CN103487755B - For predicting the system and method for power of battery transmission performance - Google Patents
For predicting the system and method for power of battery transmission performance Download PDFInfo
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- CN103487755B CN103487755B CN201210336184.XA CN201210336184A CN103487755B CN 103487755 B CN103487755 B CN 103487755B CN 201210336184 A CN201210336184 A CN 201210336184A CN 103487755 B CN103487755 B CN 103487755B
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Abstract
Name of the present invention is called " for predicting the system and method for power of battery transmission performance ".The battery management system and method that relate to predicted power transmission performance are provided.In one embodiment, a kind of for predicting the method for the power delivery performance of battery (102), comprising: the multiple battery-operated parameter (106,108,110,112) of the discharge cycle that retrieval is selected; Calculate skew (114), it indicates the interior resistance (118) of the modeling of this battery (102) and difference in the observation that generates from the calibration discharge cycle of this battery of the discharge cycle prior to this selection between resistance (116); And based on resistance output power transmission performance prediction (128) in offset correction, the interior resistance of this offset correction indicates the difference between the interior resistance (118) of the modeling of the multiple battery-operated parameter (106,108,110,112) based on the discharge cycle to selection and skew (114).
Description
Technical field
Theme disclosed herein relates to power delivery (powerdelivery) performance of prediction battery.
Background technology
Multiple power application need reliably battery-operated and operation during monitor accurately.Such as, when primary power stops providing power (such as, due to fault), the load that uninterruptible power is such as computing equipment or telecommunication apparatus for vertical (UPS) provides power for subsequent use.The accurate measurements of UPS battery charged state be convenient to user suitably store information and UPS reach electric discharge terminate before closing device.As another example, Vehicular battery provides power to promote electric vehicle.The accurate measurements of vehicle battery charging state be the operator of electric vehicle be provided in stop think vehicle battery charging before they can drive electric vehicle how long instruction.
In one approach, battery system provides the Battery Status Indicator based on charged state (SOC) or voltage.In another approach, in battery system off-line test before use, by making, the electric capacity of battery is relevant with initial resistance attempts tracking power delivery.In another method, the instruction of battery status is derived from counting discharge cycle or charging handling capacity, and based on the report on relative healths is degenerated of the battery priori test in normalized load test.
Summary of the invention
But the present inventor has identified the problem relevant with said method at this.Such as, these methods do not determine the battery spendable time under the certain loads cycle.Such as, during the light condition loaded, battery can use the longer duration than the power level place loaded at height.Correspondingly, battery is underused to avoid fault in the power system usually, or battery system has to routine test to check whether it still can provide enough backup working times.As another example, these methods provide the battery status instruction based on priori test and can not provide explains that any of test of battery behavior difference (such as, due to aging) dynamically updates.
In one embodiment, a kind of method for predicting power of battery transmission performance, comprises the multiple battery-operated parameter of the discharge cycle that retrieval is selected; The skew of the difference between the interior resistance of the observation that the calibration discharge cycle calculating the battery before the interior resistance of modeling of pilot cell and the discharge cycle of selection generates; And based on resistance output power transmission performance prediction in offset correction, the interior resistance instruction of this offset correction is based on resistance in the modeling of multiple battery-operated parameter and the difference between skew.
By building the model of the battery usage data comprising behavior in the past and interior resistance characterization, power of battery transmission performance accuracy of forecast can improve.In addition, the skew of being derived from nearest battery behavior by application dynamically updates this model, and the foreseeable accuracy of power of battery transporting can be kept, even if when battery behavior such as changes because of aging.
This concise and to the point conceptual choice described to introduce the reduced form further described herein is provided.This briefly describes the key feature or essential feature that have no intention to identify claimed subject, the scope also had no intention for limiting theme required for protection.And claimed subject is not limited to the realization solving any or all shortcoming mentioned in any part disclosed in this.Equally, the present inventor admits the problem of any identification and corresponding solution.
Accompanying drawing explanation
Non-limiting example hereafter by reading by reference to the accompanying drawings is understood by the present invention better, in the following figures:
Fig. 1 is the schematic diagram of the embodiment that battery management system of the present disclosure is shown.
Fig. 2 is the process flow diagram of an embodiment at line method of power delivery performance for predicting battery.
Fig. 3 is the process flow diagram of an embodiment of off-line method for predicting power of battery transmission performance.
Fig. 4 is the process flow diagram of an embodiment of the method for discharge cycle for selecting calibration battery power delivery performance prediction model.
