CN103487755A - System and method for predicting power transmission performance of battery - Google Patents
System and method for predicting power transmission performance of battery Download PDFInfo
- Publication number
- CN103487755A CN103487755A CN201210336184.XA CN201210336184A CN103487755A CN 103487755 A CN103487755 A CN 103487755A CN 201210336184 A CN201210336184 A CN 201210336184A CN 103487755 A CN103487755 A CN 103487755A
- Authority
- CN
- China
- Prior art keywords
- battery
- discharge cycle
- power
- transmission performance
- interior resistance
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Abstract
The invention provides a system and a method for predicting the power transmission performance of a battery and relates to a battery managing system and a method for predicting the power transmission performance. According to the embodiment, the method for predicting the power transmission performance of the battery (102) comprises the steps of searching a plurality of battery operation parameters (106, 108, 110 and 112) of the selected discharge period, calculating offset (114) which indicates the difference between modeling internal resistance (118) of the battery (102) and observation internal resistance (116) generated by the calibration discharge period, prior to the selected discharge period, of the battery, and carrying out prediction (128) of the output power transmission performance of the internal resistance based on offset correction, wherein the internal resistance based on offset correction indicates the difference between the modeling internal resistance (118) of the battery operation parameters (106, 108, 110 and 112) of the selected discharge period and the offset (114).
Description
Technical field
Theme disclosed herein relates to power delivery (power delivery) performance of predicting battery.
Background technology
The multiple power application needs battery-operated and operating period reliably to monitor accurately.For example, for example, when primary power stops providing power (, due to fault), uninterruptible power provides standby power for vertical (UPS) for the load such as computing equipment or telecommunication apparatus.The accurate monitoring of UPS battery charged state is convenient to that the user suitably stores information and closing device before UPS reaches electric discharge and finishes.As another example, Vehicular battery provides power to promote electric vehicle.The operator that the accurate monitoring of vehicle battery charging state is electric vehicle is provided at before vehicle battery charging is thought in parking they can drive electric vehicle indication how long.
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, the electric capacity by making battery is relevant with original resistance attempts following the tracks of power delivery.In another method, the indication of battery status is derived from counting discharge cycle or charging handling capacity, and the report in relatively healthy degeneration of the battery priori test based in the normalized load test.
Summary of the invention
Yet the inventor has identified the problem relevant with said method at this.For example, these methods were not determined in certain loads spendable time of battery under the cycle.For example, during the light condition loaded, battery can be used the longer duration than the power level place loaded at height.Correspondingly, battery usually underuses to avoid fault in power system, or battery system is had to routine test to check whether it still can provide enough backup working times.As another example, these methods the indication of battery status based on priori test is provided and can not provide explain battery behavior difference (such as, due to aging) any of test dynamically update.
In one embodiment, a kind of for predicting the method for power of battery transmission performance, a plurality of battery-operated parameter of the discharge cycle that comprises the retrieval selection; The skew of the difference between the interior resistance of the observation that the calibration discharge cycle of the battery before the interior resistance of the modeling of calculating pilot cell and the discharge cycle of selection generates; And the interior resistance output power transmission performance prediction based on offset correction, the interior resistance of the modeling of interior resistance indication based on a plurality of battery-operated parameters of this offset correction and the difference between skew.
Comprise the model of the battery usage data of behavior in the past and interior resistance characterization by structure, power of battery transmission performance accuracy of forecast can improve.In addition, the skew of deriving from nearest battery behavior by application dynamically updates this model, and the foreseeable accuracy of power of battery transporting can be held, though in the battery behavior such as because aging and while changing.
Provide this concise and to the point to describe to introduce the conceptual choice of the reduced form that this paper further describes.This concise and to the point key feature or essential feature that has no intention to identify claimed subject of describing, also have no intention for limiting the scope of theme required for protection.And claimed subject is not limited to solve the realization of any or all shortcoming of mentioning in this disclosed any part.Equally, the inventor admits the problem of any identification and corresponding solution.
The accompanying drawing explanation
The present invention will understand better by reading non-limiting example hereinafter by reference to the accompanying drawings, in the following drawings:
Fig. 1 is the schematic diagram that the embodiment of battery management system of the present disclosure is shown.
Fig. 2 is the process flow diagram of the embodiment at line method of the power delivery performance for predicting battery.
Fig. 3 is the process flow diagram of an embodiment of the off-line method for predicting power of battery transmission performance.
