Claims:WE CLAIM:
1. A system (10) to estimate and predict state of charge in a battery pack comprising:
a measurement module (20) configured to obtain a data representative of a plurality of terminal voltage signals corresponding to a plurality of cells of the battery pack and a plurality of terminal current signals corresponding to the plurality of cells of the battery pack;
a filtering module (30) operatively coupled to the measurement module (20) and configured to obtain a filtered signal which is the representative of the plurality of terminal voltage signals and the plurality of terminal current signals at a predefined time interval to filter noise from the data;
an identification module (40) operatively coupled to the filtering module (30) and configured to identify the cell with the lowest open circuit voltage out of the plurality of cells from filtered data using an equivalent circuit method;
an estimation module (50) operatively coupled to the identification module (40) and configured to:
estimate a first state of charge value corresponding to the lowest open circuit voltage of the battery pack using an open circuit voltage versus state of charge look up table of the plurality of cells of the battery pack;
estimate a second state of charge value of the battery pack using a coulomb counting method;
a computation module (60) operatively coupled to the estimation module (50) and configured to:
compute a weighted average function based on an upper threshold value of open circuit voltage of the battery pack, a lower threshold value of open circuit voltage of the battery pack, an instantaneous open circuit voltage of the battery pack and a state dependent voltage parameter; and
compute a temporary state of charge of the battery pack based on the first state of charge value and the second state of charge value using the weighted average function.
2. The system (10) as claimed in claim 1, wherein the computation module (60) is configured to compute the temporary state of charge in the battery pack when the lowest open circuit voltage is less than a predefined threshold voltage.
3. The system (10) as claimed in claim 1, further comprising a prediction module (170) operatively coupled to the computation module (160), wherein the prediction module is configured to predict a predicted state of charge based on the temporary state of charge and the lowest open circuit voltage.
4. The system (10) as claimed in claim 3, wherein the prediction module (170) is configured to compute a differential state of charge, wherein the differential state of charge comprises a difference of the temporary state of charge and a predicted state of charge for a previous time instance of the predefined time interval.
5. The system (10) as claimed in claim 4, wherein the prediction module (170) is configured to compute an incremental state of charge based on the differential state of charge and the predefined time interval when an absolute differential state of charge is greater than a predetermined value.
6. The system (10) as claimed in claim 5, wherein the prediction module (170) is configured to predict the predicted state of charge by distributing the incremental state of charge in the predicted state of charge for the previous time instance of the predefined time interval.
7. The system (10) as claimed in claim 3, wherein the prediction module (170) is configured to predict the predicted state of charge as the temporary state of charge when the absolute differential state of charge is less than the predetermined value.
8. The system (10) as claimed in claim 3, wherein the prediction module (170) is configured to predict the predicted state of charge as the second state of charge when the lowest open circuit voltage is greater than the predefined threshold voltage.
9. The system (10) as claimed in claim 1, wherein the identification module (40) is configured to update the system (10) with a cell having the lowest open circuit voltage in a real time.
10. The system (10) as claimed in claim 1, wherein the state dependent voltage parameter comprises an upper threshold value of the open circuit voltage when the instantaneous open circuit voltage is less than or equal to the upper threshold value of the open circuit voltage.
11. The system (10) as claimed in claim 1, wherein the state dependent voltage parameter comprises the instantaneous open circuit voltage when the instantaneous open circuit voltage is greater than the upper threshold value of the open circuit voltage.
12. A method (400) comprising:
obtaining a data representative of a plurality of terminal voltage signals corresponding to a plurality of cells of the battery pack and a plurality of terminal current signals corresponding to the plurality of cells of the battery pack; (410)
obtaining a filtered signal which is the representative of the plurality of terminal voltage signals and the plurality of terminal current signals at a predefined time interval to filter noise from the data; (420)
identifying the cell with the lowest open circuit voltage out of the plurality of cells from the filtered data using an equivalent circuit method; (430)
estimating a first state of charge value corresponding to the lowest open circuit voltage of the battery pack using an open circuit voltage versus state of charge look up table of the plurality of cells of the battery pack; (440)
estimating a second state of charge value of the battery pack using a coulomb counting method; (450)
computing a weighted average function based on an upper threshold value of open circuit voltage of the battery pack, a lower threshold value of open circuit voltage of the battery pack, an instantaneous open circuit voltage of the battery pack and a state dependent voltage parameter (460); and
computing a temporary state of charge of the battery pack based on the first state of charge value and the second state of charge value using the weighted average function. (470)
13. The method (400) as claimed in claim 12, wherein computing a temporary state of charge of the battery pack based on the first state of charge value and the second state of charge value using the weighted average function comprises computing a temporary state of charge of the battery pack based on the first state of charge value and the second state of charge value using the weighted average function when the lowest open circuit voltage is less than a predefined threshold voltage.