Embodiment
This description relates to the system and method for the power delivery performance for predicting battery.More particularly, power delivery performance prediction is realized from resistance characteristic in power discharge periodic path by modeling and dynamically updating.This interior resistance characteristic relative discharge state (such as, measuring with ampere hour) modeling.According to operating conditions, interior resistance is relative to the model of charged state or the earlier stored values of the battery-operated parameter of curve on the observation measured value, full discharge cycle of battery-operated parameter or derive from the extrapolation limiting battery-operated supplemental characteristic (such as, from the collection of partial discharge cycle).Be based upon models applying in battery-operated parameter to the approximate constant or predictable power discharge rate at electric discharge event procedure, to produce the accurately predicting of the power delivery performance of this battery.By providing the prediction of power delivery performance, such as cell runtime or distance before arrival electric discharge terminates by this battery, the virtual rating of user's this battery notified, instead of indirectly measure, such as charged state or discharge cycle number.
And the skew of being derived by resistance in the calibration cycle observation of this battery upgrades the forecast model of battery behavior in time.Due to aging change in this migration battery discharge behavior.By to offset Renewal model, the prediction of power delivery performance can keep accurate, even if when cell degradation and electric discharge behavior change.
In some implementations, the power level of the power delivery performance of prediction is evaluated, simultaneously minimum acceptable can target be user define or user adjustable.In other words, different user can select adjust in watt power flow rate, instead of always use peak power specified.Correspondingly, different user is tunable this battery management system is to provide power delivery performance prediction, and it meets their specific needs.Such as, first user setting target working time was 10 minutes of history average power stream place, and the second user sets working time target 14 minutes of full rated power stream place simultaneously.For each user-defined target and poower flow, differently assess power delivery performance prediction to consider user-defined input.Correspondingly, utilizing for multiple application and the suitable battery of user can with simple and dynamical fashion acquisition.On the contrary, needs are tested each battery at different rates or time repeated boad by the system not having user's adjustable to input, so that determine can the acceptance action and obtain suitable applications.
Battery management system described herein and method provide programmable and power of battery transmission performance fallout predictor flexibly, underuse to reduce battery and reduce off-line or fc-specific test FC.In addition, these battery management systems and method provide one to depend on the accurate instruction of the dynamic cell health state of user-defined load (SOH), and user-defined load dynamically explains nearest cell degradation and terminates regulation specific serviceable life to user is flexibly.
Fig. 1 is the schematic diagram of the embodiment that battery management system 100 of the present disclosure is shown.This battery management system 100 is coupled to battery 102 to monitor battery performance to ensure that this battery operates in proper operation border.This battery management system 100 comprises multiple battery sensor 104, prediction circuit 120 and display device 126.
Multiple battery sensor 104 is coupled to battery 102 or is positioned on battery 102.Multiple battery sensor 104 comprises any suitable equipment or the configuration of the operating parameter observing battery 102.The non-limiting example that battery sensor can realize comprises ammeter, voltmeter, ohmmeter, thermopair etc.Multiple battery sensor 104 sends the sensor signal of pilot cell operating parameter to prediction circuit 120.In the realization of accompanying drawing, multiple battery sensor 104 sends current signal 106, temperature signal 108, interior resistance signal 110 and charged state or ampere hour discharge signal 112 arrives prediction circuit 120.
Prediction circuit 120 can operate to receive from multiple battery sensor 104 sensor signal 106,108,110 and 112 and set up the mathematical model 118 of the relation between battery internal resistance and charged state (ampere hour electric discharge) that describes based on the operating parameter implementation algorithm corresponding to this sensor signal.This algorithm is based on the laboratory test results from fresh cells discharge cycle string.This model is in order to predict power of battery transmission performance (such as, the battery run-time capacity to electric discharge terminates) during discharge cycle, and it is called and operates in line model.Prediction circuit 120 operates during discharge cycle in line model, and upgrades power delivery performance prediction periodically in whole discharge cycle.Such as, every 5 seconds of prediction circuit 120 receives the prediction of sensor signal from multiple sensor 104 and correspondingly renewal power delivery performance.
The discharge cycle in the former battery discharge cycle of prediction circuit 120 storage battery 102 maps 116.Each discharge cycle maps 116 and comprises the mapping receiving the operating parameter of the duration for this discharge cycle from multiple sensor 104.In some cases, discharge cycle is the partial discharge of battery and part mapping or curve are generated by the operating parameter received during this partial discharge cycle.In some cases, discharge cycle is the full electric discharge of battery and complete maps or curve is generated by the operating parameter received during this full discharge cycle.
