicle working condition judgment;
FIG. 3 is a flowchart of a second embodiment of a method for estimating remaining driving range according to the present invention;
FIG. 4 is a schematic diagram of a cyclic electric quantity integration algorithm;
FIG. 5 is a comparison graph of the results of the global optimization of average power consumption according to one embodiment;
FIG. 6 is a comparison graph of the results of global optimization of remaining driving range for the embodiment of FIG. 5;
fig. 7 is a block diagram illustrating a structure of an apparatus for estimating a remaining driving range according to an embodiment of the present invention.
Detailed Description
The core of the invention is to provide a method, a device, equipment and a computer storage medium for estimating the remaining driving range, which can inhibit the large fluctuation of the estimated value of the remaining driving range. The invention also provides a pure electric vehicle which comprises the equipment for estimating the residual driving range and can estimate the residual driving range more quickly and accurately.
In order that those skilled in the art will better understand the disclosure, the invention will be described in further detail with reference to the accompanying drawings and specific embodiments. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, fig. 1 is a flowchart illustrating a method for estimating a remaining driving range according to a first embodiment of the present invention; the specific operation steps are as follows:
step S101: calculating the measured value of the average power consumption of the pure electric vehicle within a first preset distance;
step S102; judging the current working condition of the pure electric vehicle;
the current working condition of the pure electric vehicle can be divided into: during charging, air conditioner starting, air conditioner closing, standby, driving and running, wherein the SOC is lower than a preset threshold value; the working condition judgment can adopt a finite state machine to judge the current working condition of the pure electric vehicle according to a BMS (power management system) charging signal, a vehicle speed signal, a battery current signal, a battery SOC signal, a driver air-conditioning request signal, an air-conditioning approval signal and the like.
Referring to fig. 2, fig. 2 is a flowchart illustrating a working condition determination of a pure electric vehicle.
As shown in fig. 2, during initialization, the corresponding Drive _ States is equal to 0; when the pure electric vehicle is in a standby working condition, the standby working condition comprises: parking, feedback and sliding (the output current of the battery is not positive), the average power consumption of the pure electric vehicle is unchanged, and the corresponding Drive _ States is 1; when the pure electric vehicle is in charging, the average power consumption of the pure electric vehicle is a fixed value, and corresponds to a Drive _ States of 3; when the current working condition of the pure electric vehicle is an air conditioner switch, the average power consumption of the pure electric vehicle jumps once; when the current working condition of the pure electric vehicle is that the SOC is lower than a preset threshold value, displaying that the remaining driving range is 0, and corresponding to a Drive _ States being 4; when the current working condition of the pure electric vehicle is driving, the corresponding Drive _ States is 2. It should be noted that, in the flow of judging that the working condition of the air conditioner switch is not in the working condition, since the air conditioner switch corrects the average power consumption of the pure electric vehicle for only one period, and the calculation of the average power consumption under the working condition of the air conditioner switch is still processed in the current working condition after one period, when the working condition of the pure electric vehicle is the air conditioner switch, the correction of the corresponding multiple is directly performed on the calculation of the average power consumption value by correcting the air conditioner switch of the average power consumption of the pure electric vehicle.
Step S103: if the current working condition of the pure electric vehicle is driving, optimizing the measured value of the average power consumption by using Kalman filtering so as to obtain the optimal estimated value of the average power consumption within the first preset distance;
step S104: and estimating the remaining driving range of the pure electric vehicle by using the optimal estimated value of the average power consumption and the remaining electric quantity of the pure electric vehicle.
The remaining driving range of the pure electric vehicle is obtained by multiplying the optimal estimated value of the average power consumption calculated per period by the current remaining SOE (State of energy remaining).
In the specific embodiment, the operation condition of the pure electric vehicle is considered, the updating conditions of the average power consumption of the pure electric vehicle are different under different working conditions, and when the pure electric vehicle is driven to run, the Kalman filtering is utilized to optimize the measured value of the average power consumption of the pure electric vehicle, so that the large-amplitude fluctuation of the average power consumption is inhibited, and further, the large-amplitude fluctuation of the estimated remaining driving range is inhibited, and the estimated remaining driving range of the pure electric vehicle can be accurately estimated.
Referring to fig. 3, fig. 3 is a flowchart illustrating a method for estimating remaining driving range according to a second embodiment of the present invention.
According to the above embodiment, when the current working condition of the pure electric vehicle is driving, the method comprises the following steps:
step S301: under the working condition of driving, calculating the measured value of the average power consumption of the pure electric vehicle;
in the specific embodiment, a first-in first-out method of accumulating circulating electric quantity is adopted for calculating the average power consumption of the pure electric vehicle, a ratio of 15Km to the electric quantity consumed in the mileage is selected to represent the measured value of the average power consumption of the pure electric vehicle, the measured value of the average power consumption is updated every time the pure electric vehicle runs for 1.5Km, and a specific algorithm schematic diagram is shown in fig. 4.
