CN108414938A - Batteries of electric automobile SOH online evaluation methods based on electric vehicle monitor supervision platform - Google Patents
Batteries of electric automobile SOH online evaluation methods based on electric vehicle monitor supervision platform Download PDFInfo
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- CN108414938A CN108414938A CN201810049186.8A CN201810049186A CN108414938A CN 108414938 A CN108414938 A CN 108414938A CN 201810049186 A CN201810049186 A CN 201810049186A CN 108414938 A CN108414938 A CN 108414938A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/3644—Constructional arrangements
- G01R31/3648—Constructional arrangements comprising digital calculation means, e.g. for performing an algorithm
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/392—Determining battery ageing or deterioration, e.g. state of health
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- Tests Of Electric Status Of Batteries (AREA)
- Charge And Discharge Circuits For Batteries Or The Like (AREA)
Abstract
The batteries of electric automobile SOH online evaluation methods based on electric vehicle monitor supervision platform that the invention discloses a kind of, including three phases：First stage is that data are acquired and pre-processed, and second stage is the training for carrying out model, and three phases are power battery On-line Estimation, are predicted the power battery SOH of online driving vehicle according to monitoring system data.Operation of the present invention is stablized, and as a result reliably, solves that existing method operand is excessive, the problem excessively high to bandwidth demand amount in operational data transmission process.
Description
Technical field
The invention belongs to cell management system of electric automobile technical field, it is related to a kind of battery health of electric automobile
A kind of evaluation method, and in particular to batteries of electric automobile SOH online evaluation methods based on electric vehicle monitor supervision platform.
Technical background
Pure electric automobile (EV) and mixed power electric car (PHEV and HEV) are more and more universal in life.It is electronic
Automobile has stronger advantage in noise pollution, maintenance cost compared with internal-combustion engines vehicle.SOH, state of health are
Refer to battery health of electric automobile.
There are many methods of definition by the health status SOH of present battery, such as pass through battery current capacities and initial capacity
Ratio defines.The current also with good grounds internal resistance of cell, power density and energy density define the algorithm of SOH.
Traditional SOH estimations mode has following several:
Battery pack mathematical model is established, collection vehicle running data is fitted calculating resistance, to obtain SOH.
Estimation battery SOC (State of Charge, battery state of charge) before charging, charges with accumulative whole process
Capacity is calculated, and to obtain SOH, however above-mentioned SOH evaluation methods have shortcoming.
SOH based on mathematical model estimates the shortcomings that mode：
Battery pack mathematical model foundation needs take a substantial amount of time and energy.The foundation of mathematical model need with it is a large amount of,
Based on prolonged experiment, it is real to complete electrical performance evaluation, parameter calibration, parts (battery) verification and vehicle verification etc.
Need of work is tested to take considerable time.And material property, the technical matters that mathematical model is listened to battery list are related to production level,
Once the variation on production development, experimental data just seems less accurate.And the lifetime data that laboratory obtains is using
It is very big with actual vehicle situation difference in operating mode, cause the error in estimation.
The shortcomings that mode being estimated based on charging capacity SOH：
Method error based on charging capacity SOH estimations is excessive.The error estimated due to SOC itself 10% or so, this
Return directly contribute SOH error it is excessive.Simultaneously as the starting SOC of Vehicular charging can not be fixed, all can when charging every time
It is different, and there is no completely unifying, these are all further increased electric vehicle for used charging pile on the market at present
The error of SOH estimations.
Invention content
In order to solve the above technical problem, the present invention provides a kind of SOH evaluation methods based on extensive detection platform,
Real time and on line monitoring SOH can be reached, and operand is easy.
The technical solution adopted in the present invention is：A kind of batteries of electric automobile SOH based on electric vehicle monitor supervision platform exists
Line appraisal procedure, which is characterized in that include the following steps：
Step 1：Electric vehicle basic configuration information is obtained, classification processing is carried out to different automobile types, records electric automobile
Battery capacity, specified continual mileage；
Step 2：It is obtained by electric vehicle data monitoring platform and has reached the electric vehicle operation number that vehicle travels the service life
According to collection；Electric vehicle operation data is pre-processed, obtains parameter of the specific statistic as step 3；
Step 3：With support vector machines method find incidence relation, in step 3 data set and statistic into
Row training, obtains the model M odel of training completion0；
Step 4：The operation data of the electric vehicle just in ordinary life is obtained by electric vehicle monitor supervision platform, is carried out
Data processing obtains data matrix and statistic in turn；
Step 5：Statistic in step 2 is predicted by the model in step 3 to obtain target component estimated value M
(t)；
Step 6：SOH estimated values are obtained according to the SOH evaluation methods newly defined.
