CN117087495A - Method for estimating driving range of electric automobile based on battery aging level - Google Patents
Method for estimating driving range of electric automobile based on battery aging level Download PDFInfo
- Publication number
- CN117087495A CN117087495A CN202311212699.3A CN202311212699A CN117087495A CN 117087495 A CN117087495 A CN 117087495A CN 202311212699 A CN202311212699 A CN 202311212699A CN 117087495 A CN117087495 A CN 117087495A
- Authority
- CN
- China
- Prior art keywords
- driving
- battery
- electric
- electric automobile
- estimation model
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 76
- 230000032683 aging Effects 0.000 title claims abstract description 72
- 238000005265 energy consumption Methods 0.000 claims abstract description 26
- 238000004458 analytical method Methods 0.000 claims abstract description 13
- 238000004364 calculation method Methods 0.000 claims abstract description 10
- 230000008569 process Effects 0.000 claims description 14
- 239000011159 matrix material Substances 0.000 claims description 6
- 230000008859 change Effects 0.000 claims description 4
- 238000010276 construction Methods 0.000 claims description 4
- 230000007613 environmental effect Effects 0.000 claims description 3
- 238000010281 constant-current constant-voltage charging Methods 0.000 claims description 2
- 230000004069 differentiation Effects 0.000 claims description 2
- 238000001514 detection method Methods 0.000 abstract description 3
- 230000002159 abnormal effect Effects 0.000 description 7
- 238000010586 diagram Methods 0.000 description 6
- 230000005611 electricity Effects 0.000 description 4
- 238000007600 charging Methods 0.000 description 3
- 230000007423 decrease Effects 0.000 description 3
- 238000011161 development Methods 0.000 description 3
- 230000018109 developmental process Effects 0.000 description 3
- 238000013107 unsupervised machine learning method Methods 0.000 description 3
- 230000007547 defect Effects 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 230000036541 health Effects 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- 230000002411 adverse Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 229910052799 carbon Inorganic materials 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 238000003745 diagnosis Methods 0.000 description 1
- 238000000840 electrochemical analysis Methods 0.000 description 1
- 238000003487 electrochemical reaction Methods 0.000 description 1
- 239000000284 extract Substances 0.000 description 1
- 230000002349 favourable effect Effects 0.000 description 1
- 238000009472 formulation Methods 0.000 description 1
- 239000000446 fuel Substances 0.000 description 1
- 238000011065 in-situ storage Methods 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
- 238000000611 regression analysis Methods 0.000 description 1
- 230000001932 seasonal effect Effects 0.000 description 1
- 238000003860 storage Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000012549 training Methods 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
Classifications
-
- 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
- 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]
-
- 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
- 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
- B60L58/16—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries responding to battery ageing, e.g. to the number of charging cycles or the state of health [SoH]
-
- 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
- B60L2240/00—Control parameters of input or output; Target parameters
- B60L2240/10—Vehicle control parameters
- B60L2240/12—Speed
-
- 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
- B60L2240/00—Control parameters of input or output; Target parameters
- B60L2240/40—Drive Train control parameters
- B60L2240/54—Drive Train control parameters related to batteries
Landscapes
- Engineering & Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Sustainable Development (AREA)
- Sustainable Energy (AREA)
- Power Engineering (AREA)
- Transportation (AREA)
- Mechanical Engineering (AREA)
- Electric Propulsion And Braking For Vehicles (AREA)
Abstract
The invention provides a driving range estimation method of an electric automobile based on a battery aging level. The method comprises the following steps: acquiring operation data of the electric automobile, wherein the operation data comprises an average running speed and an electric quantity use interval, acquiring a battery aging level corresponding to the electric quantity use interval through a capacity increment analysis method, and grouping the electric quantity use intervals based on a temperature condition; constructing a nonlinear estimation model of the speed and unit energy consumption driving mileage of the electric vehicle according to the operation data of the electric vehicle; and constructing a driving distance calculation model in a battery use interval according to a nonlinear estimation model of the speed and unit energy consumption driving distance, and calculating the driving distances of the electric vehicles with different battery aging levels and temperature conditions. The method provided by the invention effectively integrates the characteristics of the actual running environment while realizing accurate estimation of the driving range of the electric automobile, and is suitable for the driving range detection and prediction system of the electric automobile in the actual complex urban traffic environment.
