CN111137169A - Estimation method and device of endurance mileage - Google Patents
Estimation method and device of endurance mileage Download PDFInfo
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- B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
- B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
- B60L58/12—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries responding to state of charge [SoC]
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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
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- B60L2260/00—Operating Modes
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- B60L2260/54—Energy consumption estimation
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Abstract
The invention relates to a technology for estimating the endurance mileage of a new energy automobile, in particular to a method and a device for estimating the endurance mileage and a computer-readable storage medium. The invention provides an estimation method of the endurance mileage, which comprises the following steps: looking up a table according to the type and the state of charge of the power battery and the working condition of the vehicle to obtain an empirical estimation value; calculating an average estimated value according to the charge state and the long-distance average energy consumption of the vehicle; calculating an instantaneous estimated value according to the state of charge and the short-distance instantaneous energy consumption of the vehicle; and according to the accuracy requirement and the stability requirement of the mileage, carrying out weighted summation on the experience estimated value, the average estimated value and the instantaneous estimated value to obtain the estimated mileage. The method and the device can keep the stability of the mileage reading while considering the accuracy of the mileage estimation, thereby improving the subjective feeling of the user and reducing the mileage anxiety of the user.
Description
Technical Field
The present invention relates to a mileage estimation technique for a new energy vehicle, and more particularly, to a mileage estimation method, an estimation device for implementing the estimation method, and a computer-readable storage medium for storing the estimation method.
Background
With the popularization of new energy automobiles, the attention of users to the endurance mileage of the automobiles is gradually increased. Accordingly, the accuracy requirements of the user for the mileage estimation will increase.
At present, the estimation mode of vehicle models sold in the market on the endurance mileage mainly comprises a table look-up method and a power consumption method. These two algorithms will be described separately below.
The table look-up method is a method which is adopted by most of current commercially available vehicle types, and the main algorithm logic is to look up a table according to the State of Charge (SOC) of a power battery and further estimate the endurance mileage of a vehicle according to the endurance performance of the working condition of a New European Driving Cycle (NEDC). The table look-up method has the advantages of high algorithm reliability and small mileage jitter. The disadvantage of the lookup table is also obvious, and the driving mileage of the vehicle cannot be truly estimated by combining different actual road conditions and driving habits of different users.
The power consumption method is a method for estimating the driving mileage of the vehicle according to the current average power consumption condition of the vehicle and the residual power battery electric quantity. The power consumption method has the advantages that the driving mileage of the vehicle can be estimated in real time by combining different actual road conditions and driving habits of different users. The power consumption method has the defects that the algorithm is complex, the jumping amplitude of the estimated endurance mileage is large, the subjective feeling of a user is poor, and the anxiety of the user is easily caused.
Therefore, in order to overcome the above-mentioned drawbacks of the prior art, there is a need in the art for a mileage estimation technique that maintains the stability of mileage reading while considering the accuracy of mileage estimation, thereby improving the subjective feeling of the user and reducing the mileage anxiety of the user.
Disclosure of Invention
The following presents a simplified summary of one or more aspects in order to provide a basic understanding of such aspects. This summary is not an extensive overview of all contemplated aspects, and is intended to neither identify key or critical elements of all aspects nor delineate the scope of any or all aspects. Its sole purpose is to present some concepts of one or more aspects in a simplified form as a prelude to the more detailed description that is presented later.
In order to overcome the defects in the prior art, the invention provides an estimation method of the endurance mileage of a new energy vehicle, an estimation device for implementing the estimation method, and a computer-readable storage medium for storing the estimation method, which are used for keeping the stability of the reading of the endurance mileage while considering the estimation accuracy of the endurance mileage, thereby improving the subjective feeling of a user and reducing the mileage anxiety of the user.
The invention provides an estimation method of the endurance mileage, which comprises the following steps: looking up a table according to the type and the state of charge of the power battery and the working condition of the vehicle to obtain an empirical estimation value; calculating an average estimated value according to the charge state and the long-distance average energy consumption of the vehicle; calculating an instantaneous estimated value according to the state of charge and the short-distance instantaneous energy consumption of the vehicle; and according to the accuracy requirement and the stability requirement of the mileage, carrying out weighted summation on the experience estimated value, the average estimated value and the instantaneous estimated value to obtain the estimated mileage.
Preferably, in some embodiments of the present invention, the operating condition may include one or more of an electric energy recovery state of the power battery, a cell temperature of the power battery, a driving mode of the vehicle, an ambient temperature, and an energy consumption of an on-board air conditioner.
Preferably, in some embodiments of the present invention, the step of obtaining the experience estimation value may further include: looking up a table according to the type of the power battery, the state of charge and the electric energy recovery state to obtain a basic estimation value; looking up a table according to the model of the power battery and the cell temperature to obtain a cell temperature correction coefficient; obtaining a driving mode correction coefficient according to the type of the power battery and the driving mode table look-up; and responding to the fact that the vehicle-mounted air conditioner is not started, and calculating the experience estimated value according to the basic estimated value, the battery core temperature correction coefficient and the driving mode correction coefficient.
Optionally, in some embodiments of the invention, the power recovery state includes, but is not limited to, a strong recovery state, a weak recovery state, and a closed recovery state.
Preferably, in some embodiments of the present invention, the estimation method may further include the steps of: responding to the starting of the vehicle-mounted air conditioner, looking up a table according to the type of the power battery to obtain the energy consumption of the vehicle-mounted air conditioner, and looking up the table according to the type of the power battery and the environment temperature to obtain an environment temperature correction coefficient; calculating an air conditioner energy consumption correction coefficient according to the ambient temperature correction coefficient and the energy consumption of the vehicle-mounted air conditioner; and calculating the experience estimated value according to the basic estimated value, the battery core temperature correction coefficient, the driving mode correction coefficient and the air conditioner energy consumption correction coefficient.