Fig. 4 is the process flow diagram of an embodiment of the method for the 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, the power delivery performance prediction is by modeling with dynamically update from the interior resistance characteristic of power discharge periodic path and realize.Should the interior relative discharge condition of resistance characteristic (for example, measuring with ampere hour) modeling.According to operating conditions, the previous storing value of the model of the relative charged state of interior resistance or the curve battery-operated parameter on the observation measured value of battery-operated parameter, full discharge cycle or for example, derived from the extrapolation of the battery-operated supplemental characteristic of restriction (, from partial discharge cycle collect).Be based upon model on battery-operated parameter and be applied to the approximate constant or predictable power discharge rate at the electric discharge event procedure, with the accurately predicting of the power delivery performance that produces this battery.By the prediction of power delivery performance is provided, for example this battery will arrive electric discharge finish before battery operation time or distance, the virtual rating of notified this battery of user, rather than indirectly tolerance, for example charged state or discharge cycle number.
And the forecast model of battery behavior is upgraded in the skew of deriving by the interior resistance of the observation of the calibration cycle since this battery in time.In this migration battery discharge behavior due to aging change.By with the skew Renewal model, the prediction of power delivery performance can keep accurately, even at cell degradation with while discharging behavior change.
In some implementations, the power level of the power delivery performance of prediction is evaluated, simultaneously minimum acceptable can target be the user define or the user adjustable.In other words, different user can select to adjust in watt the power flow rate, rather than use peak power specified always.Correspondingly, different user is tunable this battery management system is to provide the power delivery performance prediction, and it meets their specific needs.For example, first user is set the working time target 10 minutes of historical average power stream place, and the second user sets the working time target 14 minutes of full rated power stream place simultaneously.For each user-defined target and power stream, differently assess the power delivery performance prediction to consider user-defined input.Correspondingly, for multiple application and the suitable battery utilization of user, can obtain with simple and dynamical fashion.On the contrary, do not have the system of user's capable of regulating input to test each battery at different rates or time repeated boad, but obtain suitable applications to determine the acceptance action.
Battery management system described herein and method provide programmable and power of battery transmission performance fallout predictor flexibly, to reduce battery, underuse and reduce off-line or fc-specific test FC.In addition, these battery management systems and method provide a kind of accurate indication of depending on the dynamic cell health state of user-defined load (SOH), and user-defined load has dynamically been explained nearest cell degradation and the user is finished to regulation specific serviceable life is flexibly.
Fig. 1 is the schematic diagram that the embodiment of battery management system 100 of the present disclosure is shown.This battery management system 100 is coupled to battery 102 monitors battery performance and operates in the proper operation border to guarantee this battery.This battery management system 100 comprises a plurality of battery sensors 104, prediction circuit 120 and display device 126.
A plurality of battery sensors 104 are coupled to battery 102 or are positioned on battery 102.A plurality of battery sensors 104 comprise any suitable equipment or the configuration of the operating parameter of observing battery 102.The non-limiting example that battery sensor can be realized comprises ammeter, voltmeter, ohmmeter, thermopair etc.A plurality of battery sensors 104 send the sensor signal of pilot cell operating parameter to prediction circuit 120.In the realization of accompanying drawing, a plurality of battery sensors 104 send 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 the sensor signal 106,108,110 of a plurality of battery sensors 104 and 112 and operating parameter implementation algorithm based on corresponding to this sensor signal set up the mathematical model 118 of describing relation between battery internal resistance and charged state (ampere hour electric discharge).The laboratory test results of this algorithm based on from fresh cells discharge cycle string.This model is during discharge cycle for example, in order to predict power of battery transmission performance (, the battery operation time capacity finished to electric discharge), and it is called in line model and operates.Prediction circuit 120 is operating during discharge cycle in line model, and upgrades periodically the power delivery performance prediction in whole discharge cycle.For example, every 5 seconds of prediction circuit 120 receives from the sensor signal of a plurality of sensors 104 and correspondingly upgrades the prediction of power delivery performance.
The discharge cycle mapping 116 in the former battery discharge cycle of prediction circuit 120 storage batteries 102.Each discharge cycle mapping 116 comprises the mapping for the operating parameter of the duration of this discharge cycle from a plurality of sensors 104 receptions.In some cases, discharge cycle is that 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 mapping or curve are generated by the operating parameter that receives during this full discharge cycle entirely.
Due to the priori test of the discharge cycle of Resistance model for prediction in this 118 based on fresh cells, along with the time, when battery 102 is aging, the discharge cycle of observation mapping departs from the prediction generated from Resistance model for prediction 118 in this.In order to compensate this, depart from, prediction circuit 120 computing applications to Resistance model for prediction 118 in this skew 114, with the forecasting accuracy between the discharge cycle mapping 116 that keeps interior Resistance model for prediction 118 and observe.Prediction circuit 120 comes the operating parameter of self calibration discharge cycle to calculate skew 114 to interior Resistance model for prediction 118 by application.This calibration discharge cycle is the nearest discharge cycle that is selected from this discharge cycle mapping 116.This calibration discharge cycle accurately represents nearest battery behavior.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 the calibration discharge cycle is selected will be done further detailed discussion with reference to figure 4 below.