14. The method (400) as claimed in claim 12, further comprising predicting a predicted state of charge based on the temporary state of charge and the lowest open circuit voltage.
15. The method (400) as claimed in claim 12, further comprising computing a differential state of charge, wherein the differential state of charge comprises a difference of the temporary state of charge and a predicted state of charge for a previous time instance of the predefined time interval.
16. The method (400) as claimed in claim 15, further comprising computing an incremental state of charge based on the differential state of charge and the predefined time interval when an absolute differential state of charge is greater than a predetermined value.
17. The method (400) as claimed in claim 16, further comprising predicting the predicted state of charge by distributing an incremental state of charge in a predicted state of charge for the previous time instance of the predefined time interval.
18. The method (400) as claimed in claim 14, further comprising predicting the predicted state of charge as the temporary state of charge when the absolute differential state of charge is less than the predetermined value.
19. The method (400) as claimed in claim 14, further comprising predicting the predicted state of charge as the second state of charge when the lowest open circuit voltage is greater than the predefined threshold voltage.
20. The method as claimed in claim 12, further comprising updating the system with a cell having the lowest open circuit voltage in a real time.

, Description:BACKGROUND
Embodiments of a present disclosure relates to a multi cell battery and more particularly to a system and a method to estimate and predict state of charge of a battery pack.
A battery is an energy storage device of today and it is going to play even more crucial role in the future electronic devices. A wide range of devices, such as portable electronic equipment, mobile household appliances, aerospace equipment and vehicles are increasingly being powered by batteries. A charge stored by the battery is determined by the mass of active material contained in the battery which is known as cell capacity. The cell capacity represents the maximum amount of energy that can be extracted from the battery under certain specified conditions. The cell capacities may be determined using state of charge (SOC) estimates for a plurality of cells and a charge count for the battery. The state of charge (SOC) is a measure of an amount of charge available in a battery relative to the battery's capacity. The actual capacity of a battery may be used to evaluate the overall condition and performance of the battery. Various methods are available to estimate the capacity, the condition and performance of the battery.
Traditional methods estimate the cell capacity as well as predicts the actual available power in the battery system at the pack level using a single method of Coulomb counting. However, such methods result in inaccurate SoC prediction in an unbalanced battery pack or in a battery pack with weak or defective cells. Inaccurate prediction of useful energy leads to inaccurate range estimation and range anxiety.
Hence, there is a need for an improved system and method to estimate and predict a state of charge in a battery pack to address the aforementioned issues.

BRIEF DESCRIPTION
In accordance with an embodiment of the present disclosure, a system to estimate and predict state of charge of a battery pack is provided. The system includes a measurement module which is configured to obtain a data representative of a plurality of terminal voltage signals corresponding to a plurality of cells of the battery pack and a plurality of terminal current signals corresponding to the plurality of cells of the battery pack. The system also includes a filtering module which is operatively coupled to the measurement module. The filtering module is configured to obtain a filtered signal which is the representative of the plurality of terminal voltage signals and the plurality of terminal current signals at a predefined time interval to filter noise from the data. The system further includes an identification module which is operatively coupled to the filtering module. The identification module is configured to identify the cell with the lowest open circuit voltage out of the plurality of cells from filtered data using an equivalent circuit method. The system further includes an estimation module which is operatively coupled to the identification module. The estimation module is configured to estimate a first state of charge value corresponding to the lowest open circuit voltage of the battery pack using an open circuit voltage versus state of charge look up table of the plurality of cells of the battery pack. The estimation module is also configured to estimate a second state of charge value of the battery pack using a coulomb counting method. The system further includes a computation module which is operatively coupled to the estimation module. The computation module is configured to compute a weighted average function based on an upper threshold value of open circuit voltage of the battery pack, a lower threshold value of open circuit voltage of the battery pack, an instantaneous open circuit voltage of the battery pack and a state dependent voltage parameter. The computation module is also configured to compute a temporary state of charge of the battery pack based on the first state of charge value and the second state of charge value using the weighted average function.
