CN103675707A - Method for evaluating lithium ion battery peak power online - 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
- 238000000034 method Methods 0.000 title claims abstract description 16
- 238000011156 evaluation Methods 0.000 claims abstract description 14
- 230000008569 process Effects 0.000 claims abstract description 5
- 230000008859 change Effects 0.000 claims description 35
- 230000001052 transient effect Effects 0.000 claims description 32
- 238000012549 training Methods 0.000 claims description 15
- 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 5
- 238000004064 recycling Methods 0.000 claims description 3
- 230000005284 excitation Effects 0.000 claims description 2
- 238000012360 testing method Methods 0.000 description 7
- GNFTZDOKVXKIBK-UHFFFAOYSA-N 3-(2-methoxyethoxy)benzohydrazide Chemical compound COCCOC1=CC=CC(C(=O)NN)=C1 GNFTZDOKVXKIBK-UHFFFAOYSA-N 0.000 description 4
- 238000007599 discharging Methods 0.000 description 3
- 238000010586 diagram 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
- 238000013459 approach Methods 0.000 description 1
- 230000009286 beneficial 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
- 238000013461 design Methods 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
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
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Abstract
The invention discloses a method for evaluating lithium ion battery peak power online. The method comprises the following steps that influencing characteristic curves of the influence on lithium ion battery peak power by the charge state, Ohm internal resistance, temperature and sustainable time of a lithium ion battery are respectively built; all factors influencing the battery peak power are synthesized, and a battery peak power evaluation initial model is built; the battery peak power evaluation initial model is embedded into a vehicle-mounted terminal system, and the battery peak power evaluation initial model continues to be trained. The method has the advantages that the situations that due to the fact that the operation is not proper, and the battery is overcharged or overdischarged, the service life of the battery is shortened, and use cost is greatly increased are basically avoided through the method, the danger coefficient of the battery in the use process is lowered, and the reliability and stability of the operation of an electric vehicle are improved, and personal safety and equipment safety are guaranteed.
Description
Technical field
The present invention relates to lithium ion battery field, be specifically related to a kind of lithium ion battery peak power online evaluation method.
Background technology
Electrokinetic cell is as power source or the auxiliary power source of electric automobile, and its development is particularly important.Conventionally electric automobile has following basic demand to electrokinetic cell: high-energy-density, and high-specific-power, 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 are high, free from environmental pollution, and 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 continually and cross to put and can affect great on the lithium ion battery life-span.
The power characteristic of battery is weighed by peak power conventionally, 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 of weighing battery performance, particularly in high power applications occasion.Electric automobile, in actual motion, while its power use being left to larger allowance for protection battery, for meeting the power requirement of car load under different situations, need to increase number of battery cells, causes cost to strengthen, and battery weight volume is all excessive, affects the performance of car load; If the power designs of battery is smaller, may in use to over-charging of battery or mistake, put, in order sufficiently and reasonably to use and protect battery, the instantaneous input-output power that needs battery management system to provide 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 test, the test of FreedomCAR mixed pulses, Japanese JEVS test and China's 863 battery testing standards.These four kinds of methods are all mentioned and under different SOC, are carried out pulsed discharge, calculate peak power, peak power that can not real-time online prediction battery according to existing method of testing.
Summary of the invention
Object of the present invention is exactly in order to address the above problem, a kind of method of online evaluation battery peak power has been proposed, the electrokinetic cell that it can be realized using on car carries out rapid evaluation, 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 lithium ion battery peak power online evaluation method, comprises the following steps:
(1) set up respectively charge states of lithium ion battery, ohmic internal resistance, temperature and the sustainable time parameter influencing characteristic curve on battery peak power impact.
(2) choose lithium ion battery ohmic internal resistance, charge states of lithium ion battery and lithium ion battery temperature as the input parameter of power assessments initial model, choose the peak power of lithium ion battery 10s pulse as output parameter, utilize Matlab analysis of neural network algorithm to select ANFIS system made battery peak power assessment initial model, by adjusting membership function, change, initial model is carried out to parameter learning and model training.
(3) battery peak power assessment models is embedded to vehicle-mounted terminal system, according to the current charging peak power of lithium ion battery ohmic internal resistance, charge states of lithium ion battery and the lithium ion battery temperature parameter assessment lithium ion battery of on-line monitoring 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 to constant temperature oven, maintains battery constant at a certain temperature, battery is charged to charge cutoff voltage, standing; Again with preset multiplying power electric current to battery discharge to a certain state-of-charge, standing; Record the state-of-charge of current lithium ion battery, the temperature that records current lithium ion battery and open-circuit voltage OCV.