The indication of the difference between the interior resistance of the interior resistance of the modeling that this skew 114 is batteries 102 and the observation that generated by the calibration discharge cycle of this battery.When operating in line model, prediction circuit 120 is applied the interior resistance that the modeling generated by the interior Resistance model for prediction 118 of being somebody's turn to do of the operating parameter based on this current discharge cycle is arrived in this skew 114.The interior resistance output power of battery transmission performance prediction 128 of prediction circuit 120 based on offset correction is to display device 126.The interior resistance of this offset correction is based on the interior resistance of modeling of a plurality of battery-operated parameters of this current discharge cycle and the indication of the difference between 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 during current the operation, prediction circuit 120 operationally provides the moment prediction of power delivery performance of the battery 102 of next discharge cycle in off-line mode.In off-line mode, prediction circuit 120 is by selecting the battery-operated parameter of one of this discharge cycle mapping 116 a plurality of storages of retrieval 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 is retrieved in for example, discharge cycle over threshold value charged state (, the threshold quantity of ampere hour electric discharge) from recently full discharge cycle or battery discharge.Prediction circuit 120 from the calibration discharge cycle of battery 102 with on-line operation the same manner, to calculate skew 114.Prediction circuit 120 application skew 114 to the operating parameter of storage in the mapping of the discharge cycle by based on from selecting should in the interior resistance of the modeling that generates of Resistance model for prediction 118.The interior resistance output power of battery transmission performance prediction 128 of prediction circuit 120 based on offset correction is to display device 126.The interior resistance of the modeling of the battery-operated parameter of a plurality of storages of the discharge cycle mapping of interior resistance indication based on from selecting of this offset correction and the difference between 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's mode evaluated with this power delivery performance prediction is able to programme or adjustable.In other words, this power delivery performance prediction capable of regulating 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 battery 102 hypothesis power levels in the remainder operation that is applied to Resistance model for prediction 118 in this for discharge cycle.For example, 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.The interior resistance of prediction circuit 120 based on this offset correction and this user-defined power level 122 output power of battery transmission performance predictions 128 are to display device 126.In some implementations, user-defined power level 122 is programmed in prediction circuit 120 and is stored in storer.In some implementations, user-defined power level 122 receives from remote storage location, for example, from another computing equipment.In certain embodiments, user-defined power level 122 is that the user is adjustable and power delivery performance prediction 128 is adjusted with the power level in response to receiving user's adjustment.
In some implementations, prediction circuit 120 can operate to receive the user-defined object time 124 (or target range).This user-defined object time 124 is that I is accepted the tolerance limit of working time and thought application-specific definition cell health state.For example, a kind of user-defined object time of application can be set as minimum 10 minutes at the average power flow rate.In another example, the user-defined object time of different application can be set as minimum stream in full rated power in 14 minutes.As another example, user-defined target range can be set to minimum 10 meters at the average power flow rate.This example can be applied to electric vehicle.Along with the part that the output cell health state is this power delivery performance prediction 128, this prediction circuit can be considered this user-defined object time 124.Correspondingly, prediction circuit 120 is adjusted the adjustment of this cell health state in response to the user-defined object time.Correspondingly, this power delivery performance prediction can be adjusted to meet by the user needs of application-specific.In addition, this prediction can adjust dynamic tuning by the user, to compensate the variation in this battery applications.
In some implementations, the software that prediction circuit 120 can be carried out by computing equipment realizes, computing equipment for example 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 carry out hardware or firmware instructions.In some implementations, prediction circuit 120 is implemented as the combination of hardware and software.
Display device 126 is showed the visual representation of the power delivery performance prediction 128 received from prediction circuit 120.Display device 126 utilizes the display technique of any in fact type.This display device 126 can operate and show this power of battery transmission performance prediction 128.In some implementations, this power of battery transmission performance prediction 128 comprises working time 130 or finishes the excess time of interior resistance until battery 102 reaches to discharge.In some implementations, power of battery transmission performance prediction 128 comprises distance 130 until this battery reaches resistance in the electric discharge end.For example, this distance 130 is electric vehicle operating ranges (until this battery reaches resistance in the electric discharge end) in default power speed.In some implementations, power of battery transmission performance prediction 128 comprises whether this battery of indication can through-put power continue at least cell health state of object time (SOH) 132.For example, 100%SOH indicates this battery through-put power to continue at least this object time.As another example, 0%SOH indicates this battery can not continue the power level of this object time ground transmission expectation.