Step S302: judging whether the measured value of the average power consumption of the pure electric vehicle at the moment t changes or not;
when the pure electric vehicle is driven to run, due to the influences of factors such as the driving habits of a driver, the vehicle speed, the accuracy of a battery current sensor, the running road conditions and the like, the calculated measured value of the average power consumption fluctuates greatly when being updated, the deviation degree between the measured value and the actual average power consumption is increased, whether the measured value is updated or not is judged, and the current measured value of the average power consumption is optimized by using the global average power consumption of the whole vehicle running through Kalman filtering during updating.
Step S303: if yes, obtaining a predicted value of the average power consumption of the pure electric vehicle at the t moment within the preset distance according to the average power consumption of the pure electric vehicle at the t-1 moment within the preset distance;
the time update equation is as follows:
Figure GDA0002242809590000071
an optimal estimate representing the average power consumption at time t-1,
Figure GDA0002242809590000072
and (3) a predicted value representing the average power consumption at time t. In this embodiment, by using the "prediction" effect of kalman filtering, in the time update equation, the prediction that the measured value of the average power consumption at the current time is equal to the average power consumption at the previous time may modify the measured value of the average power consumption, so as to calculate the optimal estimated value of the average power consumption in the current period.
The specific derivation process of the measured value of the average power consumption at the current moment being equal to the average power consumption at the previous moment is as follows:
for pure electric vehicles, AvePC is adoptedRatedRepresents the average power consumption of the running in a product life cycle, then AvePCRatedEqual to the ratio of the total traveled mileage to the total consumed power, and if the average power consumption calculation value per travel cycle can obtain the average power consumption calculation value at the time k, the average power consumption calculation value is:
Figure GDA0002242809590000073
if:
total mileage (k-1) > mileage (k)
Total power consumption (k-1) > Power consumption (k)
Then from the score property:
AvePC(k)≈AvePC(k-1)
step S304: obtaining the prediction covariance of the predicted value according to the covariance of the average power consumption at the t-1 moment;
the time update equation two is as follows: pt|t-1＝Pt-1|t-1+Q％，Pt|t-1Representing the prediction covariance of the predicted value and the average power consumption actual value at the moment t; pt-1|t-1Representing the covariance of the optimal estimated value of the average power consumption at the time t-1; q represents the noise covariance of the prediction process, where Q may be zero or set to a minimum value (e.g., 1 e-8).
Step S305: acquiring a Kalman filtering coefficient at the t moment;
the state update formula is as follows:
Figure GDA0002242809590000081
wherein R is the measured noise covariance, Q is set to be a minimum value, the calibration is easier, R depends on the error level of average power consumption calculation under the driving running state, and the R value can be calibrated by taking the average value of the average power consumption error average value calculated per period when the whole vehicle runs at 5-100% SOC as a reference.
Step S306: acquiring an optimal estimated value of the average power consumption at the t moment according to the Kalman filtering coefficient at the t moment, the predicted value of the average power consumption at the t moment and the measured value of the average power consumption at the t moment;
the second state updating formula is:
Figure GDA0002242809590000082
wherein the content of the first and second substances,
Figure GDA0002242809590000083
an optimal estimate representing the average power consumption at time t.
Step S307: and calculating the covariance of the optimal estimated value of the average power consumption at the time t.
The state updating formula three is: pt|t＝(1-Kt)Pt|t-1Wherein P ist|tThe covariance of the optimal estimate representing the average power consumption at time t. The covariance at the moment t +1 can be predicted by utilizing the covariance at the moment t, so that the Kalman filtering algorithm can be subjected to autoregressive operation.
Collecting related signals for calculating the remaining driving range under 1 NEDC (new european automobile regulation cycle condition) cycle under the half-load condition of a certain pure electric automobile, and simulating an optimization algorithm in an MATLAB to obtain the effect as shown in fig. 5 and 6.
According to simulation comparison results, before the algorithm is not used, due to the high-power requirement at the later stage of the NEDC cycle, the average power consumption measured value is greatly reduced, so that the estimated residual driving range is rapidly reduced, the cycle is total to 11km, the residual driving range is reduced by 17.2km, and the effect is not ideal; after the global optimization algorithm is adopted, the large-amplitude jump of the average power consumption is effectively controlled, the calculated value of the residual driving range of the whole NEDC cycle is 12km lower than the actual driving value, and the descending speed of the driving range is accelerated in a section of driving range with large power demand behind the cycle, so that the change of the driving demand is reflected.