Description
Technical Field
The invention relates to the technical field of electric automobiles, in particular to a driving range estimation method of an electric automobile based on a battery aging level.
Background
Energy and environmental problems are challenges to urban traffic development at the present stage, and the global promotion of vehicle motorization is regarded as an important development direction of a future urban traffic system in all countries of the world. The popularization and application of the electric automobile strongly promote the transformation of the urban traffic system to the green low-carbon sustainable development direction, and simultaneously, the travel mode of people is changed to a great extent. Unlike traditional fuel automobiles, electric automobiles usually need to be charged in the daily operation process to complete a set travel target, and accurate estimation of the vehicle driving range is an important premise for helping drivers reasonably plan charging and travel routes; meanwhile, the capacity of the vehicle-mounted battery has direct influence on the driving range of the vehicle, and the battery inevitably generates aging phenomenon along with the running of the vehicle, and the capacity and the performance also decline along with the aging phenomenon, so that the driving range of the electric vehicle is influenced.
Therefore, the driving range estimation method for integrating the battery aging characteristic into the electric automobile is a necessary measure for guaranteeing the safe operation of the electric automobile, and meanwhile, the operation efficiency of the electric automobile can be effectively improved. The method for establishing the driving range estimation of the electric automobile integrated with the battery aging level has the following important significance:
(1) The battery aging level is integrated in the method for estimating the driving range of the electric automobile, so that the estimation result of the driving range of the automobile can be corrected in time, and the driving range estimation result of the electric automobile is closer to the actual situation.
(2) The accurate estimation of the driving range of the electric automobile integrated with the battery aging level can provide basis for battery detection and diagnosis, is beneficial to timely knowing the health state of the battery and timely replacing the battery, and improves the running efficiency of the electric automobile.
(3) The driving range estimation of the electric automobile relates to the formulation of the traveling and charging schemes of the automobile, so that the driving range estimation of the electric automobile in the actual complex running environment is accurately estimated, and the driving range estimation has an important effect on improving the running efficiency of the electric automobile.
Currently, in the prior art, a driving range estimation method of an electric automobile is generally focused on factors such as a vehicle driving state and a battery state, a driving range estimation model of the electric automobile taking the factors such as a battery charge state, a vehicle driving speed and a battery temperature into consideration is established based on a machine learning method, and the internal relation between battery aging characteristics and driving ranges is not fully considered, and meanwhile, the robustness of an estimation result is required to be improved.
The driving distance estimation method of the electric automobile in the prior art mainly has two defects: firstly, the driving range of an electric automobile is influenced by various internal and external factors such as the driving working condition of the automobile, the environment working condition and the like, the existing method aims at the lack of analysis of the aging characteristics of the battery in the actual complex operation environment, and the influence of the aging level of the battery on the driving range is ignored, so that the method has limitation in practical application; secondly, the existing driving distance estimation method of the electric automobile is generally based on ideal data, the robustness is poor, and deviation exists between a considered scene and actual complex running of the electric automobile.
Disclosure of Invention
The embodiment of the invention provides a driving range estimation method of an electric automobile based on a battery aging level, so as to effectively estimate the driving range of the electric automobile.
In order to achieve the above purpose, the present invention adopts the following technical scheme.
A driving distance estimation method of an electric automobile based on a battery aging level comprises the following steps:
acquiring operation data of an electric automobile, wherein the operation data comprises an average running speed and an electric quantity use interval, acquiring a battery aging level corresponding to the electric quantity use interval through a capacity increment analysis method, and grouping the electric quantity use interval based on a temperature condition;
constructing a nonlinear estimation model of the speed and unit energy consumption driving mileage of the electric vehicle according to the operation data of the electric vehicle;
and constructing a driving distance calculation model in a battery use interval according to the nonlinear estimation model of the speed and the unit energy consumption driving distance, and calculating the driving distances of the electric vehicles with different battery aging levels and temperature conditions.