(b) to preset multiplying power electric current to battery discharge, if do not arrive battery discharge cut-off voltage, standing, record electric discharge starts previous moment lithium ion battery terminal voltage U
1, electric discharge finishes 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 the finish time
t2, electric current transient change value Δ I
t2.
(c) to preset multiplying power electric current, battery is charged, if do not arrive battery charge cutoff voltage, standing; Record respectively the voltage change Δ U under initial time in lithium ion cell charging process
t3, curent change value Δ I
t3with the voltage change Δ U under the finish time
t4, curent change value Δ I
t4; Meanwhile, the record charging lithium ion battery terminal voltage U zero hour
3, the charging lithium ion battery terminal voltage U finish time
4.
(d) battery is carried out to constant-current discharge, change battery charge state, repeating step (a)---step (c).
(e) adjust calorstat temperature, repeating step (a)---step (d).
According to following formula, calculate the electric discharge peak value power P of lithium ion battery under different temperatures, SOC
dischargewith charging peak 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
dischargebattery discharge internal resistance, R
regenbattery charging regeneration internal resistance, Δ U
tit
i(i=1,2,3,4) voltage change constantly, Δ I
tit
i(i=1,2,3,4) curent change value constantly, OCV is the open-circuit voltage of battery under current state, V
minminimum voltage while being battery discharge, V
maxmaximum voltage while being battery recycling, U
1, U
2be respectively electric discharge the zero hour and the finish time lithium ion battery terminal voltage, U
3, U
4be respectively the terminal voltage of the charging zero hour and the charging lithium ion battery finish time.
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 is according to input data output lithium ion battery peak power, reality is exported to 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: the initial cells peak power assessment models training is write to vehicle-mounted terminal system, lithium ion battery ohmic internal resistance, state-of-charge and temperature data to the monitoring of initial cells peak power assessment models Input Online, the charging peak 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.
In described step (d), according to different charge states of lithium ion batteries, adjust the default multiplier value of suitable electric current.
The invention has the beneficial effects as follows:
(1) use this method on-line prediction battery charging peak power and electric discharge peak power, and be applied on electric automobile car-mounted terminal, can substantially avoid putting because misoperation causes to overcharge or cross to battery the shorter battery life causing, the situations such as use cost increasing, reduce the danger coefficient in battery use procedure, improve the reliability and stability of electric automobile operation, guarantee the safety of the person and equipment.
(2) the charging peak power that the present invention can real-time estimate battery and electric discharge peak power, and it is shown in real time, can effectively improve battery and utilize power, lifting vehicle power performance.
(3) treat the battery of same model, only need to extract a small amount of sample and detect, can complete peak power characteristic, can obtain battery charging and discharging peak power initial mask, reduce the loss of test sample, effectively shorten detection time.
(4) the present invention can realize the assessment of the realtime power of electrokinetic cell in electric automobile during traveling process, more approaches electrokinetic cell actual motion state, and along with the increasing of image data, and 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 and discharging state;
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) set up the influence curve of peak power characteristic
Fig. 1～Fig. 4 is the schematic diagram of the embodiment of the present invention, and the electric battery that the high power 8Ah lithium manganate battery of take is composed in series is example, with reference to above-mentioned power assessments method explanation, completes 10s peak power specificity analysis.
When a) research lithium ion battery charge and discharge peak power and SOC are related to, battery is put into constant temperature oven, maintain battery temperature constant, battery is charged to charge cutoff voltage with 1C, standing 1 hour.
B) with 1C electric current to battery discharge 6min, to SOC be 90%, standing 1h, records temperature and the open-circuit voltage OCV of current lithium ion battery.
C) with 15C electric current to battery discharge 10s, if battery does not arrive discharge cut-off voltage, standing 40s, record electric discharge starts previous moment lithium ion battery terminal voltage U
1, electric discharge finishes 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 the finish time
t2, electric current transient change value Δ I
t2; Again with 5C electric current to the battery 10s that charges, if battery does not arrive charge cutoff voltage, standing 1h, record charging starts previous moment lithium ion battery terminal voltage U
3, charging finishes 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 the finish time
t4, electric current transient change value Δ I
t4.
D) take 1C electric current is 80% to battery constant-current discharge to SOC, and standing 1h records temperature and the open-circuit voltage OCV of current lithium ion battery.