Optionally, in some embodiments of the present invention, the step of calculating the average estimated value may further include: calculating a first average energy consumption of a first long distance according to the energy consumption of each 1 kilometer of the vehicle; calculating a second average energy consumption for a second long distance based on the energy consumption of the vehicle per 50 kilometers; calculating total average energy consumption according to the first average energy consumption and the second average energy consumption; and calculating the average estimated value according to the state of charge and the total average energy consumption.
Preferably, in some embodiments of the present invention, the step of calculating the first average energy consumption for the first long distance may further include: calculating the energy consumption of the vehicle in each continuous driving 1 kilometer; storing the kilometer of energy consumption obtained by calculation into a first array to update a stack point value of the first array; and calculating the average value of all stack point values of the first array to obtain the first average energy consumption, wherein the first long distance corresponds to the number of the stack points of the first array.
Preferably, in some embodiments of the present invention, the first array may include 50 stack points. The step of calculating the second average energy consumption for the second long distance may further include: calculating the average value of 50 stack point values of the first array every 20 kilometers of the vehicle to obtain the average energy consumption of the vehicle every 50 kilometers in the past; storing the obtained average energy consumption per kilometer of the previous 50 kilometers into a second array to update a stack point value of the second array; and calculating the average value of all stack point values of the second array to obtain the second average energy consumption, wherein the second long distance corresponds to the number of the stack points of the second array.
Optionally, in some embodiments of the present invention, the step of calculating the instantaneous estimate may further include: calculating the energy consumption of the vehicle in 0.1 kilometer every time the vehicle runs for 0.1 kilometer; and calculating the instantaneous estimate based on the state of charge and the energy consumption of 0.1 km.
Optionally, in some embodiments of the present invention, the estimation method may further include the steps of: increasing a weighting coefficient of the empirical estimate in response to a signal indicative of enhanced stability; and increasing the weighting factor of the instantaneous estimate in response to a signal indicative of increased accuracy.
According to another aspect of the present invention, there is also provided a driving range estimation device for implementing the driving range estimation method.
The device for estimating the endurance mileage provided by the invention comprises a memory and a processor. The processor is coupled to the memory and configured to: looking up a table according to the type and the state of charge of the power battery and the working condition of the vehicle to obtain an empirical estimation value; calculating an average estimated value according to the charge state and the long-distance average energy consumption of the vehicle; calculating an instantaneous estimated value according to the state of charge and the short-distance instantaneous energy consumption of the vehicle; and according to the accuracy requirement and the stability requirement of the mileage, carrying out weighted summation on the experience estimated value, the average estimated value and the instantaneous estimated value to obtain the estimated mileage.
Preferably, in some embodiments of the present invention, the operating condition may include one or more of an electric energy recovery state of the power battery, a cell temperature of the power battery, a driving mode of the vehicle, an ambient temperature, and an energy consumption of an on-board air conditioner.
Preferably, in some embodiments of the present invention, the processor may be further configured to: looking up a table according to the type of the power battery, the state of charge and the electric energy recovery state to obtain a basic estimation value; looking up a table according to the model of the power battery and the cell temperature to obtain a cell temperature correction coefficient; obtaining a driving mode correction coefficient according to the type of the power battery and the driving mode table look-up; and responding to the fact that the vehicle-mounted air conditioner is not started, and calculating the experience estimated value according to the basic estimated value, the battery core temperature correction coefficient and the driving mode correction coefficient.
Optionally, in some embodiments of the invention, the power recovery state includes, but is not limited to, a strong recovery state, a weak recovery state, and a closed recovery state.
Preferably, in some embodiments of the present invention, the processor may be further configured to: responding to the starting of the vehicle-mounted air conditioner, looking up a table according to the type of the power battery to obtain the energy consumption of the vehicle-mounted air conditioner, and looking up the table according to the type of the power battery and the environment temperature to obtain an environment temperature correction coefficient; calculating an air conditioner energy consumption correction coefficient according to the ambient temperature correction coefficient and the energy consumption of the vehicle-mounted air conditioner; and calculating the experience estimated value according to the basic estimated value, the battery core temperature correction coefficient, the driving mode correction coefficient and the air conditioner energy consumption correction coefficient.
Optionally, in some embodiments of the invention, the processor may be further configured to: calculating a first average energy consumption of a first long distance according to the energy consumption of each 1 kilometer of the vehicle; calculating a second average energy consumption for a second long distance based on the energy consumption of the vehicle per 50 kilometers; calculating total average energy consumption according to the first average energy consumption and the second average energy consumption; and calculating the average estimated value according to the state of charge and the total average energy consumption.
Preferably, in some embodiments of the present invention, the processor may be further configured to: calculating the energy consumption of the vehicle in each continuous driving 1 kilometer; storing the kilometer of energy consumption obtained by calculation into a first array to update a stack point value of the first array; and calculating the average value of all stack point values of the first array to obtain the first average energy consumption, wherein the first long distance corresponds to the number of the stack points of the first array.
Preferably, in some embodiments of the present invention, the first array may include 50 stack points. The processor may be further configured to: calculating the average of 50 stack point values of the first array once every 20 kilometers of the vehicle is driven to obtain the average energy consumption of the vehicle per kilometer in the previous 50 kilometers; storing the obtained average energy consumption per kilometer of the previous 50 kilometers into a second array to update a stack point value of the second array; and calculating the average value of all stack point values of the second array to obtain the second average energy consumption, wherein the second long distance corresponds to the number of the stack points of the second array.
Optionally, in some embodiments of the invention, the processor may be further configured to: calculating the energy consumption of the vehicle in 0.1 kilometer every time the vehicle runs for 0.1 kilometer; and calculating the instantaneous estimate based on the state of charge and the energy consumption of 0.1 km.
