CN103675707B - Lithium ion battery peak power online evaluation method - Google Patents

Lithium ion battery peak power online evaluation method Download PDF

Info

Publication number
CN103675707B
CN103675707B CN201310681766.6A CN201310681766A CN103675707B CN 103675707 B CN103675707 B CN 103675707B CN 201310681766 A CN201310681766 A CN 201310681766A CN 103675707 B CN103675707 B CN 103675707B
Authority
CN
China
Prior art keywords
battery
lithium ion
ion battery
peak power
discharge
Prior art date
Application number
CN201310681766.6A
Other languages
Chinese (zh)
Other versions
CN103675707A (en
Inventor
娄婷婷
李建祥
黄德旭
曹际娜
张秉良
唐方庆
Original Assignee
国家电网公司
国网山东省电力公司电力科学研究院
山东鲁能智能技术有限公司
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 国家电网公司, 国网山东省电力公司电力科学研究院, 山东鲁能智能技术有限公司 filed Critical 国家电网公司
Priority to CN201310681766.6A priority Critical patent/CN103675707B/en
Publication of CN103675707A publication Critical patent/CN103675707A/en
Application granted granted Critical
Publication of CN103675707B publication Critical patent/CN103675707B/en

Links

Abstract

The invention discloses a kind of lithium ion battery peak power online evaluation method, comprise the following steps: set up the influencing characteristic curve that charge states of lithium ion battery, ohmic internal resistance, temperature and sustainable time affects lithium ion battery peak power respectively; Every factor of combined influence battery peak power, sets up battery peak power assessment initial model; Battery peak power assessment models is embedded vehicle-mounted terminal system, continues training peak power assessment models.Beneficial effect of the present invention: use this method can substantially avoid because misoperation causes the shorter battery life overcharging or excessively put and cause to battery, the situations such as use cost increasing, reduce the danger coefficient in battery use procedure, improve the reliability and stability that electric automobile runs, ensure the safety of the person and equipment.

Description

Lithium ion battery peak power online evaluation method
Technical field
The present invention relates to field of lithium ion battery, be specifically related to a kind of lithium ion battery peak power online evaluation method.
Background technology
Electrokinetic cell is as the power source of electric automobile or auxiliary power source, and its development is particularly important.Usual electric automobile has following basic demand to electrokinetic cell: high-energy-density, high-specific-power, and self discharge is few, longer cycle life, good charge-discharge performance, consistency of battery pack is good, and price is low, security performance is good, and working service is convenient, the problems such as non-environmental-pollution.Lithium ion battery specific energy and specific power high, free from environmental pollution, self-discharge rate is low, memory-less effect, and these advantages make it be able to widespread use, and its major defect is that anti-abuse ability is poor, overcharges and cross that put can be great to lithium ion battery aging effects continually.
The power characteristic of battery is weighed by peak power usually, and peak power refers to that battery is under current state, the peak power that can provide in a period of time Δ t.The peak power of battery is subject to the impact of the factors such as the internal resistance of cell, battery charge state (SOC), environment temperature and current impulse length, and it is one of important technology index weighing battery performance, particularly in high power applications occasion.Electric automobile is in actual motion, and when leaving larger allowance for protecting battery to the use of its power, for meeting car load power requirement in varied situations, need to increase number of battery cells, cause added cost, battery weight volume is all excessive, affects the performance of car load; If the power designs of battery is smaller, then in use may puts over-charging of battery or mistake, in order to can sufficiently and reasonably use and protect battery, need the instantaneous input-output power that battery management system provides battery to allow to entire car controller.
At present, both at home and abroad the method for testing of peak power is divided into: U.S. USABC tests, FreedomCAR mixed pulses are tested, Japanese JEVS tests and China 863 battery testing specification.These four kinds of methods are all mentioned and carry out pulsed discharge under different SOC, calculate peak power, according to existing method of testing can not real-time online prediction battery peak power.
Summary of the invention
Object of the present invention is exactly to solve the problem, propose a kind of method of online evaluation battery peak power, it can realize carrying out rapid evaluation to the electrokinetic cell that car is using, predict its current state charge and discharge peak power, avoid overcharging and excessively putting lithium ion battery.
To achieve these goals, the present invention adopts following technical scheme:
A kind of lithium ion battery peak power online evaluation method, comprises the following steps:
(1) the influencing characteristic curve that charge states of lithium ion battery, ohmic internal resistance, temperature and sustainable time parameter affect battery peak power is set up respectively.
(2) lithium ion battery ohmic internal resistance, charge states of lithium ion battery and the lithium ion battery temperature input parameter as power assessments initial model is chosen, choose the peak power of lithium ion battery 10s pulse as output parameter, Matlab analysis of neural network algorithm is utilized to select ANFIS system to set up battery peak power assessment initial model, by the change of adjustment membership function, parameter learning and model training are carried out to initial model.
(3) battery peak power assessment models is embedded vehicle-mounted terminal system, the charging peaks power current according to the lithium ion battery ohmic internal resistance of on-line monitoring, charge states of lithium ion battery and lithium ion battery temperature parameter assessment lithium ion battery and electric discharge peak power, and continue training peak power assessment models.
The concrete grammar of described step (1) is:
A lithium ion battery is placed in constant temperature oven by (), maintain battery constant at a certain temperature, charge to charge cutoff voltage to battery, leaves standstill; Be discharged to a certain state-of-charge with default multiplying power current versus cell again, leave standstill; Record the state-of-charge of current lithium ion battery, record the temperature of current lithium ion battery and open-circuit voltage OCV.
B () is discharged with default multiplying power current versus cell, if do not arrive battery discharge cut-off voltage, leave standstill, and record electric discharge starts previous moment lithium ion battery terminal voltage U 1, electric discharge terminates previous moment lithium ion battery terminal voltage U 2, record the voltage transient changing value Δ U under battery discharge initial time simultaneously t1, electric current transient change value Δ I t1with the voltage transient changing value Δ U under finish time t2, electric current transient change value Δ I t2.
C () is charged with default multiplying power current versus cell, if do not arrive battery charge cutoff voltage, leave standstill; Record the voltage change Δ U in lithium ion cell charging process under initial time respectively t3, current variation value Δ I t3with the voltage change Δ U under finish time t4, current variation value Δ I t4; Meanwhile, record charging start time lithium ion battery terminal voltage U 3, charging finish time lithium ion battery terminal voltage U 4.
D () carries out constant-current discharge to battery, change battery charge state, repeats step (a)---step (c).
E () adjustment calorstat temperature, repeats step (a)---step (d).
According to the electric discharge peak value power P of following formulae discovery lithium ion battery under different temperatures, SOC dischargewith charging peaks power P regen:
R dch-ohmic=ΔU t1/ΔI t1=ΔU t2ΔI t2
R reg-ohmic=ΔU t3/ΔI t3=ΔU t4/ΔI t4
R discharge=(U 1-U 2)/ΔI t1
R regen=(U 4-U 3)/ΔI t3
P discharge=V min×(OCV-V min)÷R discharge
P regen=V max×(V max-OCV)÷R regen
Wherein, R dch-ohmicbattery discharge ohmic internal resistance, R reg-ohmicbattery charging regeneration ohmic internal resistance, R dischargecell discharge internal resistance, R regenbattery charging regeneration internal resistance, Δ U tit ithe voltage change in (i=1,2,3,4) moment, Δ I tit ithe current variation value in (i=1,2,3,4) moment, OCV is the open-circuit voltage of battery under current state, V minminimum voltage when being battery discharge, V maxmaximum voltage when being battery recycling, U 1, U 2be respectively electric discharge start time and finish time lithium ion battery terminal voltage, U 3, U 4be respectively the terminal voltage of charging start time and charging finish time lithium ion battery.
The concrete grammar of described step (2) is: lithium ion battery ohmic internal resistance, state-of-charge and temperature data are input in battery peak power assessment initial model, produce network structure, fuzzy if-then rules device generates fuzzy rule automatically, produce weights excitation neural network, neural network exports lithium ion battery peak power according to input data, reality is exported peak power value and desired value compares, by error signal backpropagation, utilize self-adaptation and the self-learning capability of neural network, upgrade fuzzy rule, finally make error reach minimum value.
The concrete grammar of described step (3) is: by the initial cells peak power assessment models write vehicle-mounted terminal system trained, to lithium ion battery ohmic internal resistance, state-of-charge and temperature data that initial cells peak power assessment models Input Online is monitored, the charging peaks power that assessment battery is current and electric discharge peak power, and according to vehicle actual motion state acquisition battery real time status information, continue training peak power assessment models, reach the object that prediction limit, limit is revised.
According to different charge states of lithium ion batteries in described step (d), adjust suitable electric current and preset multiplier value.
The invention has the beneficial effects as follows:
(1) this method on-line prediction battery charging peaks power and electric discharge peak power is used, and be applied on electric automobile car-mounted terminal, can substantially avoid because misoperation causes the shorter battery life overcharging or excessively put and cause to battery, the situations such as use cost increasing, reduce the danger coefficient in battery use procedure, improve the reliability and stability that electric automobile runs, ensure the safety of the person and equipment.
(2) the present invention can the charging peaks power of real-time estimate battery and electric discharge peak power, and it to be shown in real time, can effectively improve battery and utilize power, lifting vehicle power performance.
(3) treat the battery of same model, only need extract a small amount of sample and detect, peak power characteristic can be completed, battery charging and discharging peak power initial mask can be obtained, reduce the loss of test sample, effectively shorten detection time.
(4) the present invention can realize the realtime power assessment of electrokinetic cell in electric automobile during traveling process, and closer to electrokinetic cell actual motion state, and increasing along with image data, assessment models can self training and reparation, and accuracy continues to improve.
Accompanying drawing explanation
Fig. 1 is the relation curve of lithium ion battery peak power of the present invention and battery SOC;
Fig. 2 is the relation curve of lithium ion battery peak power of the present invention and battery ohmic internal resistance;
Fig. 3 is the relation curve of lithium ion battery peak power of the present invention and battery temperature;
Fig. 4 is the relation curve of lithium ion battery peak power of the present invention and battery charging condition;
Fig. 5 is lithium ion battery peak power initial assessment model of the present invention;