E) repeating step c).
F) take 1C electric current is 70% to battery constant-current discharge to SOC, and standing 1h records temperature and the open-circuit voltage OCV of current lithium ion battery.
G) with 10C electric current to battery discharge 10s, if battery does not arrive discharge cut-off voltage, standing 40s, record electric discharge starts previous moment lithium ion battery terminal voltage U
1, electric discharge finishes 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 the finish time
t2, electric current transient change value Δ I
t2; Again with 10C electric current to the battery 10s that charges, if battery does not arrive charge cutoff voltage, standing 1h, record charging starts previous moment lithium ion battery terminal voltage U
3, charging finishes 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 the finish time
t4, electric current transient change value Δ I
t4.
H) take 1C electric current is 60% to battery constant-current discharge to SOC, standing 1h, and, record temperature and the open-circuit voltage OCV of current lithium ion battery, repeating step g).
I) take 1C electric current is 50% to battery constant-current discharge to SOC, standing 1h, and, record temperature and the open-circuit voltage OCV of current lithium ion battery, repeating step g).
J) take 1C electric current is 40% to battery constant-current discharge to SOC, standing 1h, and, record temperature and the open-circuit voltage OCV of current lithium ion battery, repeating step g).
K) take 1C electric current is 30% to battery constant-current discharge to SOC, and standing 1h records temperature and the open-circuit voltage OCV of current lithium ion battery, repeating step g).
L) with 5C electric current to battery discharge 10s, if battery does not arrive discharge cut-off voltage, standing 40s, record electric discharge starts previous moment lithium ion battery terminal voltage U
1, electric discharge finishes 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 the finish time
t2, electric current transient change value Δ I
t2; Again with 15C electric current to the battery 10s that charges, if battery does not arrive charge cutoff voltage, the charging of standing 1h record starts previous moment lithium ion battery terminal voltage U
3, charging finishes 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 the finish time
t4, electric current transient change value Δ I
t4.
M) take 1C electric current is 20% to battery constant-current discharge to SOC, and standing 1h records temperature and the open-circuit voltage OCV of current lithium ion battery.
N) repeating step l).
O) take 1C electric current is 10% to battery constant-current discharge to SOC, and standing 1h records temperature and the open-circuit voltage OCV of current lithium ion battery.
P) repeating step l).
Q) adjust calorstat temperature and be followed successively by-20 ℃ ,-10 ℃, 0 ℃, 10 ℃, 20 ℃, 30 ℃, 40 ℃, 50 ℃, repeat above-mentioned steps a)～p).
According to following formula, calculate the electric discharge peak value power P of lithium ion battery under different temperatures, SOC
dischargewith charging peak 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
dischargebattery discharge internal resistance, R
regenbattery charging regeneration internal resistance, Δ U
tit
i(i=1,2,3,4) voltage change constantly, Δ I
tit
i(i=1,2,3,4) curent change value constantly, OCV is the open-circuit voltage of battery under current state, V
minminimum voltage while being battery discharge, V
maxmaximum voltage while being battery recycling, U
1, U
2be respectively electric discharge the zero hour and the finish time lithium ion battery terminal voltage, U
3, U
4be respectively the terminal voltage of the charging zero hour and the charging lithium ion battery finish time.
Impact according to the different SOC of test data analysis on peak power, as shown in Figure 1; Analyze the impact of different internal resistances on peak power, as shown in Figure 2; Analyze the impact of different temperatures on peak power, as shown in Figure 3; Charging and discharging state on the impact of peak power as shown in Figure 4.
(2) set up peak power assessment initial model
ANFIS system requirements must be the Takagi-Sugeno system of zeroth order or single order output; And the input of system can be a plurality of variablees, but output can only be single variable, i.e. MISO type system, and output obtains by weighted mean de-fuzzy; The weight of strictly all rules is 1, requires the function number of output must equal fuzzy rules simultaneously.
If the peak power of electrokinetic cell 10s is regarded as to output variable, the SOC of battery, ohmic internal resistance and environment temperature are regarded as to input variable, from above-mentioned battery peak power analysis, can find out, 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, can adopt single order output.Estimation to 10s pulse peak power meets above-mentioned requirements, therefore, unlimited approximation capability according to ANFIS system to nonlinear system, can complete the estimation to the peak power of electrokinetic cell 10s pulse, therefore select ANFIS system made peak power assessment initial model, institute's established model schematic diagram as shown in Figure 5.