Preferably, the collecting operation data of the electric automobile, where the operation data includes an average running speed and an electricity consumption interval, and obtaining a battery aging level corresponding to the electricity consumption interval through a capacity increment analysis method includes:
collecting operation data of the electric automobile, wherein the operation data comprises an average running speed and an electric quantity use interval, and in the process of relatively stable current operation, deriving differentiation relative to charge capacity and voltage in a constant-current constant-voltage charging mode to obtain a capacity increment curve, thereby showing a capacity increment IC n And the trend of the voltage variation delta V along with the terminal is calculated according to the following formula:
wherein Q is n And Q n-1 Battery capacity change, Δq, respectively representing voltage steps n and n-1 n The charge capacity of the battery at the voltage step n is represented, the battery capacity Q is obtained by integrating the current I (t) at different time periods, and the calculation formula is as follows:
after obtaining a capacity increment curve of the battery, observing and recording a capacity increment peak value on the capacity increment curve, determining the aging level of the battery according to the capacity increment peak value, and obtaining the battery aging level corresponding to the electric quantity use interval.
Preferably, the constructing a nonlinear estimation model of the speed and the unit energy consumption driving mileage of the electric vehicle according to the operation data of the electric vehicle includes:
according to the running data of the electric automobile, a robust nonlinear regression method is adopted to construct a nonlinear estimation model between the average running speed V of the automobile and the unit energy consumption running mileage delta M, and the expression of the nonlinear estimation model is as follows:
wherein k is 1 ,k 2 ,k 3 ,k 4 ,k 5 ,k 6 ,k 7 Is a model parameter.
Preferably, the constructing a driving distance calculation model in a battery using section according to the nonlinear estimation model of the speed and unit energy consumption driving distance, and calculating driving distances of electric vehicles with different battery aging levels and temperature conditions includes:
aiming at different accumulated mileage intervals and temperature conditions, a driving mileage estimation model of the electric vehicle taking the battery aging level, the vehicle driving speed and the environmental temperature into consideration is established based on a robust nonlinear regression method, and the expression is shown in a formula (4):
M＝ΔM×(Z o -Z l ) In the formula (4), deltaM is obtained by a nonlinear estimation model between the average running speed and the unit energy consumption running mileage shown in the formula (1), and Z o And Z l Respectively represent an upper boundary and a lower boundary of an electric quantity using section of the electric automobile,
calculating the driving range of the electric automobile under different battery aging levels and temperature conditions according to the driving range estimation model of the electric automobile, wherein the construction process of the driving range estimation model of the electric automobile comprises the following steps:
step S1, setting an iteration cursor as I=0, estimating parameter values under different battery aging levels and temperature condition combined scenes by adopting 1st Opt, and taking the parameter values as initial estimation of nonlinear regression coefficientsDetermining an initial driving range estimation model of the electric automobile according to the given initial parameter value;
s2, calculating residual errors according to the predicted value of the initial driving range estimation model and the actual value of the data
Step S3, calculating initial weight of the driving range estimation model based on the following formula
Wherein E is i Residual error representing predicted value and actual value; c and sigma i Parameters and confidence intervals respectively;
step S4, first iteration, i=1, minimizing with weighted nonlinear least squaresAnd getResidual->Expressed in the form of a matrix, if W is a weight diagonal matrix representing the residual individuals, its solution isWherein V and M represent speed and range, respectively;
step S5, continuing to use the residual error obtained by the initial weighted nonlinear least square regressionCalculate new weight->
Step S6, new weightWill be used in the next weighted nonlinear least squares iteration, i=2, to estimate
Step S7, repeating the steps S4-S6 untilStable on one iteration result, i.e. +.>When the variation of the estimated result is not more than 0.01% of the last iteration, the convergence is considered, the unit energy consumption driving mileage delta M is obtained, and the driving mileage estimation model is obtained by substituting delta M into a formula (4).
According to the technical scheme provided by the embodiment of the invention, the method aims to overcome the defects of the existing method for estimating the driving range of the electric automobile, considers the influence of the battery aging level on the driving range of the automobile, combines with complex driving environment conditions such as external temperature and the like, fuses a capacity increment analysis method, an unsupervised machine learning method and a robust nonlinear regression method, and provides the method for estimating the driving range of the electric automobile which is integrated with the battery aging level. The method effectively integrates the characteristics of the actual running environment while realizing the accurate estimation of the driving range of the electric automobile. Therefore, the method is suitable for a driving range detection and prediction system of the electric automobile in a practical complex urban traffic environment.
Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments 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 other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a process flow diagram of a method for estimating driving range of an electric vehicle based on battery aging level according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of modeling thought of nonlinear relation between driving speed and unit energy consumption driving mileage according to an embodiment of the present invention;
fig. 3 is a schematic diagram of a construction process of a driving range estimation model of an electric vehicle according to an embodiment of the present invention.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals refer to the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the drawings are exemplary only for explaining the present invention and are not to be construed as limiting the present invention.
As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless expressly stated otherwise, as understood by those skilled in the art. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may also be present. Further, "connected" or "coupled" as used herein may include wirelessly connected or coupled. The term "and/or" as used herein includes any and all combinations of one or more of the associated listed items.
It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
For the purpose of facilitating an understanding of the embodiments of the invention, reference will now be made to the drawings of several specific embodiments illustrated in the drawings and in no way should be taken to limit the embodiments of the invention.
The battery of the electric automobile inevitably ages along with the running of the automobile, capacity and performance decline along with the running of the automobile, and further the driving range of the electric automobile is affected, so that the driving range of the electric automobile is accurately estimated by considering the aging level of the battery, and when the battery is in different aging levels, the driving range of the automobile also has obvious difference. The embodiment of the invention provides an electric vehicle driving mileage estimation method considering battery aging level and vehicle driving conditions, which can be applied to an electric vehicle battery and a driving state monitoring system to realize quick and accurate estimation of the driving mileage of the electric vehicle.
In order to accurately describe the aging characteristics of a battery pack of an electric vehicle in an actual running environment, the invention provides a method capable of representing the battery aging level through actual running data of the electric vehicle based on a capacity increment analysis method, and a driving range estimation method of the electric vehicle considering various influencing factors is established for different battery aging levels on the basis.
The process flow chart of the method for estimating the driving range of the electric automobile based on the battery aging level provided by the embodiment of the invention is shown in fig. 1, and comprises the following process steps:
step S10, acquiring operation data of the electric automobile, wherein the operation data comprise average running speed and electric quantity use intervals, acquiring battery aging levels corresponding to the electric quantity use intervals through a capacity increment analysis method, and grouping the electric quantity use intervals based on temperature conditions;
step S20, constructing a nonlinear estimation model of the speed and unit energy consumption driving mileage of the electric vehicle according to the operation data of the electric vehicle;
and step S30, constructing a driving distance calculation model in a battery use interval according to the nonlinear estimation model of the speed and unit energy consumption driving distance, and calculating the driving distances of the electric vehicles with different battery aging levels and temperature conditions.
Specifically, the step S10 includes: the invention provides a driving range estimation method of an electric vehicle based on a battery aging level, which aims at actual running data of the electric vehicle and comprises a plurality of abnormal information, wherein part of abnormal data possibly comprises useful information. The robust nonlinear regression method can effectively process outliers. According to the method, different weights are given to the data points, so that the effect of abnormal points is reduced, and the part with more influence on prediction is enhanced. Thus, a robust nonlinear regression method is employed to determine the model parameters. Meanwhile, in order to consider the influence of the battery aging level and the temperature condition on the driving range estimation result, the accumulated mileage intervals corresponding to different battery aging levels and the vehicle operation data of different seasonal combination scenes are respectively obtained by adopting a capacity increment analysis method, and the driving range estimation model capacity increment analysis method for the electric automobile is established, so that the method is an in-situ lossless electrochemical analysis method, and the electrochemical reaction inside the battery can be studied under the condition that the physical structure of the battery is not damaged. In the process of relatively stable current operation, a capacity increment curve is obtained by deriving the differential of the charge capacity to the voltage in a constant-current constant-voltage charge mode, and the capacity Increment (IC) is embodied n ) The calculation formula is as follows, with the trend of the voltage variation with terminal (DeltaV):
wherein Q is n And Q n-1 Battery capacity variations for voltage steps n and n-1, respectively; ΔQ n Indicating the charge capacity of the battery at voltage step n. The battery capacity (Q) can be obtained by integrating the current (I (t)) at different periods of time, and the calculation formula is as follows:
after obtaining the capacity increment curve of the battery, the capacity increment peak value on the curve is observed and recorded. The peak value of the capacity increment can accurately reflect the health state of the battery. As the battery aging level increases, the capacity increment peak value decreases, so that the battery aging level can be indirectly reflected by the capacity increment peak value. And determining the aging level of the battery according to the capacity increment peak value, and acquiring the battery aging level corresponding to the electric quantity use interval. The electricity consumption interval can reflect the accumulated running condition of the vehicle, and the battery aging level can be deepened as the vehicle is continuously used.