Should in Resistance model for prediction 118 and being used for predict that the specific example of corresponding algorithm of the socking out time of battery describes in this.In this example, the laboratory test results of this model (below illustrate) based on being obtained by fresh battery strings of determining battery types formerly developed and obtained.This model is used to different battery types; Yet mathematical variable can change according to battery types.
Res＝exp(a1*Temp+b1*Ah-C1*Temp*Ah)
Wherein, Res is resistance in being somebody's turn to do of this battery
Temp is the operating temperature of this battery
Ah is the amount of charge discharge during this current discharge process
A1, b1 and c1 are the battery management system particular variables for the particular battery calibration.
For the effective use in actual life or imperfect application, this model needs dynamically to dynamically update in time the particular characteristics of supplying with cell degradation and battery.The scheme that dynamically updates that is used to the observations of certainly nearest discharge cycle (being referred to as " calibration discharge cycle ") is described at this.Table X (shown in following) is showed the relative ampere hour mapping of the resistance of calibration discharge cycle.
| The ampere hour electric discharge | 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: from the relative Ah mapping of resistance of calibration cycle
Table y (shown in following, being assumed to current discharge cycle (purpose of example for this reason)) shows the relative ampere hour mapping of resistance.Discharging into the resistance that electric discharge finishes after state is R_term=Rx5.Difference between the resistance (Ry5) of the resistance (Rx5) of the ending of this calibration discharge cycle of attention existence and the ending of current discharge cycle.
| The ampere hour electric discharge | Resistance (ohm) | Average current | Temperature (degree centigrade) | 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 mapping of the resistance of current period
The purpose of this model is that accurately predicting working time is until the end of this battery discharge.Like this, for the variation of balancing battery behavior, application is displaced to the result (last row at table illustrate) of algorithm with modeling with basic coupling observations.Table xx illustrates the result of this computing rule of self-application to this calibration discharge cycle.Last row are illustrated in the skew between actual result and model prediction.These off-set values are used to proofread and correct the prediction of present discharge cycle.
| The ampere hour electric discharge | Electric discharge finishes 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 be described at this.From above-mentioned mathematical model, can solve Ah_term by the reversion model and obtain Ah_term:
Ah_term＝(loge(Res_term)+a1*Temp)/(c1*Temp)
Can be inserted into above-mentioned formula from the value in table x and solve Ah_term.
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 the prediction of observations and modeling) is Rxx1-Axx5=Oxx1.This skew is owing to a wide range of cell degradation of passing in time.This offset applications is predicted the working time of current discharge cycle in mathematical model.The skew that table yy (shown in following) shows from this calibration cycle is how to improve the prediction of this cycle based on model.
Table yy: prediction reaches the current period that electric discharge finishes the excess time of resistance
This current discharge cycle can solve Ah_term and obtains by being inserted into from the value of table y above-mentioned formula in the calculating of the offset correction of charged state.Obtain the Ah_term of offset correction in the Ah_term of the current period of modeling from the offset applications of this calibration cycle.Finally, calculated the estimated time that electric discharge finishes.
(Ah_term-Ah_obs)/Current*Seconds=excess time (for example, minute)
The prediction that the inspection of the right-hand column of table yy discloses based on algorithm is substantially consistent with the counter pair (counterpart) of their observations (reality).In other words, the result that is displaced to this modeling by applying this, accuracy of forecast can be held, even when this cell row for a change the time.
Fig. 2 is the process flow diagram of the embodiment at line method 200 of prediction power of battery transmission performance.For example the method 200 can be carried out by this battery management system 100 of Fig. 1 during the discharge cycle of battery 102.202, 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 to 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 sets the prediction threshold value.This prediction threshold value defines this predicted power transmission performance and how to assess in Resistance model for prediction in this.For example, this prediction threshold value definition prediction in which power level is assessed working time.In addition, set the interior resistance value that this prediction threshold value comprises this battery of setting definition electric discharge end.
206, the method 200 comprises retrieval observation operating parameter.This battery-operated parameter is retrieved in real time to the current discharge cycle of this battery.For example, this battery operating time comprises interior resistance and the charged state of operating current, operating temperature, observation.This observes battery-operated parameter from battery sensor, whole current discharge cycle is retrieved.For example, sensor signal receives in every 5 seconds in current discharge cycle from battery sensor.
208, the method 200 comprises calculates interior resistance skew.The indication of the difference between the interior resistance of the observation of being somebody's turn to do the interior resistance of the modeling that interior resistance skew is this battery and generating from the calibration discharge cycle of this battery.The interior resistance of this modeling is the function of charged state and the temperature of this battery.The interior resistance of this modeling is derived from the discharge cycle of fresh cells, and this calibration discharge cycle is the indication of this battery due to aging electric discharge behavior simultaneously.This offset applications compensates the difference between the interior resistance of the interior resistance of this modeling and observation in this current discharge cycle.
210, the method 200 comprises to be calculated for the electric discharge complete charge status predication current battery discharge cycle, offset correction.The interior Resistance model for prediction that this electric discharge complete charge state solves electric discharge complete charge state by reversion is predicted.Especially, the difference of resistance and this skew and the function of this temperature in the electric discharge that this electric discharge complete charge state is this battery finishes.
212, the method 200 comprises user-defined input based on real-time during current discharge cycle and the interior resistance of offset correction, exports the prediction of power of battery transmission performance.The interior resistance of this offset correction is based on the indication from difference between the interior resistance of the modeling of a plurality of battery-operated parameters of this current discharge cycle and this skew.This power delivery performance prediction can comprise the one or more of working time, distance or health status.
214, the method 200 comprises the input that receives user's adjustment.The input that this user adjusts can comprise power level and minimum threshold value working time.The input that this user adjusts is different from this user-defined input.
216, the method 200 comprises the input of the user's adjustment based on real-time during current discharge cycle and the interior resistance of offset correction, exports the prediction of power of battery transmission performance.
218, the method 200 comprises determines whether this battery discharges in this current discharge cycle.For example, determine whether the interior resistance based on this battery reaches resistance in the electric discharge end.If this battery still discharges, the method forwards 206 to.Otherwise the method is got 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.For example the method 300 can be carried out by this battery management system 100 of Fig. 1 between the discharge cycle of battery 102.302, the method 300 comprises the user-defined input of reception.304, the method 300 comprises based on this user-defined input sets the prediction threshold value.
306, the method 300 comprises the battery-operated parameter of retrieval from the storage of the previous discharge cycle of selecting.For example, this battery-operated parameter can be stored from nearest discharge cycle.As another example, this battery-operated parameter can be stored from recently full discharge cycle.
308, the method 300 comprises calculates interior resistance skew.The indication of the difference between the interior resistance of the observation of being somebody's turn to do the interior resistance of the modeling that interior resistance skew is this battery and generating from the calibration discharge cycle of this battery.The interior resistance of this modeling is the function of charged state and the temperature of this battery.The interior resistance of this modeling is derived from the discharge cycle of fresh cells, and this calibration discharge cycle is the indication of this battery due to aging electric discharge behavior simultaneously.This offset applications in the previous discharge cycle of this selection with the difference between the interior resistance of the interior resistance that compensates this modeling and observation.
310, the method 300 comprises the electric discharge complete charge status predication of the offset correction that calculates the next battery discharge cycle.The interior Resistance model for prediction that this electric discharge complete charge state solves electric discharge complete charge state by reversion is predicted.Especially, this electric discharge complete charge state is the function of the temperature of the difference of resistance and skew in electric discharge finishes and battery.
312, the method 300 comprises interior resistance and this user-defined input based on this offset correction, exports the prediction of power of battery transmission performance.The interior resistance of this offset correction is based on the indication from difference between the interior resistance of the modeling of a plurality of battery-operated parameters of this current discharge cycle and this skew.This power delivery performance prediction can comprise the one or more of working time, distance or health status.
314, the method 300 comprises the input that receives user's adjustment.The input that this user adjusts can comprise power level and minimum threshold value working time.The input that this user adjusts is different from this user-defined input.
316, the method 300 comprises the interior resistance and the input that this user adjusts based on this offset correction, exports the prediction of power of battery transmission performance.
Power of battery transmission performance that said method can be used to predict the battery discharge cycle when (that is, online) or the power of battery transmission performance of predicting the next battery discharge cycle when (, off-line).In two methods, by renewal, battery internal resistance is described and with the model of the relation between the charged state of the skew of the nearest observation operating parameter from this battery, even the accuracy of this power delivery performance prediction also is held when this cell degradation.By adjust this power of battery transmission performance prediction based on user-defined input, this prediction is tuned as application-specific and user's needs in addition.
Fig. 4 is the process flow diagram of an embodiment of method 400 of selecting the discharge cycle of calibration battery power delivery performance prediction model.For example the method 400 can be carried out by the battery management system 100 of Fig. 1.402, the method 400 comprises observes operating parameter to current discharge cycle retrieval.This observes operating parameter form retrieval with sensor signal from battery sensor.This battery-operated parameter comprises interior resistance and the charged state of operating current, operating temperature, observation.
404, the method 400 comprises based on this operating parameter determines whether this current discharge cycle is full discharge cycle.For example, this determines that whether reaching resistance in the electric discharge end based on resistance in this makes.If current 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 based on the current period calibrating patterns and being offset.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 the how complete mapping of resistance to the relation of charged state in this, consequently battery behavior accurately predicting more.
408, the method 400 comprises equals zero cycle count.This cycle count is followed the tracks of and is occurred in the discharge cycle number of full battery discharge between the cycle.
410, the method 400 comprises determines whether this cycle count is less than the cycle count threshold value.This cycle count threshold value can be set as at this model generation and any suitable discharge cycle number of observing before predicting the power delivery performance prediction that differs certain tolerance.For example, the cycle count threshold value can be set as 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 cycle is only for the partial discharge cycle with do not provide the most complete mapping of current relation between interior resistance and charged state, the discharge cycle of storage before supporting therefore current period is left in the basket.
414, the method 400 comprises to be increased this cycle count and returns to 402.
416, the method 400 comprises based on current discharge cycle calibrating patterns skew.This current discharge cycle can be used to generate this skew, although it is the partial discharge cycle, because this model need to upgrade to follow the tracks of the current behavior of battery.Although this partial discharge cycle provides the full mapping that is less than operating parameter.Useful operating parameter provides following skew, and it will, when the battery behavior does not provide skew to change with the model of the variation in the balancing battery behavior relatively, improve forecasting accuracy.
418, the method comprises that setting this cycle count equals zero and return to 402.
This written description usage example openly comprises the present invention of optimal mode, and also makes the person of ordinary skill in the relevant can put into practice the present invention, comprises and makes and use any device or system and carry out the method for any combination.The patentable scope of the present invention is defined by claim, and can comprise other example that those of ordinary skills expect.If this type of other example have with the claim literal language invariably with textural element, if or they comprise the equivalent structure key element different without essence from the claim literal language, they are defined as within the scope of claim.
Claims (20)
1. the method for the power delivery performance of predicting battery (100) comprises:
A plurality of battery-operated parameter (106,108,110,112) of the discharge cycle that retrieval is selected;
Calculate the skew (114) of the difference between the interior resistance (116) of the interior resistance (118) of modeling of the described battery of indication (102) and the observation that generates from the calibration discharge cycle of the described battery of the discharge cycle prior to described selection; And
The interior resistance output power of battery transmission performance prediction (128) of the interior resistance (118) of the modeling based on indication based on described a plurality of battery-operated parameters (106,108,110,112) and the offset correction of the difference between described skew (114).
2. the method for claim 1 further comprises:
Receive user-defined power level (122); And
Interior resistance based on described offset correction and described user-defined power level (122) output power of battery transmission performance prediction (128).
3. the method for claim 1 further comprises:
Reception is different from the power level of user's adjustment of described user-defined power level (122); And
The power of battery transmission performance prediction (128) that power level output based on resistance in described offset correction and described user adjustment is upgraded.
4. the method for claim 1, wherein said power of battery transmission performance prediction (128) comprise until described battery (102) reach electric discharge finish in excess time (13) of resistance.
5. the method for claim 1, wherein said power of battery transmission performance prediction (128) comprise until described battery (102) reach electric discharge finish in the Vehicle Driving Cycle distance (132) of resistance.
6. the method for claim 1, wherein said power of battery transmission performance prediction (128) comprises that the described battery of indication (102) whether can through-put power continues the cell health state (134) of described at least object time.
7. method as claimed in claim 6 further comprises:
Receive the user-defined object time (124); And
Adjust described cell health state (134) based on the described user-defined object time (124).
8. the method for claim 1, wherein said a plurality of battery-operated parameters comprise operating current (106), operating temperature (108), interior resistance (110) and the charged state (112) of observing.
9. the method for claim 1, described a plurality of battery-operated parameters (106,108,110,112) of wherein retrieving the discharge cycle of described selection comprise a plurality of sensor signals of the real-time reception described a plurality of battery-operated parameters of indication (106,108,110,112), and the described power of battery transmission performance prediction of real-time update (128) during current discharge cycle.
10. the method for claim 1, described a plurality of battery-operated parameters (106,108,110,112) of wherein retrieving the discharge cycle of described selection are included in the described a plurality of battery-operated parameters (106,108,110,112) of next discharge cycle retrieval before from previous discharge cycle storage, and described power of battery transmission performance prediction (128) is applied to described next discharge cycle.
11. the method for the power delivery performance of predicting battery (102) comprises:
Receive user-defined power level (122);
A plurality of battery-operated parameter of the current discharge cycle of real-time retrieval (106,108,110,112);
Calculate the skew (114) of the difference between the interior resistance (116) of the interior resistance (118) of modeling of the described battery of indication (102) and the observation that generates from the calibration discharge cycle of the described battery prior to described current discharge cycle; And
During described current discharge cycle, the interior resistance (118) of the offset correction of the interior resistance of the modeling based on indication based on described a plurality of battery-operated parameters (106,108,110,112) and the difference of described skew (114) and described user-defined power level (122) output power of battery transmission performance prediction (128).
12. method as claimed in claim 11 further comprises:
Reception is different from the power level of user's adjustment of described user-defined power level (122); And
The power of battery transmission performance prediction (128) that the power level output that interior resistance based on described offset correction and described user adjust is upgraded.
13. method 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 finishes interior resistance.
14. method 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 finishes interior resistance.
15. method as claimed in claim 11, wherein said power of battery transmission performance prediction (128) comprises whether the described battery of indication (102) can through-put power continue at least cell health state of object time (134).
16. method as claimed in claim 11, wherein when the cycle count of the discharge cycle of the appearance that there is no full discharge cycle is less than the discharge cycle threshold value, described calibration cycle is full discharge cycle, and, when the cycle count of the discharge cycle of the appearance that there is no full discharge cycle is greater than described discharge cycle threshold value, described calibration cycle is the partial discharge cycle.
17. a battery management system (100) comprising:
Battery (102);
A plurality of battery sensors (104), it can operate to transmit a plurality of sensor signals of a plurality of battery-operated parameters of indication (106,108,110,112) to prediction circuit (120);
Described prediction circuit can operate under off-line mode, battery-operated parameter (116) with retrieval from a plurality of storages of previous discharge cycle, calculate the skew (114) of the difference between the interior resistance of the interior resistance (118) of modeling of the described battery of indication (102) and the observation that generates from the calibration discharge cycle of the described battery prior to described previous discharge cycle, and between the interior resistance (118) of the modeling of the battery-operated parameter based on described a plurality of storages based on indication and described skew (114), the interior resistance output power of battery transmission performance of the offset correction of difference is predicted (128), described power of battery transmission performance prediction (128) may be used on next discharge cycle, and described prediction circuit (120) is during current discharge cycle, under line model, can operate, to receive the described a plurality of battery-operated parameters (106 of indication, 108, 110, 112) described a plurality of battery sensor signals, calculate the skew (114) of the difference between the interior resistance of the interior resistance (118) of modeling of the described battery of indication (102) and the observation that generates from the calibration discharge cycle of the described battery prior to described current discharge cycle, with the described a plurality of battery-operated parameter (106 based on described current discharge cycle based on indication, 108, 110, the interior resistance output power of battery transmission performance prediction (128) of the offset correction of difference between the interior resistance (118) of modeling 112) and described skew (114), described power of battery transmission performance prediction (128) may be used on described current discharge cycle, and
Display device (126), it can operate to receive from the described power delivery performance prediction (128) of described prediction circuit (120) and show described power of battery transmission performance prediction (128).
18. battery management system as claimed in claim 17, wherein said prediction circuit (120) can operate to receive user-defined power level (122) the interior resistance based on described offset correction and described user-defined power level (122) output power of battery transmission performance prediction (128).
19. battery management system as claimed in claim 17, wherein said power of battery transmission performance prediction (128) comprises whether the described battery of indication (102) can through-put power continue at least cell health state of object time (134), 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 system 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 finishes interior resistance, until described battery (102) reaches the distance (132) that electric discharge finishes interior resistance, and indicate described battery whether can through-put power continue at least cell health state of object time (134).
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201210336184.XA CN103487755B (en) | 2012-06-08 | 2012-06-08 | For predicting the system and method for power of battery transmission performance |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201210336184.XA CN103487755B (en) | 2012-06-08 | 2012-06-08 | For predicting the system and method for power of battery transmission performance |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN103487755A true CN103487755A (en) | 2014-01-01 |
| CN103487755B CN103487755B (en) | 2016-02-17 |
Family
ID=49828129
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201210336184.XA Active CN103487755B (en) | 2012-06-08 | 2012-06-08 | For predicting the system and method for power of battery transmission performance |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN103487755B (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| 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 |
| CN106537165A (en) * | 2014-07-15 | 2017-03-22 | 罗伯特·博世有限公司 | Method and device for measuring the electric current of a battery with multiple battery modules |
| CN106608200A (en) * | 2015-10-27 | 2017-05-03 | 北汽福田汽车股份有限公司 | Power output control method and system of electric car |
| CN110023135A (en) * | 2016-12-01 | 2019-07-16 | 沃尔沃卡车集团 | For controlling the method and system of the battery in vehicle |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1767310A (en) * | 2005-11-18 | 2006-05-03 | 上海燃料电池汽车动力系统有限公司 | Remanent electric quantity closed-loop controlling method for vehicle-carried auxiliary power accumulator set |
| CN101031810A (en) * | 2004-02-25 | 2007-09-05 | 皇家飞利浦电子股份有限公司 | Method of estimating the state-of-charge and of the use time left of a rechargeable battery, and apparatus for executing such a method |
| CN101379409A (en) * | 2006-02-09 | 2009-03-04 | 丰田自动车株式会社 | Secondary cell residual capacity estimating device and residual capacity estimating method |
| WO2009149273A1 (en) * | 2008-06-05 | 2009-12-10 | A123 Systems, Inc. | Method and system for determining state of charge of an energy delivery device |
-
2012
- 2012-06-08 CN CN201210336184.XA patent/CN103487755B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101031810A (en) * | 2004-02-25 | 2007-09-05 | 皇家飞利浦电子股份有限公司 | Method of estimating the state-of-charge and of the use time left of a rechargeable battery, and apparatus for executing such a method |
| JP2007526456A (en) * | 2004-02-25 | 2007-09-13 | コーニンクレッカ フィリップス エレクトロニクス エヌ ヴィ | Method for assessing the state of charge, method for assessing the remaining usage time of a rechargeable battery and equipment implementing such method |
| US20080150491A1 (en) * | 2004-02-25 | 2008-06-26 | Koninklijke Philips Electronics, N.V. | Method Of Estimating The State-Of-Charge And Of The Use Time Left Of A Rechageable Battery, And Apparatus For Executing Such A Method |
| CN1767310A (en) * | 2005-11-18 | 2006-05-03 | 上海燃料电池汽车动力系统有限公司 | Remanent electric quantity closed-loop controlling method for vehicle-carried auxiliary power accumulator set |
| CN101379409A (en) * | 2006-02-09 | 2009-03-04 | 丰田自动车株式会社 | Secondary cell residual capacity estimating device and residual capacity estimating method |
| WO2009149273A1 (en) * | 2008-06-05 | 2009-12-10 | A123 Systems, Inc. | Method and system for determining state of charge of an energy delivery device |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106537165A (en) * | 2014-07-15 | 2017-03-22 | 罗伯特·博世有限公司 | Method and device for measuring the electric current of a battery with multiple battery modules |
| CN106608200A (en) * | 2015-10-27 | 2017-05-03 | 北汽福田汽车股份有限公司 | Power output control method and system of electric car |
| 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 |
| CN110023135A (en) * | 2016-12-01 | 2019-07-16 | 沃尔沃卡车集团 | For controlling the method and system of the battery in vehicle |
| CN110023135B (en) * | 2016-12-01 | 2022-08-26 | 沃尔沃卡车集团 | Method and system for controlling battery in vehicle |
Also Published As
| Publication number | Publication date |
|---|---|
| CN103487755B (en) | 2016-02-17 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US8872481B2 (en) | Systems and methods for predicting battery power-delivery performance | |
| JP7063440B2 (en) | Battery management device and method | |
| US10447046B2 (en) | Secondary battery management system with remote parameter estimation | |
| US20210044119A1 (en) | Method of performing a state of health estimation for a rechargeable battery energy storage system | |
| JP6556649B2 (en) | Storage battery evaluation device, storage battery, storage battery evaluation method, and program | |
| KR102335296B1 (en) | Wireless Network based Battery Management System | |
| US9557387B2 (en) | Testing individual cells within multi-cell battery applications | |
| JP5743634B2 (en) | Deterioration measuring device, secondary battery pack, deterioration measuring method, and program | |
| EP3719515B1 (en) | Evaluation device, evaluation method, and computer program | |
| US20140055100A1 (en) | Battery state estimation system, battery control system, battery system, and battery state estimation method | |
| JP2018147827A (en) | Charge and discharge control device, service condition creation device, program and power storage system | |
| Chen et al. | Modelling of lithium-ion battery for online energy management systems | |
| EP3832331B1 (en) | Cell state estimation device and cell control device | |
| CN103487755A (en) | System and method for predicting power transmission performance of battery | |
| US20140300323A1 (en) | Vehicle battery system balancing systems and methods | |
| CA2931084A1 (en) | Power storage system and charging method for secondary battery | |
| US20150369876A1 (en) | Deterioration determination method, manufacturing method of electric storage device, deterioration determination device, and storage medium | |
| CN108107374A (en) | The battery power quantity display method and device of terminal device | |
| JP5583057B2 (en) | Battery module, battery pack system, battery adjustment method and program | |
| JP5674713B2 (en) | System and method for predicting battery power supply performance | |
| US20180198176A1 (en) | Storage battery management system | |
| JP5851514B2 (en) | Battery control device, secondary battery system | |
| EP2672562B1 (en) | Systems and methods for predicting battery power-delivery performance | |
| KR101736717B1 (en) | Energy storage apparatus and method for controlling therof | |
| EP3602724B1 (en) | Controlling a battery charge level |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant |