CN107329094A - Power battery state of health estimation method and device - Google Patents
Power battery state of health estimation method and device Download PDFInfo
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- CN107329094A CN107329094A CN201710731729.XA CN201710731729A CN107329094A CN 107329094 A CN107329094 A CN 107329094A CN 201710731729 A CN201710731729 A CN 201710731729A CN 107329094 A CN107329094 A CN 107329094A
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- 238000000034 method Methods 0.000 title claims abstract description 49
- 230000036541 health Effects 0.000 title abstract description 6
- 230000032683 aging Effects 0.000 claims abstract description 41
- 238000007600 charging Methods 0.000 claims description 88
- 230000003862 health status Effects 0.000 claims description 56
- 238000010606 normalization Methods 0.000 claims description 50
- 238000012545 processing Methods 0.000 claims description 25
- 230000007935 neutral effect Effects 0.000 claims description 23
- 238000011156 evaluation Methods 0.000 claims description 22
- 230000005611 electricity Effects 0.000 claims description 21
- 238000003062 neural network model Methods 0.000 claims description 4
- 238000013528 artificial neural network Methods 0.000 abstract description 18
- 238000004364 calculation method Methods 0.000 abstract description 3
- 210000004027 cell Anatomy 0.000 description 98
- 229910001416 lithium ion Inorganic materials 0.000 description 32
- HBBGRARXTFLTSG-UHFFFAOYSA-N Lithium ion Chemical compound [Li+] HBBGRARXTFLTSG-UHFFFAOYSA-N 0.000 description 30
- 238000003483 aging Methods 0.000 description 27
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- 238000006243 chemical reaction Methods 0.000 description 5
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- 238000005259 measurement Methods 0.000 description 4
- 238000003860 storage Methods 0.000 description 4
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 description 3
- 230000008901 benefit Effects 0.000 description 3
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- 238000004590 computer program Methods 0.000 description 3
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- 238000005516 engineering process Methods 0.000 description 3
- 230000002427 irreversible effect Effects 0.000 description 3
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 2
- 125000004122 cyclic group Chemical group 0.000 description 2
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- 208000032953 Device battery issue Diseases 0.000 description 1
- SOXUFMZTHZXOGC-UHFFFAOYSA-N [Li].[Mn].[Co].[Ni] Chemical compound [Li].[Mn].[Co].[Ni] SOXUFMZTHZXOGC-UHFFFAOYSA-N 0.000 description 1
- 239000011149 active material Substances 0.000 description 1
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
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Abstract
The application provides a method and a device for estimating the state of health of a power battery, wherein the method comprises the following steps: normalizing the voltage curves corresponding to the power battery in N aging states to generate N normalized voltage curves, wherein N is a positive integer greater than 1; selecting a reference curve from the N normalized voltage curves according to a preset standard; fitting the reference curve by using a neural network to obtain a voltage curve of the power battery; and estimating the state of health of the power battery according to the voltage curve of the power battery. Therefore, the SOH of the power battery is estimated by a method of fitting a voltage curve, the estimation accuracy is high, the calculation amount is small, the cost is low, the operation mode is simple and easy to realize, and the influence of the overcharge and the overdischarge of the battery on the service life of the battery is avoided.
Description
Technical field
The application is related to field of new energy technologies, more particularly to a kind of electrokinetic cell health status evaluation method and device.
Background technology
With being further exacerbated by for expanding economy and environmental problem and energy problem, the environmental consciousness of people gradually increases
By force, the electric automobile using vehicle power as power also gradually receives the favor of people.Lithium ion battery is used as pure electric automobile
Key energy unit, its performance directly affects the performance indications of pure electric automobile.In order to ensure lithium ion battery is extremely multiple
Can be safe and reliable under miscellaneous vehicle running environment and efficiently run, to health status (the State of of electrokinetic cell
Health, abbreviation SOH) it is monitored and seems necessary.
Existing power battery SOH estimation method, can estimate the reality of battery first with systematic parameter evaluation method
Capacity and battery ohmic internal resistance, then handle the actual capacity and ohmic internal resistance of estimation using redundancy processing method, recycle real
Border capacity and ohmic internal resistance estimate the health status of battery respectively, and it is true by the relation weighting processing between them to obtain battery
Health status, then using the health status value of filtration combined weighted processing method treatment of battery.However, this mode, is present as follows
Drawback：1) SOH of off-line measurement vehicle mounted dynamic battery is needed, operate inconvenience；2) it is likely to result in overcharging and mistake for battery
Put, influence the service life of battery；3) it is too high to electric current, voltage, temperature sampling precision required precision, realize engineer applied cost
It is larger；4) error of the sampling precision in vehicle operating mode under high current is not considered.
The content of the invention
The application is intended at least solve one of technical problem in correlation technique to a certain extent.
Therefore, the application proposes a kind of electrokinetic cell health status evaluation method, realize by being fitted voltage curve
Method estimates electrokinetic cell SOH, and not only estimation precision is high, operand is small, cost is low, and mode of operation is simple, easily realize, keeps away
The influence for overcharging and crossing battery of being rivals in a contest of battery is exempted from.
The application also proposes a kind of electrokinetic cell health status estimation device.
The application first aspect embodiment proposes a kind of electrokinetic cell health status evaluation method, including：To described dynamic
Power battery corresponding voltage curve under N number of ageing state is normalized, and generates N bar normalized voltage curves, wherein,
N is the positive integer more than 1；According to default standard, datum curve is chosen from the N bars normalized voltage curve；Utilize god
Through network, processing is fitted to the datum curve, the voltage curve of the electrokinetic cell is obtained；According to the electrokinetic cell
Voltage curve, estimate the health status of the electrokinetic cell.
The electrokinetic cell health status evaluation method that the embodiment of the present application is provided, first to electrokinetic cell in N number of aging shape
Corresponding voltage curve is normalized under state, generates N bar normalized voltage curves, then bent from N bars normalized voltage
Datum curve is chosen in line, and utilizes neutral net, processing is fitted to datum curve, the voltage for obtaining electrokinetic cell is bent
Line, so as to the voltage curve according to electrokinetic cell, estimates the health status of electrokinetic cell.Hereby it is achieved that passing through fitting
The method estimation electrokinetic cell SOH of voltage curve, not only estimation precision is high, operand is small, cost is low, and mode of operation it is simple,
Easily realize, it is to avoid the influence for overcharging and crossing battery of being rivals in a contest of battery.