Based on obtaining charging data and capacity increment peak values of the electric vehicle in a certain period, integrating accumulated mileage data and the capacity increment peak values of the electric vehicle, and identifying battery aging levels in different accumulated mileage intervals by adopting an unsupervised machine learning method, so as to obtain a corresponding relation between the accumulated mileage and the battery aging levels.
Specifically, the step S20 includes:
the robust nonlinear regression theory is introduced into the driving range estimation of the electric automobile. The robust nonlinear regression method is not constrained by whether actual data obeys the assumed distribution or not, has strong capability of resisting the influence of abnormal values, and the established driving range estimation model can better reflect the actual running scene of the electric automobile. The robust nonlinear regression method selects a proper weight function to reduce the influence of the abnormal value on the parameter estimation value as much as possible, and extracts useful information contained in the abnormal value as much as possible when the adverse influence of the abnormal value is reduced, so that the driving range estimation model of the electric automobile can obtain the optimal estimation value under the condition of external interference.
The schematic diagram of modeling thought of nonlinear relation between the running speed and the unit energy consumption running mileage of the electric automobile is shown in fig. 2. After the running data of the electric automobile are obtained through methods such as online acquisition and the like, a model frame is determined according to the nonlinear relation characteristics of the running speed and the unit energy consumption running mileage; and then, a robust nonlinear regression method is adopted, and a nonlinear model library of speed and driving mileage is established by combining different battery aging levels and different external temperature condition data.
In the case of data, the primary solution to nonlinear regression analysis is to determine the nonlinear model architecture between dependent and independent variables. Firstly, constructing a nonlinear estimation model between the average running speed (V) and the unit energy consumption running mileage (delta M), wherein the expression of the model is as follows:
wherein k is 1 ,k 2 ,k 3 ,k 4 ,k 5 ,k 6 ,k 7 Is a model parameter.
The nonlinear estimation model presented in the formula (1) is applied to constructing a driving range estimation model for different battery aging levels and temperature conditions, wherein the battery aging levels and the temperature conditions are mainly realized through data selection, namely, the data under the corresponding battery aging levels and temperature conditions are used as the basis in modeling, and the nonlinear estimation model shown in the formula (1) is used for fitting a corresponding driving range estimation model library by combining a battery state of charge.
Specifically, the step S30 includes: based on the method, a driving range estimation model of the electric vehicle considering the battery aging level, the vehicle driving speed and the environment temperature is established based on a robust nonlinear regression method aiming at different accumulated mileage intervals and temperature conditions, and the expression is shown in a formula (4). Wherein DeltaM is obtained by a nonlinear estimation model between the average running speed (V) and the unit energy consumption running mileage (DeltaM) shown in the formula (1), and the running mileage can be obtained by further substituting the battery capacity on the basis. Considering that in the actual use process of the electric automobile, for the reasons of protecting the battery, keeping enough safe electric quantity and the like, a driver usually sets an electric quantity use interval of the battery in advance, so that a maximum change interval [ Z ] of the battery is introduced into a driving range estimation model o ,Z l ]Wherein Z is o And Z l Respectively representing the upper and lower bounds of the electricity usage interval. The range in the battery usage interval can be obtained by the formula (4).
M＝ΔM□(Z o -Z l ) (4)
Fig. 3 is a schematic diagram of a construction process of a driving range estimation model of an electric vehicle according to an embodiment of the present invention, which specifically includes:
step S1, setting an iteration cursor as I=0, estimating parameter values under different battery aging levels and temperature condition combined scenes by adopting 1st Opt, and taking the parameter values as initial estimation of nonlinear regression coefficients
Step S2, calculating residual errors according to the predicted value of the initial estimation model and the actual value of the dataAnd used to calculate the initial weights. The initial estimation model is a model determined by given initial parameter values, and is updated continuously through iteration, and the predicted values are optimized gradually.