Fig. 6 is each input parameter membership function change curve in lithium ion battery peak power initial model of the present invention.
Embodiment:
Below in conjunction with accompanying drawing and embodiment, the present invention will be further described:
(1) influence curve of peak power characteristic is set up
Fig. 1 ~ Fig. 4 is the schematic diagram of the embodiment of the present invention, and the electric battery be composed in series for high power 8Ah lithium manganate battery, illustrates with reference to above-mentioned power evaluation method, completes 10s peak power specificity analysis.
When a) studying lithium ion battery charge and discharge peak power and SOC relation, battery is put into constant temperature oven, maintain battery temperature constant, with 1C, charge cutoff voltage is charged to battery, leave standstill 1 hour.
B) with 1C current versus cell electric discharge 6min, be 90% to SOC, leave standstill 1h, record temperature and the open-circuit voltage OCV of current lithium ion battery.
C) with 15C current versus cell electric discharge 10s, if battery does not arrive discharge cut-off voltage, leave standstill 40s, record electric discharge starts previous moment lithium ion battery terminal voltage U 1, electric discharge terminates previous moment lithium ion battery terminal voltage U 2, record the voltage transient changing value Δ U under battery discharge initial time simultaneously t1, electric current transient change value Δ I t1with the voltage transient changing value Δ U under finish time t2, electric current transient change value Δ I t2; Again with 5C current versus cell charging 10s, if battery does not arrive charge cutoff voltage, leave standstill 1h, record charging starts previous moment lithium ion battery terminal voltage U 3, charging terminates previous moment lithium ion battery terminal voltage U 4, and record the voltage transient changing value Δ U under battery charging initial time t3, electric current transient change value Δ I t3with the voltage transient changing value Δ U under finish time t4, electric current transient change value Δ I t4.
D) with 1C current versus cell constant-current discharge to SOC for 80%, leave standstill 1h, record temperature and the open-circuit voltage OCV of current lithium ion battery.
E) step c) is repeated.
F) with 1C current versus cell constant-current discharge to SOC for 70%, leave standstill 1h, record temperature and the open-circuit voltage OCV of current lithium ion battery.
G) with 10C current versus cell electric discharge 10s, if battery does not arrive discharge cut-off voltage, leave standstill 40s, record electric discharge starts previous moment lithium ion battery terminal voltage U 1, electric discharge terminates previous moment lithium ion battery terminal voltage U 2, record the voltage transient changing value Δ U under battery discharge initial time simultaneously t1, electric current transient change value Δ I t1with the voltage transient changing value Δ U under finish time t2, electric current transient change value Δ I t2; Again with 10C current versus cell charging 10s, if battery does not arrive charge cutoff voltage, leave standstill 1h, record charging starts previous moment lithium ion battery terminal voltage U 3, charging terminates previous moment lithium ion battery terminal voltage U 4, and record the voltage transient changing value Δ U under battery charging initial time t3, electric current transient change value Δ I t3with the voltage transient changing value Δ U under finish time t4, electric current transient change value Δ I t4.
H) with 1C current versus cell constant-current discharge to SOC for 60%, leave standstill 1h, record temperature and the open-circuit voltage OCV of current lithium ion battery, repeat step g).
I) with 1C current versus cell constant-current discharge to SOC for 50%, leave standstill 1h, record temperature and the open-circuit voltage OCV of current lithium ion battery, repeat step g).
J) with 1C current versus cell constant-current discharge to SOC for 40%, leave standstill 1h, record temperature and the open-circuit voltage OCV of current lithium ion battery, repeat step g).
K) with 1C current versus cell constant-current discharge to SOC for 30%, leave standstill 1h, record temperature and the open-circuit voltage OCV of current lithium ion battery, repeat step g).
L) with 5C current versus cell electric discharge 10s, if battery does not arrive discharge cut-off voltage, leave standstill 40s, record electric discharge starts previous moment lithium ion battery terminal voltage U 1, electric discharge terminates previous moment lithium ion battery terminal voltage U 2, record the voltage transient changing value Δ U under battery discharge initial time simultaneously t1, electric current transient change value Δ I t1with the voltage transient changing value Δ U under finish time t2, electric current transient change value Δ I t2; Again with 15C current versus cell charging 10s, if battery does not arrive charge cutoff voltage, leave standstill the charging of 1h record and start previous moment lithium ion battery terminal voltage U 3, charging terminates previous moment lithium ion battery terminal voltage U 4, and record the voltage transient changing value Δ U under battery charging initial time t3, electric current transient change value Δ I t3with the voltage transient changing value Δ U under finish time t4, electric current transient change value Δ I t4.
M) with 1C current versus cell constant-current discharge to SOC for 20%, leave standstill 1h, record temperature and the open-circuit voltage OCV of current lithium ion battery.
N) step l is repeated).
O) with 1C current versus cell constant-current discharge to SOC for 10%, leave standstill 1h, record temperature and the open-circuit voltage OCV of current lithium ion battery.
P) step l is repeated).
Q) adjust calorstat temperature be followed successively by-20 DEG C ,-10 DEG C, 0 DEG C, 10 DEG C, 20 DEG C, 30 DEG C, 40 DEG C, 50 DEG C, repeat above-mentioned steps a) ~ p).
According to the electric discharge peak value power P of following formulae discovery lithium ion battery under different temperatures, SOC dischargewith charging peaks power P regen:
R dch-ohmic=ΔU t1/ΔI t1=ΔU t2/ΔI t2
R reg-ohmic=ΔU t3/ΔI t3=ΔU t4/ΔI t4
R discharge=(U 1-U 2)/ΔI t1
R regen=(U 4-U 3)/ΔI t3
P discharge=V min×(OCV-V min)÷R discharge
P regen=V max×(V max-OCV)÷R regen
Wherein, R dch-ohmicbattery discharge ohmic internal resistance, R reg-ohmicbattery charging regeneration ohmic internal resistance, R dischargecell discharge internal resistance, R regenbattery charging regeneration internal resistance, Δ U tit ithe voltage change in (i=1,2,3,4) moment, Δ I tit ithe current variation value in (i=1,2,3,4) moment, OCV is the open-circuit voltage of battery under current state, V minminimum voltage when being battery discharge, V maxmaximum voltage when being battery recycling, U 1, U 2be respectively electric discharge start time and finish time lithium ion battery terminal voltage, U 3, U 4be respectively the terminal voltage of charging start time and charging finish time lithium ion battery.
According to the different SOC of test data analysis on the impact of peak power, as shown in Figure 1; Analyze the impact of different internal resistance on peak power, as shown in Figure 2; Analyze different temperatures to the impact of peak power, as shown in Figure 3; Charging and discharging state on the impact of peak power as shown in Figure 4.
(2) peak power assessment initial model is set up
ANFIS system requirements must be the Takagi-Sugeno system that zeroth order or single order export; And the input of system can be multiple variable, but output can only be single variable, i.e. MISO type system, is exported and obtained by weighted mean de-fuzzy; The weight of strictly all rules is 1, requires that the function number exported must equal fuzzy rules simultaneously.
If the peak power of electrokinetic cell 10s is regarded as output variable, the SOC of battery, ohmic internal resistance and environment temperature are regarded as input variable, as can be seen from above-mentioned battery peak power analysis, the peak power model of lithium battery is nonlinear completely, and the output quantity of model is this single argument of peak power of 10s, and single order can be adopted to export.Above-mentioned requirements is met to the estimation of 10s pulse peak power, therefore, according to the unlimited approximation capability of ANFIS system to nonlinear system, the estimation of the peak power to electrokinetic cell 10s pulse can be completed, therefore select ANFIS system to set up peak power assessment initial model, institute's established model schematic diagram as shown in Figure 5.
According to the data of power analysis process, to the training of assessment initial model, according to ANFIS structure, according to given data collection, be fixed the fuzzy division of quantity, set quantity and the degree of membership type of the membership function of each input language variable, adopt the mode of combined training that cut-off error is set to 5, iterative steps is set to 50 steps; The fuzzy interval of four input variables divides based on grid type generating mode, and dividing number is 2,4,3,5; Subordinate function is set to Gaussian; Output variable is set to linear. after data training, and model respectively inputs membership function change as shown in Figure 6, and now system relative error is about 0.8%, meets permissible error requirement.
(3) model insertion, assessment limit, limit is trained
By the initial cells peak power assessment models write vehicle-mounted terminal system trained, the charging peaks power that the battery SOC of foundation on-line monitoring, ohmic internal resistance, temperature evaluation battery are carved at this moment and electric discharge peak power, and according to vehicle actual motion state acquisition battery real time status information, continue training peak power assessment models, reach the object that prediction limit, limit is revised.
By reference to the accompanying drawings the specific embodiment of the present invention is described although above-mentioned; but not limiting the scope of the invention; one of ordinary skill in the art should be understood that; on the basis of technical scheme of the present invention, those skilled in the art do not need to pay various amendment or distortion that creative work can make still within protection scope of the present invention.

Claims (5)

1. a lithium ion battery peak power online evaluation method, is characterized in that, comprise the following steps:
(1) the influencing characteristic curve that charge states of lithium ion battery, ohmic internal resistance, temperature and sustainable time parameter affect battery peak power is set up respectively;
(2) lithium ion battery ohmic internal resistance, charge states of lithium ion battery and the lithium ion battery temperature input parameter as power assessments initial model is chosen, choose the peak power of lithium ion battery 10s pulse as output parameter, Matlab analysis of neural network algorithm is utilized to select ANFIS system to set up initial cells peak power assessment models, by the change of adjustment membership function, parameter learning and model training are carried out to initial cells peak power assessment models;
(3) initial cells peak power assessment models is embedded vehicle-mounted terminal system, the charging peaks power current according to the lithium ion battery ohmic internal resistance of on-line monitoring, charge states of lithium ion battery and lithium ion battery temperature parameter assessment lithium ion battery and electric discharge peak power, and continue training initial cells peak power assessment models.
2. a kind of lithium ion battery peak power online evaluation method as claimed in claim 1, it is characterized in that, the concrete grammar of described step (1) is:
A lithium ion battery is placed in constant temperature oven by (), maintain battery constant at a certain temperature, charge to charge cutoff voltage to battery, leaves standstill; Be discharged to a certain state-of-charge with default multiplying power current versus cell again, leave standstill; Record the state-of-charge of current lithium ion battery, record the temperature of current lithium ion battery and open-circuit voltage OCV;
B () is discharged with default multiplying power current versus cell, if do not arrive battery discharge cut-off voltage, leave standstill, record electric discharge start time lithium ion battery terminal voltage U 1, electric discharge finish time lithium ion battery terminal voltage U 2, record the voltage transient changing value Δ U under battery discharge initial time simultaneously t1, electric current transient change value Δ I t1with the voltage transient changing value Δ U under finish time t2, electric current transient change value Δ I t2;
C () is charged with default multiplying power current versus cell, if do not arrive battery charge cutoff voltage, leave standstill; Record the voltage transient changing value Δ U in lithium ion cell charging process under initial time respectively t3, electric current transient change value Δ I t3with the voltage transient changing value Δ U under finish time t4, electric current transient change value Δ I t4; Meanwhile, record charging start time lithium ion battery terminal voltage U 3, charging finish time lithium ion battery terminal voltage U 4;
D () carries out constant-current discharge to battery, change battery charge state, repeats step (a)---step (c);
E () adjustment calorstat temperature, repeats step (a)---step (d);
According to the electric discharge peak value power P of following formulae discovery lithium ion battery under different temperatures, SOC dischargewith charging peaks power P regen:
R dch-ohmic=ΔU t1/ΔI t1=ΔU t2/ΔI t2
R reg-ohmic=ΔU t3/ΔI t3=ΔU t4/ΔI t4
R discharge=(U 1-U 2)/ΔI t1
R regen=(U 4-U 3)/ΔI t3
P discharge=V min×(OCV-V min)÷R discharge
P regen=V max×(V max-OCV)÷R regen
Wherein, R dch-ohmicbattery discharge ohmic internal resistance, R reg-ohmicbattery charging regeneration ohmic internal resistance, R dischargecell discharge internal resistance, R regenbattery charging regeneration internal resistance, Δ U t1, Δ I t1be respectively the voltage under battery discharge initial time, electric current transient change value, Δ U t2, Δ I t2be respectively the voltage under battery discharge finish time, electric current transient change value, Δ U t3, Δ I t3be respectively voltage, the electric current transient change value under battery charging initial time, Δ U t4, Δ I t4be respectively voltage, the electric current transient change value under battery charging finish time, OCV is the open-circuit voltage of battery under current state, V minminimum voltage when being battery discharge, V maxmaximum voltage when being battery recycling, U 1,u 2be respectively electric discharge start time and finish time lithium ion battery terminal voltage, U 3, U 4be respectively the terminal voltage of charging start time and charging finish time lithium ion battery.
3. a kind of lithium ion battery peak power online evaluation method as claimed in claim 1, it is characterized in that, the concrete grammar of described step (2) is: by lithium ion battery ohmic internal resistance, state-of-charge and temperature data are input in initial cells peak power assessment models, produce network structure, fuzzy if-then rules device generates fuzzy rule automatically, produce weights excitation neural network, neural network exports lithium ion battery peak power according to input data, reality is exported peak power value and desired value compares, by error signal backpropagation, utilize self-adaptation and the self-learning capability of neural network, upgrade fuzzy rule, error is finally made to reach minimum value.
4. a kind of lithium ion battery peak power online evaluation method as claimed in claim 1, it is characterized in that, the concrete grammar of described step (3) is: by the initial cells peak power assessment models write vehicle-mounted terminal system trained, to the lithium ion battery ohmic internal resistance of initial cells peak power assessment models Input Online monitoring, state-of-charge and temperature data, the charging peaks power that assessment battery is current and electric discharge peak power, and according to vehicle actual motion state acquisition battery real time status information, continue training initial cells peak power assessment models, reach the object that prediction limit, limit is revised.
5. a kind of lithium ion battery peak power online evaluation method as claimed in claim 2, is characterized in that, according to different charge states of lithium ion batteries in described step (d), adjust suitable electric current and preset multiplier value.
CN201310681766.6A 2013-12-13 2013-12-13 Lithium ion battery peak power online evaluation method CN103675707B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201310681766.6A CN103675707B (en) 2013-12-13 2013-12-13 Lithium ion battery peak power online evaluation method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201310681766.6A CN103675707B (en) 2013-12-13 2013-12-13 Lithium ion battery peak power online evaluation method

Publications (2)

Publication Number Publication Date
CN103675707A CN103675707A (en) 2014-03-26
CN103675707B true CN103675707B (en) 2016-01-20

Family

ID=50313861

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201310681766.6A CN103675707B (en) 2013-12-13 2013-12-13 Lithium ion battery peak power online evaluation method

Country Status (1)

Country Link
CN (1) CN103675707B (en)

Families Citing this family (23)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103995232B (en) * 2014-04-21 2017-01-04 中通客车控股股份有限公司 A kind of detection method of lithium iron phosphate dynamic battery group peak value charge-discharge performance
US9419314B2 (en) * 2014-05-12 2016-08-16 GM Global Technology Operations LLC Systems and methods for determining battery system power capability
CN104267354B (en) * 2014-10-29 2017-03-01 哈尔滨工业大学 A kind of peak power Forecasting Methodology of electrokinetic cell
US20160131701A1 (en) * 2014-11-07 2016-05-12 Ford Global Technologies, Llc Battery testing system and method
CN104391251B (en) * 2014-11-18 2017-04-26 郑州日产汽车有限公司 Data acquisition method of electric vehicle battery management system
CN104537268B (en) * 2015-01-19 2018-08-21 重庆长安汽车股份有限公司 A kind of battery maximum discharge power evaluation method and device
US10527681B2 (en) * 2015-03-31 2020-01-07 Gs Yuasa International Ltd. Degradation estimator for energy storage device, energy storage apparatus, input-output control device for energy storage device, and method for controlling input and output of energy storage device
CN104793048B (en) * 2015-04-14 2018-01-16 清华大学 The computational methods and device of adaptation loss power
CN106324507A (en) * 2015-06-26 2017-01-11 北汽福田汽车股份有限公司 Performance testing method and system of power battery
CN106646240A (en) * 2015-10-29 2017-05-10 宝山钢铁股份有限公司 Method for estimating maximum discharge power of lithium battery
CN106125000A (en) * 2016-08-18 2016-11-16 中国电力科学研究院 A kind of method of testing of lithium battery ohmic internal resistance based on dipulse electric current
CN106249170B (en) * 2016-08-31 2018-11-23 简式国际汽车设计(北京)有限公司 A kind of electrokinetic cell system power rating estimation method and device
CN107102271A (en) * 2017-05-25 2017-08-29 宁德时代新能源科技股份有限公司 The evaluation method of battery pack peak power, device and system
CN107436412B (en) * 2017-07-31 2020-03-27 成都雅骏新能源汽车科技股份有限公司 Method for estimating power of power battery based on self-learning
CN108363009B (en) * 2017-12-26 2020-01-14 浙江大学 Method for realizing online estimation of maximum allowable power of lithium ion battery
CN110927592A (en) * 2018-08-31 2020-03-27 华为技术有限公司 Method and device for estimating peak power of battery
CN110286326A (en) * 2018-11-23 2019-09-27 天津力神电池股份有限公司 The method of rapid evaluation lithium-ion-power cell pulse power charging and discharging capabilities
CN109633465A (en) * 2018-11-29 2019-04-16 北京交通大学 A kind of peak power method for rapidly testing of lithium ion battery
CN109613438A (en) * 2018-12-17 2019-04-12 欣旺达电动汽车电池有限公司 A kind of SOC-OCV relationship evaluation method
CN109799459B (en) * 2019-01-25 2021-02-12 欣旺达电子股份有限公司 Method for testing power of lithium ion power battery cell, testing device and storage medium
CN110048444B (en) * 2019-05-31 2020-07-31 闽江学院 Lead-acid battery fuzzy control method based on SOC state estimation
CN110261789A (en) * 2019-05-31 2019-09-20 蜂巢能源科技有限公司 The pulsed discharge power evaluation method and battery management system of power battery pack
CN110994053B (en) * 2019-12-18 2021-04-09 北京理工大学 Active management method and system for power battery performance

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH08123561A (en) * 1994-10-20 1996-05-17 Meidensha Corp Method and device for maximum output following control for photovoltaic power generation system
JP2000023390A (en) * 1998-07-02 2000-01-21 Honda Access Corp Power unit for vehicle
JP2002078206A (en) * 2000-08-29 2002-03-15 Nissin Electric Co Ltd Battery power storage system
CN102244393A (en) * 2010-05-12 2011-11-16 通用电气公司 System and method for photovoltaic plant power curve measurement and health monitoring
CN102306943A (en) * 2011-09-15 2012-01-04 河北工业大学 Lithium ion battery management system
CN102323553A (en) * 2011-05-31 2012-01-18 惠州市亿能电子有限公司 Method for testing battery peak power
CN102576055A (en) * 2009-10-16 2012-07-11 宝马股份公司 Method for determining and/or predicting the maximum performance capacity of a battery

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH08123561A (en) * 1994-10-20 1996-05-17 Meidensha Corp Method and device for maximum output following control for photovoltaic power generation system
JP2000023390A (en) * 1998-07-02 2000-01-21 Honda Access Corp Power unit for vehicle
JP2002078206A (en) * 2000-08-29 2002-03-15 Nissin Electric Co Ltd Battery power storage system
CN102576055A (en) * 2009-10-16 2012-07-11 宝马股份公司 Method for determining and/or predicting the maximum performance capacity of a battery
CN102244393A (en) * 2010-05-12 2011-11-16 通用电气公司 System and method for photovoltaic plant power curve measurement and health monitoring
CN102323553A (en) * 2011-05-31 2012-01-18 惠州市亿能电子有限公司 Method for testing battery peak power
CN102306943A (en) * 2011-09-15 2012-01-04 河北工业大学 Lithium ion battery management system

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
功率型锂离子动力电池的内阻特性;郭宏榆 等;《北京交通大学学报》;20111031;第35卷(第5期);119-123 *
动力电池组峰值功率估计算法研究;张彩萍 等;《系统仿真学报》;20100630;第22卷(第6期);1524-1527 *