The second face of the application embodiment proposes a kind of electrokinetic cell health status estimation device, including：First processing mould
Block, for corresponding voltage curve to be normalized under N number of ageing state to the electrokinetic cell, generates N bar normalizings
Change voltage curve, wherein, N is the positive integer more than 1；Module is chosen, for according to default standard, from N bars normalization
Datum curve is chosen in voltage curve；Second processing module, for utilizing neutral net, place is fitted to the datum curve
Reason, obtains the voltage curve of the electrokinetic cell；Estimation block, for the voltage curve according to the electrokinetic cell, estimates institute
State the health status of electrokinetic cell.
The electrokinetic cell health status estimation device that the embodiment of the present application is provided, first to electrokinetic cell in N number of aging shape
Corresponding voltage curve is normalized under state, generates N bar normalized voltage curves, then bent from N bars normalized voltage
Datum curve is chosen in line, and utilizes neutral net, processing is fitted to datum curve, the voltage for obtaining electrokinetic cell is bent
Line, so as to the voltage curve according to electrokinetic cell, estimates the health status of electrokinetic cell.Hereby it is achieved that passing through fitting
The method estimation electrokinetic cell SOH of voltage curve, not only estimation precision is high, operand is small, cost is low, and mode of operation it is simple,
Easily realize, it is to avoid the influence for overcharging and crossing battery of being rivals in a contest of battery.
Brief description of the drawings
Of the invention above-mentioned and/or additional aspect and advantage will become from the following description of the accompanying drawings of embodiments
Substantially and be readily appreciated that, wherein：
Fig. 1 is the lithium ion battery ageing cycle charging and discharging currents schematic diagram of the application one embodiment；
Fig. 2 be the application one embodiment different temperatures under battery actual capacity with ageing cycle number of times attenuation ratio compared with
Figure；
Fig. 3 be the application one embodiment different degree of agings under the internal resistance of cell with battery SOC change compare figure；
Fig. 4 be the application one embodiment different degree of agings under charging voltage curve ratio relatively scheme；
Fig. 5 be the application one embodiment different degree of agings under discharge voltage curve ratio relatively scheme；
Fig. 6 is the flow chart of the electrokinetic cell health status evaluation method of the application one embodiment；
Fig. 7 is the electrokinetic cell normalization charging voltage curve map of the application one embodiment；
Fig. 8 is the electrokinetic cell normalization discharge voltage profile figure of the application one embodiment；
Fig. 9 is that the normalization charging voltage curve ratio under the different ageing cycle number of times of the application one embodiment is relatively schemed；
Figure 10 is that the normalization discharge voltage profile under the different ageing cycle number of times of the application one embodiment compares figure；
Figure 11 is artificial neuron's model structure of the application one embodiment；
Figure 12 is the basic block diagram of the single-input single-output multilayer neutral net forward of the application one embodiment；
Figure 13 is the BP neural network training process figure of the application one embodiment；
Figure 14 is the BP neural network training error change curve of the application one embodiment；
Figure 15 is the BP neural network sample value of the application one embodiment and the comparison diagram of trained values；
Figure 16 is the 593rd charging cycle SOH of electrokinetic cell of the application one embodiment estimation curve；
Figure 17 is that the electrokinetic cell health status of the application one embodiment estimates the structural representation of device.
Embodiment
Embodiments herein is described below in detail, the example of the embodiment is shown in the drawings, wherein from beginning to end
Same or similar label represents same or similar element or the element with same or like function.Below with reference to attached
The embodiment of figure description is exemplary, it is intended to for explaining the application, and it is not intended that limitation to the application.
Specifically, existing power battery SOH estimation method, can estimate battery first with systematic parameter evaluation method
Actual capacity and battery ohmic internal resistance, the actual capacity and ohmic internal resistance of estimation are then handled using redundancy processing method, then
Estimate the health status of battery respectively using actual capacity and ohmic internal resistance, and electricity is obtained by the relation weighting processing between them
The real health status in pond, then using the health status value of filtration combined weighted processing method treatment of battery.This mode, is present as follows
Drawback：1) SOH of off-line measurement vehicle mounted dynamic battery is needed, operate inconvenience；2) it is likely to result in overcharging and mistake for battery
Put, influence the service life of battery；3) it is too high to electric current, voltage, temperature sampling precision required precision, realize engineer applied cost
It is larger；4) error of the sampling precision in vehicle operating mode under high current is not considered.
Various embodiments of the present invention are in view of the above-mentioned problems, propose a kind of electrokinetic cell health status evaluation method, first to dynamic
Power battery corresponding voltage curve under N number of ageing state is normalized, and generates N bar normalized voltage curves, then
Datum curve is chosen from N bar normalized voltage curves, and utilizes neutral net, processing is fitted to datum curve, is obtained
The voltage curve of electrokinetic cell, so as to the voltage curve according to electrokinetic cell, estimates the health status of electrokinetic cell.By
This, realizes and estimates electrokinetic cell SOH by the method for being fitted voltage curve, and not only estimation precision is high, operand is small, cost
It is low, and mode of operation is simple, easily realize, it is to avoid the influence for overcharging and crossing battery of being rivals in a contest of battery.
Below with reference to the accompanying drawings the electrokinetic cell health status evaluation method and device of the embodiment of the present invention described.
For clear explanation scheme provided in an embodiment of the present invention, the chemical reaction from inside battery is to electrokinetic cell first
Capacity attenuation mechanism analyzed.The embodiment of the present invention is illustrated by taking lithium-ion-power cell as an example.
Lithium ion battery ideally, in addition to the abjection and insertion of lithium ion between both positive and negative polarity, does not occur other
Any reaction, will not occur the irreversible consumption of lithium ion.But actual lithium ion battery, there is side reaction at any moment
Occur and different degrees of irreversible consumption, such as active material dissolving, electrolyte decomposition and lithium metal deposition.Actual electricity
Cell system, it is any that the side reaction of lithium ion can be lost in charge and discharge process, it is likely to change the balance of battery capacity.
Once this change occurs, that this changes is irreversible, and growth that can be over time and Operation mode cycle are cumulative,
And battery performance generation is had a strong impact on.Specifically include：
1) dissolving of positive electrode causes disproportionated reaction to cause battery capacity to be lost, such as Mn dissolving.
