CN109747654B - A kind of hybrid vehicle control parameter scaling method towards operating condition - Google Patents
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Abstract
The invention discloses a kind of hybrid vehicle control parameter scaling method towards operating condition, is related to Development of HEV Technology field.Method mainly include establish operating condition sample, based on particle swarm algorithm to five steps such as the optimization control parameter of the Optimization about control parameter under each independent operating condition, the operating mode feature index screening based on correlation, multiple linear regression analysis, new operating condition calibration.Fully consider the relationship between operating mode feature and optimization control parameter, establish the multiple linear regression model between optimization control parameter and operating mode feature index, control parameter can be demarcated rapidly for different operating conditions, on the one hand facilitate the influence for understanding operating condition to optimization control parameter, on the one hand optimization control parameter is quickly determined convenient for calibration personnel, shorten scalar period.
Description
Technical field
The invention belongs to Development of HEV Technology fields, in particular to a kind of hybrid vehicle control parameter calibration side
Method.
Background technique
Energy conservation is one of main target of automobile hybrid power, and hybrid vehicle is two or more dynamic due to being related to
Power source, coupled relation is complicated, and energy management control strategy (calling control strategy in the following text) and key control parameter therein have oil consumption
Great influence, therefore derive it is a series of using mimimum fuel consumption as the Optimization about control parameter method of target, as dynamic programming algorithm,
Minimum equivalent fuel consumption method, genetic algorithm, particle swarm algorithm etc..Wherein dynamic programming algorithm can achieve under certain specific operation
Economy it is optimal, but needing to carry out complicated control role extracting can just be applied in practical control, the essence of Rule Extraction
True degree also affects practical oil consumption；Minimum equivalent fuel consumption method is optimal for target with instantaneous fuel economy, is unable to reach
The economy of entire operating condition is optimal；The intelligent optimization algorithms such as genetic algorithm, particle swarm algorithm not only can be applied to global optimum, but also
It can be applied to instantaneous optimal, and be the direct optimization to control parameter, the specific value of available optimization control parameter.
Specific operation is based on to the optimization of control parameter above, that is, the parameter obtained after optimizing can not make automobile in institute
Having under driving cycle has optimal fuel economy, and operating condition is different, and control parameter needs to make corresponding adjustment.For demarcating people
For member, control parameter is optimized and can be taken a significant amount of time under each operating condition, extends the development cycle；In addition, automobile is real
Often there is bigger difference and often variation in the operating condition used when border driving cycle and offline optimization, and the control in controller is joined
Number is unable to on-line proving, causes the fuel economy of hybrid vehicle that cannot give full play to, and method common at present is first to exist
Offline optimization is carried out to control parameter under a large amount of operating conditions, the corresponding optimization control parameter of each operating condition is obtained, then carries out operating condition
Identification and prediction, then the on-line proving control parameter by way of tabling look-up.When the effective range of this method depends on offline optimization
Operating condition quantity, when automobile running working condition is not among table, this method failure, and increasing offline operating condition quantity can increase greatly
Add time cost.Therefore, the relationship between operating mode feature and optimization control parameter is found for the offline mark of raising control strategy
Determine efficiency and on-line proving validity is of great significance.
China Patent Publication No. is CN104071161A, publication date 2014-10-01, discloses a kind of plug-in mixing
Power vehicle operating mode's switch and the method for energy control, identify operating condition using support vector machines first, operating condition are divided into several
Then particular types control engine torque using different blur methods under variety classes operating condition, to optimize fuel oil warp
Ji property can not embody operating mode feature if operating condition is only divided into limited Ganlei by this method in detail, and blur method simulation people's sentences
It is disconnected, it is equivalent to the relationship rule of thumb made between operating mode feature and control parameter, effect of optimization is limited；Chinese patent is public
The number of opening is CN102717797A, publication date 2012-10-10, discloses a kind of hybrid vehicle energy management method and energy
Management system is measured, this method is calibration with motor output torque using fuel consumption, engine emission, battery SOC as cost function
Amount, solves the problems, such as energy management using Stochastic Dynamic Programming Method, different from this patent optimization method, and does not probe into best control
The relationship of parameter processed and operating mode feature.
Summary of the invention
To overcome the shortcomings of the prior art, the present invention provides a kind of hybrid vehicle control parameter towards operating condition
Scaling method probes into hybrid electric vehicle and sails operating condition and make the relationship and rule between the optimal control parameter of fuel economy
Rule.Under different operating conditions, the control parameter that hybrid vehicle reaches Best Economy is different, illustrates optimization control parameter and work
Linearly or nonlinearly relationship is certainly existed between certain features of condition, and linear relationship is easier to be summarised and use.With power
It learns segment and expands operating condition sample as independent operating condition, calculate separately out the ginseng of the Optimal Control under each operating condition with particle swarm algorithm
Number, then using statistical method analysis and the highest operating mode feature index of optimization control parameter correlation and these indexs and
Linear relationship between optimization control parameter, and multiple linear regression model is established, according to regression model, obtaining new operating condition
After correlated characteristic index, can calculate makes the optimal control parameter of automobile fuel ecomomy under the operating condition, completes control parameter
Calibration.
To achieve the above object, a kind of hybrid vehicle control parameter mark towards operating condition according to an embodiment of the present invention
Determine method, including the following contents:
First, operating condition sample is established, specifically includes the following steps:
1. selecting several standard cycle state of cyclic operation first, all dynamics of each standard cycle operating condition are then extracted
Segment regards each dynamics segment as an independent operating condition, and the division methods of the dynamics segment are to be originated with operating condition
Starting point of the point as first dynamics segment, first 1 speed point after speed experience is greater than zero using after starting point as
The end point of first dynamics segment, starting point of the end point of the above dynamics segment as next dynamics segment,
And so on, if speed is always zero after the last one dynamics segment starting point, give up the section, all state of cyclic operation move
The sum of mechanics segments is sample number, is calculated as N；
2. calculating 20 characteristic index C of each independent operating condition1,C2,C3......C20, 20 characteristic indexs successively refer to
Parking ratio, stop frequency accelerate ratio, deceleration ratio, cruise ratio, average acceleration, peak acceleration, acceleration standard
Difference, average retardation rate, maximum deceleration, deceleration standard deviation, average speed of operation, speed standard deviation, speed root mean square, highest
Speed, 0-20km/h speed ratio, 20-40km/h speed ratio, 40-60km/h speed ratio, 60-80km/h speed ratio,
80-100km/h speed ratio, each characteristic index CxThe vector for being N for unitary prime number, x are subscript, represent 1,2,
3......20；
Second, the Optimization about control parameter of each independent operating condition, specifically includes the following steps:
1. the determination control parameter P to be optimized；
2. being optimized under each independent operating condition to P using particle swarm algorithm, with the minimum fitness of fuel consumption
Function, using battery SOC balance as constraint condition, using P as particle position, setting the number of iterations K is as optimization termination condition, most
It obtains making the smallest optimization control parameter P of fuel consumption under each independent operating condition eventuallyb, PbThe vector for being N for an element；
Third, the operating mode feature index screening based on correlation, specifically includes the following steps:
1. calculating the related coefficient between each characteristic index using the relevance formula in linear regression analysisSuch as
Shown in formula (1), the related coefficient between each characteristic index and optimization control parameterAs shown in formula (2):
Subscript x, y indicate characteristic index code name in formula, are 1,2,3......20；
2. filtering out and the higher operating mode feature index of optimization control parameter correlation, setting threshold value R1, retain allCharacteristic index, remaining rejecting；
3. continuing to reduce characteristic index quantity according to the correlation between each operating mode feature index, threshold value R is set2, in step
Suddenly it is 2. found all in the middle operating mode feature index retainedCharacteristic index to Cx、Cy, it is special to be considered as duplicate operating condition
Index is levied, reservation is higher with optimization control parameter correlation, i.e.,WithIn the corresponding operating mode feature of the larger value
Index rejects the corresponding operating mode feature index of smaller value；
The operating mode feature index number remained after screening step described above is denoted as M, M≤20；
4th, multiple linear regression analysis, specifically includes the following steps:
1. the level of significance α of model is arranged, optimization control parameter P is establishedbIt is polynary between M operating mode feature index
Linear regression model (LRM), shown in model such as formula (3):
Pb=β0+β1·C1+β2·C2+......+βM·CM+ε (3)
In formula, β0For constant term, β1、β2……βMFor regression coefficient, ε is random error；
2. residual analysis obtains the residual error r of each sample according to the multiple linear regression model1、r2……rNAnd it is corresponding
Confidence interval rint1、rint2……rintNIf the confidence interval of certain sample does not include zero point, then it is assumed that the sample data is different
Often, it is rejected, then re-establishes multiple linear regression model shown in formula (3)；
3. carrying out T to model to examine, if operating mode feature index number when into the step is Me, first update Me, if it exists
The regression coefficient β not examined by Tw, then the smallest β of lower label is rejectedwCorresponding CwAnd it enters step 4., remaining operating condition at this time
Index number is Mo, Mo=Me-1；The regression coefficient that do not examined if it does not exist by T, then remaining operating condition index number is Me, into
Enter step 5.；
4. keeping level of significance α constant, optimization control parameter P is establishedbWith MoPolynary line between a operating mode feature index
Property regression model simultaneously carries out F inspection, if F upchecks, enters step 3.；If the test fails by F, newest be removed is restored
An operating mode feature index and enter step 5., remaining operating condition index number is M at this timee；
5. with the M finally retainedeA operating mode feature index and PbBetween multiple linear regression model as final mask,
As shown in formula (4):
5th, for the new operating condition to be demarcated, calculate the M finally retainedeA operating mode feature index, and substituted into
Formula (4) obtains the corresponding optimization control parameter of the operating condition.
Compared with prior art, the present invention the relationship between operating mode feature and optimization control parameter is fully considered, for not
Control parameter can be demarcated rapidly with operating condition, optimize the fuel economy of automobile.Operating condition is divided with dynamics segment, it will be each dynamic
Mechanics segment regards an independent operating condition as, has expanded sample size, improve it is subsequent using statistical method when precision；Use particle
Control parameter under group's each independent operating condition of algorithm optimization, effect of optimization is preferable and can directly obtain the specific of optimization control parameter
Numerical value；Correlation analysis between operating mode feature index and optimization control parameter and each index can be filtered out to best control
Parameter processed influences maximum index, reduces the number of arguments of subsequent multiple linear regression model, simplifies and calculates；To the more of foundation
First linear regression model (LRM) carries out T detection and helps further to reduce operating mode feature index number, improves model accuracy.The present invention one
Aspect facilitates the influence for understanding operating condition to optimization control parameter, and Optimal Control ginseng is on the one hand quickly determined convenient for calibration personnel
Number shortens scalar period.
Detailed description of the invention
Above-mentioned and/or additional aspect and advantage combination following accompanying drawings of the invention will be to will become bright in the description of embodiment
It shows and is readily appreciated that, in which:
The position Fig. 1 overview flow chart according to an embodiment of the present invention；
Fig. 2 is to divide operating condition schematic diagram by dynamics segment according to the embodiment of the present invention；
Fig. 3 is the operating mode feature index matrix schematic diagram according to the embodiment of the present invention；
Fig. 4 is the operating mode feature index according to the embodiment of the present invention and phase between optimization control parameter and operating mode feature index
Relationship number table schematic diagram；
Fig. 5 is the multiple linear regression analysis flow chart according to the embodiment of the present invention.
Specific embodiment
The embodiments described below with reference to the accompanying drawings are exemplary, for explaining only the invention, and should not be understood as
Limitation of the present invention.
Due to the calculation method of 20 operating mode feature indexs, particle swarm optimization algorithm, the residual error point in multiple linear regression
Analysis, F inspection, T inspection etc. are universal method, therefore details are not described herein.
A kind of hybrid vehicle control parameter scaling method towards operating condition of the present invention, including the following contents:
First, establish operating condition sample.Due to a kind of hybrid vehicle control parameter towards operating condition of the present invention
Scaling method is related to the multiple linear regression analysis in statistics, it is necessary to assure sample size is sufficient, therefore to divide dynamics piece
The method exptended sample capacity of section, the foundation of operating condition sample specifically includes the following steps:
1. selecting several standard cycle operating conditions first, the standard cycle operating condition fingering rule are defined, automobile industry is logical
Typical travel operating condition, such as NEDC operating condition, specific standard cycle operating condition and number are voluntarily true as needed by calibration personnel
It is fixed.Then all dynamics segments for extracting each standard cycle operating condition regard each dynamics segment as an independent work
Condition, the division methods of the dynamics segment are using operating condition starting point as the starting point of first dynamics segment, with starting
End point of first zero speed o'clock as first dynamics segment after speed experience is greater than zero after point, the above dynamics
Starting point of the end point of segment as next dynamics segment, and so on, if after the last one dynamics segment starting point
Speed is always zero, then gives up the section, and the sum of dynamics segments of all state of cyclic operation is sample number, is calculated as N.
With reference to attached drawing 2, by taking NEDC operating condition as an example, NEDC operating condition is divided into 13 according to the dynamics segment division methods
A independent operating condition.
2. calculating 20 characteristic index C of each independent operating condition1,C2,C3......C20.20 characteristic indexs successively refer to
Parking ratio, stop frequency accelerate ratio, deceleration ratio, cruise ratio, average acceleration, peak acceleration, acceleration standard
Difference, average retardation rate, maximum deceleration, deceleration standard deviation, average speed of operation, speed standard deviation, speed root mean square, highest
Speed, 0-20km/h speed ratio, 20-40km/h speed ratio, 40-60km/h speed ratio, 60-80km/h speed ratio,
80-100km/h speed ratio, each characteristic index CxThe vector for being N for unitary prime number, x are subscript, represent 1,2,
3......20。
With reference to attached drawing 3, each independent operating condition has 20 characteristic indexs, that is, each characteristic index corresponds to N number of value,
The characteristic index of all independent operating conditions indicates that the xth column of matrix indicate the xth of N number of independent operating condition with the matrix that a 20 row N are arranged
The value of a characteristic index.
Second, the Optimization about control parameter of each independent operating condition, specifically includes the following steps:
1. the determination control parameter P to be optimized.A kind of hybrid vehicle towards operating condition of the present invention controls ginseng
Number scaling methods have versatility, different control parameters can be demarcated, it is therefore desirable to the control parameter of calibration by demarcate personnel
Voluntarily choose.
2. being optimized under each independent operating condition to P using particle swarm algorithm.With the minimum fitness of fuel consumption
Function, using battery SOC balance as constraint condition, using P as particle position, setting the number of iterations K is used as optimization termination condition, by
The principle of optimality of particle swarm algorithm can obtain making fuel consumption most under each independent operating condition it is found that when the K of setting is sufficiently large
Small optimization control parameter Pb, PbThe vector for being N for an element, i.e., each corresponding optimal control parameter of independent operating condition.
Third, the operating mode feature index screening based on correlation.20 operating mode feature indexs be not all with
Optimization control parameter is in significant correlation, needs to filter out the higher index of correlation, rejects the not high index of correlation；This
Outside, there may be the higher correlations of degree between certain operating mode feature indexs, it means that can be with an index come generation
For remaining higher index of property associated therewith.The screening of operating mode feature index can reduce the number of index, retain and best control
The higher operating mode feature index of dependence on parameter processed simplifies subsequent multiple linear regression model and its confidence level, screening process
Specifically includes the following steps:
1. calculating the related coefficient between each characteristic index using the relevance formula in linear regression analysisSuch as
Shown in formula (1), the related coefficient between each characteristic index and optimization control parameterAs shown in formula (2):
Subscript x, y indicate characteristic index code name in formula, are 1,2,3......20；
2. filtering out and the higher operating mode feature index of optimization control parameter correlation, setting threshold value R1, retain allCharacteristic index, remaining rejecting
3. continuing to reduce characteristic index quantity according to the correlation between each operating mode feature index, threshold value R is set2, in step
Suddenly it is 2. found all in the middle operating mode feature index retainedCharacteristic index to Cx、Cy, it is considered as duplicate operating condition
Characteristic index, reservation is higher with optimization control parameter correlation, i.e.,WithIn the corresponding operating condition of the larger value it is special
Index is levied, the corresponding operating mode feature index of smaller value is rejected；
The operating mode feature index number remained after screening step described above is denoted as M, M≤20.
With reference to attached drawing 4, data are the related coefficients being calculated according to formula (1), formula (2) in cell, such as the 2nd row the 1st
ColumnIndicate operating mode feature index C1With optimization control parameter PbBetween related coefficient.According to formula (1), formula (2) it is found that becoming
Amount and the related coefficient of itself are 1, therefore table diagonal entry is 1；Element above diagonal line and its about diagonal line pair
The element of title is equal, such asTherefore element value no longer calculates above diagonal line.
4th, multiple linear regression analysis.What expression above step determined in the form of mathematical model joins with Optimal Control
Multiple linear regression relationship between the higher M operating mode feature index of data/coherency and optimization control parameter, analysis model
Reasonability simultaneously makes corresponding adjustment.With reference to Fig. 5, multiple linear regression analysis specifically includes the following steps:
1. the level of significance α of model is arranged, optimization control parameter P is establishedbIt is polynary between M operating mode feature index
Linear regression model (LRM), as shown in formula (3):
Pb=β0+β1·C1+β2·C2+......+βM·CM+ε (3)
In formula, β0For constant term, β1、β2……βMFor regression coefficient, ε is random error.
2. residual analysis, the purpose for carrying out residual analysis to formula (3) institute's representation model is to find the sample of data exception, delete
After exceptional sample, model accuracy is higher.For each sample, its residual error and corresponding confidence interval can be obtained, according to each
The residual error r of sample1、r2……rNAnd corresponding confidence interval rint1、rint2……rintN, according to multiple linear regression correlation
Theory, if the confidence interval of certain sample does not include zero point, then it is assumed that the sample data is abnormal, is rejected, is then re-established
Multiple linear regression model shown in formula (3).
3. carrying out T to model to examine, the purpose that T is examined is in the formula that finds (3) to PbThe weaker operating mode feature of interpretability
Index is simultaneously rejected, and after rejecting an index, multiple linear regression model variation, the result for carrying out T inspection again can change, therefore every
One operating mode feature index of secondary rejecting.
If operating mode feature index number when into the step is Me, first update Me, Me≤M.It is not examined if it exists by T
Regression coefficient βw, then its corresponding operating mode feature index CwTo PbInterpretability is weaker, rejects the smallest C of lower labelwAnd enter
4., remaining operating condition index number is M to step at this timeo, Mo=Me-1；The regression coefficient that do not examined if it does not exist by T, then it is remaining
Operating condition index number is Me, enter step 5..
4. keeping level of significance α constant, optimization control parameter P is establishedbWith MoPolynary line between a operating mode feature index
Property regression model simultaneously carries out F inspection, if F upchecks, enters step 3.；If the test fails by F, newest be removed is restored
An operating mode feature index and enter step 5., remaining operating condition index number is M at this timee。
It is the linear of optimization control parameter and each operating mode feature index in the established multiple linear regression model of verifying that F, which is examined,
Whether relationship is significantly set up on the whole, if not passing through, which cannot be used, therefore one operating mode feature index of every rejecting is just
A F is carried out to examine.
5. with the M finally retainedeA operating mode feature index and PbBetween multiple linear regression model as final mask,
As shown in formula (4):
5th, for the new operating condition to be demarcated, calculate the M finally retainedeA operating mode feature index, and substituted into
Formula (4) obtains the corresponding optimization control parameter of the operating condition.
Claims (1)
1. a kind of hybrid vehicle control parameter scaling method towards operating condition, which is characterized in that including the following contents:
First, operating condition sample is established, specifically includes the following steps:
1. selecting several standard cycle operating conditions first, all dynamics segments of each standard cycle operating condition are then extracted, it will
Each dynamics segment regards an independent operating condition as, and the division methods of the dynamics segment are using operating condition starting point as the
The starting point of one dynamics segment, first zero speed o'clock after speed experience is greater than zero using after starting point are dynamic as first
The end point of mechanics segment, starting point of the end point of the above dynamics segment as next dynamics segment, and so on,
If speed is always zero after the last one dynamics segment starting point, give up the section, the dynamics segment of all state of cyclic operation
The sum of number is sample number, is calculated as N；
2. calculating 20 characteristic index C of each independent operating condition1,C2,C3......C20, 20 characteristic indexs successively refer to parking
Ratio, stop frequency, accelerate ratio, deceleration ratio, cruise ratio, average acceleration, peak acceleration, acceleration standard deviation,
Average retardation rate, maximum deceleration, deceleration standard deviation, average speed of operation, speed standard deviation, speed root mean square, highest vehicle
Speed, 0-20km/h speed ratio, 20-40km/h speed ratio, 40-60km/h speed ratio, 60-80km/h speed ratio, 80-
100km/h speed ratio, each characteristic index CxThe vector for being N for unitary prime number, x are subscript, represent 1,2,
3......20；
Second, the Optimization about control parameter of each independent operating condition, specifically includes the following steps:
1. the determination control parameter P to be optimized；
2. P is optimized under each independent operating condition using particle swarm algorithm, with the minimum fitness function of fuel consumption,
Using battery SOC balance as constraint condition, using P as particle position, setting the number of iterations K is final to obtain as optimization termination condition
Make the smallest optimization control parameter P of fuel consumption under to each independent operating conditionb, PbThe vector for being N for an element；
Third, the operating mode feature index screening based on correlation, specifically includes the following steps:
1. calculating the related coefficient between each characteristic index using the relevance formula in linear regression analysisSuch as formula (1)
Shown, between each characteristic index and optimization control parameter related coefficientAs shown in formula (2):
Subscript x, y indicate characteristic index code name in formula, are 1,2,3......20；
2. filtering out and the higher operating mode feature index of optimization control parameter correlation, setting threshold value R1, retain allCharacteristic index, remaining rejecting；
3. continuing to reduce characteristic index quantity according to the correlation between each operating mode feature index, threshold value R is set2, step 2. in
In the operating mode feature index of reservation, find allCharacteristic index to Cx、Cy, it is considered as duplicate operating mode feature and refers to
Mark, reservation is higher with optimization control parameter correlation, i.e.,WithIn the corresponding operating mode feature of the larger value refer to
Mark rejects the corresponding operating mode feature index of smaller value；
The operating mode feature index number remained after screening step described above is denoted as M, M≤20；
4th, multiple linear regression analysis, specifically includes the following steps:
1. the level of significance α of model is arranged, optimization control parameter P is establishedbWith the multiple linear between M operating mode feature index
Regression model, shown in model such as formula (3):
Pb=β0+β1·C1+β2·C2+......+βM·CM+ε (3)
In formula, β0For constant term, β1、β2……βMFor regression coefficient, ε is random error；
2. residual analysis obtains the residual error r of each sample according to the multiple linear regression model1、r2……rNAnd corresponding confidence
Section rint1、rint2……rintNIf the confidence interval of certain sample does not include zero point, then it is assumed that the sample data is abnormal, will
It is rejected, and multiple linear regression model shown in formula (3) is then re-established；
3. carrying out T to model to examine, if operating mode feature index number when into the step is Me, first update Me, do not lead to if it exists
Cross the regression coefficient β of T inspectionw, then the smallest β of lower label is rejectedwCorresponding CwAnd it enters step 4., remaining operating condition index at this time
Number is Mo, Mo=Me-1；The regression coefficient that do not examined if it does not exist by T, then remaining operating condition index number is Me, into step
Suddenly 5.；
4. keeping level of significance α constant, optimization control parameter P is establishedbWith MoMultiple linear between a operating mode feature index returns
Return model and carry out F inspection, if F upchecks, enters step 3.；If the test fails by F, newest one be removed is restored
5. a operating mode feature index simultaneously enters step, remaining operating condition index number is M at this timee；
5. with the M finally retainedeA operating mode feature index and PbBetween multiple linear regression model as final mask, such as formula
(4) shown in:
5th, for the new operating condition to be demarcated, calculate the M finally retainedeA operating mode feature index, and substituted into formula
(4), the corresponding optimization control parameter of the operating condition is obtained.
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| CN110816291B (en) * | 2019-11-11 | 2021-05-11 | 常熟理工学院 | Distributed driving automobile energy efficiency optimization control method of second-order oscillation particle swarm |
| CN110926827A (en) * | 2019-11-30 | 2020-03-27 | 河南科技大学 | Automatic optimization and calibration system for vehicle control parameters |
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| CN102717797B (en) * | 2012-06-14 | 2014-03-12 | 北京理工大学 | Energy management method and system of hybrid vehicle |
| CN103112450B (en) * | 2013-02-27 | 2015-06-24 | 清华大学 | Real-time optimized control method for plug-in parallel hybrid electric vehicle |
| CN104071161B (en) * | 2014-04-29 | 2016-06-01 | 福州大学 | A kind of method of plug-in hybrid-power automobile operating mode's switch and energy management and control |
| CN106004865B (en) * | 2016-05-30 | 2019-05-10 | 福州大学 | Mileage ADAPTIVE MIXED power vehicle energy management method based on operating mode's switch |
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