Also Published As

Publication number Publication date
CN103675707A (en) 2014-03-26

Similar Documents

Publication Publication Date Title
Shen et al. Design and real-time controller implementation for a battery-ultracapacitor hybrid energy storage system
Wang et al. A method for state-of-charge estimation of LiFePO4 batteries at dynamic currents and temperatures using particle filter
Hannan et al. A review of lithium-ion battery state of charge estimation and management system in electric vehicle applications: Challenges and recommendations
Ouyang et al. A dynamic capacity degradation model and its applications considering varying load for a large format Li-ion battery
CN105676134B (en) A kind of SOH evaluation methods of vehicle lithium-ion power battery
Swierczynski et al. Lifetime Estimation of the Nanophosphate $\hbox {LiFePO} _ {4}\hbox {/C} $ Battery Chemistry Used in Fully Electric Vehicles
Ramadan et al. Extended kalman filter for accurate state of charge estimation of lithium-based batteries: a comparative analysis
CN102981125B (en) A kind of electrokinetic cell SOC method of estimation based on RC equivalent model
Zhang et al. Battery modelling methods for electric vehicles-A review
Chen et al. A new state-of-health estimation method for lithium-ion batteries through the intrinsic relationship between ohmic internal resistance and capacity
CN103941210B (en) The checking monitoring system of a kind of BMS and method thereof
CN102540084B (en) Method for determining a state of a rechargeable battery device in real time
Kim Nonlinear state of charge estimator for hybrid electric vehicle battery
Pei et al. Online peak power prediction based on a parameter and state estimator for lithium-ion batteries in electric vehicles
CN103197251B (en) A kind of discrimination method of dynamic lithium battery Order RC equivalent model
Jiang et al. Fundamentals and applications of lithium-ion batteries in electric drive vehicles
CN103412206B (en) A kind of automatic test pilot system of charging equipment of electric automobile of multi-state
Watrin et al. Multiphysical lithium-based battery model for use in state-of-charge determination
Lee et al. Comparison of passive cell balancing and active cell balancing for automotive batteries
CN102937704B (en) Method for identifying RC (resistor-capacitor) equivalent model of power battery
CN105954679B (en) A kind of On-line Estimation method of lithium battery charge state
CN101762800B (en) Battery managing system testing platform
Chen et al. Loss-minimization-based charging strategy for lithium-ion battery
Yang et al. A fractional-order model-based battery external short circuit fault diagnosis approach for all-climate electric vehicles application
Chiang et al. Online estimation of internal resistance and open-circuit voltage of lithium-ion batteries in electric vehicles

Legal Events

Date Code Title Description
PB01 Publication
SE01 Entry into force of request for substantive examination
C10 Entry into substantive examination
GR01 Patent grant
C14 Grant of patent or utility model
CP01 Change in the name or title of a patent holder

Address after: 100031 Xicheng District West Chang'an Avenue, No. 86, Beijing

Co-patentee after: Electric Power Research Institute of State Grid Shandong Electric Power Company

Patentee after: State Grid Co., Ltd.

Co-patentee after: National Network Intelligent Technology Co., Ltd.

Address before: 100031 Xicheng District West Chang'an Avenue, No. 86, Beijing

Co-patentee before: Electric Power Research Institute of State Grid Shandong Electric Power Company

Patentee before: State Grid Corporation

Co-patentee before: Shandong Luneng Intelligent Technology Co., Ltd.

Address after: 100031 Xicheng District West Chang'an Avenue, No. 86, Beijing

Co-patentee after: Electric Power Research Institute of State Grid Shandong Electric Power Company

Patentee after: State Grid Co., Ltd.

Co-patentee after: National Network Intelligent Technology Co., Ltd.

Address before: 100031 Xicheng District West Chang'an Avenue, No. 86, Beijing

Co-patentee before: Electric Power Research Institute of State Grid Shandong Electric Power Company

Patentee before: State Grid Corporation

Co-patentee before: Shandong Luneng Intelligent Technology Co., Ltd.

CP01 Change in the name or title of a patent holder
TR01 Transfer of patent right

Effective date of registration: 20210302

Address after: 100031 No. 86 West Chang'an Avenue, Beijing, Xicheng District

Patentee after: STATE GRID CORPORATION OF CHINA

Patentee after: ELECTRIC POWER RESEARCH INSTITUTE OF STATE GRID SHANDONG ELECTRIC POWER Co.

Patentee after: Shandong Luneng Software Technology Co.,Ltd. intelligent electrical branch

Address before: 100031 No. 86 West Chang'an Avenue, Beijing, Xicheng District

Patentee before: STATE GRID CORPORATION OF CHINA

Patentee before: ELECTRIC POWER RESEARCH INSTITUTE OF STATE GRID SHANDONG ELECTRIC POWER Co.

Patentee before: National Network Intelligent Technology Co.,Ltd.

TR01 Transfer of patent right