2) phase change of positive electrode：Electrode, which is undergone phase transition, during deintercalation as normal such as lithium ion causes host's lattice variations,
These changes reduce particle and electrode and intergranular electrochemical reaction；For another example electrode is occurred when over-charging of battery or mistake are put
Phase transformation cause the reduction of positive electrode reversible capacity, be also the one of the main reasons of capacity attenuation.
3) reduction of electrolyte：In charging, because electrolyte the unstability of carbon-containing electrode occurs reduction reaction,
Electrolyte reduction consumes solvent and electrolyte, and the capacity and cycle life of lithium ion battery are had undesirable effect.
4) caused capacitance loss is overcharged：When over-charging of battery, lithium ion will be deposited on negative electrode active material surface.
Such result is that on the one hand reversible active lithium-ion number is reduced, and the lithium metal on the other hand deposited is easily in electrolyte
In salt and the molecule of solvent chemically react, the chemical substance such as lithium fluoride (LiF) can block electrode hole, ultimately result in battery
The loss and the decline in life-span of capacity.
5) solid electrolyte interface caused by high temperature etc. (Solid Electrolyte Interface, abbreviation SEI) film point
Solution：The negative material of lithium ion battery typically uses graphite, in the charge state, when temperature is more than 60 degrees Celsius, negative pole table
The SEI films in face can be destroyed, and active lithium-ion is lost.When charging again, SEI films are re-formed.So multiple high temperature fills
Electricity, SEI films are constantly destroyed and rebuild, and active lithium-ion is constantly lost, and cause the decay of battery capacity to accelerate.
6) self discharge is lost：It is electric caused by the spontaneous chemical reaction of inside battery when battery is not at working condition
Tankage fails, and the capacity lost is largely reversible, directly affects the shelf life of battery.
7) current collector material is corroded：The corrosion or local corrosion phenomenon occurred in collection liquid surface can cause battery
Electrode reaction resistance increases and internal resistance increases, so that the efficiency for charge-discharge for directly contributing lithium ion battery is low and capacitance loss.
The use from battery is to factors pair such as charging/discharging voltage, charging and discharging currents, depth of discharge and temperature below
The influence of battery capacity is analyzed, and proposes several common counters of lithium ion battery life-span decline.
In practice, the factor of influence battery is relatively more, including battery types, temperature, environment etc..
1) influence of the charging/discharging voltage to battery life：Over-charging of battery refers to that when battery charges battery reaches limitation electricity
Pressure but continues to charge, and this causes metal-lithium ion to be deposited on electrode surface, hinders the motion of reactive compound, cell reaction speed
Rate lowers, simultaneously because the blocking action of barrier film increases the internal resistance of cell, accelerates electrode polarization, so as to be produced not to battery capacity
Reversible damage.Battery is crossed to put equally to be had than large effect to battery life, and research shows that capacity is minimum in power battery pack
Cell determine the overall performance quality of battery pack, and this inconsistency is easier the guiding discharge point phase and crosses the hair put
It is raw, make battery vicious circle accelerated ageing.
2) influence of the temperature to battery life：Temperature often raises 1 DEG C, lithium ion battery failure speed increase about 7%.Work as lithium
When ion battery environment temperature is relatively low, inside battery catalytic activity is relatively low, and the viscosity and resistance of electrolyte also just compare high, lithium
The activity reduction of ion, so as to cause the chemical reaction of inside battery to be difficult to carry out.When environment temperature is reduced to below 0 DEG C,
Battery can the capacity that can send only have under normal temperature 50% or so, when environment temperature is reduced to the lowest limit temperature of battery
When spending, battery possibly even fails.In contrast, when environment temperature is too high, if battery radiating is bad, high temperature can destroy electricity
Chemical balance inside pond, accelerates the corrosion and ageing of battery material, so as to reduce battery life.
3) influence of the depth of discharge (Depthof Discharge, abbreviation DOD) to battery life：Residual capacity (State
Of Charge, abbreviation SOC) application be usually different, relative batteries cycle life be also it is different, electric discharge
Depth is bigger, and the cycle life influence on battery is more serious.
4) influence of the charge-discharge magnification to battery life：In the case of high current charge-discharge, battery temperature rise is obvious,
Battery polarization phenomenon is serious, and battery balancing system is destroyed, so as to influence the life-span of battery.
In the ageing process of lithium-ion-power cell, the performance of lithium ion battery progressively decays with cycle-index.It is main
The reduction of capacity, the rise of charging platform, the reduction of discharge platform, internal resistance increase, power drop can actually be filled by showing as battery
Increase of low and charging interval etc..Experiment gathered data during these battery charging and discharging agings, can serve as weighing apparatus
Measure the parameter of battery SOH situations.
In embodiments of the present invention, the circulation of the nickel manganese cobalt lithium ion battery (LiNMC) produced with SANYO GS company is old
Change data instance and carry out analytic explanation.The major parameter that LiNMC lithium ion batteries producer is set is as shown in table 1：
The main specifications parameter of the LiNMC batteries of table 1
In actually demarcation, the initial capacity of battery and factory-designed rated capacity are different, the embodiment of the present invention
Initial capacity 0.9264Ah using measuring is rated capacity, and the ageing cycle test of battery is as shown in Figure 1.In each charge and discharge
In electricity circulation, LiNMC batteries are with 1C constant currents charge and discharge to blanking voltage.
1) battery actually can charge/discharge capacity reduction
With the aging of battery, under identical charge-discharge mechanism lithium ion battery actually can charge and discharge obtain capacity and be decreased obviously, such as
Shown in Fig. 2, before 500 circulations, the speed of the actual active volume decay of battery is slower, battery capacity after 500 circulations
Rate of decay increase.And with the reduction of temperature, the rate of ageing increase of battery.After about 800 circulations, battery it is available
Electricity accounts for the ratio of specified electric quantity less than 80%, and aging out of use index has been reached in electric automobile practical application.
2) the internal resistance increase of lithium ion battery
With the increase of cycle-index, the internal resistance of battery is slowly varying, under different degree of agings, the internal resistance of cell with
SOC variation tendency, as shown in Figure 3.
Internal resistance can simply be described as with battery SOC and SOH relation, with the aging of battery, the healthy shape of battery
State declines, internal resistance increase, while internal resistance increases also with SOC decline.Height is presented in internal resistance and SOH and SOC relation
It is non-linear.In practice, obvious change just occurs in battery capacity decay at least more than 30% in the ohmic internal resistance of battery,
And IEEE regulation battery capacities drop to 80% and just should more renew battery, meanwhile, the internal resistance of lithium ion battery is small-signal, accurate
Really measure relatively difficult, nowadays the internal resistance measurement instrument of each manufacturer production to the difference of the measured value of same battery also than larger.
