CN110852378A - Road condition kinematics segment extraction method based on navigation system - Google Patents
Road condition kinematics segment extraction method based on navigation system Download PDFInfo
- Publication number
- CN110852378A CN110852378A CN201911094517.0A CN201911094517A CN110852378A CN 110852378 A CN110852378 A CN 110852378A CN 201911094517 A CN201911094517 A CN 201911094517A CN 110852378 A CN110852378 A CN 110852378A
- Authority
- CN
- China
- Prior art keywords
- electric vehicle
- road
- pure electric
- driving
- condition
- 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
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/213—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
- G06F18/2135—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods based on approximation criteria, e.g. principal component analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
Landscapes
- Engineering & Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Theoretical Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Artificial Intelligence (AREA)
- Bioinformatics & Computational Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Evolutionary Biology (AREA)
- Evolutionary Computation (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Electric Propulsion And Braking For Vehicles (AREA)
Abstract
The invention discloses a road condition kinematics segment extraction method based on a navigation system, which is used for collecting actual road running condition data of a pure electric vehicle, extracting kinematics segments, and extracting and classifying feature values of the kinematics segments based on a principal component analysis and a cluster analysis method. The invention provides a feasible solution for improving the economy and the service life of the pure electric vehicle and realizes the efficient and stable running of the electric vehicle under the complex running condition by taking the modeling and the energy optimization management of the pure electric vehicle power system as research objects, adopting a research method combining theoretical analysis, model construction and experimental research, taking the theoretical analysis as a core, the model construction as a basis and optimizing the energy management strategy of the system as a key point, and finally carrying out experimental verification and researching and optimizing key basic theories and technical problems related to the comprehensive working condition and the control of the pure electric vehicle power system.
Description
Technical Field
The invention relates to the technical field of new energy automobiles, in particular to a road working condition kinematics fragment extraction method based on a navigation system.
Background
In order to deal with the problems of energy shortage and environmental pollution, the development of new energy automobiles is more and more concerned by governments and society of various countries, and pure electric automobiles have the outstanding advantages of high efficiency, zero emission and the like and are one of the important directions of automobile development, but the performance of power batteries still has the technical bottleneck at the present stage, and the driving range of the pure electric automobiles is obviously reduced along with the decline of the performance of the power batteries; especially under the urban driving working conditions of frequent starting, acceleration, braking and the like, the energy consumption of the electric automobile is increased, the driving range is obviously shortened, the use of the pure electric automobile is severely limited, how to reasonably adjust the energy distribution of the electric automobile under the complex road condition, reduce the energy consumption of the whole automobile and prolong the driving range of the pure electric automobile is a main problem concerned by the industry and students, along with the development of intelligent transportation and power battery technologies in recent years, the construction and prediction of the driving working conditions of the pure electric automobile are carried out according to the actual road driving working condition data, and meanwhile, the battery modeling and state estimation method is combined to carry out optimization management on the energy dissipation process of the pure electric automobile, so that the method becomes an effective way for improving the driving range of the pure electric automobile.
At present, methods such as feature extraction, cluster analysis, Markov process theory, discrete wavelet transformation and the like are mainly adopted for constructing the vehicle running condition, the method is applied to vehicle fuel consumption and emission analysis testing, power system optimization design and control, and plays an important role, and domestic and foreign scholars predict road conditions and vehicle running conditions under complex road conditions based on an artificial neural network method and the Markov process theory and are mainly used for guiding the optimization design of a vehicle energy management strategy.
The battery model describes a mathematical relationship between influence factors of a battery and working characteristics of the battery, and related main factors comprise voltage, current, temperature, internal resistance, cycle working times, power, SOC, self-discharge and the like.
The current control theory method applied to the energy management of the electric automobile mainly comprises (1) a rule-based logic threshold control strategy which is simple, easy to implement and good in robustness, but belongs to static control, cannot ensure the optimality of the control effect, usually cannot achieve the optimization targets of economy and emission, needs a developer to repeatedly debug the automobile type, and has poor algorithm portability for different automobile types, (2) a fuzzy logic-based control strategy which has self-correcting and self-adapting capabilities and can be simply or complexly controlled according to the order of magnitude of input parameters, but cannot achieve flexible control under various driving conditions and working conditions due to the unchanged set parameters, has a complex system, and is difficult to formulate fuzzy rules and establish membership functions, there is no clear making method, (3) the control strategy based on the optimization algorithm, the control strategy based on the optimization algorithm mainly includes the global optimization control strategy and the transient optimization control strategy, the global optimization control strategy-generally requires the optimization action search under the condition that the global disturbance (i.e. power demand) is known, the economy and emission of the vehicle are set as the control target, various system variables are used as the optimized constraint condition, the optimization model is established, and finally the corresponding energy distribution value is calculated, the strategy includes the global optimization control strategy based on the multi-objective variable mathematical programming, dynamic programming and the minimum value theory, but the global optimization method can accurately solve the global optimal energy management control decision under the condition that the vehicle state and the running condition information are completely known, and the real-time control is difficult to realize, the transient optimization control strategy converts a global optimization control problem into a series of transient optimization problems, the minimum energy consumption strategy and the minimum power loss can be taken as targets, and the solved result is a sequence of transient optimal results.
Disclosure of Invention
The invention aims to provide a road working condition kinematics segment extraction method based on a navigation system, which has the advantages of improving the economy, the service life and the driving range of a new energy automobile and solves the problems of high energy dissipation and low driving range of a pure electric automobile.
In order to achieve the purpose, the invention provides the following technical scheme: a road condition kinematics segment extraction method based on a navigation system is characterized by comprising the following steps:
acquiring actual road running condition data of the pure electric vehicle, and extracting a kinematic segment;
extracting and classifying the characteristic values of the kinematic segments based on a principal component analysis and cluster analysis method, and constructing a comprehensive working condition according with the running condition characteristics of the pure electric vehicle;
introducing multiple time scale kinematic parameters for describing quasi-steady state characteristics of a driving state in a kinematic segment class, between classes and in a switching process between different kinematic segments.
As described above, the method for extracting the road condition kinematics segment based on the navigation system, wherein optionally, the collecting of the actual road driving condition data of the pure electric vehicle specifically includes:
acquiring actual road driving condition data of the pure electric vehicle;
acquiring navigation map information, storing the actual road running condition data and suggesting a corresponding relation with a specific road section of the navigation map information;
and taking the unit actual driving distance as a reference, and storing the road driving condition data and the corresponding relation with the specific road section according to the sequence.
The method for extracting the road condition kinematics segment based on the navigation system may optionally include the road condition information and the driving parameters of the pure electric vehicle driven under the corresponding road condition information.
The method for extracting the road condition kinematics segment based on the navigation system as described above, wherein optionally, the driving parameters of the line electric vehicle include power consumption, power consumption per actual driving distance, driving speed, driving acceleration and vibration amplitude of a seat in the vehicle;
the corresponding road condition information includes road unevenness.
The method for extracting the road condition kinematics segment based on the navigation system optionally further comprises displaying a recommended driving speed if the route in the current navigation map information comprises the stored road condition information and the driving parameter information of the pure electric vehicle; meanwhile, the newly acquired actual road running condition data of the pure electric vehicle is used for replacing the existing road condition information and the running parameter information of the pure electric vehicle.
The method for extracting the road condition kinematics segment based on the navigation system comprises the following steps of optionally obtaining the recommended running speed according to the stored road condition information and the current electric quantity information of the pure electric vehicle through simulation calculation; specifically, the controller for simulation calculation is connected with the pure electric vehicle through a network.
The method for extracting road condition kinematics segments based on the navigation system may optionally store the road driving condition data and the corresponding relationship with the specific road segment, specifically, store the road driving condition data and the corresponding relationship with the specific road segment in a cloud storage manner.