Preferably, the electric vehicle operation data collection for having reached the vehicle traveling service life described in step 2 includes electronic vapour
Vehicle power battery SOC monitoring, running time, Vehicle Speed and electric automobile during traveling mileage.
Preferably, in step 2, it is described that electric vehicle operation data is pre-processed, it is in each row of electric vehicle
It sails and starts to carry out record MILEST (t) to total kilometres；When being started running to automobile, the SOC on vehicle CAN bus is remembered
Record SOCST (t)；At the end of electric vehicle travels every time to total kilometres into record MILEEND (T)；To running car knot
Shu Shi, the SOC on vehicle CAN bus carry out record SOCEND (T)；The speed signal for recording this section of travel distance is denoted as matrix
V=[v1v2...vn].
Preferably, specific statistic described in step 2 includes increment CZSOC (t), the single row of single traveling SOC
Sail increment CZMILE (t), unit increment P (t) and the single Statistical Speed CZV (t) of mileage number；
CZSOC (t)=SOCST (t)-SOCEND (t)；
CZMILE (t)=MILEEND (t)-MILEST (t)；
P (t)=CZMILE (t)/CZSOC (t)；
Wherein, it is travelled every time in electric vehicle and starts to carry out record MILEST (t) to total kilometres, while automobile is opened
Begin when driving, the SOC on vehicle CAN bus carries out record SOCST (t), to total travel at the end of electric vehicle travels every time
Mileage into record MILEEND (T), while to running car at the end of, the SOC on vehicle CAN bus carries out record SOCEND
(T)；Speed signal matrix V=[v1v2...vn] of this section of travel distance is recorded simultaneously.
Preferably, in step 3, model M odel0Input is running data, is exported as unit increment M (t).
Preferably, target component estimated value M (t) is the estimated value of parameter P (t) in step 5.
Preferably, the SOH newly defined in step 6 is SOH (t), SOH (t)=P (t)/Mo=M (t)/M0, wherein P (t)
For unit increment, M0For continual mileage under electric vehicle standard condition, M (t) is the estimated value of P (t).
Compared with prior art, the present invention having the following advantages that：
1, the present invention carries out analysis calculating by the running data generated in electric vehicle driving conditions, is not related in battery
Portion's mechanism has versatility to the cell health state prediction of most of electric automobile power battery.
2, this method operand is small, can carry out operation in vehicle-mounted small intelligent terminal.
3, most of electric automobile power battery in operation is lithium ion battery, and SOH onboard has been used to monitor greatly
Majority is by under lab, being monitored, being formed by resistance and battery capacity under fixed or regularly changing load
The table of composition obtains the estimation to battery health of electric automobile when vehicle is run by look-up table.However in reality
In traveling, the discharge scenario of battery is determined by the operating condition of electric vehicle, sufficiently complex.Actual vehicle data and experimental data
Differ widely, the error that actual battery health status is predicted will be caused using the data in laboratory as the ginseng divine force that created the universe.This system
Formation model be the generation travelled based on vehicle data, be the data of most closing to reality operating mode, for this data estimate
The health status of battery then can be to avoid defect intrinsic in laboratory method in actual condition.
Description of the drawings
Fig. 1 is the flow chart of the embodiment of the present invention；
Fig. 2 is that the electric vehicle monitor supervision platform initial data the 1st of the embodiment of the present invention is arranged to the 6th row part；
Fig. 3 is that the electric vehicle monitor supervision platform initial data the 7th of the embodiment of the present invention is arranged to the 12nd row part；
Fig. 4 is that the electric vehicle monitor supervision platform initial data the 13rd of the embodiment of the present invention is arranged to the 18th row part；
Fig. 5 is that the electric vehicle monitor supervision platform initial data the 19th of the embodiment of the present invention is arranged to the 22nd row part；
Fig. 6 is being distributed for the estimation of electric automobile power battery SOH based on data under platform for the embodiment of the present invention.
Specific implementation mode
Understand for the ease of those of ordinary skill in the art and implement the present invention, with reference to the accompanying drawings and embodiments to this hair
It is bright to be described in further detail, it should be understood that implementation example described herein is merely to illustrate and explain the present invention, not
For limiting the present invention.
As Internet of Things develops, electric vehicle monitor supervision platform and electric automobile monitoring system flourished in recent years, big portion
Divide the data for the real time monitoring for having been carried out one minute 2 times -60 times.The present invention can be provided based on most of monitor supervision platform
Essential information is the infrastructure device implemented.
Referring to Fig.1, a kind of batteries of electric automobile SOH online evaluations based on electric vehicle monitor supervision platform provided by the invention
Method includes the following steps：
S1. electric vehicle basic configuration information is obtained, classification processing, record electric vehicle vehicle electricity are carried out to different automobile types
Tankage, specified continual mileage.
The present embodiment finds out continual mileage M under electric vehicle standard condition according to the information of vehicles of electric vehicle0.
S2. certain history running data for reaching the progress of service life electric vehicle vehicle of logarithm platform is analyzed.Data
From electric vehicle monitor supervision platform, data are divided into the primary real-time upload data of 30S between being.The method can be equally used for
The real-time upload data of different time intervals.
It is travelled every time in electric vehicle and starts to carry out record MILEST (t) to total kilometres, while to automobile starting row
When sailing, the SOC on vehicle CAN bus carries out record SOCST (t), to total kilometres at the end of electric vehicle travels every time
Into record MILEEND (T), while to running car at the end of, the SOC on vehicle CAN bus carries out record SOCEND (T).Together
When record the speed signal of this section of travel distance and be denoted as matrix V=[v1v2...vn]
The present embodiment is according to electric automobile power battery SOC monitoring, running time, Vehicle Speed and electric vehicle
Mileage travelled is the online evaluation means of analysis foundation, flat in the electric vehicle monitoring data different from this platform data source form
In platform, the method also can be used.
Seek increment CZSOC (t)=SOCST (t)-SOCEND (t) of single traveling SOC；
Seek increment CZMILE (t)=MILEEND (t)-MILEST (t) of single mileage travelled number；
Unit increment P (t)=CZMILE (t)/CZSOC (t) * 100；
Remember single Statistical Speed：
S3. single according to single traveling SOC increment CZSOC (t), the increment CZMILE (t) of single mileage travelled number in S3
Secondary Statistical Speed CZV (t), the form mileage MILEST (t) that vehicle starts running are trained as support vector machines (SVM),
The model M odel that output training is completed0。
S4. according to non-scrap-car during road traveling, the data monitored of large-scale data monitor supervision platform into
The integration of row above step.Data are as shown in S2, i.e. CZSOC (t), CZMILE (t), P (t), V.
S5. it is carried out the statistic in S2, S3 step as input parameter to CZMILE's (t) (single mileage travelled number)
Estimation；Target component estimated value M (t) is exported, M (t) is the estimation of P (t).
S6. according to the SOH formula newly defined in electric vehicle monitor supervision platform
Have
SOH (t)=P (t)/M0
Wherein P (t) is unit increment, M0For continual mileage M under electric vehicle standard condition0.M (t) is P (t) in step 4
Estimated value, therefore：
SOH (t)=M (t)/M0
When the historical data for the training vehicle for participating in training is more, obtained cell health state valuation is more accurate.
Extremely see Fig. 2, Fig. 3, Fig. 4 and Fig. 5, the respectively row of electric vehicle monitor supervision platform initial data the 1st of the present embodiment
6th row part, the 7th row to the 12nd row part, the 13rd row to the 18th row part, the 18th row to the 22nd row part；
Wherein the 1st is classified as electric vehicle monitor supervision platform as " time " sequence in electric automobile during traveling or charging process, surveys
Amount interval time is 30s.2nd is classified as electric vehicle " total kilometrage " row, indicates that electric vehicle history travels milimeter number.3rd is classified as
Batteries of electric automobile state of charge (battery SOC) indicates batteries of electric automobile state of charge, i.e. electric automobile power battery electricity
Account for percentage when being full of.4th is classified as charging batteries of electric automobile state, when power battery is charging, " charging shape
State " be charging, when electric vehicle just in motion, " charged state " be non-charged state.5th is classified as charger output
Charging voltage, when electric vehicle charges on charging pile, numerical value is the output charging voltage of charger.6th is classified as charging
The charging current of machine output, when electric vehicle charges on charging pile, numerical value is the output charging current of charger.
Fig. 3 is that the electric vehicle monitor supervision platform initial data the 7th of the embodiment of the present invention is arranged to the 12nd row part；Wherein " vehicle
Fast signal " be electric vehicle monitor supervision platform initial data the 7th row, numerical value indicate be electric vehicle in the process of moving
Speed." firing key signal " is the 8th row of electric vehicle monitor supervision platform initial data, and keyed ignition shape is inserted into electric vehicle
It is " ON " under state, is " OFF " when being not inserted into key." VCU error code " is the 9th row of electric vehicle monitor supervision platform initial data,
It indicates the signal code of new-energy automobile entirety controller (VCU) failure." motor status and signal strength " supervises for electric vehicle
The 10th row for controlling platform initial data, indicate the working condition of motor, when motor is driven, export as " motor drives
It is dynamic ", when motor is generated electricity, exports and be shown as " no work " for " electric power generation " when motor is not worked.It is " remaining
Mileage travelled " is the 11st row of electric vehicle monitor supervision platform initial data, indicates the remaining driving mileage of electric vehicle." system
Dynamic on off state " is the 12nd row of electric vehicle monitor supervision platform initial data, indicates whether electric vehicle is braked, when
When electric vehicle is braked, output is that " having braking " is shown as " brakeless " when not braked.
Fig. 4 is that the electric vehicle monitor supervision platform initial data the 13rd of the embodiment of the present invention is arranged to the 18th row part；" shift hand
Handle position " is the 13rd row of electric vehicle monitor supervision platform initial data, indicates electric automobile gearshift handle position, and N is neutral gear,
D is forward gear, and R is reverse gear, and P is parking position switch." DC bus-bar voltage " is the 14th row of electric vehicle monitor supervision platform initial data,
It indicates the numerical value of voltage on busbar." DC bus current " is the 15th row of electric vehicle monitor supervision platform initial data, table
Show the numerical value of electric current on busbar." motor status " is the 16th row of electric vehicle monitor supervision platform initial data, indicates motor
Operating status." motor speed " is the 17th row of electric vehicle monitor supervision platform initial data, indicates turn of motor at runtime
Speed." motor feedback torque " is the 18th row of electric vehicle monitor supervision platform initial data, indicates the torque that motor is fed back above.
Fig. 5 is that the electric vehicle monitor supervision platform initial data the 19th of the embodiment of the present invention is arranged to the 22nd row part；" battery fills
Electric discharge total current " is the 19th row of electric vehicle monitor supervision platform initial data, indicates battery charging and discharging total current." electric leakage inspection
Survey " be electric vehicle monitor supervision platform initial data the 20th row, when electric vehicle leaks electricity, numerical value 1, when not occurring
When electric leakage, numerical value 0；" battery SOC (after processing) " is the 21st row of electric vehicle monitor supervision platform initial data, electric vehicle
Battery state of charge (battery SOC), treated, and battery SOC is the battery charge corrected according to more normal operation method inside electric vehicle
State, that is, battery SOC (after processing)." external charging signal (power battery charging instruction) " is that electric vehicle monitor supervision platform is original
22nd row of data, have indicated whether that charging pile plug is inserted into and has signal communication, are " external if having signal communication
Charging ", nothing are then " no external charging ".
It is being distributed for the estimation of electric automobile power battery SOH based on data under platform for the present embodiment see Fig. 6,
Wherein X-axis is electric vehicle trip number, and Y-axis is cell health state (SOH).
It should be understood that the part that this specification does not elaborate belongs to the prior art.
It should be understood that the above-mentioned description for preferred embodiment is more detailed, can not therefore be considered to this
The limitation of invention patent protection range, those skilled in the art under the inspiration of the present invention, are not departing from power of the present invention
Profit requires under protected ambit, can also make replacement or deformation, each fall within protection scope of the present invention, this hair
It is bright range is claimed to be determined by the appended claims.
Claims (7)
1. a kind of batteries of electric automobile SOH online evaluation methods based on electric vehicle monitor supervision platform, which is characterized in that including with
Lower step：
Step 1：Electric vehicle basic configuration information is obtained, classification processing, record electric vehicle vehicle electricity are carried out to different automobile types
Tankage, specified continual mileage；
Step 2：The electric vehicle operation data collection for having reached vehicle and travelling the service life is obtained by electric vehicle data monitoring platform；
Electric vehicle operation data is pre-processed, obtains parameter of the specific statistic as step 3；
Step 3：With support vector machines method find incidence relation, in step 3 data set and statistic instruct
Practice, obtains the model M odel of training completion0；
Step 4：The operation data of the electric vehicle just in ordinary life is obtained by electric vehicle monitor supervision platform, carries out data
It handles and then obtains data matrix and statistic；
Step 5：Statistic in step 2 is predicted to obtain target component estimated value M (t) by the model in step 3；
Step 6：SOH estimated values are obtained according to the SOH evaluation methods newly defined.
2. the batteries of electric automobile SOH online evaluation methods according to claim 1 based on electric vehicle monitor supervision platform,
It is characterized in that：In step 2, the electric vehicle operation data collection for having reached the vehicle traveling service life includes electric powered motor electricity
Pond SOC monitoring, running time, Vehicle Speed and electric automobile during traveling mileage.