Because this interior Resistance model for prediction 118 is based on the priori test of the discharge cycle of fresh cells, along with the time is when battery 102 is aging, the discharge cycle of observation maps the prediction departing from and generate from this interior Resistance model for prediction 118.Depart to compensate this, prediction circuit 120 computing application to this interior Resistance model for prediction 118 skew 114, map forecasting accuracy between 116 to keep interior Resistance model for prediction 118 and the discharge cycle observed.The operating parameter that prediction circuit 120 carrys out self calibration discharge cycle by application calculates skew 114 to interior Resistance model for prediction 118.This calibration discharge cycle is selected from the nearest discharge cycle that this discharge cycle maps 116.The nearest battery behavior of this calibration discharge cycle accurate representation.In some instances, this calibration cycle is the partial discharge cycle.In some cases, this calibration discharge cycle is full discharge cycle.The example that calibration discharge cycle is selected will do discussion detailed further with reference to figure 4 below.
This skew 114 is instructions of the difference in the interior resistance of the modeling of battery 102 and the observation that generated by the calibration discharge cycle of this battery between resistance.When operating in line model, prediction circuit 120 applies resistance in this skew 114 to the modeling of this interior Resistance model for prediction 118 generation by the operating parameter based on this current discharge cycle.Prediction circuit 120 exports power of battery transmission performance prediction 128 to display device 126 based on resistance in offset correction.The interior resistance of this offset correction is the instruction of the difference in the modeling based on the multiple battery-operated parameter of this current discharge cycle between resistance and this skew.In line model, this power of battery transmission performance prediction 128 may be used on this current discharge cycle.
In some implementations, when this battery in discharge cycle current do not operate time, prediction circuit 120 operationally provides the moment of the power delivery performance of the battery 102 of next discharge cycle to predict in off-line mode.In off-line mode, the battery-operated parameter of prediction circuit 120 by selecting one of this discharge cycle mapping 116 to retrieve multiple storage from previous discharge cycle.In some implementations, the battery-operated parameter of this storage is retrieved from the nearest discharge cycle of major part.In some implementations, this storage battery operating parameter exceedes in the discharge cycle of threshold value charged state (such as, the threshold quantity of ampere hour electric discharge) from recently full discharge cycle or battery discharge and retrieves.Prediction circuit 120 from the calibration discharge cycle of battery 102 to calculate skew 114 with on-line operation the same manner.Prediction circuit 120 applies resistance in skew 114 to the modeling by generating based on this interior Resistance model for prediction 118 from the operating parameter stored in the discharge cycle mapping selected.Prediction circuit 120 exports power of battery transmission performance prediction 128 to display device 126 based on resistance in offset correction.The interior resistance instruction of this offset correction is based on the difference in the modeling of the battery-operated parameter of the multiple storages mapped from the discharge cycle selected between resistance and skew.In off-line mode, this power of battery transmission performance prediction 128 may be used on next discharge cycle.
In some implementations, prediction circuit 120 is that user is able to programme or adjustable in the mode that this power delivery performance prediction is evaluated.In other words, this power delivery performance prediction adjustable meets by the needs of user-defined different application.Especially, prediction circuit 120 can operate to receive user-defined power level 122.This user-defined power level 122 is power levels that battery 102 supposes in the remainder operation being applied to this interior Resistance model for prediction 118 for discharge cycle.Such as, user-defined power level can be set at full rate or maximum power levels.Another example, user-defined power level can be set at average or Partial Power level.Prediction circuit 120 exports power of battery transmission performance prediction 128 to display device 126 based on resistance in this offset correction and this user-defined power level 122.In some implementations, user-defined power level 122 is programmed in prediction circuit 120 and also stores in memory.In some implementations, user-defined power level 122 receives, such as, from another computing equipment from remote storage location.In certain embodiments, user-defined power level 122 is that the adjustable and power delivery performance prediction 128 of user is adjusted with in response to the power level receiving user's adjustment.
In some implementations, prediction circuit 120 can operate to receive user-defined object time 124 (or target range).This user-defined object time 124 is that most I accepts the tolerance limit of working time and thinks application-specific definition cell health state.Such as, a kind of user-defined object time of application can be set as that minimum 10 minutes at average power flow rate.In another example, the user-defined object time of different application can be set as that minimum 14 minutes at full rated power stream.As another example, user-defined target range can be set to minimum 10 meters at average power flow rate.This example can be applied to electric vehicle.Along with exporting the part that cell health state is this power delivery performance prediction 128, this prediction circuit can consider this user-defined object time 124.Correspondingly, prediction circuit 120 adjusts the adjustment of this cell health state in response to the user-defined object time.Correspondingly, this power delivery performance prediction can be adjusted by user the needs meeting application-specific.In addition, this prediction carrys out dynamic tuning, to compensate the change in this battery applications by user's adjustment.
In some implementations, the software that prediction circuit 120 performs by computing equipment realizes, and computing equipment such as comprises the microcontroller of processor and storer.In some implementations, prediction circuit 120 is implemented as one or more hardware or firmware logic machines configurations to perform hardware or firmware instructions.In some implementations, prediction circuit 120 is implemented as the combination of hardware and software.
The visual representation of the power delivery performance prediction 128 received from prediction circuit 120 shown by display device 126.Display device 126 utilizes the display technique of any in fact type.This display device 126 can operate this power of battery transmission performance of display prediction 128.In some implementations, this power of battery transmission performance prediction 128 comprises working time 130 or until battery 102 reaches electric discharge terminate excess time of interior resistance.In some implementations, power of battery transmission performance prediction 128 comprises distance and 130 terminates interior resistance until this battery reaches electric discharge.Such as, this distance 130 is electric vehicle operating ranges (until this battery reaches electric discharge terminate interior resistance) in predetermined power speed.In some implementations, power of battery transmission performance prediction 128 comprises this battery of instruction and whether through-put power can continue the cell health state (SOH) 132 of at least object time.Such as, 100%SOH indicates this battery through-put power can continue at least this object time.As another example, 0%SOH indicates this battery to transmit the power level of expectation with can not continuing this object time.
This interior Resistance model for prediction 118 and be used for the particular example of corresponding algorithm of the socking out time predicting battery and describe in this.In this illustration, this model (illustrating below) is formerly developed based on the laboratory test results obtained by fresh battery strings of determining battery types and is obtained.This model is used to different battery types; But mathematical variable can change according to battery types.
Res＝exp(a1*Temp+b1*Ah-C1*Temp*Ah)
Wherein, Res is this interior resistance of this battery
Temp is the operating temperature of this battery
The amount of charge discharge during Ah is this current discharge process
A1, b1 and c1 are the battery management system particular variables for particular battery calibration.
In order to the effective use in actual life or imperfect application, this model needs the particular characteristics dynamically dynamically updating supply cell degradation and battery in time.The scheme that dynamically updates from the observations of nearest discharge cycle (being referred to as " calibration discharge cycle ") is utilized to describe at this.Table X (below) shows that the relative ampere hour of resistance of calibration discharge cycle maps.
| Ampere hour discharges | Resistance | Average current | Temperature | Excess time |
| Ax1 | Rx1 | Cx1 | Tx1 | Sx1 |
| Ax2 | Rx2 | Cx2 | Tx2 | Sx2 |
| Ax3 | Rx3 | Cx3 | Tx3 | Sx3 |
| Ax4 | Rx4 | Cx4 | Tx4 | Sx4 |
| Ax5 | Rx5 | Cx5 | Tx5 | Sx5 |
Table x: the relative Ah of resistance carrying out self-calibration cycle maps
Table y (below, being assumed to the present discharge cycle (for this reason the object of example)) shows that the relative ampere hour of resistance maps.The resistance discharged into after electric discharge ending state is R_term=Rx5.Note there is the difference between the resistance (Rx5) of ending of this calibration discharge cycle and the resistance (Ry5) of the ending in present discharge cycle.
| Ampere hour discharges | Resistance (ohm) | Average current | Temperature (degree Celsius) | Excess time (second) |
| Ay1 | Ry1 | Cy1 | Ty1 | Sy1 |
| Ay2 | Ry2 | Cy2 | Ty2 | Sy2 |
| Ay3 | Ry3 | Cy3 | Ty3 | Sy3 |
| Ay4 | Ry4 | Cy4 | Ty4 | Sy4 |
| Ay5 | Ry5 | Cy5 | Ty5 | Sy5 |
Table y: the relative Ah of resistance of current period maps
The object of this model is that accurately predicting working time is until this battery discharge terminates.Like this, in order to the change of balancing battery behavior, application is displaced to algorithm to have the result (illustrating at last row of table) of the modeling of basic coupling observations.Table xx illustrates the result of this computing rule of self-application to this calibration discharge cycle.Last row illustrate the skew between actual result and model prediction.These off-set values are used to the prediction correcting present discharge cycle.
| Ampere hour discharges | Electric discharge terminates the ampere hour at resistance place | Skew |
| Axx1 | Rxx1 | Oxx1 |
| Axx2 | Rxx2 | Oxx2 |
| Axx3 | Rxx3 | Oxx3 |
| Axx4 | Rxx4 | Oxx4 |
| Axx5 | Rxx5 | Cxx5 |
Table X X: calibration cycle: the calculating of skew
The calculating of the charged state of Ay1 will describe at this.From above-mentioned mathematical model, solve Ah_term by inversed shape and obtain Ah_term:
Ah_term＝(loge(Res_term)+a1*Temp)/(c1*Temp)
Above-mentioned formula can be inserted into solve Ah_term from the value in table x.
Ah_term＝(loge(Rx5+a1*Tx1/(c1*Tx15)＝Rxx1
Ah_term is known as Axx5, as observed from calibration cycle.Like this, this skew (difference between observations and the prediction of modeling) is Rxx1-Axx5=Oxx1.This skew is owing to a wide range of cell degradation of passing in time.This offset applications predicts the working time in present discharge cycle in mathematical model.Table yy (below shown in) skew shown from this calibration cycle how to improve the prediction of this cycle based on model.
Table yy: prediction reaches the current period that electric discharge terminates the excess time of resistance
This current discharge cycle obtains to solve Ah_term by the value from table y is inserted into above-mentioned formula in the calculating of the offset correction of charged state.The Ah_term of offset correction is obtained from the offset applications of this calibration cycle in the Ah_term of the current period of modeling.Finally, the estimated time that electric discharge terminates is calculated.
(Ah_term-Ah_obs)/Current*Seconds=excess time (such as, minute)
It is consistent that the inspection of the right-hand column of table yy discloses the basic counter pair (counterpart) observing (reality) with them of prediction based on algorithm.In other words, by applying the result that this is displaced to this modeling, accuracy of forecast can be kept, though when this cell row for a change time.
Fig. 2 is the process flow diagram of an embodiment at line method 200 of prediction power of battery transmission performance.Such as the method 200 performs during the discharge cycle of battery 102 by this battery management system 100 of Fig. 1.202, the method 200 comprises the user-defined input of reception.In some implementations, this user-defined input comprises user-defined power level.In some implementations, this user defines input and comprises the user-defined object time.In some implementations, this user-defined input comprises user-defined target range.
204, the method 200 comprises based on this user-defined input setting prediction threshold value.How this predicted power transmission performance of this prediction threshold definitions is assessed in this interior Resistance model for prediction.Such as, the prediction of this prediction threshold definitions in which power level is assessed working time.In addition, the internal resistance value that this prediction threshold value comprises this battery that setting definition electric discharge terminates is set.
206, the method 200 comprises retrieval and observes operating parameter.The present discharge cycle of this battery-operated parameter to this battery is retrieved in real time.Such as, this battery operating time comprises operating current, operating temperature, the interior resistance of observation and charged state.The battery-operated parameter of this observation is from battery sensor, retrieve the whole present discharge cycle.Such as, sensor signal receives for every 5 seconds in the present discharge cycle from battery sensor.
208, the method 200 comprises resistance shift in calculating.This interior resistance shift is the instruction of the difference in the interior resistance of the modeling of this battery and the observation that generates from the calibration discharge cycle of this battery between resistance.The interior resistance of this modeling is the charged state of this battery and the function of temperature.The interior resistance of this modeling is derived from the discharge cycle of fresh cells, and this calibration discharge cycle is the instruction of this battery due to aging electric discharge behavior simultaneously.This offset applications compensates the difference in this modeling in resistance and observation between resistance in this current discharge cycle.
210, the method 200 comprise calculate for the present battery discharge cycle, the electric discharge complete charge status predication of offset correction.This electric discharge complete charge state solves Resistance model for prediction in electric discharge complete charge state by reversion and predicts.Especially, this electric discharge complete charge state is that the electric discharge of this battery terminates interior resistance and the difference of this skew and the function of this temperature.