Optionally, in some embodiments of the present invention, the processor may be further configured to: increasing a weighting coefficient of the empirical estimate in response to a signal indicative of enhanced stability; and increasing the weighting factor of the instantaneous estimate in response to a signal indicative of increased accuracy.
According to another aspect of the present invention, a computer-readable storage medium is also provided herein.
The present invention provides the above computer readable storage medium having stored thereon computer instructions. The computer instructions, when executed by the processor, may implement any of the above methods of estimating range.
Drawings
The above features and advantages of the present disclosure will be better understood upon reading the detailed description of embodiments of the disclosure in conjunction with the following drawings. In the drawings, components are not necessarily drawn to scale, and components having similar relative characteristics or features may have the same or similar reference numerals.
Fig. 1 illustrates a flow chart of a driving range estimation method provided according to an aspect of the present invention.
FIG. 2 illustrates obtaining an empirical estimate S according to an embodiment of the present invention1Is a schematic flow diagram.
FIG. 3 illustrates a calculated average estimate S provided according to an embodiment of the present invention2Is a schematic flow diagram.
Fig. 4 is a schematic structural diagram illustrating a mileage estimation apparatus provided according to another aspect of the present invention.
Reference numerals
101-104 endurance mileage estimation method;
201-207 obtaining an experience evaluation value;
301-304 calculating an average estimate;
estimating device of 40 endurance mileage;
41 a memory;
42 a processor.
Detailed Description
The following description of the embodiments of the present invention is provided for illustrative purposes, and other advantages and effects of the present invention will become apparent to those skilled in the art from the present disclosure. While the invention will be described in connection with the preferred embodiments, there is no intent to limit its features to those embodiments. On the contrary, the invention is described in connection with the embodiments for the purpose of covering alternatives or modifications that may be extended based on the claims of the present invention. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. The invention may be practiced without these particulars. Moreover, some of the specific details have been left out of the description in order to avoid obscuring or obscuring the focus of the present invention.
In the description of the present invention, it should be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
Additionally, the terms "upper," "lower," "left," "right," "top," "bottom," "horizontal," "vertical" and the like as used in the following description are to be understood as referring to the segment and the associated drawings in the illustrated orientation. The relative terms are used for convenience of description only and do not imply that the described apparatus should be constructed or operated in a particular orientation and therefore should not be construed as limiting the invention.
It will be understood that, although the terms first, second, third, etc. may be used herein to describe various elements, regions, layers and/or sections, these elements, regions, layers and/or sections should not be limited by these terms, but rather are used to distinguish one element, region, layer and/or section from another element, region, layer and/or section. Thus, a first component, region, layer or section discussed below could be termed a second component, region, layer or section without departing from some embodiments of the present invention.
As described above, the existing lookup table method cannot really estimate the driving mileage of the vehicle in combination with different actual road conditions and driving habits of different users, and the existing power consumption method has a complex algorithm and a large jumping amplitude of the estimated driving mileage, which leads to poor subjective feeling of the user and easily causes anxiety of the user.
In order to overcome the defects in the prior art, the invention provides an estimation method of the endurance mileage of a new energy vehicle, an estimation device for implementing the estimation method, and a computer-readable storage medium for storing the estimation method, which are used for keeping the stability of the reading of the endurance mileage while considering the estimation accuracy of the endurance mileage, thereby improving the subjective feeling of a user and reducing the mileage anxiety of the user.
The method for estimating the endurance mileage of the new energy vehicle can be implemented in an estimation device of the endurance mileage. In some embodiments, the means for estimating range may include a memory and a processor. The memory may be provided with a computer-readable storage medium having stored thereon computer instructions. The processor may be coupled to the memory and configured to execute computer instructions stored on the computer-readable storage medium to implement the method for estimating range. It is understood that the processor includes, but is not limited to, a Vehicle Control Unit (VCU) of the new energy Vehicle. In some embodiments, the method for estimating the driving range of the new energy vehicle provided by the invention can be implemented by the VCU of the new energy vehicle. Alternatively, in other embodiments, the method for estimating the mileage of the new energy vehicle provided by the present invention may also be implemented by other processor modules dedicated to estimating the mileage of the new energy vehicle.
Referring to fig. 1, fig. 1 is a schematic flow chart illustrating a driving range estimation method according to an aspect of the present invention.
As shown in fig. 1, the method for estimating the endurance mileage provided by the present invention may include the steps of:
101: obtaining an empirical estimation value S by looking up a table according to the type and the state of charge of the power battery and the working condition of the vehicle1。
Obtaining the empirical estimate S1The method can be implemented by combining a table look-up method, and is mainly used for providing theoretical reference data with high reliability and small mileage jump for the estimation of the cruising mileage of the new energy automobile. In some embodiments, the VCU may perform table lookup according to the specific model and State of Charge (SOC) of the power battery, and estimate the cruising range of the vehicle according to the cruising performance of the New European Driving Cycle (NEDC) working condition.
In some preferred embodiments, the basic estimation obtained by table lookup may be further combined with specific operating conditions such as an electric energy recovery state of the power battery, a battery core temperature of the power battery, a driving mode of the vehicle, an external environment temperature of the vehicle, and an energy consumption of the vehicle-mounted air conditionerThe calculated value is corrected to obtain more reliable and accurate experience estimated value S1。
Referring further to fig. 2, fig. 2 illustrates obtaining an empirical estimate S according to an embodiment of the present invention1Is a schematic flow diagram.