In this embodiment, the average power consumption measured value of the pure electric vehicle obtained by the cyclic electric quantity integral calculation is theoretically an average power consumption accurate value, but the measured value obtained in the actual calculation has a large deviation for the following reasons: (1) the accuracy of the vehicle speed sensor and the battery feedback current is insufficient. The accuracy of the current fed back by a vehicle speed sensor and a battery used by an automobile is not high, the current is suitable for real-time control, when the current is subjected to integral operation, the integral calculated value of 10ms per period is small, the controller can generate accuracy loss when performing single-accuracy calculation on the current, and the accuracy of the integral fraction part of electric quantity is required to be higher than that of mileage integration, so that the calculated average power consumption has two errors: the sensor detects errors and the average power consumption calculation accuracy lacks errors, and the more the accumulation period is, the larger the error accumulation is, so that the calculated average power consumption value is not accurate and has larger errors. (2) The average power consumption of the whole vehicle is influenced by the sliding and feedback working conditions in the running working condition. Although the phenomenon of overlarge accumulated data value can be avoided by using a first-in first-out circulating average power consumption calculation method, the power consumption value is reduced or unchanged during feedback and sliding, the mileage still increases, the SOE increases due to feedback, the calculated average power consumption is inaccurate at the moment, and the calculated value of the remaining driving mileage is larger, so that the power consumption and mileage accumulation during feedback and sliding cannot be considered during calculating the average power consumption, but the calculated average power consumption only represents the driving average power consumption and cannot represent the average power consumption during normal driving of the whole vehicle, and the calculated average power consumption is lower than the normally calculated average power consumption.
As can be seen from the above, the measured value obtained by using the cyclic electric quantity integral calculation is inaccurate, so that the average power consumption measured value needs to be globally optimized by using the prediction function of kalman filtering, and the remaining driving distance calculated by using the optimized optimal estimated value of the average power consumption tends to be slowly reduced without large fluctuation; the Kalman filtering can be utilized to enable the average power consumption of the pure electric vehicle to be converged quickly, large accumulated mileage and accumulated electric quantity are not needed, the average power consumption can be converged to be close to the average power consumption when a driver runs on the vehicle every time, and the fluctuation condition of the average power consumption depends on the power requirement of the driver. Therefore, the sensitivity of average power consumption to the change of the working condition is effectively reduced, and the embodiment of the power consumption degree of the working condition is not lost.
Referring to fig. 7, fig. 7 is a block diagram illustrating a structure of an apparatus for estimating remaining driving range according to an embodiment of the present invention; the specific device may include:
the measuring module 100 is used for calculating a measured value of average power consumption of the pure electric vehicle within a first preset distance;
the judging module 200 is used for judging the current working condition of the pure electric vehicle;
the optimization module 300 is configured to optimize the measured value of the average power consumption by using kalman filtering when the current working condition of the pure electric vehicle is driving so as to obtain an optimal estimated value of the average power consumption within the first preset distance;
and the estimation module 400 is configured to estimate the remaining driving range of the pure electric vehicle by using the optimal estimated value of the average power consumption and the remaining electric quantity of the pure electric vehicle.
The device for estimating the remaining range of the present embodiment is used for implementing the method for estimating the remaining range, and therefore, specific embodiments of the device for estimating the remaining range may be found in the foregoing embodiments of the method for estimating the remaining range, for example, the measuring module 100, the determining module 200, the optimizing module 300, and the estimating module 400 are respectively used for implementing steps S101, S102, S103, and S104 in the method for estimating the remaining range, so that specific embodiments thereof may refer to descriptions of corresponding embodiments of various parts, and are not described herein again.
An embodiment of the present invention further provides an apparatus for estimating a remaining driving range, including: a memory for storing a computer program; a processor for implementing the steps of one of the above-described methods of estimating a remaining driving range when executing the computer program.
Embodiments of the present invention also provide a computer-readable storage medium for estimating a remaining driving range, on which a computer program is stored, which, when being executed by a processor, carries out the above-mentioned steps of the method for estimating a remaining driving range.
The embodiment of the invention also provides a pure electric vehicle which comprises the equipment for estimating the residual driving range.
The embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same or similar parts among the embodiments are referred to each other. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description.
Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
The method and apparatus for estimating remaining driving range according to the present invention are described in detail above. The principles and embodiments of the present invention are explained herein using specific examples, which are presented only to assist in understanding the method and its core concepts. It should be noted that, for those skilled in the art, it is possible to make various improvements and modifications to the present invention without departing from the principle of the present invention, and those improvements and modifications also fall within the scope of the claims of the present invention.