Step S3, calculating initial weights based on the following
Wherein E is i Residual error representing predicted value and actual value; c and sigma i Parameters and confidence intervals, respectively.
Step S4, first iteration, i=1, minimizing with weighted nonlinear least squaresAnd getResidual->Expressed in the form of a matrix, if W is a weight diagonal matrix representing the residual individuals, its solution isWhere V and M represent speed and range, respectively.
Step S5, continuing to use the residual error obtained by the initial weighted nonlinear least square regressionCalculate new weight->
Step S6, new weightWill be used in the next weighted nonlinear least squares iteration, i=2, to estimate
Step S7, repeating the steps S4-S6 untilStable on one iteration result, i.e. +.>When the variation of the estimated result does not exceed 0.01% of the previous iteration, the convergence is considered, the unit energy consumption driving mileage delta M can be obtained at the moment, and the unit energy consumption driving mileage delta M is substituted into the formula (4) to obtain the driving mileage estimation model.
The input data of the electric vehicle driving distance estimation model obtained based on the training of the method are the average driving speed (V) and the electric quantity using interval [ Z ] o ,Z l ]The output data is a range (M), wherein the input data is processed by a capacity increment analysis method to determine corresponding battery aging levels before being substituted into the model, and the data is grouped based on temperature conditions, so that a range estimation model library under different combinations of battery aging levels and temperature conditions can be obtained.
In estimating the range of an electric vehicle, it is a common practice to obtain an estimated value by substituting a value of a variable into a model based on test data. However, in the actual running process of the vehicle, the running speed of the electric vehicle is changed in a complex traffic network, so that the speed in the current state of charge cannot represent the later running state. Therefore, the method for obtaining the accumulated mileage by solving the segmented mileage and overlapping the segmented mileage when estimating the driving mileage realizes accurate estimation of the driving mileage. In fig. 2, i increases by 1 for every 1% change in state of charge, and the velocity takes the velocity average at the current state of charge.
In summary, by applying the method provided by the invention, accurate estimation of the driving range of the electric automobile under different battery aging levels, vehicle driving conditions and external environment conditions can be realized, and effective information is provided for the driver of the electric automobile in the aspects of accurately knowing the driving range of the vehicle, timely replacing the battery and the like.
The driving range estimation method of the electric vehicle integrated with the battery aging level has higher execution efficiency, and has the advantages that:
(1) The provided method adopts a capacity increment analysis method, can effectively utilize actual measurement operation data of the electric automobile, identify the aging trend characteristics of the battery in a real complex operation environment, and breaks through the bottleneck that the traditional constant temperature battery test data estimation method based on a laboratory is difficult to reflect the actual operation condition of the electric automobile;
(2) An unsupervised machine learning method is introduced in the execution process of the method, and the battery aging level which is difficult to intuitively acquire is effectively related to the accumulated mileage of the vehicle, so that favorable conditions are created for exploring the driving mileage estimation method of the electric vehicle under different battery aging levels;
(3) The method establishes the driving range estimation model of the electric vehicle facing to the combined scene of different battery aging levels and external temperature conditions by adopting a robust nonlinear regression method, effectively utilizes useful information contained in data outliers, and adds a nonlinear relation between the vehicle driving speed and the energy consumption into the model. Therefore, the method provided by the invention not only can realize accurate estimation of the driving range of the electric automobile, but also has better practicability.
Those of ordinary skill in the art will appreciate that: the drawing is a schematic diagram of one embodiment and the modules or flows in the drawing are not necessarily required to practice the invention.
From the above description of embodiments, it will be apparent to those skilled in the art that the present invention may be implemented in software plus a necessary general hardware platform. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a storage medium, such as a ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the embodiments or some parts of the embodiments of the present invention.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for apparatus or system embodiments, since they are substantially similar to method embodiments, the description is relatively simple, with reference to the description of method embodiments in part. The apparatus and system embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
The present invention is not limited to the above-mentioned embodiments, and any changes or substitutions that can be easily understood by those skilled in the art within the technical scope of the present invention are intended to be included in the scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.
