CN103675707B - Lithium ion battery peak power online evaluation method - Google Patents
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- 229910001416 lithium ion Inorganic materials 0.000 title claims abstract description 95
- HBBGRARXTFLTSG-UHFFFAOYSA-N Lithium ion Chemical compound [Li+] HBBGRARXTFLTSG-UHFFFAOYSA-N 0.000 title claims abstract description 93
- 238000011156 evaluation Methods 0.000 title claims abstract description 14
- 238000012549 training Methods 0.000 claims abstract description 12
- 238000000034 method Methods 0.000 claims abstract description 11
- 230000001052 transient effect Effects 0.000 claims description 40
- 238000013528 artificial neural network Methods 0.000 claims description 8
- 230000006870 function Effects 0.000 claims description 7
- 230000008929 regeneration Effects 0.000 claims description 6
- 238000011069 regeneration method Methods 0.000 claims description 6
- 238000004458 analytical method Methods 0.000 claims description 5
- 238000012544 monitoring process Methods 0.000 claims description 4
- 238000004064 recycling Methods 0.000 claims description 3
- 230000005284 excitation Effects 0.000 claims description 2
- 230000009286 beneficial effect Effects 0.000 abstract description 2
- 238000012360 testing method Methods 0.000 description 6
- GNFTZDOKVXKIBK-UHFFFAOYSA-N 3-(2-methoxyethoxy)benzohydrazide Chemical compound COCCOC1=CC=CC(C(=O)NN)=C1 GNFTZDOKVXKIBK-UHFFFAOYSA-N 0.000 description 4
- 238000010586 diagram Methods 0.000 description 2
- 238000007599 discharging Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000003912 environmental pollution Methods 0.000 description 2
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 description 1
- 235000015429 Mirabilis expansa Nutrition 0.000 description 1
- 244000294411 Mirabilis expansa Species 0.000 description 1
- 230000003679 aging effect Effects 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 238000013480 data collection Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- QHGJSLXSVXVKHZ-UHFFFAOYSA-N dilithium;dioxido(dioxo)manganese Chemical compound [Li+].[Li+].[O-][Mn]([O-])(=O)=O QHGJSLXSVXVKHZ-UHFFFAOYSA-N 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000003780 insertion Methods 0.000 description 1
- 230000037431 insertion Effects 0.000 description 1
- 229910052744 lithium Inorganic materials 0.000 description 1
- 235000013536 miso Nutrition 0.000 description 1
- 238000010998 test method Methods 0.000 description 1
- 238000005303 weighing Methods 0.000 description 1
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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
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.
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| 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 |
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| US10035426B2 (en) * | 2016-03-10 | 2018-07-31 | Ford Global Technologies, Llc | Battery power management in hybrid vehicles |
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| CN113910976B (en) * | 2021-09-29 | 2023-04-28 | 北汽福田汽车股份有限公司 | Power battery control method and device of electric automobile and electric automobile |
| CN114264966A (en) * | 2021-12-06 | 2022-04-01 | 阳光储能技术有限公司 | Method and device for evaluating state of charge of battery, terminal device and medium |
| CN114523878B (en) * | 2022-03-29 | 2023-10-24 | 蜂巢能源科技股份有限公司 | Lithium ion battery lithium precipitation safety early warning method and device |
| CN115993539B (en) * | 2023-03-22 | 2023-06-06 | 国民技术股份有限公司 | Method and device for predicting SOP of battery based on real-time temperature |
Citations (7)
| 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 |
-
2013
- 2013-12-13 CN CN201310681766.6A patent/CN103675707B/en active Active
Patent Citations (7)
| 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)
| 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;《***仿真学报》;20100630;第22卷(第6期);1524-1527 * |
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