The method for extracting the road condition kinematics segment based on the navigation system optionally adopts a dimensionality reduction processing and fuzzy clustering method to complete the construction of the quasi-steady-state process typical driving condition of the pure electric vehicle;
performing parameter identification on the established equivalent circuit model of the lithium ion power battery pack by adopting a least square method;
performing SOC and SOH joint estimation on the lithium ion power battery pack by adopting a combined algorithm of extended Kalman filtering and ampere-hour integration;
the energy management system of the pure electric vehicle is established by a hierarchical control theory, the top strategy is an energy management strategy, and the bottom strategy is an execution layer.
Compared with the prior art, the invention has the following beneficial effects:
the invention can continuously acquire the road working condition based on the navigation system to extract the kinematics segments, can acquire the road information of each road, can be favorable for calculating better recommended speed based on the road information, is favorable for driving on different roads at better speed in the driving process, and is favorable for acquiring better economy and comfort. More importantly, the invention can provide a thought for setting the vehicle speed in automatic driving.
In addition, the modeling and energy optimization management of the pure electric vehicle power system are taken as research objects, a research method combining theoretical analysis, model construction and experimental research is planned to be adopted, the theoretical analysis is taken as a core, the model construction is taken as a basis, the optimization of the system energy management strategy is taken as a key point, finally, the experimental verification is carried out, and the key basic theory and the technical problem related to the comprehensive working condition and control of the pure electric vehicle power system are researched and optimized.
Drawings
FIG. 1 is a flow chart of the steps of the present invention;
FIG. 2 is a flowchart illustrating the steps of extracting a kinematic fragment according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1 and fig. 2, the invention provides a road condition kinematics segment extraction method based on a navigation system, which comprises the steps of collecting actual road running condition data of a pure electric vehicle, extracting kinematics segments, extracting and classifying kinematic segment characteristic values based on a principal component analysis and a clustering analysis method, constructing a comprehensive working condition according with the running condition characteristics of the pure electric vehicle, introducing multi-time scale kinematics parameters for describing quasi-steady state characteristics of a running state in a kinematics segment class, a class and a switching process among different kinematics segments, adopting a dimension reduction processing and fuzzy clustering method to complete the construction of a typical running condition of the quasi-steady state process of the pure electric vehicle, adopting a least square method to carry out parameter identification on an established lithium ion power battery pack equivalent circuit model, adopting a combined algorithm of extended Kalman filtering and ampere-hour integration to carry out combined estimation on the SOC and the SOH of the lithium ion power battery pack, the energy management system of the pure electric vehicle is established by a hierarchical control theory, the top strategy is an energy management strategy, and the bottom strategy is an execution layer.
Specifically, the method for acquiring actual road running condition data of the pure electric vehicle and extracting the kinematics segment specifically comprises the following steps:
acquiring actual road driving condition data of the pure electric vehicle; specifically, the actual road driving condition data includes road data and driving condition data of the pure electric vehicle.
Acquiring navigation map information, storing the actual road running condition data and suggesting a corresponding relation with a specific road section of the navigation map information; specifically, the road information corresponding to each road segment is recorded.
And taking the unit actual driving distance as a reference, and storing the road driving condition data and the corresponding relation with the specific road section according to the sequence. For example, corresponding data is recorded in units of 100 meters, respectively.
As a preferred embodiment, the kinematic segment includes road condition information and driving parameters of the pure electric vehicle driving under the corresponding road condition information. More specifically, the driving parameters of the line electric vehicle include power consumption, power consumption per actual driving distance, driving speed, driving acceleration, and amplitude of seat vibration in the vehicle; the corresponding road condition information includes road unevenness.
As a preferred embodiment, the method further includes displaying a recommended driving speed if the route in the current navigation map information includes stored road condition information and driving parameter information of the pure electric vehicle; meanwhile, the newly acquired actual road running condition data of the pure electric vehicle is used for replacing the existing road condition information and the running parameter information of the pure electric vehicle. Therefore, the change information of the road can be modified, and the accuracy of the data is favorably ensured.
As a better implementation mode, the recommended driving speed is obtained by simulation calculation according to the stored road condition information and the current electric quantity information of the pure electric vehicle; specifically, the controller for simulation calculation is connected with the pure electric vehicle through a network. Specifically, the result may be calculated as an optimization algorithm for the most energy-saving or comfort purpose.
As a preferred embodiment, the road driving condition data and the corresponding relationship with the specific road section are stored, specifically, stored in a cloud storage manner.
The comprehensive driving condition research:
(1) driving condition state division, model event classification and model event set determination
The definition set S represents the state of the driving condition, the time sequence x (t), (t e {1, 2.. once, n }) represents the value of the driving condition at time t, the driving condition is classified into different states, and thus the state sequence S (t) (t e {, 2.. once, n }) is formed, S (t) is a state set which comprises a limited number of state elements, S (t) is S, and therefore, the driving condition state is:
S＝{s1，s2，...，sk} (1)；
each time series is to be attributed to a set state, namely X (1) belongs to S;
(2) state transition probability matrix calculation
And (3) solving a transition probability matrix by adopting a statistical method, wherein mi represents the times of the state si occurring in different time periods, mij represents the times of transition from the state si to the state mj, and the transition probability is obtained by the following formula of Pij:
in the formula (2), the more accurate the value of Pij is, the more accurate the obtained prediction result is, so that the soundness of the historical data and the actual quantity are crucial to the solution of the transition probability Pij, and after the transition probability is solved, the transition probability matrix is calculated by the following expression:
(3) driving state prediction
Firstly, state division is carried out on test data, assuming that Y (t) represents the test data, Y (t) represents the prediction data, Y (i) represents the predicted initial data, and s 'i is the state of Y (i), and then the state of the initial data to be positioned in the next step can be predicted according to the obtained transition probability matrix P and the state s' i of the current initial data Y (i), wherein the expression is as follows:
battery model construction, parameter identification and state estimation method research
In order to meet the requirements of an electric automobile energy management system for offline and online estimation of battery states, the accuracy and complexity of a model are comprehensively considered, a first-order RC equivalent circuit is selected for modeling a battery pack, a battery average model of the battery pack is established based on the first-order RC equivalent circuit model under a Matlab/Simscape platform, the developed first-order RC equivalent circuit model comprises a temperature calculation sub-model, an SOC estimation sub-model, an open-circuit voltage description sub-model, an ohmic internal resistance description sub-model, a polarization internal resistance and polarization capacitance description sub-model, a mixed pulse power performance test pulse charge-discharge test is carried out, and battery model parameters are extracted online by adopting a recursive least square method;
in order to improve the Soc estimation precision of the battery pack, the SoC estimation algorithm of the battery pack is researched based on a battery pack average equivalent circuit model, and the SOc estimation algorithm is formed by adding a Kalman filtering correction link on the basis of an ampere-hour integration method;
the method comprises the steps of calculating the capacity of the battery pack under different circulation states by taking the condition that the battery is not over charged/discharged as a constraint condition based on battery parameter information in a battery pack circulation test, analyzing the relevance between the capacity of the battery pack and IC and DV curves by referring to an ICA (independent component analysis) and DVA (digital video analysis) method principle, representing the health state of the battery pack by using the characteristic values and the transformation coefficients of the IC and DV curves of the battery pack, developing a new online identification algorithm of the characteristic points and the transformation coefficients of the IC and DV curves of the battery pack, carrying out cluster analysis on the voltage curves of the battery, and further realizing online estimation on the capacity of the battery pack based on the relationship between the characteristic points and the transformation coefficients of the.
Energy loss model construction and energy dissipation mechanism analysis
On the basis of the constructed running comprehensive working condition of the pure electric vehicle, on the basis of battery pack modeling and state estimation research, the characteristics of energy loss, transmission and conversion efficiency and the like of main components such as a super capacitor, a DC-DC converter and a motor and auxiliary systems in the power system of the pure electric vehicle under different working conditions are analyzed through experiments, a model of electromechanical coupling energy flow loss mechanism of the power system of the pure electric vehicle is established, and the energy dissipation mechanism and the smoothing mechanism of the power system of the pure electric vehicle under the comprehensive working condition and the quasi-steady working condition are analyzed according to the energy flow loss model of the power system of the pure electric vehicle and the running working condition construction result;
after the working condition construction is completed, dividing energy consumption into a plurality of types, establishing a fuzzy rule base, respectively inputting kinematic fragment data of the constructed working condition, including finite time domain comprehensive working condition and quasi-steady state working condition data, of the pure electric vehicle power system according to the established energy flow loss model of the pure electric vehicle, then performing power system energy flow analysis and total energy consumption calculation according to the kinematic fragment as a unit, and establishing corresponding relations between the running working conditions of the pure electric vehicle at different time scales and energy consumption; and establishing a fuzzy rule base between the characteristic parameters and the energy consumption according to the fuzzy rule base.
Energy management system construction
Introducing a hierarchical control principle to establish a pure electric vehicle energy management system, wherein the control system comprises two hierarchical control strategies, the top strategy is an energy management strategy, an energy optimization management model is established by adopting a Markov decision theory and is responsible for monitoring the energy flow of the whole pure electric vehicle power system, and the target power of a motor is determined according to the current state of the system; the bottom layer strategy is an execution control layer, power distribution and motor control are carried out according to the current state of the system, and the requirements of the pure electric vehicle on dynamic performance are met.
Energy optimization management strategy study
The method comprises the steps of taking the optimal energy consumption of a pure electric vehicle in the whole comprehensive working condition interval as a target under the comprehensive working condition, taking the optimal comprehensive power performance and economical efficiency of the pure electric vehicle under the quasi-steady working condition as a target, taking the current state of the electric vehicle as a constraint condition, establishing a random dynamic programming optimization proposition based on a Markov decision theory and solving the problem, integrating the optimization proposition into a Markov decision process with a limited domain based on a driving state transition probability matrix, wherein the optimization proposition is the Markov decision process with the limited domain and can be solved by a bottom-up recursion method, the optimization process has different solving time according to different discrete granularity, the continuous online solving can not meet the requirement of real-time control of the electric vehicle, a processing mode of discrete solving online table lookup can be adopted, all possible initial values are traversed during offline solving, and the result is stored in a table, when the online operation is carried out, the current optimal control vector value is directly obtained in a table look-up mode.
Experimental protocol
(1) Running condition data acquisition and processing test scheme
The GPS/GPRS-based vehicle-mounted data acquisition device is used for acquiring and processing basic data of a driving experiment road and acquiring characteristic information of driving conditions such as a track, displacement and speed of a vehicle.
(2) Battery test and equivalent circuit model parameter identification test scheme
Based on HPPC battery characteristic test specifications, a battery simulator is adopted to perform battery pack testing under a pulse cycle discharge working condition, parameters in the established battery pack equivalent circuit model are identified, and the battery simulator is used to perform characteristic testing and theoretical model verification on the super capacitor.
(3) Battery state estimation algorithm verification test scheme
The SOC and SOH estimation algorithm verification test is completed through a battery pack testing system, a battery state estimation algorithm is developed by adopting a battery management system hardware-in-loop simulation platform, an established battery pack model is loaded to the BMS hardware-in-loop simulation platform, voltage, current and temperature information of a single battery is acquired in real time by using a data acquisition board card, the Soc state and capacity of the battery pack are estimated based on the developed SOC and SOH estimation algorithm, and the accuracy and efficiency of the algorithm are verified.
(4) Control strategy test experimental scheme
The control strategy test aims at carrying out online test on the energy management strategy and verifying the validity and correctness of the strategies of the methods such as the classification of driving conditions, the feature extraction, the battery state estimation, the Markov decision and the like, the test verification is divided into two stages of bench test verification and finished automobile test verification, and the bench test verification is that the test verification can be carried out on the power system of the pure electric automobile through an inertia simulation test bench of the pure electric automobile; after the bench test verifies, in order to enable the running condition of the vehicle to be close to the running condition on the actual road, the experimental working condition is extracted from the working condition data collected by the actual running condition, the road running resistance is simulated on the hub test bench, the control effect of the energy management strategy is experimentally verified, and the energy management system is realized through a dSPACE rapid prototype and a BMS hardware in-loop simulation platform.
The method comprises the steps of constructing a complex running working condition of the pure electric vehicle, and constructing a running state transfer matrix of the pure electric vehicle based on a Markov process; constructing an equivalent circuit model of the lithium ion power battery pack, and exploring a coupling mechanism between a driving working condition and the SOC and SOH of the battery pack; constructing a pure electric vehicle power system electromechanical coupling energy flow loss mechanism model, and exploring system energy dissipation mechanisms under various complex working conditions; the method comprises the steps of establishing a pure electric vehicle energy management system, providing energy optimization management strategies under different time scales, and creatively solving the core key problems of pure electric vehicle power system modeling and energy optimization under random continuous working conditions.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (8)
1. A road condition kinematics segment extraction method based on a navigation system is characterized by comprising the following steps:
acquiring actual road running condition data of the pure electric vehicle, and extracting a kinematic segment;
extracting and classifying the characteristic values of the kinematic segments based on a principal component analysis and cluster analysis method, and constructing a comprehensive working condition according with the running condition characteristics of the pure electric vehicle;
introducing multiple time scale kinematic parameters for describing quasi-steady state characteristics of a driving state in a kinematic segment class, between classes and in a switching process between different kinematic segments.
2. The method for extracting the road condition kinematics segment based on the navigation system according to claim 1, wherein the step of collecting the actual road running condition data of the pure electric vehicle specifically comprises the following steps:
acquiring actual road driving condition data of the pure electric vehicle;
acquiring navigation map information, storing the actual road running condition data and suggesting a corresponding relation with a specific road section of the navigation map information;
and taking the unit actual driving distance as a reference, and storing the road driving condition data and the corresponding relation with the specific road section according to the sequence.
3. The navigation system-based road condition kinematics fragment extraction method according to claim 2, wherein the kinematics fragment comprises road condition information and driving parameters of the pure electric vehicle driving under the corresponding road condition information.
4. The navigation system based road condition kinematics fragment extraction method according to claim 3, wherein the driving parameters of the linear electric vehicle comprise power consumption, power consumption per actual driving distance, driving speed, driving acceleration and vibration amplitude of seats in the vehicle;
the corresponding road condition information includes road unevenness.
5. The navigation system-based road condition kinematics segment extraction method according to claim 3, further comprising displaying a recommended driving speed if the route in the current navigation map information includes stored road condition information and driving parameter information of the pure electric vehicle; meanwhile, the newly acquired actual road running condition data of the pure electric vehicle is used for replacing the existing road condition information and the running parameter information of the pure electric vehicle.
6. The method for extracting the road condition kinematics fragment based on the navigation system according to claim 5, wherein the recommended driving speed is obtained by simulation calculation according to the stored road condition information and the current electric quantity information of the pure electric vehicle; specifically, the controller for simulation calculation is connected with the pure electric vehicle through a network.
7. The navigation system-based road condition kinematics fragment extraction method according to any one of claims 2 to 6, wherein the road driving condition data and the corresponding relation with the specific road section are stored in a cloud storage manner.
8. The method for extracting the road condition kinematics segment based on the navigation system according to any one of claims 1 to 6, further comprising the steps of completing construction of a quasi-steady-state process typical driving condition of the pure electric vehicle by adopting a dimension reduction processing and fuzzy clustering method;
performing parameter identification on the established equivalent circuit model of the lithium ion power battery pack by adopting a least square method;
performing SOC and SOH joint estimation on the lithium ion power battery pack by adopting a combined algorithm of extended Kalman filtering and ampere-hour integration;
the energy management system of the pure electric vehicle is established by a hierarchical control theory, the top strategy is an energy management strategy, and the bottom strategy is an execution layer.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911094517.0A CN110852378A (en) | 2019-11-11 | 2019-11-11 | Road condition kinematics segment extraction method based on navigation system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911094517.0A CN110852378A (en) | 2019-11-11 | 2019-11-11 | Road condition kinematics segment extraction method based on navigation system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110852378A true CN110852378A (en) | 2020-02-28 |
Family
ID=69601144
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911094517.0A Pending CN110852378A (en) | 2019-11-11 | 2019-11-11 | Road condition kinematics segment extraction method based on navigation system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110852378A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111907342A (en) * | 2020-07-31 | 2020-11-10 | 江苏理工学院 | Working condition identification control method of pure electric vehicle |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103434509B (en) * | 2013-07-10 | 2016-03-02 | 大连理工大学 | A kind of control system of hybrid-power bus and power-control method thereof |
| CN108445418A (en) * | 2018-05-17 | 2018-08-24 | 福建省汽车工业集团云度新能源汽车股份有限公司 | A kind of battery dump energy evaluation method and storage medium |
| CN110126841A (en) * | 2019-05-09 | 2019-08-16 | 吉林大学 | EV Energy Consumption model prediction method based on road information and driving style |
-
2019
- 2019-11-11 CN CN201911094517.0A patent/CN110852378A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103434509B (en) * | 2013-07-10 | 2016-03-02 | 大连理工大学 | A kind of control system of hybrid-power bus and power-control method thereof |
| CN108445418A (en) * | 2018-05-17 | 2018-08-24 | 福建省汽车工业集团云度新能源汽车股份有限公司 | A kind of battery dump energy evaluation method and storage medium |
| CN110126841A (en) * | 2019-05-09 | 2019-08-16 | 吉林大学 | EV Energy Consumption model prediction method based on road information and driving style |
Non-Patent Citations (1)
| Title |
|---|
| 潘登: "混合动力汽车城市循环工况构建及运行工况多尺度预测", 《中国优秀硕士学位论文全文数据库》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111907342A (en) * | 2020-07-31 | 2020-11-10 | 江苏理工学院 | Working condition identification control method of pure electric vehicle |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110780203B (en) | SOC (state of charge) online estimation method for battery pack of pure electric vehicle | |
| Sun et al. | Velocity predictors for predictive energy management in hybrid electric vehicles | |
| CN103745110B (en) | Method of estimating operational driving range of all-electric buses | |
| Lee et al. | A novel big data modeling method for improving driving range estimation of EVs | |
| CN103472398B (en) | Based on the electrokinetic cell SOC method of estimation of spreading kalman particle filter algorithm | |
| CN103745111B (en) | Pure electric passenger vehicle continual mileage Forecasting Methodology | |
| Kim et al. | Estimation of Li-ion battery state of health based on multilayer perceptron: As an EV application | |
| CN109934403A (en) | Charge load Analysis prediction technique in electric car resident region based on mathematical model | |
| CN109508826B (en) | Electric vehicle cluster schedulable capacity prediction method based on gradient lifting decision tree | |
| CN103793605A (en) | Lithium iron phosphate power battery equivalent circuit model parameter estimation method based on particle swarm algorithm | |
| CN106295860A (en) | A kind of electric automobile scale charge requirement Forecasting Methodology based on Monte Carlo Analogue Method | |
| CN112163372B (en) | SOC estimation method of power battery | |
| CN103983919A (en) | Battery life prediction method based on GM (1, N) gray model | |
| CN114707292B (en) | Analysis method for voltage stability of distribution network containing electric automobile | |
| CN106998086A (en) | MW class energy-accumulating power station battery management method and its system | |
| CN103983920A (en) | Method for establishing model of power battery of electric vehicle | |
| Wang et al. | A driving-behavior-based SoC prediction method for light urban vehicles powered by supercapacitors | |
| CN115063184A (en) | Electric vehicle charging demand modeling method, system, medium, equipment and terminal | |
| Montazeri-Gh et al. | Driving condition recognition for genetic-fuzzy HEV control | |
| CN107067130A (en) | A kind of quick charge station method for planning capacity based on electric automobile markov charge requirement analysis model | |
| Wang et al. | Research on electric vehicle (EV) driving range prediction method based on PSO-LSSVM | |
| Xia et al. | A predictive energy management strategy for multi-mode plug-in hybrid electric vehicle based on long short-term memory neural network | |
| CN110852378A (en) | Road condition kinematics segment extraction method based on navigation system | |
| CN112765726A (en) | Service life prediction method and device | |
| CN109117972A (en) | A kind of charge requirement of electric car determines method |
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 | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20200228 |