Claims (4)
1. The method for estimating the driving range of the electric automobile based on the battery aging level is characterized by comprising the following steps of:
acquiring operation data of an electric automobile, wherein the operation data comprises an average running speed and an electric quantity use interval, acquiring a battery aging level corresponding to the electric quantity use interval through a capacity increment analysis method, and grouping the electric quantity use interval based on a temperature condition;
constructing a nonlinear estimation model of the speed and unit energy consumption driving mileage of the electric vehicle according to the operation data of the electric vehicle;
and constructing a driving distance calculation model in a battery use interval according to the nonlinear estimation model of the speed and the unit energy consumption driving distance, and calculating the driving distances of the electric vehicles with different battery aging levels and temperature conditions.
2. The method according to claim 1, wherein the collecting operation data of the electric vehicle, the operation data including an average running speed and a power usage interval, and obtaining a battery aging level corresponding to the power usage interval by a capacity increment analysis method includes:
collecting operation data of the electric automobile, wherein the operation data comprises an average running speed and an electric quantity use interval, and in the process of relatively stable current operation, deriving differentiation relative to charge capacity and voltage in a constant-current constant-voltage charging mode to obtain a capacity increment curve, thereby showing a capacity increment IC n And the trend of the voltage variation delta V along with the terminal is calculated according to the following formula:
wherein Q is n And Q n-1 Respectively represent voltage stepsBattery capacity change, Δq, for long n and n-1 n The charge capacity of the battery at the voltage step n is represented, the battery capacity Q is obtained by integrating the current I (t) at different time periods, and the calculation formula is as follows:
after obtaining a capacity increment curve of the battery, observing and recording a capacity increment peak value on the capacity increment curve, determining the aging level of the battery according to the capacity increment peak value, and obtaining the battery aging level corresponding to the electric quantity use interval.
3. The method of claim 2, wherein the constructing a nonlinear estimation model of the speed and the unit energy consumption driving mileage of the electric vehicle according to the operation data of the electric vehicle comprises:
according to the running data of the electric automobile, a robust nonlinear regression method is adopted to construct a nonlinear estimation model between the average running speed V of the automobile and the unit energy consumption running mileage delta M, and the expression of the nonlinear estimation model is as follows:
wherein k is 1 ,k 2 ,k 3 ,k 4 ,k 5 ,k 6 ,k 7 Is a model parameter.
4. The method of claim 3, wherein constructing a driving distance calculation model in a battery use interval according to the nonlinear estimation model of the speed and unit energy consumption driving distance, and calculating driving distances of electric vehicles with different battery aging levels and temperature conditions comprises:
aiming at different accumulated mileage intervals and temperature conditions, a driving mileage estimation model of the electric vehicle taking the battery aging level, the vehicle driving speed and the environmental temperature into consideration is established based on a robust nonlinear regression method, and the expression is shown in a formula (4):
M＝ΔM×(Z o -Z l ) (4)
wherein DeltaM is obtained by a nonlinear estimation model between the average running speed and the unit energy consumption running mileage shown in the formula (1), Z o And Z l Respectively represent an upper boundary and a lower boundary of an electric quantity using section of the electric automobile,
calculating the driving range of the electric automobile under different battery aging levels and temperature conditions according to the driving range estimation model of the electric automobile, wherein the construction process of the driving range estimation model of the electric automobile comprises the following steps:
step S1, setting an iteration cursor as I=0, estimating parameter values under different battery aging levels and temperature condition combined scenes by adopting 1st Opt, and taking the parameter values as initial estimation of nonlinear regression coefficientsDetermining an initial driving range estimation model of the electric automobile according to the given initial parameter value;
s2, calculating residual errors E according to the predicted value of the initial driving range estimation model and the actual value of the data i (0) ；
Step S3, calculating initial weight of the driving range estimation model based on the following formula
Wherein E is i Residual error representing predicted value and actual value; c and sigma i Parameters and confidence intervals respectively;
step S4, first iteration, i=1, minimizing with weighted nonlinear least squaresAnd get->Residual E i (1) . Expressed in the form of a matrix, if W is a weight diagonal matrix representing the residual individuals, its solution isWherein V and M represent speed and range, respectively;
step S5, continuing to use the residual E obtained by the initial weighted nonlinear least square regression i (1) Calculating new weights
Step S6, new weightI=2 to be used in the next weighted nonlinear least squares iteration, to estimate +.>
Step S7, repeating the steps S4-S6 untilStable on one iteration result, i.e. +.>When the variation of the estimated result is not more than 0.01% of the last iteration, the convergence is considered, the unit energy consumption driving mileage delta M is obtained, and the driving mileage estimation model is obtained by substituting delta M into a formula (4).