As shown in FIG. 2, in some embodiments of the present invention, an empirical estimate S is obtained1May further comprise:
201: looking up a table according to the type of the power battery, the charge state of the power battery and the electric energy recovery state of the vehicle to obtain a basic estimation value S;
202: obtaining a cell temperature correction coefficient X according to the model of the power battery and a cell temperature look-up table of the power battery1；
203: obtaining a driving mode correction coefficient X according to the model of the power battery and the driving mode look-up table2(ii) a And
204: responding to the fact that the vehicle-mounted air conditioner is not started, and correcting the coefficient X according to the basic estimated value S and the cell temperature1And driving mode correction factor X2Calculating an empirical estimate S1。
In step 201, the type of the power battery may be determined according to the type parameter of the power battery and the code of the power battery. In some embodiments, the VCU may determine the code of the power battery based on the number of temperature sensors in the power battery.
In some embodiments, the VCU may obtain the basic estimation S by querying a reference data file of the mileage. In particular, the reference data file for range may be tabular data (e.g., MAP). In some embodiments, the abscissa of the reference MAP file of the driving range may be the state of charge (SOC) of the power battery, and the ordinate thereof may be the power battery code. The VCU can acquire the state of charge (SOC) of the power battery, and then reads the reading of the corresponding cell according to the SOC of the power battery and the code of the power battery to acquire the corresponding basic estimation value S.
In some preferred embodiments, the electric energy recovery state of the vehicle includes, but is not limited to, a strong recovery state, a weak recovery state, and a shut down recovery state, indicating the strength of the electric energy feedback to the power battery. Each power recovery state may correspond to a reference MAP file of endurance mileage. The VCU may preferably obtain the current power recovery status from the power battery, and then query the corresponding reference MAP file according to the current power recovery status to obtain a more reliable and accurate basic estimation value S.
In step 202, the VCU may obtain the cell temperature correction coefficient X by querying the temperature correction MAP files of different battery packs1. In some embodiments, the abscissa of the temperature correction MAP file for different battery packs may be the cell temperature, and the ordinate thereof may be the power battery code. The VCU can read the reading of the temperature sensor in the power battery to obtain the electric core temperature of the power battery, and then read the reading of the corresponding cell to obtain the corresponding electric core temperature correction coefficient X according to the code number of the power battery and the electric core temperature of the power battery1。
In step 203, the VCU may obtain the driving mode correction coefficient X by querying the driving model correction MAP file of different battery packs2. The driving modes include, but are not limited to, a Sport (Sport) mode, a Normal (Normal) mode, and an economy (Eco) mode of the vehicle. In some embodiments, the abscissa of the driving model modified MAP file for different battery packs may be the driving mode selection, while the ordinate may be the power battery code. The VCU can read the current driving mode selection information of the vehicle, and then read the reading of the corresponding cell to obtain the corresponding driving mode correction coefficient X according to the code of the power battery and the current driving mode of the vehicle2。
As shown in fig. 2, in some embodiments, the VCU may determine whether an on-board air conditioner of the vehicle is in an on state. If the vehicle-mounted air conditioner is in the off state, it can be determined that the energy consumption P of the vehicle-mounted air conditioner is not required to be considered when estimating the driving mileage, so that the step 204 is skipped to start the empirical estimation value S1And (4) calculating.
In particular, the empirical estimate S1Can be according to the formula S1＝S×X1×X2To calculate. By combining vehicle electric energyThe basic estimated value S is corrected by real-time actual working conditions such as the receiving state, the power battery cell temperature and the vehicle driving mode, the defect that the driving mileage of the vehicle cannot be really estimated by combining different actual road conditions and driving habits of different users in the traditional table look-up method can be effectively overcome, and therefore a more reliable and accurate experience estimated value S can be obtained1。
Preferably, in other embodiments, if the vehicle-mounted air conditioner is in an on state, the empirical estimation value S is obtained1The step of (a) may further comprise:
205: responding to the start of the vehicle-mounted air conditioner, looking up a table according to the type of the power battery to acquire the energy consumption P of the vehicle-mounted air conditioner, and looking up a table according to the type of the power battery and the ambient temperature to acquire an ambient temperature correction coefficient X3；
206: correcting the coefficient X according to the ambient temperature3And calculating the energy consumption correction coefficient X of the air conditioner by using the energy consumption P of the vehicle-mounted air conditioner4(ii) a And
207: according to the basic estimated value S and the cell temperature correction coefficient X1Driving mode correction factor X2And air conditioner energy consumption correction coefficient X4Calculating an empirical estimate S1。
In step 205, the VCU may obtain the reference energy consumption P of the air conditioner by querying the energy consumption data file of the air conditioner for starting different battery packs. In some embodiments, the VCU may modify the coefficient X according to the cell temperature and the basic estimated value S1Driving mode correction factor X2And calculating the reference energy consumption P of the air conditioner to obtain an empirical estimation value S1。
In some preferred embodiments, the VCU may further obtain the ambient temperature correction coefficient X by querying an electric energy MAP file consumed by the air conditioner at different outdoor temperatures3. The abscissa of the MAP file of the electric energy consumed by the air conditioner at different outdoor temperatures may be the outdoor temperature, and the ordinate thereof may be the power battery code. The VCU can firstly read the reading of the temperature sensor outside the vehicle to obtain the outdoor temperature, and then read the reading of the corresponding cell to obtain the corresponding environment temperature correction coefficient X according to the code number of the power battery and the outdoor temperature3。
Thereafter, in step 206, the VCU may be formulated according to the formula
Calculating to obtain the energy consumption correction coefficient X of the air conditioner4. The energy consumption correction coefficient X of the air conditioner4The influence of the actual energy consumption P' of the vehicle-mounted air conditioner on the endurance mileage can be more accurately reflected.