3. the batteries of electric automobile SOH online evaluation methods according to claim 1 based on electric vehicle monitor supervision platform,
It is characterized in that：It is described that electric vehicle operation data is pre-processed in step 2, it is to travel to start pair every time in electric vehicle
Total kilometres carry out record MILEST (t)；When being started running to automobile, the SOC on vehicle CAN bus carries out record SOCST
(t)；At the end of electric vehicle travels every time to total kilometres into record MILEEND (T)；At the end of running car, vehicle
SOC in CAN bus carries out record SOCEND (T)；Record this section of travel distance speed signal be denoted as matrix V=
[v1v2...vn]。
4. the batteries of electric automobile SOH online evaluation methods according to claim 1 based on electric vehicle monitor supervision platform,
It is characterized in that：In step 2, the specific statistic includes increment CZSOC (t), the single mileage travelled number of single traveling SOC
Increment CZMILE (t), unit increment P (t) and single Statistical Speed CZV (t)；
CZSOC (t)=SOCST (t)-SOCEND (t)；
CZMILE (t)=MILEEND (t)-MILEST (t)；
P (t)=CZMILE (t)/CZSOC (t)；
Wherein, it is travelled every time in electric vehicle and starts to carry out record MILEST (t) to total kilometres, while to automobile starting row
When sailing, the SOC on vehicle CAN bus carries out record SOCST (t), to total kilometres at the end of electric vehicle travels every time
Into record MILEEND (T), while to running car at the end of, the SOC on vehicle CAN bus carries out record SOCEND (T)；Together
When record speed signal matrix V=[the v1 v2 ... vn] of this section of travel distance.
5. the batteries of electric automobile SOH online evaluation methods according to claim 1 based on electric vehicle monitor supervision platform,
It is characterized in that：In step 3, model M odel0Input is running data, is exported as unit increment M (t).
6. the batteries of electric automobile SOH online evaluation methods according to claim 4 based on electric vehicle monitor supervision platform,
It is characterized in that：Target component estimated value M (t) is the estimated value of parameter P (t) in step 5.
7. the batteries of electric automobile SOH based on electric vehicle monitor supervision platform according to claim 1-6 any one is online
Appraisal procedure, it is characterised in that：The SOH newly defined in step 6 is SOH (t), SOH (t)=P (t)/Mo=M (t)/M0, wherein P
(t) it is unit increment, M0For continual mileage under electric vehicle standard condition, M (t) is the estimated value of P (t).
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| CN109711007A (en) * | 2018-12-11 | 2019-05-03 | 北京匠芯电池科技有限公司 | The method of power lithium-ion battery security performance Nondestructive |
| CN109927575A (en) * | 2019-02-28 | 2019-06-25 | 福建工程学院 | A kind of battery performance detection method for direct-current charging post |
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| CN110632528A (en) * | 2019-11-04 | 2019-12-31 | 桂林电子科技大学 | Lithium battery SOH estimation method based on internal resistance detection |
| CN110861496A (en) * | 2019-11-27 | 2020-03-06 | 安徽江淮汽车集团股份有限公司 | Battery SOH value adjusting method and system |
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| CN110632528A (en) * | 2019-11-04 | 2019-12-31 | 桂林电子科技大学 | Lithium battery SOH estimation method based on internal resistance detection |
| CN110632528B (en) * | 2019-11-04 | 2021-08-31 | 桂林电子科技大学 | Lithium battery SOH estimation method based on internal resistance detection |
| CN110861496A (en) * | 2019-11-27 | 2020-03-06 | 安徽江淮汽车集团股份有限公司 | Battery SOH value adjusting method and system |
| CN113759269A (en) * | 2021-11-10 | 2021-12-07 | 武汉理工大学 | Method and system for monitoring health state of battery of electric vehicle |
| CN115214410A (en) * | 2022-06-24 | 2022-10-21 | 安徽大学江淮学院 | Electric automobile electric energy online intelligent monitoring guide system based on big data analysis |
| CN115214410B (en) * | 2022-06-24 | 2023-03-10 | 安徽大学江淮学院 | Electric automobile electric energy online intelligent monitoring guide system based on big data analysis |
| CN115424443A (en) * | 2022-09-20 | 2022-12-02 | 安徽江淮汽车集团股份有限公司 | Vehicle abnormity monitoring method based on driving data |
| CN115424443B (en) * | 2022-09-20 | 2024-04-26 | 安徽江淮汽车集团股份有限公司 | Vehicle abnormity monitoring method based on driving data |
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