Data according to 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 cut-off error of combined training to be set to 5, iterative steps is set to 50 steps; The fuzzy interval of four input variables is divided 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 is respectively inputted membership function and changed as shown in Figure 6, and now system relative error is about 0.8%, meets permissible error requirement.
(3) model insertion, the training of assessment limit, limit
The initial cells peak power assessment models training is write to vehicle-mounted terminal system, the charging peak power of carving at this moment according to battery SOC, ohmic internal resistance, the temperature evaluation battery of on-line monitoring 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.
Although above-mentioned, by reference to the accompanying drawings the specific embodiment of the present invention is described; but be 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 modifications that creative work can make or distortion still in protection scope of the present invention.
Claims (5)
1. a lithium ion battery peak power online evaluation method, is characterized in that, comprises the following steps:
(1) set up respectively charge states of lithium ion battery, ohmic internal resistance, temperature and the sustainable time parameter influencing characteristic curve on battery peak power impact;
(2) choose lithium ion battery ohmic internal resistance, charge states of lithium ion battery and lithium ion battery temperature as the input parameter of power assessments initial model, choose the peak power of lithium ion battery 10s pulse as output parameter, utilize Matlab analysis of neural network algorithm to select ANFIS system made battery peak power assessment initial model, by adjusting membership function, change, initial model is carried out to parameter learning and model training;
(3) battery peak power assessment models is embedded to vehicle-mounted terminal system, according to the current charging peak power of lithium ion battery ohmic internal resistance, charge states of lithium ion battery and the lithium ion battery temperature parameter assessment lithium ion battery of on-line monitoring and electric discharge peak power, and continue training peak power assessment models.
2. a kind of lithium ion battery peak power online evaluation method as claimed in claim 1, is characterized in that, the concrete grammar of described step (1) is:
(a) lithium ion battery is placed in to constant temperature oven, maintains battery constant at a certain temperature, battery is charged to charge cutoff voltage, standing; Again with preset multiplying power electric current to battery discharge to a certain state-of-charge, standing; Record the state-of-charge of current lithium ion battery, the temperature that records current lithium ion battery and open-circuit voltage OCV;
(b) to preset multiplying power electric current to battery discharge, if do not arrive battery discharge cut-off voltage, standing, record electric discharge starts previous moment lithium ion battery terminal voltage U
1, electric discharge finishes 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 the finish time
t2, electric current transient change value Δ I
t2;
(c) to preset multiplying power electric current, battery is charged, if do not arrive battery charge cutoff voltage, standing; Record respectively the voltage change Δ U under initial time in lithium ion cell charging process
t3, curent change value Δ I
t3with the voltage change Δ U under the finish time
t4, curent change value Δ I
t4; Meanwhile, the record charging lithium ion battery terminal voltage U zero hour
3, the charging lithium ion battery terminal voltage U finish time
4;
(d) battery is carried out to constant-current discharge, change battery charge state, repeating step (a)---step (c);
(e) adjust calorstat temperature, repeating step (a)---step (d);
According to following formula, calculate the electric discharge peak value power P of lithium ion battery under different temperatures, SOC
dischargewith charging peak 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
dischargebattery discharge internal resistance, R
regenbattery charging regeneration internal resistance, Δ U
tit
i(i=1,2,3,4) voltage change constantly, Δ I
tit
i(i=1,2,3,4) curent change value constantly, OCV is the open-circuit voltage of battery under current state, V
minminimum voltage while being battery discharge, V
maxmaximum voltage while being battery recycling, U
1, U
2be respectively electric discharge the zero hour and the finish time lithium ion battery terminal voltage, U
3, U
4be respectively the terminal voltage of the charging zero hour and the charging lithium ion battery finish time.
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 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 is according to input data output lithium ion battery peak power, reality is exported to 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.
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: the initial cells peak power assessment models training is write to vehicle-mounted terminal system, lithium ion battery ohmic internal resistance to the monitoring of initial cells peak power assessment models Input Online, state-of-charge and temperature data, the charging peak 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.
5. a kind of lithium ion battery peak power online evaluation method as claimed in claim 2, is characterized in that, in described step (d), according to different charge states of lithium ion batteries, adjusts the default multiplier value of suitable electric current.
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| CN104267354A (en) * | 2014-10-29 | 2015-01-07 | 哈尔滨工业大学 | Peak power prediction method for power battery |
| CN104391251A (en) * | 2014-11-18 | 2015-03-04 | 郑州日产汽车有限公司 | Data acquisition method of electric vehicle battery management system |
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