In step 207, the VCU may be based on formula S1＝S×X1×X2×X4To calculate an empirical estimate S1Wherein, in the step (A),
the corrected estimated driving range data is secondarily corrected by further combining with actual working conditions such as energy consumption P, outdoor temperature and the like of the vehicle-mounted air conditioner, and the estimated driving range of the vehicle can be truly estimated by further combining with different actual road conditions and driving habits of different users, so that a more reliable and accurate experience estimated value S is obtained1。
As shown in fig. 1, the method for estimating the endurance mileage provided by the present invention may further include:
102: calculating average estimated value S according to the charge state of the power battery and the long-distance average energy consumption of the vehicle2。
The above average estimated value S2The method can be calculated and obtained according to the actual long-distance energy consumption performance of the new energy vehicle, and is mainly used for providing actual reference data with high stability and small mileage jump for the estimation of the cruising mileage of the new energy vehicle. In some embodiments, the VCU may calculate the average energy consumption P per kilometer for 50 kilometers ahead of the vehicle, respectively1And an average energy consumption per kilometer P of 130 kilometers ahead of the vehicle2Then according to P1And P2To calculate the above average estimated value S2。
Referring further to fig. 3, fig. 3 illustrates a calculated average estimate S provided according to an embodiment of the present invention2Is a schematic flow diagram.
As shown in the figure3, in some embodiments of the invention, the average estimate S is calculated2May further comprise:
301: calculating a first average energy consumption P of a first long distance according to the energy consumption of each 1 kilometer of the vehicle1。
In some embodiments, the first long distance may be 50 km. Specifically, the VCU may first determine whether the vehicle is in a traveling state based on the vehicle speed of the vehicle. In response to the vehicle being in a driving state, the VCU may calculate a consumption of the SOC every 1 km of the vehicle, and store the calculation result in the first array including 50 stack points to update one stack point value of the first array. In some preferred embodiments, the VCU may calculate the consumption of the SOC only once when the vehicle continuously travels for 1 km, so as to avoid abnormal excessive energy consumption to the first average energy consumption P at the moment of starting the vehicle1Creating excessive interference.
In some embodiments, the VCU may sequentially store the calculation results one by one in chronological order into the stack points of the first array. In response to a calculation result being stored in the first array, the calculation result (i.e., the stack point value indicating the SOC consumption condition of 51 km before the vehicle travels) stored in the first array at the earliest time is replaced to realize the update of the stack point value. In some preferred embodiments, in response to a vehicle power down, the VCU may store the stack values of the first array of stack points in a powered Erasable Programmable read only memory (EEPROM). After the vehicle is powered on again, the VCU can directly call the stored stack point values of all stack points from the EEPROM and fill the stack points from the first stack point of the first array, so that the previous energy consumption data are recovered. By storing the stack point values of the stack points of the first array into the EEPROM, the multi-section discontinuous driving energy consumption can be comprehensively counted, thereby ensuring that the average estimated value S is calculated2The data volume of the new energy vehicle is sufficient, and the actual long-distance energy consumption condition of the new energy vehicle can be accurately and stably represented.
In some embodiments, the VCU may average the stack values of all 50 stack points in the first array to obtain the vehicleFirst average energy consumption P of 50 km before driving1. The first average energy consumption P1Indicating the average energy consumption per kilometer 50 kilometers before the vehicle is driven.
Those skilled in the art will understand that there is a corresponding relationship between the first long distance of 50 km and the first array including 50 stack points. The specific values are merely illustrative and are not intended to limit the scope of the present invention. Based on the same concept, in other embodiments, the first array may include n stackpoints. The VCU may calculate the consumption of the SOC every 1 km of the vehicle in continuous travel and store the calculation result in the first array. At this time, the first average energy consumption P obtained by averaging all stack point values of the first array is obtained1The average energy consumption per kilometer n kilometers before the vehicle is driven will be indicated.
As shown in FIG. 3, in some embodiments of the present invention, an average estimate S is calculated2The step (d) may further comprise:
302: calculating a second average energy consumption P of the second long distance according to the energy consumption of each 50 kilometers of the vehicle2。
In some embodiments, the second long distance may be 130 km, corresponding to a second number of 5 stack points. Specifically, the VCU may calculate an average value of 50 stackpoint values of the first array once every 20 km traveled by the vehicle, and store the calculation result in the second array including 5 stackpoint values to update one stackpoint value of the second array. At this time, the stack point values of the 5 stack points of the second array will respectively indicate the average energy consumption per kilometer of the 130 th to 81 th kilometers, the average energy consumption per kilometer of the 110 th to 61 th kilometers, the average energy consumption per kilometer of the 90 th to 41 th kilometers, the average energy consumption per kilometer of the 70 th to 21 th kilometers, and the average energy consumption per kilometer of the 50 th to 1 st kilometers ahead of the vehicle. In some embodiments, the VCU may average the stack values of all 5 stack points in the second array to obtain a second average energy consumption P of 130 kilometers before the vehicle travels2. The second average energy consumption P2Indicating the average energy consumption per kilometer at 130 kilometers before the vehicle is driven. Calculating a second average energy consumption P by repeatedly using partial stack values2The average estimated value S can be set without violating2On the premise of accuracy, the average estimated value S is further improved2Stability of (2).
In some embodiments, the VCU may not have to store the stack values of the second array in response to a vehicle power down. After the vehicle is powered on again, the VCU may directly retrieve and calculate the average stack point value of the 50 stack points of the first array, and fill the average stack point value into the 5 stack points of the second array respectively to maintain the average estimated value S2The stability of (2).