212, the method 200 comprises based on resistance in user-defined input real-time during the present discharge cycle and offset correction, exports the prediction of power of battery transmission performance.The interior resistance of this offset correction is the instruction of difference between resistance and this skew in the modeling based on the multiple battery-operated parameter from this current discharge cycle.This power delivery performance prediction can comprise working time, distance or health status one or more.
214, the method 200 comprises the input receiving user's adjustment.The input of this user adjustment can comprise power level and minimum working time threshold value.The input of this user adjustment is different from this user-defined input.
216, the method 200 comprise based on user real-time during the present discharge cycle adjustment input and offset correction in resistance, export the power of battery transmission performance prediction.
218, the method 200 comprises determines whether this battery discharges in discharge cycle in this prior.Such as, determine that whether reaching electric discharge based on resistance in this battery terminates interior resistance.If this battery still discharges, the method forwards 206 to.Otherwise the method gets back to other operations.
Fig. 3 is the process flow diagram of an embodiment of the off-line method 300 of prediction power of battery transmission performance.Such as the method 300 performs between the discharge cycle of battery 102 by this battery management system 100 of Fig. 1.302, the method 300 comprises the user-defined input of reception.304, the method 300 comprises based on this user-defined input setting prediction threshold value.
306, the method 300 comprises the battery-operated parameter of retrieval from the storage of the previous discharge cycle selected.Such as, this battery-operated parameter can store from nearest discharge cycle.As another example, this battery-operated parameter can store from nearest full discharge cycle.
308, the method 300 comprises resistance shift in calculating.This interior resistance shift is the instruction of the difference in the interior resistance of the modeling of this battery and the observation that generates from the calibration discharge cycle of this battery between resistance.The interior resistance of this modeling is the charged state of this battery and the function of temperature.The interior resistance of this modeling is derived from the discharge cycle of fresh cells, and this calibration discharge cycle is the instruction of this battery due to aging electric discharge behavior simultaneously.This offset applications in the previous discharge cycle of this selection to compensate the difference in this modeling in resistance and observation between resistance.
310, the method 300 comprises the electric discharge complete charge status predication of the offset correction calculating the next battery discharge cycle.This electric discharge complete charge state solves Resistance model for prediction in electric discharge complete charge state by reversion and predicts.Especially, this electric discharge complete charge state is the function that electric discharge terminates the difference of interior resistance and skew and the temperature of battery.
312, the method 300 comprises based on resistance in this offset correction and this user-defined input, exports the prediction of power of battery transmission performance.The interior resistance of this offset correction is the instruction of difference between resistance and this skew in the modeling based on the multiple battery-operated parameter from this current discharge cycle.This power delivery performance prediction can comprise working time, distance or health status one or more.
314, the method 300 comprises the input receiving user's adjustment.The input of this user adjustment can comprise power level and minimum working time threshold value.The input of this user adjustment is different from this user-defined input.
316, the method 300 comprises the input based on resistance in this offset correction and this user adjustment, exports the prediction of power of battery transmission performance.
Power of battery transmission performance or power of battery transmission performance when predicting next battery discharge cycle (that is, off-line) when said method can be used to predict battery discharge cycle (that is, online).In two methods, by upgrade describe battery internal resistance and with the skew of the nearest observation operating parameter from this battery charged state between the model of relation, even if the accuracy of this power delivery performance prediction is also kept when this cell degradation.In addition by predicting based on user-defined this power of battery transmission performance of input adjustment, this prediction is tuned as application-specific and user's needs.
Fig. 4 is the process flow diagram of an embodiment of the method 400 of the discharge cycle selecting calibration battery power delivery performance prediction model.Such as the method 400 performs by the battery management system 100 of Fig. 1.402, the method 400 comprises observes operating parameter to the retrieval of present discharge cycle.This observation operating parameter is retrieved with the form of sensor signal from battery sensor.This battery-operated parameter comprises operating current, operating temperature, the interior resistance of observation and charged state.
404, based on this operating parameter, the method 400 comprises determines whether this current discharge cycle is full discharge cycle.Such as, this determines that whether reaching electric discharge based on this interior resistance terminates interior resistance to make.If the present discharge cycle is defined as full discharge cycle, the method moves on to 406.Otherwise the method moves on to 410.
406, the method 400 comprises and offseting based on current period calibrating patterns.In other words, current period is chosen as the calibration cycle for derived offset, because current period is full discharge cycle.The full electric discharge of this battery provides this interior resistance to more complete map of the relation of charged state, consequently battery behavior more accurately predicting.
408, the method 400 comprises makes cycle count equal zero.This cycle count follows the tracks of the discharge cycle number occurred between the full battery discharge cycle.
410, the method 400 comprises determines whether this cycle count is less than cycle count threshold value.This cycle count threshold might be set is any suitable discharge cycle number before this model generation predicts with observation the power delivery performance prediction differing certain tolerance.Such as, cycle count threshold might be set is 200 discharge cycles.If this cycle count is defined as being less than this cycle count threshold value, the method moves on to 412.Otherwise the method moves on to 416.
412, the method 400 comprises ignores current period as calibration cycle.In other words this current period is only the partial discharge cycle and does not provide the most complete map of current relation between interior resistance and charged state, therefore the discharge cycle that current period stores before being left in the basket and supporting.
414, the method 400 comprises to be increased this cycle count and returns 402.
416, the method 400 comprises based on present discharge period regulation model offset.This current discharge cycle can be used to generate this skew, although it is the partial discharge cycle, because this model needs to upgrade with the current behavior of following the tracks of battery.Although this partial discharge cycle provides the full mapping being less than operating parameter.Operating parameter can be used to provide following skew, its by battery behavior relative to do not provide offset change with the model of the change in balancing battery behavior time, improve forecasting accuracy.
418, the method comprises this cycle count of setting and equals zero and return 402.
This written description uses example openly to comprise the present invention of optimal mode, and also makes person of ordinary skill in the relevant put into practice the present invention, comprises the method making and use any device or system and perform any combination.The patentable scope of the present invention is defined by claim, and can comprise other example that those of ordinary skill in the art expect.If this type of other example have with claim literal language invariably with textural element, if or they comprise from claim literal language without the different equivalent structural elements of essence, then they are defined as within the scope of claim.
Claims (20)
1., for predicting a method for the power delivery performance of battery (102), comprising:
The multiple battery-operated parameter (106,108,110,112) of the discharge cycle that retrieval is selected;
The skew (114) of the difference in the interior resistance (118) calculating the modeling of the described battery of instruction (102) and the observation generated from the calibration discharge cycle of the described battery of the discharge cycle prior to described selection between resistance (110); And
Export power of battery transmission performance based on instruction based on the interior resistance of the offset correction of the difference between the interior resistance (118) of the modeling of described multiple battery-operated parameter (106,108,110,112) and described skew (114) and predict (128).
2. the method for claim 1, comprises further:
Receive user-defined power level (122); And
Power of battery transmission performance prediction (128) is exported based on resistance in described offset correction and described user-defined power level (122).
3. method as claimed in claim 2, comprises further:
Reception is different from the power level of user's adjustment of described user-defined power level (122); And
Power level based on resistance in described offset correction and described user adjustment exports power of battery transmission performance prediction (128) upgraded.
4. the method for claim 1, wherein said power of battery transmission performance prediction (128) comprises until described battery (102) reaches the excess time (13) that electric discharge terminates interior resistance.
5. the method for claim 1, wherein said power of battery transmission performance prediction (128) comprises until described battery (102) reaches the vehicle operating range (132) that electric discharge terminates interior resistance.
6. the method for claim 1, whether wherein said power of battery transmission performance prediction (128) comprises the described battery of instruction (102) through-put power can continue the cell health state (134) of at least object time.
7. method as claimed in claim 6, comprises further:
Receive the user-defined object time (124); And
Described cell health state (134) is adjusted based on described user-defined object time (124).
8. the method for claim 1, wherein said multiple battery-operated parameter comprises operating current (106), operating temperature (108), the interior resistance (110) observed and charged state (112).
9. the method for claim 1, the described multiple battery-operated parameters (106,108,110,112) wherein retrieving the discharge cycle of described selection comprise multiple sensor signals of the described multiple battery-operated parameter (106,108,110,112) of real-time reception instruction, and power of battery transmission performance described in real-time update predicts (128) during the present discharge cycle.
10. the method for claim 1, the described multiple battery-operated parameters (106,108,110,112) wherein retrieving the discharge cycle of described selection retrieve the described multiple battery-operated parameters (106,108,110,112) stored from previous discharge cycle before being included in next discharge cycle, and described power of battery transmission performance prediction (128) is applied to described next discharge cycle.
11. 1 kinds, for predicting the method for the power delivery performance of battery (102), comprising:
Receive user-defined power level (122);
The multiple battery-operated parameter (106,108,110,112) in real-time retrieval present discharge cycle;
The skew (114) of the difference in the interior resistance (118) calculating the modeling of the described battery of instruction (102) and the observation generated from the calibration discharge cycle of the described battery prior to the described present discharge cycle between resistance (116); And
During the described present discharge cycle, export power of battery transmission performance based on instruction based on the interior resistance (118) of the interior resistance of modeling of described multiple battery-operated parameter (106,108,110,112) and the offset correction of the difference of described skew (114) and described user-defined power level (122) and predict (128).
12. methods as claimed in claim 11, comprise further:
Reception is different from the power level of user's adjustment of described user-defined power level (122); And
Power level based on resistance in described offset correction and described user adjustment exports power of battery transmission performance prediction (128) upgraded.
13. methods as claimed in claim 11, wherein said power of battery transmission performance prediction (128) comprises until described battery (102) reaches the excess time (130) that electric discharge terminates interior resistance.
14. methods as claimed in claim 11, wherein said power of battery transmission performance prediction (128) comprises until described battery (102) reaches the distance (132) that electric discharge terminates interior resistance.
15. methods as claimed in claim 11, whether wherein said power of battery transmission performance prediction (128) comprises the described battery of instruction (102) through-put power can continue the cell health state (134) of at least object time.
16. methods as claimed in claim 11, wherein when the cycle count of the discharge cycle not having the appearance of full discharge cycle is less than discharge cycle threshold value, described calibration discharge cycle is full discharge cycle, and when the cycle count of the discharge cycle not having the appearance of full discharge cycle is greater than described discharge cycle threshold value, described calibration discharge cycle is the partial discharge cycle.
17. 1 kinds of battery management systems (100), comprising:
Battery (102);
Multiple battery sensor (104), it can operate to transmit and indicate multiple sensor signals of multiple battery-operated parameter (106,108,110,112) to prediction circuit (120);
Described prediction circuit can operate in disconnection mode, with the battery-operated parameter of retrieval from multiple storages of previous discharge cycle, the skew (114) of the difference in the interior resistance (118) calculating the modeling of the described battery of instruction (102) and the observation generated from the calibration discharge cycle of the described battery prior to described previous discharge cycle between resistance, and based on instruction based in the modeling of the battery-operated parameter of described multiple storage between resistance (118) and described skew (114) the interior resistance of the offset correction of difference export power of battery transmission performance and predict (128), described power of battery transmission performance prediction (128) may be used on next discharge cycle, and described prediction circuit (120) is during the present discharge cycle, can operate under line model, to receive the described multiple battery-operated parameter (106 of instruction, 108, 110, 112) described multiple battery sensor signals, the skew (114) of the difference in the interior resistance (118) calculating the modeling of the described battery of instruction (102) and the observation generated from the calibration discharge cycle of the described battery prior to the described present discharge cycle between resistance, with based on instruction based on the described multiple battery-operated parameter (106 in described present discharge cycle, 108, 110, 112) between the interior resistance (118) of modeling and described skew (114), the interior resistance of the offset correction of difference exports power of battery transmission performance prediction (128), described power of battery transmission performance prediction (128) may be used on the described present discharge cycle, and
Display device (126), it can operate the described power delivery performance prediction (128) that receives from described prediction circuit (120) and show described power of battery transmission performance prediction (128).
18. battery management systems as claimed in claim 17, wherein said prediction circuit (120) can operate to receive user-defined power level (122) and export power of battery transmission performance prediction (128) based on resistance in described offset correction and described user-defined power level (122).
19. battery management systems as claimed in claim 17, whether wherein said power of battery transmission performance prediction (128) comprises the described battery of instruction (102) through-put power can continue the cell health state (134) of at least object time, and described prediction circuit can operate to receive the user-defined object time (124), and adjust described cell health state based on the described user-defined object time.
20. battery management systems as claimed in claim 17, wherein said power of battery transmission performance prediction (128) comprises until described battery (102) reaches the excess time (130) that electric discharge terminates interior resistance, until described battery (102) reaches electric discharge terminate the distance (132) of interior resistance, and indicate described battery whether through-put power can continue the cell health state (134) of at least object time.
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| CN106608200B (en) * | 2015-10-27 | 2019-12-31 | 北京宝沃汽车有限公司 | Electric automobile power output control method and system |
| CN105678077A (en) * | 2016-01-07 | 2016-06-15 | 北京北交新能科技有限公司 | Online prediction method of power performance of lithium ion battery for hybrid power vehicle |
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