In accordance with another embodiment of the present disclosure, a method to estimate and predict state of charge of a battery pack is provided. The method includes obtaining a data representative of a plurality of terminal voltage signals corresponding to a plurality of cells of the battery pack and a plurality of terminal current signals corresponding to the plurality of cells of the battery pack. The method also includes obtaining a filtered signal which is the representative of the plurality of terminal voltage signals and the plurality of terminal current signals at a predefined time interval to filter noise from the data. The method further includes identifying the cell with the lowest open circuit voltage out of the plurality of cells from filtered data using an equivalent circuit method. The method further includes estimating a first state of charge value corresponding to the lowest open circuit voltage of the battery pack using an open circuit voltage versus state of charge look up table of the plurality of cells of the battery pack. The method further includes estimating a second state of charge value of the battery pack using a coulomb counting method. The method further includes computing a weighted average function based on an upper threshold value of open circuit voltage of the battery pack, a lower threshold value of open circuit voltage of the battery pack, an instantaneous open circuit voltage of the battery pack and a state dependent voltage parameter. The method further includes computing a temporary state of charge of the battery pack based on the first state of charge value and the second state of charge value using the weighted average function.
To further clarify the advantages and features of the present invention, a more particular description of the invention will follow by reference to specific embodiments thereof, which are illustrated in the appended figures. It is to be appreciated that these figures depict only typical embodiments of the invention and are therefore not to be considered limiting in scope. The invention will be described and explained with additional specificity and detail with the appended figures.
BRIEF DESCRIPTION OF THE DRAWINGS
The disclosure will be described and explained with additional specificity and detail with the accompanying figures in which:
FIG. 1 is a block diagram representation of a system to estimate and predict state of charge of a battery pack in accordance with an embodiment of the present disclosure;
FIG. 2 is a block diagram representation of an exemplary system to estimate and predict the state of charge of the battery pack of FIG. 1 in accordance with an embodiment of the present disclosure;
FIG. 3 is a schematic representation of one embodiment of FIG. 1, depicts a method flow chart to estimate and predict the state of charge of the battery pack in accordance with an embodiment of the present disclosure; and
FIG. 4 is a flow chart representing the steps involved in a method to estimate and predict state of charge of the battery pack of FIG. 1 in accordance with an embodiment of the present disclosure.
Further, those skilled in the art will appreciate that elements in the figures are illustrated for simplicity and may not have necessarily been drawn to scale. Furthermore, in terms of the construction of the device, one or more components of the device may have been represented in the figures by conventional symbols, and the figures may show only those specific details that are pertinent to understanding the embodiments of the present disclosure so as not to obscure the figures with details that will be readily apparent to those skilled in the art having the benefit of the description herein.
DETAILED DESCRIPTION
For the purpose of promoting an understanding of the principles of the disclosure, reference will now be made to the embodiment illustrated in the figures and specific language will be used to describe them. It will nevertheless be understood that no limitation of the scope of the disclosure is thereby intended. Such alterations and further modifications in the illustrated system, and such further applications of the principles of the disclosure as would normally occur to those skilled in the art are to be construed as being within the scope of the present disclosure.
The terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process or method that comprises a list of steps does not include only those steps but may include other steps not expressly listed or inherent to such a process or method. Similarly, one or more devices or sub-systems or elements or structures or components preceded by "comprises... a" does not, without more constraints, preclude the existence of other devices, sub-systems, elements, structures, components, additional devices, additional sub-systems, additional elements, additional structures or additional components. Appearances of the phrase "in an embodiment", "in another embodiment" and similar language throughout this specification may, but not necessarily do, all refer to the same embodiment.
In the following specification and the claims, reference will be made to a number of terms, which shall be defined to have the following meanings. The singular forms “a”, “an”, and “the” include plural references unless the context clearly dictates otherwise.
Embodiments of the present disclosure relate to a system to estimate and predict state of charge of a battery pack. The system includes a measurement module which is configured to obtain a data representative of a plurality of terminal voltage signals corresponding to a plurality of cells of the battery pack and a plurality of terminal current signals corresponding to the plurality of cells of the battery pack. The system also includes a filtering module which is operatively coupled to the measurement module. The filtering module is configured to obtain a filtered signal which is the representative of the plurality of terminal voltage signals and the plurality of terminal current signals at a predefined time interval to filter noise from the data. The system further includes an identification module which is operatively coupled to the filtering module. The identification module is configured to identify the cell with the lowest open circuit voltage out of the plurality of cells from filtered data using an equivalent circuit method. The system further includes an estimation module which is operatively coupled to the identification module. The estimation module is configured to estimate a first state of charge value corresponding to the lowest open circuit voltage of the battery pack using an open circuit voltage versus state of charge look up table of the plurality of cells of the battery pack. The estimation module is also configured to estimate a second state of charge value of the battery pack using a coulomb counting method. The system further includes a computation module which is operatively coupled to the estimation module. The computation module is configured to compute a weighted average function based on an upper threshold value of open circuit voltage of the battery pack, a lower threshold value of open circuit voltage of the battery pack, an instantaneous open circuit voltage of the battery pack and a state dependent voltage parameter. The computation module is also configured to compute a temporary state of charge of the battery pack based on the first state of charge value and the second state of charge value using the weighted average function.
FIG. 1 is a block diagram representation of a system (10) to estimate and predict state of charge of a battery pack in accordance with an embodiment of the present disclosure. The system (10) includes a measurement module (20) which is configured to obtain a data representative of a plurality of terminal voltage signals corresponding to a plurality of cells of the battery pack and a plurality of terminal current signals corresponding to the plurality of cells of the battery pack. The system (10) also includes a filtering module (30) which is operatively coupled to the measurement module (20). The filtering module (30) is configured to obtain a filtered signal which is the representative of the plurality of terminal voltage signals and the plurality of terminal current signals at a predefined time interval to filter noise from the data. The noise is a data with a large amount of additional meaningless information.
The system (10) further includes an identification module (40) which is operatively coupled to the filtering module. The identification module (40) is configured to identify the cell with the lowest open circuit voltage out of the plurality of cells from filtered data using an equivalent circuit method. The lowest open circuit voltage using the equivalent circuit method is derived using a plurality of equations stated below:
v_oc [k]=v_(term_avg ) [k]- i_avg [k]*R0[k]+v_c1 [k]+v_c2 [k]
v_c1 [k]= v_c1 [k-1]*e^(-?t/(tau_1 [k] ))- i_avg [k]*R1[k]*(1 -e^(-?t/(tau_1 [k] )) )
v_c2 [k]= v_c2 [k-1]*e^(-?t/(tau_2 [k] ))- i_avg [k]*R2[k]*(1 -e^(-?t/(tau_2 [k] )) )
Where, v_(term_avg ) is the averaged terminal voltage, i_avg is the average current. Voltage and current are averaged over the predefined time interval such as last 60 seconds.
v_c1 and v_c2 are the voltages across the capacitors. Initialize capacitor voltages to 0, such as v_c1=0, v_c2=0. R0, R1, R2, tau1, tau2 are obtained using a look up table. In such case, a discharge current is assumed to be negative.
In one embodiment, the identification module (40) may be configured to update the system (10) with a cell having the lowest open circuit voltage in a real time at every predefined time interval.
Furthermore, the system (10) includes an estimation module (50) which is operatively coupled to the identification module (40). The estimation module (50) is configured to estimate a first state of charge value corresponding to the lowest open circuit voltage of the battery pack using an open circuit voltage versus state of charge look up table of the plurality of cells of the battery pack. The estimation module (50) is also configured to estimate a second state of charge value of the battery pack using a coulomb counting method. The second state of charge using the coulomb counting method is derived using an equation stated below:
SoC_cc [k] = SoC_cc [k-1]+i*t_s
Where, i is a current of a cell with the lowest voltage, ts is the sampling time and SoC_cc [k-1] is coulomb counting state of charge at previous state.
Moreover, the system (10) further includes a computation module (60) operatively coupled to the estimation module (50). The computation module (60) is configured to compute a weighted average function based on an upper threshold value of open circuit voltage of the battery pack, a lower threshold value of open circuit voltage of the battery pack, an instantaneous open circuit voltage of the battery pack and a state dependent voltage parameter. The weighted average function is derived using an equation stated below:
wt= ((v_ocv [k] - v_(oc?v_lower?_thresh ))/(?v_ocv?_fac -v_(oc?v_lower?_thresh ) ))^a
Where ?v_ocv?_fac is the state dependent voltage parameter, v_(oc?v_lower?_thresh ) is the lower threshold open circuit voltage, ?v_ocv?_(upper_thresh ) is the upper threshold open circuit voltage, v_ocv [k] is the instantaneous open circuit voltage and an exponent ‘a’ is defined to be greater than 0.
In some embodiments, the state dependent voltage parameter may be an upper threshold value of the open circuit voltage when the instantaneous open circuit voltage is less than or equal to the upper threshold value of the open circuit voltage. In another embodiment, the state dependent voltage parameter may be the instantaneous open circuit voltage when the instantaneous open circuit voltage is greater than the upper threshold value of the open circuit voltage. In a specific embodiment, the upper threshold value of the open circuit voltage and the lower threshold value of open circuit voltage are selected based on chemistry of Lithium-ion cell.
The computation module (60) is also configured to compute a temporary state of charge of the battery pack based on the first state of charge value and the second state of charge value using the weighted average function. The temporary state of charge is derived using an equation stated below:
temp_soc=wt*SoC_cc [k] +(1 -wt)*SoC_ocv [k]
Where SoC_ocv [k] is the first state of charge corresponding to the lowest open circuit voltage of the battery pack using an open circuit voltage versus state of charge look up table of the cell and SoC_cc [k] is the second state of charge corresponding to state of charge using coulomb counting method.
In one embodiment, the computation module (60) may be configured to compute the temporary state of charge in the battery pack when the lowest open circuit voltage is less than a predefined threshold voltage.
FIG. 2 is a block diagram representation of an exemplary system (10) to estimate and predict the state of charge of the battery pack (110) of FIG. 1 in accordance with an embodiment of the present disclosure. The system (10) includes a measurement module (120) which is configured to obtain a data representative of a plurality of terminal voltage signals corresponding to a plurality of cells of the battery pack and a plurality of terminal current signals corresponding to the plurality of cells of the battery pack. The measurement module (120) is substantially similar to the measurement module (20) of FIG. 1.
The system (10) also includes a filtering module (130) which is operatively coupled to the measurement module (120). The filtering module (130) is substantially similar to the filtering module (30) of FIG. 1. The filtering module (130) is configured to obtain a filtered which is the representative of the plurality of terminal voltage signals and the plurality of terminal current signals at a predefined time interval to filter noise from the data.
The system (10) further includes an identification module (140) which is operatively coupled to the filtering module (130). The identification module (140) is substantially similar to identification module (40) of FIG. 1. The identification module (140) is configured to identify the cell with the lowest open circuit voltage out of the plurality of cells from the filtered data using an equivalent circuit method. In one embodiment, the identification module (140) may be configured to update the system (10) with a cell having the lowest open circuit voltage in a real time at every predefined time interval.
The system (10) further includes an estimation module (150) which is operatively coupled to the identification module (140). The estimation module (150) is substantially similar to the estimation module (50) of FIG. 1. The estimation module (150) is configured to estimate a first state of charge value corresponding to the lowest open circuit voltage of the battery pack (110) using an open circuit voltage versus state of charge look up table of the plurality of cells of the battery pack. The estimation module (150) is also configured to estimate a second state of charge value of the battery pack (110) using a coulomb counting method.
Furthermore, the system (10) includes a computation module (160) operatively coupled to the estimation module (150). The computation module (160) is substantially similar to the computation module (60) of FIG. 1. The computation module (160) is configured to compute a weighted average function based on an upper threshold value of open circuit voltage of the battery pack, a lower threshold value of open circuit voltage of the battery pack, an instantaneous open circuit voltage of the battery pack and a state dependent voltage parameter. In one embodiment, the state dependent voltage parameter may include an upper threshold value of the open circuit voltage when the instantaneous open circuit voltage is less than or equal to the upper threshold value of the open circuit voltage. In another embodiment, the state dependent voltage parameter may include the instantaneous open circuit voltage when the instantaneous open circuit voltage is greater than the upper threshold value of the open circuit voltage.
Moreover, the computation module (160) is also configured to compute a temporary state of charge of the battery pack (110) based on the first state of charge value and the second state of charge value using the weighted average function. In a specific embodiment, the computation module (160) may be configured to compute the temporary state of charge in the battery pack (110) when the lowest open circuit voltage is less than a predefined threshold voltage.
In some embodiments, the system (10) may include a prediction module (170) which is operatively coupled to the computation module (160). The prediction module (170) is configured to predict a predicted state of charge based on the temporary state of charge and the lowest open circuit voltage. In such embodiment, the prediction module (170) may also be configured to compute a differential state of charge. The differential state of charge may include a difference of the temporary state of charge and a predicted state of charge for a previous time instance of the predefined time interval. In a specific embodiment, the prediction module (170) may be configured to predict the predicted state of charge by distributing the incremental state of charge in the predicted state of charge for the previous time instance of the predefined time interval.
In one embodiment, the prediction module (170) may further be configured to predict the predicted state of charge by distributing the incremental state of charge in the predicted state of charge for the previous time instance of the time interval. In some embodiments, the prediction module (170) may be configured to predict the predicted state of charge as the temporary state of charge when the absolute differential state of charge is less than the predetermined value. In another embodiment, the prediction module (170) may be configured to predict the predicted state of charge as the second state of charge when the lowest open circuit voltage is greater than the predefined threshold voltage.
FIG. 3(a) is a schematic representation of one embodiment of FIG. 1, depicts a method flow chart (200) to estimate and predict the state of charge of the battery pack and FIG. 3(b) is a continued flow chart representation of FIG. 3(a) in accordance with an embodiment of the present disclosure. The system to estimate and predict the state of charge of the battery pack sets a plurality of parameters such as check time, flag, cell selection, counter to initiate the estimation and prediction (step 210). For example, initially the check time is set to 60 seconds. The system evaluates the situation and receive the data in every 60 seconds. All the counters and flags set to 0 in the initial default condition. Firstly, the system measures voltage and current of each of a plurality of cells of the battery pack at every 1 second (step 220).
Further, the system performs filtering of cell terminal voltage and current values to filter noise from measured voltage and current data in every 60 seconds. Then, the system computes open circuit voltage of each of the plurality of cells using an equivalent circuit method and identifies the cell with the lowest open circuit voltage out of the plurality of cells (step 230). The system further estimates a first state of charge value corresponding to the lowest open circuit voltage of the battery pack using an open circuit voltage versus state of charge look up table of the plurality of cells of the battery pack. Also, the system estimates a second state of charge value of the battery pack using a coulomb counting method (step 240). Then the system evaluates, if the open circuit voltage (OCV) of the cell with the lowest OCV is greater than a predefined threshold value (step 250) then the system predicts that the state of charge of the battery is the second state of charge which is calculated using coulomb counting method (step 260).
Similarly, if the open circuit voltage (OCV) of the cell with the lowest OCV is less than a predefined threshold value (step 250) then the system computes a temporary state of charge of the battery pack based on the first state of charge value and the second state of charge value using the weighted average function (270). The weighted average function is computed based on an upper threshold value of open circuit voltage, a lower threshold value of open circuit voltage, an instantaneous open circuit voltage and a state dependent voltage parameter. Here, the weighted average function is a function of the state dependent voltage parameter.
Based on a value of the state dependent voltage parameter the weightage of the methods is evaluated to calculate the temporary state of charge. The state dependent voltage parameter obtains an upper threshold value of the open circuit voltage when the instantaneous open circuit voltage is less than or equal to the upper threshold value of the open circuit voltage. Similarly, the state dependent voltage parameter obtains the instantaneous open circuit voltage when the instantaneous open circuit voltage is greater than the upper threshold value of the open circuit voltage. In such case, the value of the weighted average function becomes equal to 1 and the temporary state of charge is the second state of charge which is calculated using the coulomb counting method.
Furthermore, to predict a correct temporary state of charge of the battery pack, the system computes a predicted state of charge based on the temporary state of charge and the lowest open circuit voltage. To compute the predicted state of charge, the system computes a differential state of charge which is a difference of the temporary state of charge and a predicted state of charge at previous time instance. For example, this condition is evaluated at a time instance of 180 seconds from when the process has started. So, the differential state of charge is a difference of the temporary state of charge and the predicted state of charge at 120 seconds. Moreover, if the product of average current and the differential state of charge is greater than a predetermined value such as zero then this indicates the battery is discharging with a proper rate (step 280).
Further, the system evaluates, if an absolute value of differential state of charge is less than a predefined value such as 1 (step 290), then the temporary state of charge is predicted as the predicted state of charge (step 300). Otherwise, an incremental state of charge is calculated based on the differential state of charge and the predefined time interval (step 310). For example, if the battery is discharging from 40 percent to 35 percent at 60 second time interval; then the state of charge is calculated as (35-40)/60 and such incremental state of charge is distributed to the predicted state of charge at previous time instance to predict the predicted state of charge (step 320). The flag value is set at 1 when a cell with a lowest open circuit voltage is less than a threshold value (step 330). Such flag value indicates that such cell should be evaluated at every time instance.
FIG. 4 is a flow chart representing the steps involved in a method (400) to estimate and predict state of charge of the battery pack of FIG. 1 in accordance with an embodiment of the present disclosure. The method (400) includes obtaining a data representative of a plurality of terminal voltage signals corresponding to a plurality of cells of the battery pack and a plurality of terminal current signals corresponding to the plurality of cells of the battery pack in step 410. The method (400) also includes obtaining a filtered signal which is the representative of the plurality of terminal voltage signals and the plurality of terminal current signals at a predefined time interval to filter noise from the data step 420.
The method (400) also includes identifying the cell with the lowest open circuit voltage out of the plurality of cells from the filtered data using an equivalent circuit method in step 430. The method (400) further includes estimating a first state of charge value corresponding to the lowest open circuit voltage of the battery pack using an open circuit voltage versus state of charge look up table of the plurality of cells of the battery pack in step 440.
The method (400) further includes estimating a second state of charge value of the battery pack using a coulomb counting method in step 450. The method (400) further includes computing a weighted average function based on an upper threshold value of open circuit voltage of the battery pack, a lower threshold value of open circuit voltage of the battery pack, an instantaneous open circuit voltage of the battery pack and a state dependent voltage parameter in step 460. The method (400) further includes computing a temporary state of charge of the battery pack based on the first state of charge value and the second state of charge value using the weighted average function in step 470.
In one embodiment, computing a temporary state of charge of the battery pack based on the first state of charge value and the second state of charge value using the weighted average function may include computing a temporary state of charge of the battery pack based on the first state of charge value and the second state of charge value using the weighted average function when the lowest open circuit voltage is less than a predefined threshold voltage.
In some embodiments, the method (400) may include predicting a predicted state of charge based on the temporary state of charge and the lowest open circuit voltage. In a specific embodiment, computing a differential state of charge, wherein the differential state of charge may include a difference of the temporary state of charge and a predicted state of charge for a previous time instance of the predefined time interval. In such embodiment, the method (400) may include computing an incremental state of charge based on the differential state of charge and the predefined time interval when an absolute differential state of charge is greater than a predetermined value. In such embodiment, the method (400) may include predicting the predicted state of charge by distributing an incremental state of charge in a predicted state of charge for the previous time instance of the predefined time interval.
In one embodiment, the method (400) may also include predicting the predicted state of charge as the temporary state of charge when the absolute differential state of charge is less than the predetermined value. In another embodiment, the method (400) may include predicting the predicted state of charge as the second state of charge when the lowest open circuit voltage is greater than the predefined threshold voltage. In a specific embodiment, the method (400) may further include updating the system with a cell having the lowest open circuit voltage in a real time.
Various embodiments of the system to estimate and predict state of charge of a battery pack described above enables efficient computational method for predicting the state of charge. The system takes care of inaccuracies due to measurement circuit or sensor errors and predicts smooth state of charge of the battery pack.
Furthermore, the system takes care of ill-defined open circuit voltages versus state of charge curves in Lithium ion phosphate cells and may be extended for Lithium ion cells of all chemistries.
It will be understood by those skilled in the art that the foregoing general description and the following detailed description are exemplary and explanatory of the disclosure and are not intended to be restrictive thereof.
While specific language has been used to describe the disclosure, any limitations arising on account of the same are not intended. As would be apparent to a person skilled in the art, various working modifications may be made to the method in order to implement the inventive concept as taught herein.
The figures and the foregoing description give examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be split into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, order of processes described herein may be changed and are not limited to the manner described herein. Moreover, the actions of any flow diagram need not be implemented in the order shown; nor do all of the acts need to be necessarily performed. Also, those acts that are not dependent on other acts may be performed in parallel with the other acts. The scope of embodiments is by no means limited by these specific examples.