So the estimation that the method for being monitored or being recognized by internal resistance carries out cell health state is relatively difficult.
3) the charging voltage platform rise of lithium ion battery
In cyclic process, significant change can also occur therewith for charging platform.As shown in figure 4, entering under 22 DEG C of environment temperatures
Row ageing cycle is tested, and with the progress of circulation, charging voltage platform is being stepped up, under 1C charging current effect, electricity
Pond battery reaches that blanking voltage 4.1V time is shorter and shorter, and the electricity that constant-current charge can be filled with is fewer and fewer.
4) discharge voltage plateau of lithium ion battery reduces
Discharge voltage plateau and charging voltage platform are on the contrary, discharge voltage plateau is reduced with the progress of cycle-index.
As shown in figure 5, the reduction of discharge voltage plateau, is on the one hand due to the pressure drop increase in the electric internal resistance increase of cell degradation, internal resistance,
On the other hand be battery it is actual can discharge and recharge reduce, cause the increase of battery actual discharge multiplying power.These are also to cause battery
The reason for charging voltage platform is raised.
As platform is reduced, battery discharge capability under high current operating mode can be reduced, corresponding to the power water of electric automobile
Flat to decline, the electricity that lithium ion battery is released under the limitation of identical discharge cut-off voltage reduces；Average electricity in discharge process
Pressure drop is low, as shown in table 2.
Average discharge volt compares under the different degree of agings of table 2
By above-mentioned analysis, compared to the internal resistance and impedance of battery, the voltage data bag in battery charge and discharge process
SOC and SOH information containing more batteries, also relatively more accurate and cost is low for the measurement of cell voltage in addition, is more suitable for using
In battery SOH estimation.
Below in conjunction with the accompanying drawings, electrokinetic cell health status evaluation method provided in an embodiment of the present invention is illustrated.
Fig. 6 is the schematic flow sheet of the electrokinetic cell health status evaluation method of one embodiment of the invention.
As shown in fig. 6, the electrokinetic cell health status evaluation method includes：
Step 601, to electrokinetic cell, corresponding voltage curve is normalized under N number of ageing state, generates N bars
Normalized voltage curve, wherein, N is the positive integer more than 1.
Wherein, electrokinetic cell health status evaluation method provided in an embodiment of the present invention, can be carried by the embodiment of the present invention
The electrokinetic cell health status estimation device of confession, hereinafter referred to as estimates that device is performed.
Specifically, step 601 can be accomplished by the following way：
To electrokinetic cell under N number of ageing state corresponding N bars charging voltage curve, and N bars discharge voltage profile difference
It is normalized.
It is understood that during battery charging and discharging, the curve that battery terminal voltage changes with time is at certain
Be in degree it is closely similar, therefore, in embodiments of the present invention, can be using time shaft length and magnitude of voltage dimensional variation
Method, the charging voltage of battery and discharge voltage profile are normalized.
When implementing, the starting voltage that battery can charge is labeled as Voc, and the blanking voltage of charging is designated as Vec, put
The starting voltage of electricity is designated as Vod, and the blanking voltage of electric discharge is designated as Ved, and the time started is all set to To, and the end time is all set to Te,
Beginning and ends of the Voc and Vec for voltage axis under charging operating mode is chosen respectively, and it is voltage axis under electric discharge operating mode to choose Vod and Ved
Beginning and end.Then voltage axis is normalized with Vec-Voc and Vod-Ved respectively, then again returned time shaft with Te-To
One changes, and is shown below.
Charging voltage is normalized：
Va=(V-Voc)/(Vec-Voc) (1)
Discharge voltage is normalized：
Va=(V-Ved)/(Vod-Ved) (2)
Time coordinate is normalized：
Ta=(T-To)/(Te-To) (3)
Wherein, Va is the charging/discharging voltage after normalization, and Ta is the time after normalization, and V is actual samples voltage, and T is
The actual samples time.
In the embodiment of the present invention, by the experimental data of above-mentioned LiNMC lithium ion batteries, the charging that can obtain battery rises
Beginning Voc voltage is 3.1V, and charge cutoff voltage Vec is 4.1V, and discharge inception voltage Vod is 3.9V, and discharge cut-off voltage Ved is
2.6V, by the normalization processing method of the embodiment of the present invention, can respectively obtain the 389th charging as shown in Figure 7 and Figure 8
With the voltage curve after the 389th electric discharge normalization.
After charging/discharging voltage curve is normalized it can be seen from Fig. 7 and Fig. 8, the beginning of battery charge and discharge process and knot
All got up by corresponding constraint at beam two ends.For the charging voltage curve after normalization, charging voltage and charging interval are all
Since 0, terminate to 1.And the discharge voltage profile after normalizing, discharge voltage is since 1, to 0 end, and discharge time is still
Since 0, terminate to 1.
Step 602, according to default standard, datum curve is chosen from N bar normalized voltage curves.
Wherein, default standard, for characterizing the rule that datum curve is chosen from N bar normalized voltage curves.
Specifically, step 602 can be accomplished by the following way：
The area S that N bars normalization charging voltage curve is surrounded with time shaft is determined respectivelyci, and N bars normalization discharge voltage
The area S that curve is surrounded with time shaftfi, wherein i is the positive integer more than or equal to 1, and less than or equal to N；
Respectively according to each SciValue and each SfiValue, determine N bars normalization charging voltage curve between uniformity and N
Uniformity between bar normalization discharge voltage profile；
It is determined that the N bars normalization charging voltage curve between uniformity, than the N bars normalize discharge voltage profile
Between uniformity it is good when, determine that N bars normalization charging voltage curve includes the datum curve.
It is understood that with the recycling of lithium-ion-power cell, due to battery itself some factors or
The improper operation of some of battery in use, can cause battery actually can discharge and recharge reduction, cause the health of battery
Degree SOH declines.
In embodiments of the present invention, it is necessary to redefine SOH.The SOH of the embodiment of the present invention refers to battery in an aging
(from the lower blanking voltage of charging to upper blanking voltage, or being discharged in state of cyclic operation from lower blanking voltage to upper blanking voltage) to fill
The ratio of specified electric quantity when the maximum electricity and battery for entering or releasing dispatch from the factory.Therefore, under the SOH value of battery different times,
Voltage curve relative uniformity degree after normalization seems extremely important.
In order to assess under different degree of agings (i.e. different SOH values), the uniformity journey after the normalization of charging/discharging voltage curve
Degree, the embodiment of the present invention is using the method for comparing the size that voltage curve and time shaft are surrounded after normalization.Due to reality
The charging/discharging voltage data point for testing collection is discrete discontinuous, therefore when reference area, can be inserted using linear
The computational methods of value, more can obtain accurately and easily result, calculation formula is as follows：
According to the size for calculating the area that curve is surrounded after normalization, charging voltage curve after normalization can be evaluated respectively
With the quality of discharge voltage profile uniformity.
Specifically, when the size difference that voltage curve is surrounded after the normalization of different cycle-indexes is smaller, normalization
The uniformity of voltage curve is preferable afterwards.
Charging voltage curve and discharge voltage profile uniformity after being normalized with reference to instantiation to above-mentioned evaluation
The process of quality is illustrated.
Specifically, in being tested to the ageing cycle of above-mentioned LiNMC lithium ion batteries, can be by environment temperature control 22
DEG C, using 1C constant currents charge and discharge to battery cutoff voltage, choose experimental data the 17th, 173,267,389,491,593,673,
777th, 884 charging/discharging voltage curves are normalized, and the charging voltage curve and discharge voltage profile after normalization are respectively such as
Shown in Fig. 9 and Figure 10.
Charging voltage curve and discharge voltage profile after being normalized it can be seen from Fig. 9 and Figure 10 have good one
Cause property.From the tendency of Fig. 9 and Figure 10 curve can be seen that relative to normalization discharge voltage profile for, charging voltage
The uniformity of curve is more preferable.
By using the method for mathematical linear difference, the area that every normalized curve and transverse axis surrounded can be calculated big
Small, result and area the relative ratio difference of calculating are as shown in Table 3 and Table 4.
Charging voltage normalized curve consistency analysis under the different cycle-indexes of table 3
| Period | SOH | Area | It is filled with electricity (Ah) | Area ratio (%) |
| 17 | 0.7437 | 0.8241 | 0.68895 | 5.31 |
| 173 | 0.6485 | 0.8451 | 0.60081 | 2.90 |
| 267 | 0.5863 | 0.8564 | 0.54317 | 1.60 |
| 389 | 0.5406 | 0.8645 | 0.50080 | 0.67 |
| 491 | 0.4993 | 0.8703 | 0.46252 | 0 |
| 593 | 0.4509 | 0.8769 | 0.41769 | 0.79 |
| 673 | 0.4026 | 0.8815 | 0.37295 | 1.29 |
| 777 | 0.3792 | 0.8823 | 0.35125 | 1.38 |
| 884 | 0.3328 | 0.8845 | 0.30828 | 1.63 |
Discharge voltage normalized curve is analyzed under the different cycle-indexes of table 4
| Period | SOH | Area | Release electricity (Ah) | Area ratio (%) |
| 17 | 0.7444 | 0.8235 | 0.68964 | 13.35 |
| 173 | 0.6447 | 0.7758 | 0.59726 | 6.79 |
| 267 | 0.5763 | 0.7546 | 0.53385 | 3.87 |
| 389 | 0.5273 | 0.7412 | 0.48848 | 2.02 |
| 491 | 0.4836 | 0.7265 | 0.44798 | 0 |
| 593 | 0.4346 | 0.7109 | 0.40263 | 2.15 |
| 673 | 0.3862 | 0.6926 | 0.35775 | 4.67 |
| 777 | 0.3632 | 0.6909 | 0.33647 | 4.90 |
| 884 | 0.3174 | 0.6657 | 0.29404 | 8.37 |
From the point of view of the comparative result and the regularity of distribution of area, the charging voltage curve conformity degree after normalization is substantially good
In discharge voltage profile.Therefore the embodiment of the present invention can select lithium ion battery charging voltage curve to carry out SOH estimations, you can
To choose datum curve from N bars normalization charging voltage curve.
It is understood that the starting stage tested in circulating battery, because battery performance is unstable, therefore the mistake shown
Bigger error of the difference compared with performance after stable, the starting point estimated from the actual use situation and SOH of battery considers, can ignore
The error of battery early stage SOH estimations.Moreover it is possible to find out that datum curve is more as cycle-index deviates, its error also can be with
Increase, this be unfavorable for away from datum curve cycle-index SOH estimation.That is only when the actual healthy shape of battery
When condition is close to SOH value based on datum curve, SOH estimated results are just more accurate.
Therefore, in embodiments of the present invention, it can be chosen from N bars normalization charging voltage curve and surround size
A moderate curve, such as the 491st voltage curve of charging cycle is used as datum curve.
Step 603, using neutral net, processing is fitted to datum curve, the voltage curve of electrokinetic cell is obtained.
Wherein, neutral net, can be reverse transfer (Backpropagation, abbreviation BP) neutral net, recurrent neural
Any type of neutral net such as network, convolutional neural networks.
In a kind of preferably way of realization, need the precision after curve matching higher due to the embodiment of the present invention, therefore
Reverse transfer (Back Propagation, abbreviation BP) neutral net can be selected to carry out curve fitting.
BP neural network is a kind of error signal along backpropagation, and working signal is along forward-propagating and neuron containing multilayer
Feedforward neural network.
Artificial neural network (Artificial Neural Networks, abbreviation ANNs) is a kind of simulation biological brain god
The mathematical modeling for information processing set up through cynapse link structure.One complete nerve network system is by many god
Be linked to each other composition through first (component units of neutral net), neuron be it is a kind of with multi input, single output it is substantially single
Member, its structure is as shown in figure 11.
X in Figure 11j(j=1,2 ..., are n) input signal of neuron, yiFor output signal, siIt is an externally input signal,
θiFor neuron threshold values, uiFor the state of inside neurons, wjiFor the weighted value of neuron input signal.According to neutral net
General principle, above-mentioned model can be represented with formula (5)：
When inside neurons do not set state, y can be madei=ui, h=f, from Sigmoid functions, its expression formula is such as
Shown in formula (6)；
One single neuron is not of practical significance, only connects substantial amounts of neuron according to certain rules,
Function of the neutral net to information processing could be realized, and embodies its superiority.Single-input single-output (SISO) multilayer is forward
The basic result of neutral net is as shown in figure 12, wherein neuron spread pattern layer distributed, to be formed required for neutral net
Input layer, output layer and the hidden layer of centre, and every layer of neuron can only regard the output of preceding layer as input.Neuron
Node is divided into input block and the class of computing unit two, and signal is after preceding layer network processes, then is transferred to next layer network, whole
Simultaneously feedback signal is not present in individual process.
The method that the learning method of BP neural network uses Minimum Mean Square Error, its learning process is divided into two kinds, is respectively just
To propagation learning process and back propagation learning process.In forward-propagating learning process, signal enters implicit through input layer first
Layer, output layer is transmitted to by the processing of hidden layer again.If output result is not inconsistent with desired value, error will be along road before
Footpath backpropagation, progressively changes the weights of each neuron in the process, until neutral net output valve and desired value it
Between error reach minimum.
The embodiment of the present invention mainly can approach arbitrary nonlinear function this feature using BP neural network, to battery
Charging voltage curve makees curvilinear regression.For other non-linear regression methods in mathematical method, BP nerve nets are used
Network has the advantages that precision height, method are easy, and if being accomplished by root first with other non-linear regression methods in mathematics
Nonlinear mathematical model is built according to known curve, then nonlinear model is linearized and solved, and precision is by constructed model
Influence, it can be seen from battery charge characteristic, mathematical modeling may use piecewise function, complicate the issue.Consider,
The embodiment of the present invention is fitted battery charging voltage curve from BP neural network.
Specifically, step 603 can be accomplished by the following way：
According to datum curve, each magnitude of voltage and each time value of electrokinetic cell are determined；
Using each magnitude of voltage as input, each time value is output, and default neural network model is trained, and obtains power
The voltage curve of battery.
When implementing, each magnitude of voltage and each time value, Ran Houji of electrokinetic cell can be determined first according to datum curve
It is trained in BP neural network.
Specifically, in program, transmission function can be based on Matlab Neural Network Toolbox, will in the .M files write
Shown in the training parameter of BP neural network is defined as follows.
By the curve to be fitted of the embodiment of the present invention is by a relatively simple, therefore can be implicit from single input, list
The BP neural network of layer, the structure of single output, transmission function log-sigmoid types (logsig), output of neuron input
Transmission function select purelin types, training function select be based on trainlm types.Because the precision of matched curve is by direct shadow
SOH in future estimation precision is rung, therefore the number of hidden layer neuron can be chosen to be 12, maximum respectively in the embodiment of the present invention
Frequency of training epochs can be set as 2000 times, and learning rate can be set as 0.5, expect to obtain minimum within the most short time
Mean square error.
In BP neural network program, electricity after the normalization that will can be determined according to the datum curve chosen in step 602
Pressure value is used as the input of neutral net, the normalization time value of the datum curve after being output as based on training.
Ytrain=u5；
Xtrain=t5：
Net=newff ([0,1], [12,1], { ' logsig ', ' purelin ' }, ' trainlm ')；
Net.trainParam.epochs=2000；
Net.trainParam.lr=0.5；
Net.trainParam.goal=0.0001；
Net=train (net, ytrain, xtrain)；
Xvalidaition=sim (net, ytrain)；
E=xvalidation-xtrain：
By training, BP neural network training result as shown in fig. 13 that can be obtained.It can be observed from fig. 13 that due to
Selected the trainlm type training functions based on Levenberg-Marquardt, the mean square deviations of sample value and trained values can with compared with
Fast speed reaches the target of the embodiment of the present invention.After being trained by four generations, the mean square deviation of sample points and trained values is by generation
0.0000122 is decremented to, as shown in figure 14.
The curve of sample value after the sample value of sampling and training is illustrated in fig. 15 shown below.Figure 15 a and Figure 15 b are respectively
The BP neural network sample value and the comparison diagram of trained values of 491 ageing cycles and the 173rd ageing cycle.It can be seen by Figure 15
Arrive, trained output data, curve similarity has reached the degree for making people satisfied.
Step 604, according to the voltage curve of electrokinetic cell, the health status of electrokinetic cell is estimated.
It is understood that the health status SOH of estimation battery is actually that estimation battery is actual under current state
Maximum charging and discharging capabilities.The general principle of SOH estimations is electricity and this period being actually filled with certain time in battery
Inside it is filled with the ratio of the percentage of total electricity shared by electricity.Because battery is in different SOH, the performance characteristic of its voltage is not
Equally, therefore with voltage curve fitting process the battery under different health status can be estimated.
When implementing, it is possible to use below equation estimation battery SOH：
Wherein, I is the specified charging current of electrokinetic cell, and C is the rated capacity of electrokinetic cell.
Specifically, when battery charges, can respectively after two moment of t1, t2, magnitude of voltage V1, V2 for monitoring battery
With the voltage curve according to electrokinetic cell, determine that two magnitudes of voltage distinguish corresponding Capacity Ratio c1, c2 respectively.It is possible thereby to estimate
The SOH of battery.
Assuming that two pieces of batteries for having SOH value different need estimation, wherein battery X and battery Y charging the t1 moment all just
Benefit is in the A points on datum curve, if the value (c1, v1) of A points, after identical time interval after Δ t, and battery X is in charging
The t3 moment be in B points on curve, the values of B points is (c2, v2), and battery Y is in the C points on curve, C at the t3 moment of charging
The value of point is (c3, v3), and c3 ＞ c2 calculate battery X and battery Y SOH value respectively, and result of calculation is as follows：
Wherein, because time interval Δ t is identical, c3 ＞ c2 can then obtain SOHY＜ SOHX.In fact, due to battery Y
Health status SOH is relatively poor, and capacity attenuation is more, and electricity fills charging platform rising, after the identical charging interval, battery Y
Voltage rise very fast, therefore SOH value is relatively small.
With reference to instantiation, the estimation precision of electrokinetic cell health status evaluation method in the embodiment of the present invention is entered
Row analysis.
Specifically, can choose bent on the basis of the normalized curve of above-mentioned 491st cycle charging in the embodiment of the present invention
Line, estimate respectively battery the 17th, 173,267,389,491,593,673,777,884 cycle chargings when SOH.593rd
The relation of magnitude of voltage is as shown in figure 16 after the SOH of secondary charging voltage curve estimation and normalization.
As shown in Figure 16, because battery is at charging process initial stage, the unstable and lithium of inside battery chemical property from
The estimation result error that the charging platform of sub- battery is caused is larger, with the progress of charging process, and battery performance tends towards stability, and
Charge after later stage abolition of plateau, estimation result can convergence stationary value.
Battery SOH estimation result is as shown in table 5 under different cycle-indexes.
SOH estimation result under the different cycle-indexes of table 5
| Cycle-index | Actual SOH (%) | Estimate SOH (%) | Error |
| 17 | 0.7437 | 0.6725 | 9.574% |
| 173 | 0.6485 | 0.5922 | 8.682% |
| 267 | 0.5863 | 0.5782 | 1.382% |
| 389 | 0.5406 | 0.5366 | 0.74% |
| 491 | 0.4993 | 0.4976 | 0.34% |
| 593 | 0.4509 | 0.4472 | 0.821% |
| 673 | 0.4026 | 0.4111 | 2.113% |
| 777 | 0.3792 | 0.3632 | 4.219% |
| 884 | 0.3328 | 0.3617 | 8.684% |
By above-mentioned analysis, because battery charging chemical property at initial stage is unstable and chargin level of lithium ion battery
Platform causes estimation result error larger, with the progress of charging process, and the chemical property of battery tends towards stability, and the charging later stage puts down
Platform disappears, and estimation result just levels off to stably.Due to have selected curve on the basis of 491 normalized charging voltage curves, because
This is when estimating the SOH near 491 times, and the precision of estimation is of a relatively high, when deviateing 491 periods farther out, estimation error
Also it is relatively large.
Electrokinetic cell health status evaluation method provided in an embodiment of the present invention, first to electrokinetic cell in N number of aging shape
Corresponding voltage curve is normalized under state, generates N bar normalized voltage curves, then bent from N bars normalized voltage
Datum curve is chosen in line, and utilizes neutral net, processing is fitted to datum curve, the voltage for obtaining electrokinetic cell is bent
Line, so as to the voltage curve according to electrokinetic cell, estimates the health status of electrokinetic cell.Hereby it is achieved that passing through fitting
The method estimation electrokinetic cell SOH of voltage curve, not only estimation precision is high, operand is small, cost is low, and mode of operation it is simple,
Easily realize, it is to avoid the influence for overcharging and crossing battery of being rivals in a contest of battery.
In order to realize above-described embodiment, the application also proposes a kind of electrokinetic cell health status estimation device.
Figure 17 is that the electrokinetic cell health status of the application one embodiment estimates the structural representation of device.
As shown in figure 17, electrokinetic cell health status estimation device, including：First processing module 1701, selection module
1702nd, Second processing module 1703, estimation block 1704.
Wherein, first processing module 1701, for corresponding voltage curve to be carried out under N number of ageing state to electrokinetic cell
Normalized, generates N bar normalized voltage curves, wherein, N is the positive integer more than 1；
Module 1702 is chosen, for according to default standard, datum curve to be chosen from N bar normalized voltage curves；
Second processing module 1703, for utilizing neutral net, processing is fitted to datum curve, electrokinetic cell is obtained
Voltage curve；
Estimation block 1704, for the voltage curve according to electrokinetic cell, estimates the health status of electrokinetic cell.
Specifically, electrokinetic cell health status estimation device provided in an embodiment of the present invention, can perform implementation of the present invention
The electrokinetic cell health status evaluation method that example is provided.
In a kind of possible way of realization of the present invention, above-mentioned first processing module 1701, specifically for：
To electrokinetic cell under N number of ageing state corresponding N bars charging voltage curve, and N bars discharge voltage profile difference
It is normalized.
In the alternatively possible way of realization of the present invention, above-mentioned selection module 1702, specifically for：
The area S that N bars normalization charging voltage curve is surrounded with time shaft is determined respectivelyci, and N bars normalization discharge voltage
The area S that curve is surrounded with time shaftfi, wherein i is the positive integer more than or equal to 1, and less than or equal to N；
Respectively according to each SciValue and each SfiValue, determine N bars normalization charging voltage curve between uniformity and N
Uniformity between bar normalization discharge voltage profile；
It is determined that N bars normalization charging voltage curve between uniformity, than N bar normalize discharge voltage profile between it is consistent
Property it is good when, determine N bars normalization charging voltage curve include datum curve.
In the alternatively possible way of realization of the present invention, above-mentioned Second processing module 1703, specifically for：
According to datum curve, each magnitude of voltage and each time value of electrokinetic cell are determined；
Using each magnitude of voltage as input, each time value is output, and default neural network model is trained, and obtains power
The voltage curve of battery.
In the alternatively possible way of realization of the present invention, above-mentioned estimation block 1704, specifically for：
Determine the current magnitude of voltage of electrokinetic cell；
Voltage curve based on electrokinetic cell, it is determined that capability value corresponding with current magnitude of voltage；
According to the rated capacity value of capability value and electrokinetic cell, the health status of electrokinetic cell is estimated.
It should be noted that the foregoing explanation to electrokinetic cell health status evaluation method embodiment is also applied for this
The electrokinetic cell health status estimation device of embodiment, here is omitted.
The electrokinetic cell health status estimation device that the embodiment of the present application is provided, first to electrokinetic cell in N number of aging shape
Corresponding voltage curve is normalized under state, generates N bar normalized voltage curves, then bent from N bars normalized voltage
Datum curve is chosen in line, and utilizes neutral net, processing is fitted to datum curve, the voltage for obtaining electrokinetic cell is bent
Line, so as to the voltage curve according to electrokinetic cell, estimates the health status of electrokinetic cell.Hereby it is achieved that passing through fitting
The method estimation electrokinetic cell SOH of voltage curve, not only estimation precision is high, operand is small, cost is low, and mode of operation it is simple,
Easily realize, it is to avoid the influence for overcharging and crossing battery of being rivals in a contest of battery.
The invention also provides a kind of computer-readable recording medium, store thereon by computer program, the program is located
When managing device execution, the electrokinetic cell health status evaluation method as described in above-mentioned embodiment is realized.
The invention also provides a kind of computer program product, when the instruction in the computer program product is by processor
During execution, the electrokinetic cell health status evaluation method as described in above-mentioned embodiment is performed.
In the description of this specification, reference term " one embodiment ", " some embodiments ", " example ", " specifically show
The description of example " or " some examples " etc. means to combine specific features, structure, material or the spy that the embodiment or example are described
Point is contained at least one embodiment of the application or example.
In addition, term " first ", " second " are only used for describing purpose, and it is not intended that indicating or implying relative importance
Or the implicit quantity for indicating indicated technical characteristic.Thus, define " first ", the feature of " second " can express or
Implicitly include at least one this feature.
Any process described otherwise above or method description are construed as in flow chart or herein, represent to include
Module, fragment or the portion of the code of one or more executable instructions for the step of realizing specific logical function or process
Point, and the scope of the preferred embodiment of the application includes other realization, wherein can not be by shown or discussion suitable
Sequence, including according to involved function by it is basic simultaneously in the way of or in the opposite order, carry out perform function, this should be by the application
Embodiment person of ordinary skill in the field understood.
It should be appreciated that each several part of the application can be realized with hardware, software, firmware or combinations thereof.Above-mentioned
In embodiment, the software that multiple steps or method can in memory and by suitable instruction execution system be performed with storage
Or firmware is realized.If, and in another embodiment, can be with well known in the art for example, realized with hardware
Any one of row technology or their combination are realized：With the logic gates for realizing logic function to data-signal
Discrete logic, the application specific integrated circuit with suitable combinational logic gate circuit, programmable gate array (PGA), scene
Programmable gate array (FPGA) etc..
Those skilled in the art are appreciated that to realize all or part of step that above-described embodiment method is carried
Rapid to can be by program to instruct the hardware of correlation to complete, described program can be stored in a kind of computer-readable storage medium
In matter, the program upon execution, including one or a combination set of the step of embodiment of the method.
Storage medium mentioned above can be read-only storage, disk or CD etc..Although having been shown and retouching above
Embodiments herein is stated, it is to be understood that above-described embodiment is exemplary, it is impossible to be interpreted as the limit to the application
System, one of ordinary skill in the art can be changed to above-described embodiment, change, replace and become within the scope of application
Type.
Claims (10)
1. a kind of electrokinetic cell health status evaluation method, it is characterised in that including：
To the electrokinetic cell, corresponding voltage curve is normalized under N number of ageing state, generation N bar normalization electricity
Buckle line, wherein, N is the positive integer more than 1；
According to default standard, datum curve is chosen from the N bars normalized voltage curve；
Using neutral net, processing is fitted to the datum curve, the voltage curve of the electrokinetic cell is obtained；
According to the voltage curve of the electrokinetic cell, the health status of the electrokinetic cell is estimated.
2. the method as described in claim 1, it is characterised in that described corresponding under N number of ageing state to the electrokinetic cell
Voltage curve be normalized, including：
To the electrokinetic cell under N number of ageing state corresponding N bars charging voltage curve, and N bars discharge voltage profile difference
It is normalized.
3. method as claimed in claim 2, it is characterised in that described to choose benchmark from the N bars normalized voltage curve
Curve, including：
The area S that N bars normalization charging voltage curve is surrounded with time shaft is determined respectivelyci, and N bars normalization discharge voltage profile
The area S surrounded with time shaftfi, wherein i is the positive integer more than or equal to 1, and less than or equal to N；
Respectively according to each SciValue and each SfiValue, determine N bars normalization charging voltage curve between uniformity and N bars return
One changes the uniformity between discharge voltage profile；
It is determined that the N bars normalization charging voltage curve between uniformity, than the N bars normalization discharge voltage profile between
When uniformity is good, determine that the N bars normalization charging voltage curve includes the datum curve.
4. the method as described in claim 1, it is characterised in that the utilization neutral net, intends the datum curve
Conjunction is handled, and obtains the voltage curve of the electrokinetic cell, including：
According to the datum curve, each magnitude of voltage and each time value of the electrokinetic cell are determined；
Using each magnitude of voltage as input, each time value is output, and default neural network model is trained, and obtains described
The voltage curve of electrokinetic cell.
5. the method as described in claim 1, it is characterised in that the voltage curve according to the electrokinetic cell, estimates institute
The health status of electrokinetic cell is stated, including：
Determine the current magnitude of voltage of the electrokinetic cell；
Based on the voltage curve of the electrokinetic cell, it is determined that capability value corresponding with the current magnitude of voltage；
According to the rated capacity value of the capability value and the electrokinetic cell, the health status of the electrokinetic cell is estimated.
6. a kind of electrokinetic cell health status estimates device, it is characterised in that including：
First processing module, for place to be normalized in corresponding voltage curve under N number of ageing state to the electrokinetic cell
Reason, generates N bar normalized voltage curves, wherein, N is the positive integer more than 1；
Module is chosen, for according to default standard, datum curve to be chosen from the N bars normalized voltage curve；
Second processing module, for utilizing neutral net, processing is fitted to the datum curve, the electrokinetic cell is obtained
Voltage curve；
Estimation block, for the voltage curve according to the electrokinetic cell, estimates the health status of the electrokinetic cell.
7. device as claimed in claim 6, it is characterised in that the first processing module, specifically for：
To the electrokinetic cell under N number of ageing state corresponding N bars charging voltage curve, and N bars discharge voltage profile difference
It is normalized.
8. device as claimed in claim 7, it is characterised in that the selection module, specifically for：
The area S that N bars normalization charging voltage curve is surrounded with time shaft is determined respectivelyci, and N bars normalization discharge voltage profile
The area S surrounded with time shaftfi, wherein i is the positive integer more than or equal to 1, and less than or equal to N；
Respectively according to each SciValue and each SfiValue, determine N bars normalization charging voltage curve between uniformity and N bars return
One changes the uniformity between discharge voltage profile；
It is determined that the N bars normalization charging voltage curve between uniformity, than the N bars normalization discharge voltage profile between
When uniformity is good, determine that the N bars normalization charging voltage curve includes the datum curve.
9. device as claimed in claim 6, it is characterised in that the Second processing module, specifically for：
According to the datum curve, each magnitude of voltage and each time value of the electrokinetic cell are determined；
Using each magnitude of voltage as input, each time value is output, and default neural network model is trained, and obtains described
The voltage curve of electrokinetic cell.
10. device as claimed in claim 6, it is characterised in that the estimation block, specifically for：
Determine the current magnitude of voltage of the electrokinetic cell；
Based on the voltage curve of the electrokinetic cell, it is determined that capability value corresponding with the current magnitude of voltage；
According to the rated capacity value of the capability value and the electrokinetic cell, the health status of the electrokinetic cell is estimated.
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