It can be understood by those skilled in the art that the above-mentioned solution for calculating the average stack point value of the 50 stack points of the first array to update the stack point value of one stack point of the second array is only a preferred embodiment provided by the present invention, and is mainly used for reducing the data collection load and the data processing load of the vehicle, and is not used for limiting the protection scope of the present invention. Optionally, in other embodiments, the second group may also include 130 stack points for storing energy consumption per kilometer of the previous 130-1 kilometers of the vehicle, and repeatedly collecting and storing a large amount of data for the VCU to calculate the same second average energy consumption P according to the same data2。
Those skilled in the art will also appreciate that the average energy consumption per kilometer P for the first 130 kilometers is calculated as a function of the average energy consumption per kilometer per 50 kilometers as described above2The technical scheme is only the embodiment provided by the invention and is mainly used for clearly showing the intention of the invention to collect a large amount of energy consumption data by different statistical units, thereby remarkably embodying the invention to reduce the average estimated value S of data error pair2The concept of interference is not intended to limit the scope of the invention. Alternatively, in other embodiments, one skilled in the art can also count other units (e.g., average energy consumption per kilometer P per 10 kilometers)1And an average power consumption per kilometer P per 100 kilometers2Etc.) calculate the average energy consumption data P and achieve the same technical effect. It should be noted that P is increased1And P2The difference of statistical units will help to reduce the average estimation S of the same interference factor pair2The interference situation of (1).
As shown in the figure3, in some embodiments of the invention, the average estimate S is calculated2The step (d) may further comprise:
303: according to the first average energy consumption P1And a second average energy consumption P2Calculating the total average energy consumption P; and
304: calculating an average estimated value S according to the state of charge and the total average energy consumption P of the power battery2。
In some embodiments, the VCU may be based on a formula for every 100 meters of vehicle travel
Calculating the total average energy consumption P of the primary vehicle according to a formula
Calculating to obtain average estimated value S2Wherein, SOCAt presentIndicating the current state of charge of the power cell. In some embodiments, the VCU may determine the distance traveled by the vehicle based on an increment of an Odograph (ODO) reading of the vehicle.
By first calculating a first average energy consumption P1And a second average energy consumption P2To obtain the total average energy consumption P, and calculating the average estimated value S according to the total average energy consumption P2The method can reflect the actual long-distance energy consumption performance of the new energy vehicle, thereby providing an actual reference data which can reflect the actual energy consumption condition of the vehicle and has the advantages of high stability, small mileage bounce and the like for the estimation of the cruising mileage of the new energy vehicle.
As shown in fig. 1, the method for estimating the endurance mileage provided by the present invention may further include:
103: calculating an instantaneous estimation value S according to the state of charge of the power battery and the short-distance instantaneous energy consumption of the vehicle3。
The above-mentioned instantaneous estimate S3The method can be calculated and obtained according to the actual short-distance instantaneous energy consumption performance of the new energy vehicle, and is mainly used for providing actual reference data with sensitive response and high accuracy for the estimation of the cruising mileage of the new energy vehicle.
In some embodiments, the VCU may calculate the energy consumption of the vehicle for every 100 meters of vehicle traveled, i.e., the VCU may calculate the energy consumption of the vehicle for that 100 meters
The VCU may then follow the formula
Calculating an instantaneous estimate S3Wherein, SOCAt presentIndicating the current state of charge of the power cell.
It will be understood by those skilled in the art that the above-described instantaneous estimate S is calculated every 100 meters3The scheme provided by the invention is only one embodiment provided by the invention, and is mainly used for displaying the characteristic of short statistical distance, and is not used for limiting the protection scope of the invention. Based on the same concept, in other embodiments, the VCU may also calculate the energy consumption of the vehicle in the previous 50 meters distance every 200 meters to achieve similar technical effects.
In some preferred embodiments, the VCU may selectively calculate the instantaneous estimate S based on the actual speed of the vehicle3The separation distance of (a). For example: the VCU may select the instantaneous estimate S to be calculated every 100 meters in response to the vehicle speed being 60km/h3Alternatively, the instantaneous estimate S may be calculated every 50 m in response to the vehicle speed being 30km/h3To ensure the instantaneous estimate S3The sampling rate of (c).
As shown in fig. 1, the method for estimating the endurance mileage provided by the present invention may further include:
104: according to the accuracy requirement and the stability requirement of the endurance mileage, the experience estimated value S is subjected to1Average estimated value S2And an instantaneous estimate S3Weighted summation to obtain estimated range SGeneral assembly。
VCU may be based on formula SGeneral assembly＝a×S1+b×S2+c×S3To calculate the estimated driving range SGeneral assemblyWhere a is an empirical estimate S1The weighting coefficient of (2); b is the average estimated value S2The weighting coefficient of (2); c is instantaneousEstimate S3The weighting coefficient of (2). In some embodiments, a commissioning personnel of the vehicle may estimate S based on the range mileageGeneral assemblyThe values of a, b and c are determined according to the accuracy requirement and the stability requirement. If better accuracy is required, the value of c can be set to be larger. If better stability is required, the value of a can be set to be larger. In some embodiments, a debugger can determine the corresponding b according to the values of a and c to coordinate the estimated value S of the driving rangeGeneral assemblyThe accuracy and stability of the expression are shown.
In some preferred embodiments, the vehicle' S VCU may adjust the range estimate S on-site based on a user selected actionGeneral assemblyThe accuracy and stability of the expression are shown. In particular, the VCU may increase the empirical estimate S in response to the operating signal indicating enhanced stability1To enhance the estimated driving range value SGeneral assemblyThe stability performance of (2); the instantaneous estimate S may also be increased in response to a signal indicating increased accuracy3To enhance the estimated driving range SGeneral assemblyThe accuracy of the method is shown.
While, for purposes of simplicity of explanation, the methodologies are shown and described as a series of acts, it is to be understood and appreciated that the methodologies are not limited by the order of acts, as some acts may, in accordance with one or more embodiments, occur in different orders and/or concurrently with other acts from that shown and described herein or not shown and described herein, as would be understood by one skilled in the art.
As described above, the method for estimating the driving range of the new energy vehicle provided by the invention can be implemented in a driving range estimation device. According to another aspect of the present invention, there is also provided a driving range estimation device for implementing the driving range estimation method.
Referring to fig. 4, fig. 4 is a schematic structural diagram illustrating a driving range estimation apparatus according to another aspect of the present invention.
As shown in fig. 4, the estimation device 40 of driving range provided by the present invention includes a memory 41 and a processor 42. The processor 42 is coupled to the memory 41, and is configured to implement the endurance mileage estimation method provided in any one of the above embodiments, and can achieve the corresponding technical effects.
According to another aspect of the present invention, a computer-readable storage medium is also provided herein. The computer readable storage medium has computer instructions stored thereon. When executed by the processor 42, the computer instructions may implement the endurance mileage estimation method provided in any one of the above embodiments, and achieve the corresponding technical effects.
Those of skill in the art would understand that information, signals, and data may be represented using any of a variety of different technologies and techniques. For example, data, instructions, commands, information, signals, bits (bits), symbols, and chips that may be referenced throughout the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields or particles, or any combination thereof.
Those of skill would further appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. 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 various illustrative logical modules, and circuits described in connection with the embodiments disclosed herein may be implemented or performed with a general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration.
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 RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC. The ASIC may reside in a user terminal. In the alternative, the processor and the storage medium may reside as discrete components in a user terminal.
In one or more exemplary embodiments, the functions described may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software as a computer program product, the functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Computer-readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage media may be any available media that can be accessed by a computer. By way of example, and not limitation, such computer-readable media can comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. Any connection is properly termed a computer-readable medium. For example, if the software is transmitted from a web site, server, or other remote source using a coaxial cable, fiber optic cable, twisted pair, Digital Subscriber Line (DSL), or wireless technologies such as infrared, radio, and microwave, then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of medium. Disk (disk) and disc (disc), as used herein, includes Compact Disc (CD), laser disc, optical disc, Digital Versatile Disc (DVD), floppy disk and blu-ray disc where disks (disks) usually reproduce data magnetically, while discs (discs) reproduce data optically with lasers. Combinations of the above should also be included within the scope of computer-readable media.
The previous description of the disclosure is provided to enable any person skilled in the art to make or use the disclosure. Various modifications to the disclosure will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other variations without departing from the spirit or scope of the disclosure. Thus, the disclosure is not intended to be limited to the examples and designs described herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (21)
1. A method for estimating driving range, comprising:
looking up a table according to the type and the state of charge of the power battery and the working condition of the vehicle to obtain an empirical estimation value;
calculating an average estimated value according to the charge state and the long-distance average energy consumption of the vehicle;
calculating an instantaneous estimated value according to the state of charge and the short-distance instantaneous energy consumption of the vehicle; and
and weighting and summing the experience estimation value, the average estimation value and the instantaneous estimation value according to the accuracy requirement and the stability requirement of the mileage so as to obtain the estimated mileage.
2. The estimation method of claim 1, wherein the operating condition includes one or more of a state of electric energy recovery of the power battery, a cell temperature of the power battery, a driving mode of the vehicle, an ambient temperature, and an energy consumption of an on-board air conditioner.
3. The estimation method of claim 2, wherein the step of obtaining an empirical estimate comprises:
looking up a table according to the type of the power battery, the state of charge and the electric energy recovery state to obtain a basic estimation value;
looking up a table according to the model of the power battery and the cell temperature to obtain a cell temperature correction coefficient;
obtaining a driving mode correction coefficient according to the type of the power battery and the driving mode table look-up; and
and responding to the fact that the vehicle-mounted air conditioner is not started, and calculating the experience estimated value according to the basic estimated value, the battery core temperature correction coefficient and the driving mode correction coefficient.
4. The estimation method according to claim 3, wherein the electric power recovery state includes a strong recovery state, a weak recovery state, and a shut-down recovery state.
5. The estimation method of claim 3, further comprising:
responding to the starting of the vehicle-mounted air conditioner, looking up a table according to the type of the power battery to obtain the energy consumption of the vehicle-mounted air conditioner, and looking up the table according to the type of the power battery and the environment temperature to obtain an environment temperature correction coefficient;
calculating an air conditioner energy consumption correction coefficient according to the ambient temperature correction coefficient and the energy consumption of the vehicle-mounted air conditioner; and
and calculating the experience estimated value according to the basic estimated value, the cell temperature correction coefficient, the driving mode correction coefficient and the air conditioner energy consumption correction coefficient.
6. The estimation method of claim 1, wherein the step of calculating an average estimate comprises:
calculating a first average energy consumption of a first long distance according to the energy consumption of each 1 kilometer of the vehicle;
calculating a second average energy consumption for a second long distance based on the energy consumption of the vehicle per 50 kilometers;
calculating total average energy consumption according to the first average energy consumption and the second average energy consumption; and
and calculating the average estimated value according to the state of charge and the total average energy consumption.
7. The estimation method according to claim 6, wherein the step of calculating the first average energy consumption for the first long distance comprises:
calculating the energy consumption of the vehicle in each continuous driving 1 kilometer;
storing the kilometer of energy consumption obtained by calculation into a first array to update a stack point value of the first array; and
and calculating the average value of all stack point values of the first array to obtain the first average energy consumption, wherein the first long distance corresponds to the number of the stack points of the first array.
8. The estimation method of claim 7, wherein the first array comprises 50 stack points, and the step of calculating the second average energy consumption for the second long distance comprises:
calculating the average value of 50 stack point values of the first array every 20 kilometers of the vehicle to obtain the average energy consumption of the vehicle every 50 kilometers in the past;
storing the obtained average energy consumption per kilometer of the previous 50 kilometers into a second array to update a stack point value of the second array; and
and calculating the average value of all stack point values of the second array to obtain the second average energy consumption, wherein the second long distance corresponds to the number of the stack points of the second array.
9. The estimation method of claim 1, wherein the step of calculating the instantaneous estimate comprises:
calculating the energy consumption of the vehicle in 0.1 kilometer every time the vehicle runs for 0.1 kilometer; and
and calculating the instantaneous estimated value according to the state of charge and the energy consumption of 0.1 kilometer.
10. The estimation method of claim 1, further comprising:
increasing a weighting coefficient of the empirical estimate in response to a signal indicative of enhanced stability; and
the weighting factor of the instantaneous estimate is increased in response to a signal indicating increased accuracy.
11. An apparatus for estimating a driving range, comprising:
a memory, and
a processor coupled to the memory and configured to:
looking up a table according to the type and the state of charge of the power battery and the working condition of the vehicle to obtain an empirical estimation value;
calculating an average estimated value according to the charge state and the long-distance average energy consumption of the vehicle;
calculating an instantaneous estimated value according to the state of charge and the short-distance instantaneous energy consumption of the vehicle; and
and weighting and summing the experience estimation value, the average estimation value and the instantaneous estimation value according to the accuracy requirement and the stability requirement of the mileage so as to obtain the estimated mileage.
12. The estimation device of claim 11, wherein the operating condition includes one or more of a state of electrical energy recovery of the power battery, a cell temperature of the power battery, a driving mode of the vehicle, an ambient temperature, and an energy consumption of an on-board air conditioner.
13. The estimation device of claim 12, wherein the processor is further configured to:
looking up a table according to the type of the power battery, the state of charge and the electric energy recovery state to obtain a basic estimation value;
looking up a table according to the model of the power battery and the cell temperature to obtain a cell temperature correction coefficient;
obtaining a driving mode correction coefficient according to the type of the power battery and the driving mode table look-up; and
and responding to the fact that the vehicle-mounted air conditioner is not started, and calculating the experience estimated value according to the basic estimated value, the battery core temperature correction coefficient and the driving mode correction coefficient.
14. The estimation device of claim 13 wherein the power recovery state includes a strong recovery state, a weak recovery state and a closed recovery state.
15. The estimation device of claim 13, wherein the processor is further configured to:
responding to the starting of the vehicle-mounted air conditioner, looking up a table according to the type of the power battery to obtain the energy consumption of the vehicle-mounted air conditioner, and looking up the table according to the type of the power battery and the environment temperature to obtain an environment temperature correction coefficient;
calculating an air conditioner energy consumption correction coefficient according to the ambient temperature correction coefficient and the energy consumption of the vehicle-mounted air conditioner; and
and calculating the experience estimated value according to the basic estimated value, the cell temperature correction coefficient, the driving mode correction coefficient and the air conditioner energy consumption correction coefficient.
16. The estimation device of claim 11, wherein the processor is further configured to:
calculating a first average energy consumption of a first long distance according to the energy consumption of each 1 kilometer of the vehicle;
calculating a second average energy consumption for a second long distance based on the energy consumption of the vehicle per 50 kilometers;
calculating total average energy consumption according to the first average energy consumption and the second average energy consumption; and
and calculating the average estimated value according to the state of charge and the total average energy consumption.
17. The estimation device of claim 16, wherein the processor is further configured to:
calculating the energy consumption of the vehicle in each continuous driving 1 kilometer;
storing the kilometer of energy consumption obtained by calculation into a first array to update a stack point value of the first array; and
and calculating the average value of all stack point values of the first array to obtain the first average energy consumption, wherein the first long distance corresponds to the number of the stack points of the first array.
18. The evaluation device of claim 17, wherein the first array comprises 50 stack points,
the processor is further configured to:
calculating the average value of 50 stack point values of the first array every 20 kilometers of the vehicle to obtain the average energy consumption of the vehicle every 50 kilometers in the past;
storing the obtained average energy consumption per kilometer of the previous 50 kilometers into a second array to update a stack point value of the second array; and
and calculating the average value of all stack point values of the second array to obtain the second average energy consumption, wherein the second long distance corresponds to the number of the stack points of the second array.
19. The estimation device of claim 11, wherein the processor is further configured to:
calculating the energy consumption of the vehicle in 0.1 kilometer every time the vehicle runs for 0.1 kilometer; and
and calculating the instantaneous estimated value according to the state of charge and the energy consumption of 0.1 kilometer.
20. The estimation device of claim 11, wherein the processor is further configured to:
increasing a weighting coefficient of the empirical estimate in response to a signal indicative of enhanced stability; and
the weighting factor of the instantaneous estimate is increased in response to a signal indicating increased accuracy.
21. A computer-readable storage medium having stored thereon computer instructions which, when executed by a processor, implement the estimation method of any one of claims 1-10.
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| CN114056187B (en) * | 2021-12-13 | 2024-04-05 | 爱驰汽车有限公司 | Vehicle endurance mileage calculation method, device, equipment and storage medium |
| CN114572055A (en) * | 2022-03-17 | 2022-06-03 | 一汽解放汽车有限公司 | Endurance mileage estimation method, endurance mileage estimation device, computer equipment and storage medium |
| CN114572055B (en) * | 2022-03-17 | 2023-08-18 | 一汽解放汽车有限公司 | Method, device, computer equipment and storage medium for estimating endurance mileage |
| CN115320390A (en) * | 2022-08-30 | 2022-11-11 | 广汽埃安新能源汽车有限公司 | Method and device for updating remaining endurance of electric vehicle, electronic equipment and storage medium |
| CN115320390B (en) * | 2022-08-30 | 2024-05-03 | 广汽埃安新能源汽车有限公司 | Method and device for updating remaining endurance of electric automobile, electronic equipment and storage medium |
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