CN111090048A - Vehicle-mounted data self-adaptive time interval transmission method for new energy automobile - Google Patents
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Abstract
The invention relates to a new energy automobile vehicle-mounted data self-adaptive time interval transmission method, and belongs to the field of vehicle-mounted data processing. The method comprises the following steps: s1, selecting dynamic working condition data of the power battery of the actual energy automobile under laboratory conditions, and collecting technical parameters of the battery; s2 intercepting a section of voltage, temperature or current data, extracting wavelet decomposition coefficients by using haar wavelet transform, and performing wavelet reconstruction after processing the coefficients; s3, recording the initial time of each section and the corresponding original voltage, temperature or current data according to the reconstructed voltage, temperature or current data to obtain the reduced-dimension voltage, temperature or current data, and recording other vehicle-mounted data at the corresponding time; s4 models and estimates the state of the processed battery data. The method can obtain the vehicle-mounted data transmitted at the adaptive time interval, ensures the integrity of the data under severe working conditions, has higher modeling and state estimation precision, and has more advantages compared with the transmission at the current fixed time interval.
Description
Technical Field
The invention belongs to the field of vehicle-mounted data processing, and relates to a new energy vehicle-mounted data self-adaptive time interval transmission method.
Background
With the advent of the internet of things and the automobile intelligent era, a large amount of vehicle data is accessed into a cloud data center through the internet of things, and mass data can be collected into various types of data uploaded by an intelligent vehicle-mounted terminal in real time at high frequency in the data center. The data are collected, cleaned, converted, stored, analyzed in real time and offline and value mined, and various large automobile enterprises construct large data platforms thereof to realize all-weather real-time monitoring on information such as battery states, vehicle states and geographic positions of new energy automobiles.
Data communication among the vehicle-mounted terminal, the vehicle enterprise platform and the public platform of the new energy automobile currently conforms to the technical specification of electric automobile remote service and management system (GT32960-2016) of the national standard. The standard indicates that the transmission time interval of real-time data uploaded to the enterprise platform by the vehicle-mounted terminal should not exceed 30s at most. It is understood that the time interval adopted by each enterprise big data platform is mostly fixed for 10s, which results in that data uploaded to the platform may be lost under severe working conditions or short-time working condition changes, so that some data with useful value is missed. Therefore, a transmission method capable of adapting the time interval is needed, so that more data points can be transmitted under severe working conditions and fewer data points can be transmitted under mild working conditions.
Disclosure of Invention
In view of the above, the invention aims to provide a new energy automobile vehicle-mounted data adaptive time interval transmission method, which is used for realizing adaptive time interval data transmission of vehicle-mounted data and ensuring the accuracy of a big data analysis result.
In order to achieve the purpose, the invention provides the following technical scheme:
a new energy automobile vehicle-mounted data self-adaptive time interval transmission method specifically comprises the following steps:
s1: selecting dynamic working condition data of the power battery of the actual energy automobile under laboratory conditions, and collecting technical parameters of the battery;
s2: intercepting a section of vehicle-mounted battery data (selected according to self needs) such as voltage, temperature or current, extracting a wavelet decomposition coefficient by using haar wavelet transform, and performing wavelet reconstruction after processing the coefficient;
s3: recording the initial time of each section and the corresponding original voltage, temperature or current data according to the reconstructed voltage, temperature or current data to obtain the reduced-dimension voltage, temperature or current data, and recording other vehicle-mounted battery data at the corresponding time;
s4: modeling and state estimation are carried out on the processed battery data, and estimation accuracy of the current fixed interval processing method is compared.
Further, the step S2 specifically includes the following steps:
s21: for the intercepted voltage, temperature or current data, the data length needs to satisfy the m power of 2, if not, the data length is complemented by 0;
s22: carrying out haar wavelet decomposition on the voltage, temperature or current data, and carrying out descending arrangement on wavelet coefficients;
s23: selecting a threshold value delta, setting all the sorted wavelet coefficients smaller than the delta to be 0, namely setting the numerical value of the original wavelet coefficient smaller than the threshold value to be 0;
s24: and integrating the wavelet coefficients at the moment, and performing wavelet reconstruction to obtain a series of piecewise constant data.
Further, the step S3 specifically includes the following steps:
s31: if the reconstructed data length exceeds the original length N, truncating from N;
s32: and recording the initial moment corresponding to each section of the first point, finding the voltage, temperature or current value of the original data at the moment to form a new group of voltage, temperature or current data, and recording other vehicle-mounted data at the corresponding moment.
Further, the step S4 specifically includes the following steps:
s41: establishing a battery model, such as a first-order equivalent circuit model;
s42: selecting a proper parameter identification and state estimation algorithm;
s43: and (4) training the model by using the new self-adaptive interval battery data and the battery data extracted at fixed intervals respectively to obtain the estimation precision of the comparison state.
Further, the step S41 includes establishing a thermal model.
Further, in step S42, the state estimation algorithm adopted specifically is: joint estimation using Recursive Least Squares (RLS) and Extended Kalman Filter (EKF) is not limited to this algorithm.
Further, in the step S43, the State estimation mainly specifically includes performing a State of charge (SOC) estimation.
The invention has the beneficial effects that:
1) the self-adaptive time interval transmission method adopted by the invention automatically extracts the vehicle-mounted data according to the vehicle running state, so that the vehicle-mounted terminal can transmit more data points under severe working conditions and transmit less data points under mild working conditions;
2) the invention mainly applies the characteristic of haar wavelet transform, has simple algorithm and obvious applicability and feasibility;
3) the vehicle-mounted data obtained according to the method is more fit with actual data, and has higher accuracy in later data analysis and data mining.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objectives and other advantages of the invention may be realized and attained by the means of the instrumentalities and combinations particularly pointed out hereinafter.
Drawings
For the purposes of promoting a better understanding of the objects, aspects and advantages of the invention, reference will now be made to the following detailed description taken in conjunction with the accompanying drawings in which:
fig. 1 is a flowchart illustrating an implementation of a new energy vehicle-mounted data adaptive time interval transmission method according to the present invention;
FIG. 2 is a comparison of reconstructed voltage data and original data;
FIG. 3 is a first order equivalent circuit model;
FIG. 4 is a graph of adaptive time interval data and SOC estimation results;
fig. 5 shows data and SOC estimation results at fixed 10s intervals.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention in a schematic way, and the features in the following embodiments and examples may be combined with each other without conflict.
Referring to fig. 1 to 3, fig. 1 is a preferred adaptive time interval transmission method for new energy vehicle-mounted data of a new energy vehicle according to the present invention, which specifically includes the following steps:
s1: selecting dynamic working condition data of a laboratory or real vehicle power battery, selecting a group of ternary square battery charging and discharging data of a middle-navigation lithium battery under the laboratory condition, and collecting technical parameters of the battery;
s2: intercepting a section of voltage data (or other vehicle-mounted data such as temperature, current and the like can be selected according to the self requirement), extracting wavelet decomposition coefficients by using haar wavelet transform, and performing wavelet reconstruction after processing the coefficients. The method specifically comprises the following steps:
s21: for intercepted voltage data UoriginThe data length is recorded as N, and if N is not equal to the k power of 2, the data length is complemented by 0;
s22: performing haar wavelet decomposition on the voltage data, performing descending arrangement on wavelet coefficients, and calling a Matlab function waverec (·) to realize wavelet transformation;
[C,L]＝wavedec(Uorigin,3,'haar')
wherein C is a wavelet coefficient, and C ═ C1,C2,…,Ci,…,Cn],n＝2kAnd L is the number of corresponding wavelet coefficients, and haar represents that haar wavelet decomposition is adopted.
S23: selecting a threshold value delta, setting all the sorted wavelet coefficients smaller than the delta to be 0, namely setting the numerical value of the original wavelet coefficient smaller than the threshold value to be 0;
f(Ci＜δ),Ci＝0
the wavelet coefficient at this time is recorded as Crec。
S24: and integrating the wavelet coefficients at the moment and then performing wavelet reconstruction to obtain a series of piecewise constant data.
Urec＝waverec(Crec,L,'haar')
S3: and recording the initial time of each section and the corresponding original voltage data according to the reconstructed voltage data to obtain the voltage data after the dimensionality reduction, and recording other vehicle-mounted data at the corresponding time. The method specifically comprises the following steps:
s31: if the reconstructed data length exceeds the original length N, cutting off from the position N, and FIG. 2 is the comparison between the reconstructed voltage data and the original data;
s32: recording the initial moment corresponding to each section of the first point, finding the voltage value of the original data at the moment to form a new group of voltage data, and recording other vehicle-mounted data at the corresponding moment;
A＝[(t1,U1),(t2,U2),…,(ti,Ui),…,(tm,Um)]
wherein m is the number of subsequences, tiFor each sub-sequence initial time, UiIs tiCorresponding UoriginVoltage data of (1).
The voltage sequence at this time is Ua＝[U1,U2,…,Ui,…,Um]
S4: modeling and state estimation are carried out on the processed battery data, and estimation accuracy of the current fixed time interval processing method is compared. The method specifically comprises the following steps:
s41: establishing a battery model, wherein a first-order equivalent circuit model is selected in the example, as shown in FIG. 3;
s42: selecting a proper parameter identification and state estimation algorithm, wherein RLS and EKF joint estimation is adopted, and the estimation process comprises the following steps:
initializing parameters:
q, R, wherein,
respectively an initial parameter value of the RLS and an initial value of an error covariance matrix of parameter estimation;
q and R are respectively the state initial value of the EKF, the state estimation error covariance matrix initial value, the system noise covariance matrix and the observation noise covariance.
Time update of state variables:
wherein,
parameter estimation of RLS:
wherein, λ is forgetting factor, yk＝Ut,k-OCVk。
State update of state variables:
wherein,
s43: and respectively training the model by using the new self-adaptive time interval battery data and the battery data extracted at fixed intervals, and comparing the state estimation precision.
Fig. 4 shows the data and SOC estimation result of the adaptive time interval using the method of the present invention, and fig. 5 shows the data and SOC estimation result of the fixed 10s time interval, and it can be seen from comparing fig. 4 and fig. 5 that the SOC estimation error of the method of the present invention is within 1%, and the SOC estimation error of the data of the fixed 10s time interval is within 2%, obviously, the adaptive time interval method proposed by the present invention is more advantageous.
Finally, the above embodiments are only intended to illustrate the technical solutions of the present invention and not to limit the present invention, and although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions, and all of them should be covered by the claims of the present invention.
Claims (7)
1. A new energy automobile vehicle-mounted data self-adaptive time interval transmission method is characterized by comprising the following steps:
s1: selecting dynamic working condition data of the power battery of the actual energy automobile under laboratory conditions, and collecting technical parameters of the battery;
s2: intercepting a section of voltage, temperature or current data, extracting a wavelet decomposition coefficient by using haar wavelet transform, and performing wavelet reconstruction after processing the coefficient;
s3: recording the initial time of each section and the corresponding original voltage, temperature or current data according to the reconstructed voltage, temperature or current data to obtain the reduced-dimension voltage, temperature or current data, and recording other vehicle-mounted battery data at the corresponding time;
s4: and modeling and state estimation are carried out on the processed battery data.
2. The adaptive time interval transmission method for the vehicle-mounted data of the new energy vehicle as claimed in claim 1, wherein the step S2 specifically includes the following steps:
s21: for the intercepted voltage, temperature or current data, the data length needs to satisfy the m power of 2, if not, the data length is complemented by 0;
s22: carrying out haar wavelet decomposition on the voltage, temperature or current data, and carrying out descending arrangement on wavelet coefficients;
s23: selecting a threshold value delta, setting all the sorted wavelet coefficients smaller than the delta to be 0, namely setting the numerical value of the original wavelet coefficient smaller than the threshold value to be 0;
s24: and integrating the wavelet coefficients at the moment, and performing wavelet reconstruction to obtain a series of piecewise constant data.
3. The adaptive time interval transmission method for the vehicle-mounted data of the new energy vehicle as claimed in claim 1, wherein the step S3 specifically includes the following steps:
s31: if the reconstructed data length exceeds the original length N, truncating from N;
s32: and recording the initial moment corresponding to each section of the first point, finding the voltage, temperature or current value of the original data at the moment to form a new group of voltage, temperature or current data, and recording other vehicle-mounted data at the corresponding moment.
4. The adaptive time interval transmission method for the vehicle-mounted data of the new energy vehicle as claimed in claim 1, wherein the step S4 specifically includes the following steps:
s41: establishing a battery model;
s42: selecting a proper parameter identification and state estimation algorithm;
s43: and training the model by using the new self-adaptive interval battery data to obtain state estimation.
5. The adaptive time interval transmission method for the vehicle-mounted data of the new energy vehicle according to claim 4, wherein the step S41 further comprises establishing a thermal model.
6. The adaptive time interval transmission method for the vehicle-mounted data of the new energy vehicle according to claim 4, wherein in the step S42, the adopted state estimation algorithm specifically comprises: joint estimation using Recursive Least Squares (RLS) and Extended Kalman Filter (EKF) is used.
7. The on-board data adaptive time interval transmission method for the new energy vehicle according to claim 4, wherein in the step S43, the State estimation is specifically a State of Charge (SOC) estimation.
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