Claims (8)

1. A method of estimating remaining range, comprising:
calculating the measured value of the average power consumption of the pure electric vehicle within a first preset distance;
judging the current working condition of the pure electric vehicle;
if the current working condition of the pure electric vehicle is driving, optimizing the measured value of the average power consumption by using Kalman filtering so as to obtain the optimal estimated value of the average power consumption within the first preset distance;
estimating the remaining driving range of the pure electric vehicle by using the optimal estimated value of the average power consumption and the remaining electric quantity of the pure electric vehicle;
if the current working condition of the pure electric vehicle is driving, optimizing the measured value of the average power consumption by using kalman filtering so as to obtain the optimal estimated value of the average power consumption within the first preset distance includes:
judging whether the measured value of the average power consumption of the pure electric vehicle at the time t within a first preset distance changes or not;
if yes, obtaining a predicted value of the average power consumption of the pure electric vehicle at the t moment within the first preset distance according to the average power consumption of the pure electric vehicle at the t-1 moment within the first preset distance;
obtaining the prediction covariance of the predicted value according to the covariance of the average power consumption at the t-1 moment;
acquiring a Kalman filtering coefficient at the t moment according to the prediction covariance so as to calculate the covariance of the optimal estimated value of the average power consumption at the t moment;
and obtaining the optimal estimated value of the average power consumption at the t moment according to the Kalman filtering coefficient at the t moment, the predicted value of the average power consumption at the t moment and the measured value of the average power consumption at the t moment.
2. The method of claim 1, wherein calculating the measure of the average power consumption of the pure electric vehicle within the first preset distance comprises:
calculating the average power consumption measured value of the pure electric vehicle within the first preset distance by utilizing the ratio of the first preset distance to the consumed electric quantity of the pure electric vehicle within the first preset distance; and recalculating the measured value of the average power consumption once every time the pure electric vehicle runs for a second preset distance.
3. The method of claim 1, wherein the determining the current operating condition of the electric-only vehicle comprises: and judging the current working condition of the pure electric vehicle by adopting a finite state machine according to a charging signal of a power management system, a vehicle speed signal, a battery current signal, a battery charge state signal, a driver air conditioner request signal and an air conditioner approval signal.
4. The method of claim 3, wherein the determining the current operating condition of the electric-only vehicle comprises:
judging whether the state of charge of the battery of the pure electric vehicle is lower than a preset threshold value or not, and if so, judging that the remaining driving range is zero; if not, judging whether the pure electric vehicle is in a charging state;
if the pure electric vehicle is in a charging state, determining that the average power consumption is a fixed value, and if the pure electric vehicle is in a non-charging state, determining that the pure electric vehicle is in a standby working condition;
when the pure electric vehicle is in a standby working condition, judging whether the pure electric vehicle is in a charging state and/or the battery charge state is lower than a preset threshold value, and if not, driving the pure electric vehicle to run under the working condition.
5. An apparatus for estimating a remaining driving range, comprising:
the measuring module is used for calculating the measured value of the average power consumption of the pure electric vehicle within a first preset distance;
the judging module is used for judging the current working condition of the pure electric vehicle;
the optimization module is used for optimizing the measured value of the average power consumption by using Kalman filtering when the current working condition of the pure electric vehicle is driving so as to obtain the optimal estimated value of the average power consumption within the first preset distance;
the estimation module is used for estimating the remaining driving range of the pure electric vehicle by utilizing the optimal estimated value of the average power consumption and the remaining electric quantity of the pure electric vehicle;
the optimization module is specifically configured to:
judging whether the measured value of the average power consumption of the pure electric vehicle at the time t within a first preset distance changes or not;
if yes, obtaining a predicted value of the average power consumption of the pure electric vehicle at the t moment within the first preset distance according to the average power consumption of the pure electric vehicle at the t-1 moment within the first preset distance;
obtaining the prediction covariance of the predicted value according to the covariance of the average power consumption at the t-1 moment;
acquiring a Kalman filtering coefficient at the t moment according to the prediction covariance so as to calculate the covariance of the optimal estimated value of the average power consumption at the t moment;
and obtaining the optimal estimated value of the average power consumption at the t moment according to the Kalman filtering coefficient at the t moment, the predicted value of the average power consumption at the t moment and the measured value of the average power consumption at the t moment.
6. An apparatus for estimating remaining driving range, comprising:
a memory for storing a computer program;
a processor for implementing the steps of the method of estimating a remaining range as claimed in any one of claims 1 to 4 when executing the computer program.
7. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method of estimating a remaining range according to any one of claims 1 to 4.
8. A pure electric vehicle comprising an apparatus for estimating a remaining driving range according to claim 6.
CN201710972520.2A 2017-10-18 2017-10-18 Method, device and equipment for estimating remaining driving range and pure electric vehicle Expired - Fee Related CN107933317B (en)

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