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311212699.3A CN117087495A (en) | 2023-09-19 | 2023-09-19 | Method for estimating driving range of electric automobile based on battery aging level |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311212699.3A CN117087495A (en) | 2023-09-19 | 2023-09-19 | Method for estimating driving range of electric automobile based on battery aging level |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117087495A true CN117087495A (en) | 2023-11-21 |
Family
ID=88777066
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311212699.3A Pending CN117087495A (en) | 2023-09-19 | 2023-09-19 | Method for estimating driving range of electric automobile based on battery aging level |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117087495A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117728566A (en) * | 2023-12-05 | 2024-03-19 | 国网安徽省电力有限公司黄山供电公司 | Mobile energy storage remote control method and system |
-
2023
- 2023-09-19 CN CN202311212699.3A patent/CN117087495A/en active Pending
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117728566A (en) * | 2023-12-05 | 2024-03-19 | 国网安徽省电力有限公司黄山供电公司 | Mobile energy storage remote control method and system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Li et al. | Big data driven lithium-ion battery modeling method based on SDAE-ELM algorithm and data pre-processing technology | |
| CN110341690B (en) | PHEV energy management method based on deterministic strategy gradient learning | |
| CN112265546B (en) | Networked automobile speed prediction method based on time-space sequence information | |
| CN111007399B (en) | Lithium battery state of charge prediction method based on improved generation countermeasure network | |
| Tian et al. | Consistency evaluation and cluster analysis for lithium-ion battery pack in electric vehicles | |
| Caliwag et al. | Hybrid VARMA and LSTM method for lithium-ion battery state-of-charge and output voltage forecasting in electric motorcycle applications | |
| CN111323719A (en) | Method and system for online determination of health state of power battery pack of electric automobile | |
| CN104977545A (en) | Power battery charge state estimation method and system | |
| CN110673039B (en) | Lithium iron phosphate battery SOC charging online correction method based on big data | |
| US20220179009A1 (en) | Method and Apparatus for Operating a System for Providing Predicted States of Health of Electrical Energy Stores for a Device Using Machine Learning Methods | |
| CN104459551A (en) | Electric vehicle power battery state-of-energy estimation method | |
| CN103616647A (en) | Battery remaining capacity estimation method for electric car battery management system | |
| CN112557907A (en) | SOC estimation method of electric vehicle lithium ion battery based on GRU-RNN | |
| CN111352032A (en) | Lithium battery dynamic peak power prediction method | |
| CN112163372B (en) | SOC estimation method of power battery | |
| CN117087495A (en) | Method for estimating driving range of electric automobile based on battery aging level | |
| CN116298936A (en) | Intelligent lithium ion battery health state prediction method in incomplete voltage range | |
| CN115219913A (en) | Power battery full-life-cycle management system based on capacity increment method | |
| CN113868884B (en) | Power battery multi-model fault-tolerant fusion modeling method based on evidence theory | |
| CN102959791B (en) | For determining the method for at least one state of multiple secondary battery unit, accumulator and motor vehicles | |
| CN116047308A (en) | Lithium battery SOC estimation method based on PID control and DEKF | |
| CN116523177A (en) | Vehicle energy consumption prediction method and device integrating mechanism and deep learning model | |
| Xu et al. | Safety warning analysis for power battery packs in electric vehicles with running data | |
| CN113420444A (en) | Lithium ion battery SOC estimation method based on parameter online identification | |
| US20230305073A1 (en) | Method and apparatus for providing a predicted aging state of a device battery based on a predicted usage pattern |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |