CN106408123A - Optimal charging current estimation method based on neural network model - Google Patents
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Abstract
The invention discloses an optimal charging current estimation method based on a neural network model. The optimal charging current estimation method comprises the steps of: (1) acquiring a neural network thermal model; (2) configuring a charging current range according to the model of a vehicle to be charged, wherein the upper bound of the charging current range is the maximum charging current, and the lower bound of the charging current range is the minimum charging current; (3) configuring parameters of an evaluation function, wherein the parameters of the evaluation function include allowable longest charging time, allowable shortest charging time, allowable maximum temperature, allowable minimum temperature, an evaluation weight of a temperature function and an evaluation weight of a time function; (4) and acquiring an optimal current according to the charging current range, the parameters of the evaluation function and the neural network thermal model, and regarding the optimal current as an initial charging current. The optimal charging current estimation method is high in adaptivity and flexible, can adapt to charging initial states of different battery packs, can estimate the optimal charging current in the current state, and ensures that the situations of overlong charging time and overhigh charging temperature cannot occur.
Description
Technical field
The invention belongs to electric automobile field, more particularly, to a kind of optimum electricity that charges based on neural network model
The method of stream estimation.
Background technology
Charging technique is one of key technology in electric vehicle engineering.Therefore, the selection of charging current have very heavy
Want meaning.Rational charging current, not only can ensure to charge safety moreover it is possible to shortening the charging interval, controlling charging temperature not lose
Control, avoid battery overaging in charging, improve set of cells service life.
At present, the charging method of electric automobile is substantially and is charged with fixing charging current, the method charge mode
Single, underaction, it is impossible to be well adapted for different set of cells initial state of charge, such as charges and starts different environment temperature
Degree, different battery pack temperature, different set of cells dump energy etc. all can be different to the demand of charging current, if any feelings
All it is charged with fixing charging current under condition, charging current and the unmatched situation of actual demand easily occur, leads to fill
The adverse consequencess such as electric overlong time, charging temperature are too high, cell degradation aggravation, seriously possibly even cause the safe thing that charges
Therefore.
Content of the invention
For the defect of prior art, it is an object of the invention to provide a kind of optimum charging based on neural network model
The method of electric current estimation is it is intended to solve to be charged leading to easily occur charging current with fixing charging current in prior art
The unmatched situation with actual demand, the charging interval is long, charging temperature is too high and the problem of cell degradation aggravation.
The invention provides a kind of method of the optimum charging current estimation based on neural network model, including following steps
Suddenly：
(1) obtain neutral net thermal model；
(2) according to automobile type configuration charging current scope to be charged, the upper bound of described charging current scope is maximum charge
Electric current, the lower bound of described charging current scope is minimum charge current；
(3) configure the parameter of evaluation function, wherein, the parameter of described evaluation function includes：The longest charging interval of permission,
The shortest charging interval allowing, the maximum temperature allowing, the minimum temperature allowing, the evaluation weight of temperature funtion and time function
Evaluation weight；
(4) optimal current is obtained according to charging current scope, the parameter of evaluation function and neutral net thermal model, and by institute
State optimal current as initial charge current.
The present invention adopts neural network algorithm, based on charging neural network model, according to different charging original states,
Line estimates the optimum charging current under current battery state.This method adaptivity is strong, flexible and changeable, can adapt to difference
Set of cells charging original state, estimates the optimum charging current under current state, ensure that simultaneously and the charging interval will not occur
Situations such as long, charging temperature is too high.
Brief description
Fig. 1 is nerve in the method based on the optimum charging current estimation of neural network model provided in an embodiment of the present invention
The building method flowchart of network thermal model；
Fig. 2 is in the method based on the optimum charging current estimation of neural network model provided in an embodiment of the present invention
Flowchart.
Specific embodiment
In order that the objects, technical solutions and advantages of the present invention become more apparent, below in conjunction with drawings and Examples, right
The present invention is further elaborated.It should be appreciated that specific embodiment described herein is only in order to explain the present invention, and
It is not used in the restriction present invention.
The present invention adopts neural network algorithm, based on charging neural network model, according to different charging original states,
Line estimates the optimum charging current under current battery state.This method adaptivity is strong, flexible and changeable, can adapt to difference
Set of cells charging original state, estimates the optimum charging current under current state, ensure that simultaneously and the charging interval will not occur
Situations such as long, charging temperature is too high.
In the present invention, neural network algorithm has very strong nonlinear fitting ability, can map arbitrarily complicated nonlinear dependence
System, and learning rules are simple, are easy to computer and realize.Have very strong robustness, memory ability, non-linear mapping capability with
And powerful self-learning capability.Using neural network algorithm, in conjunction with charging electric vehicle data, a high adaptation can be constructed
Property, high-precision charging thermal model.
On the basis of thermal model, set up the charging current evaluation function with regard to temperature rise and charging interval.Evaluation function is used for
Weigh the superiority-inferiority of the charging current of estimation.Finally, choose and evaluate score value highest charging current as optimum charging current.
Neutral net in method based on the optimum charging current estimation of neural network model provided in an embodiment of the present invention
The main flow of the building method of thermal model is as shown in figure 1, the building method of neutral net thermal model is：
(1) gather the daily operation data of electric automobile, including battery pack temperature, electric current, voltage, ambient temperature etc..
(2) filter out the data of live part from all operation datas, by each charging current charging, time, just
Beginning temperature, final temperature, voltage etc., as the attribute in a record of charging, charge every time and can generate a record.
(3) using all record of chargings of vehicle history charge data generation as the training sample of neural network model, sample
Initial temperature in this record, electric current, voltage, charging interval etc. as the input variable of model, using temperature rise of charging as output
Variable, builds the nerve covering the different charging original states such as different charging currents, charging interval, different initial charge temperature
Network thermal model.
Xp1=(P_input-x1_step1_xoffset) * x1_step1_gain+x1_step1_ymin；
A11=b1+IW1_1*Xp1；
A1=2/ (1+exp (- 2*a11)) 1；
A2=b2+LW2_1*a1；
Y=(a2-y1_step1_ymin)/y1_step1_gain+y1_step1_xoffset；
Wherein, P_input is the input vector of temperature model, and input vector comprises：Charging current, charging interval, environment
Temperature, battery initial temperature；Y is model assessment temperature rise out；X1_step1_xoffset is 4*1 matrix；x1_step1_
Gain is 4*1 matrix；X1_step1_ymin is 1*1 matrix；B1 is 3*1 matrix；IW1_1 is 3*4 matrix；B2 is 1*1 matrix；
LW2_1 is 1*3 matrix；Y1_step1_ymin is 1*1 matrix；Y1_step1_gain is 1*1 matrix；y1_step1_xoffset
It is 1*1 matrix.
As shown in Fig. 2 in-service evaluation function calculates, the step in conjunction with neutral net thermal model is：
(1) setting minimax allows charging current, the longest the shortest permission charging interval.
(2) setting temperature, charging interval weight.
(3) obtain current charging original state (battery pack temperature, ambient temperature, dump energy etc.)
(4) different electric currents, charging interval value are substituted into neutral net thermal model, predict temperature rise value using thermal model.
(5) in-service evaluation function, to estimation, the temperature rise value obtaining, charging interval give a mark, and fractional value record
Get off.
In embodiments of the present invention, evaluation function is：Score=Tscore+tscore；Wherein, score is PTS；
Tscore is temperature score；Tscore is time score；And Tscore=afa_T* (1- ((y+Tcell0)-Tcell0)/
(maxtemp-Tcell0))*100；Tscore=beta_t* (1- (time-besttime)/(badtime-besttime)) *
100；Afa_T+beta_t=1；Afa_T is temperature score weight；Beta_t is time score weight；Y is temperature rise, and Tcell0 is
Charging initial cells temperature, maxtemp is to allow highest charging temperature；Time is when time charging interval, and besttime is to permit
Permitted the corresponding the shortest charging interval under maximum charging current, badtime is corresponding under allowing minimum charge current the longest fills
The electric time.
(6) repeat (4th)～(5) step, find fractional value highest record, and with the corresponding electricity of score value tidemark
Flow valuve is as optimum charging current.
The present invention uses neural network algorithm, using the charge data of electric automobile as foundation, builds charging thermal model.And
Construct evaluation function, combined charge thermal model, provide the evaluation methodology of a set of charging current.Based on neural network model, lead to
Cross evaluation function, find, estimate optimum charging current.
The present invention adopts neural network algorithm, based on charging neural network model, according to different charging original states,
Line estimates the optimum charging current under current battery state.This method adaptivity is strong, flexible and changeable, can adapt to difference
Set of cells charging original state, estimates the optimum charging current under current state, ensure that simultaneously and the charging interval will not occur
Situations such as long, charging temperature is too high.
As it will be easily appreciated by one skilled in the art that the foregoing is only presently preferred embodiments of the present invention, not in order to
Limit the present invention, all any modification, equivalent and improvement made within the spirit and principles in the present invention etc., all should comprise
Within protection scope of the present invention.
Claims (6)
1. a kind of method of the optimum charging current estimation based on neural network model is it is characterised in that comprise the steps：
(1) obtain neutral net thermal model；
(2) according to automobile type configuration charging current scope to be charged, the upper bound of described charging current scope is maximum charging current,
The lower bound of described charging current scope is minimum charge current；
(3) configure the parameter of evaluation function, wherein, the parameter of described evaluation function includes：The longest charging interval of permission, permission
The shortest charging interval, the maximum temperature allowing, the minimum temperature allowing, the commenting of the evaluation weight of temperature funtion and time function
Valency weight；
(4) according to charging current scope, the parameter of evaluation function and neutral net thermal model obtain optimal current, and by described
Excellent electric current is as initial charge current.
2. the method for claim 1 is it is characterised in that described neutral net thermal model is：
Xp1=(P_input-x1_step1_xoffset) * x1_step1_gain+x1_step1_ymin；
A11=b1+IW1_1*Xp1；
A1=2/ (1+exp (- 2*a11)) 1；
A2=b2+LW2_1*a1；
Y=(a2-y1_step1_ymin)/y1_step1_gain+y1_step1_xoffset；
Wherein, P_input is the input vector of temperature model, and described input vector comprises：Charging current, charging interval, environment
Temperature, battery initial temperature；Y is model assessment temperature rise out；X1_step1_xoffset is 4*1 matrix x1_step1_
Gain is 4*1 matrix, and x1_step1_ymin is 1*1 matrix, and b1 is 3*1 matrix, and IW1_1 is 3*4 matrix, and b2 is 1*1 matrix,
LW2_1 is 1*3 matrix, and y1_step1_ymin is 1*1 matrix, and y1_step1_gain is 1*1 matrix, y1_step1_xoffset
It is 1*1 matrix.
3. method as claimed in claim 1 or 2 is it is characterised in that described evaluation function is：Score=Tscore+
tscore；
Wherein, score is PTS；Tscore is temperature score；Tscore is time score；Tscore=afa_T* (1- ((y
+Tcell0)-Tcell0)/(maxtemp-Tcell0))*100；Tscore=beta_t* (1- (time-besttime)/
(badtime-besttime))*100；Afa_T+beta_t=1；Afa_T is temperature score weight；Beta_t is time score
Weight；Y is temperature rise, and Tcell0 is charging initial cells temperature, and maxtemp is to allow highest charging temperature；Time is to fill when secondary
The electric time, besttime be allow maximum charging current under the corresponding the shortest charging interval, badtime be allow minimum fill
The corresponding the longest charging interval under electric current.
4. the method as described in any one of claim 1-3 it is characterised in that in step (4) obtain optimal current step
It is specially：
(4.1) obtain current charging original state, described charging original state includes ambient temperature and the initial maximum temperature that charges；
(4.2) begin stepping through all charging current values from minimum charge current in the range of described charging current, and obtain and fill
The electric current value corresponding charging interval；
(4.3) the corresponding temperature rise of present charging current value is obtained according to described charging interval and neutral net thermal model；
(4.4) evaluation function value is obtained according to described temperature rise and evaluation function；
(4.5) judging whether described evaluation function value is maximum, if so, then obtaining optimal current, if being otherwise back to step
(4.2).
5. method as claimed in claim 4 it is characterised in that the described charging interval be：Time=(1-SOC) * Capacity/
Current；Wherein, SOC is current residual electricity, and Capacity is battery capacity, and Current is charging current.
6. method as claimed in claim 4 is it is characterised in that in step (4.5), described judges whether evaluation function value is maximum
It is specially：
From the beginning of allowing minimum charge current, the electric current every 1A is scanned traversal up to maximum allowable charging current；Each
The current value of scanning and corresponding charging interval, ambient temperature, battery initial temperature one group of Neural Network Temperature model of composition
Input, input is imported to corresponding charging temperature rise in model, can be estimated；The charging that the current value of each scanning produces
Temperature rise and corresponding charging interval are input in evaluation function, calculate the score under current working, namely each current value
The score of a charging effect all can be drawn, choose the maximum corresponding current value of operating mode of score as optimum charging current.
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106877431A (en) * | 2017-03-01 | 2017-06-20 | 安文科技有限公司 | Charging pile for electric vehicle network load balancing method and electric vehicle charging device |
| CN107963073A (en) * | 2017-12-12 | 2018-04-27 | 江铃汽车股份有限公司 | A kind of electricity-generating control method of hybrid vehicle P0 pattern motors |
| CN107972508A (en) * | 2017-11-27 | 2018-05-01 | 南京晓庄学院 | A kind of electric automobile charge power control method and control device |
| CN109346787A (en) * | 2018-09-21 | 2019-02-15 | 北京机械设备研究所 | A kind of electric automobile power battery adaptive optimization charging method |
| CN109633450A (en) * | 2018-11-23 | 2019-04-16 | 成都云材智慧数据科技有限公司 | A kind of lithium battery charging detection system neural network based |
| CN109934473A (en) * | 2019-02-28 | 2019-06-25 | 深圳智链物联科技有限公司 | Charge health index methods of marking, device, terminal device and storage medium |
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Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106877431A (en) * | 2017-03-01 | 2017-06-20 | 安文科技有限公司 | Charging pile for electric vehicle network load balancing method and electric vehicle charging device |
| CN107972508A (en) * | 2017-11-27 | 2018-05-01 | 南京晓庄学院 | A kind of electric automobile charge power control method and control device |
| CN107963073A (en) * | 2017-12-12 | 2018-04-27 | 江铃汽车股份有限公司 | A kind of electricity-generating control method of hybrid vehicle P0 pattern motors |
| CN107963073B (en) * | 2017-12-12 | 2020-02-04 | 江铃汽车股份有限公司 | Power generation control method for P0 mode motor of hybrid electric vehicle |
| CN109346787A (en) * | 2018-09-21 | 2019-02-15 | 北京机械设备研究所 | A kind of electric automobile power battery adaptive optimization charging method |
| CN109633450A (en) * | 2018-11-23 | 2019-04-16 | 成都云材智慧数据科技有限公司 | A kind of lithium battery charging detection system neural network based |
| CN109934473A (en) * | 2019-02-28 | 2019-06-25 | 深圳智链物联科技有限公司 | Charge health index methods of marking, device, terminal device and storage medium |
| CN109934473B (en) * | 2019-02-28 | 2021-10-15 | 深圳智链物联科技有限公司 | Charging health index scoring method and device, terminal equipment and storage medium |
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