Development of a Proposed Performance Standard for Battery Storage System connected to a Domestic/ Small Commercial Solar PV system Battery Capacity Estimation Methodology and Validation using ITP data
Report Number: PP198127-AUME-MS03-TEC-01-R-01-A
Project Partners
Funding Partners
Revision History Revision No 1
Date
Authored by
Reviewed by
21/5/19
Dr. Sajeeb Saha, Finna Mathew, Dr. Md Enamul Haque Dr. Sajeeb Saha, Finna Mathew, Dr. Md Enamul Haque Dr. Sajeeb Saha, Finna Mathew, Dr. Md Enamul Haque Dr. Sajeeb Saha, Finna Mathew, Dr. Md Enamul Haque
Dr. Apel Mahmud, Dr. Shama Islam
2
14/06/19
3
21/06/2019
4
28/06/2019
Dr. Apel Mahmud, Dr. Shama Islam Dr. Apel Mahmud, Dr. Shama Islam Dr. Apel Mahmud, Dr. Shama Islam
Approved by
DNV GL Approval Reviewed 30/05/19. Please revise content as per comments. Reviewed 18/06/19. Please revise content as per comments. Reviewed 28/06/19. Please revise content as per comments. Approved 28/06/19.
The views expressed herein are not necessarily the views of the Australian Government, and the Australian Government does not accept responsibility for any information or advice contained herein.
DEAKIN UNIVERSITY
Deakin University CRICOS Provider Code: 00113B
Contents Executive Summary ...................................................................................................................................... 1 Introduction ................................................................................................................................................... 2 Preliminaries ................................................................................................................................................. 3 Basics of the coulomb counting (CC) method .............................................................................................. 6 Rationale behind the choice of Coulomb counting (CC) approach .............................................................. 9 Validation of Coulomb counting approach using ITP renewable data ......................................................... 9 Proposed Improved Coulomb Counting approach ...................................................................................... 21 Performance Evaluation of the Proposed Approach using ITP data ........................................................... 25 Future work ................................................................................................................................................. 29 Conclusion .................................................................................................................................................. 31 References ................................................................................................................................................... 32
List of tables Table 1: Existing SoC Estimation Techniques ............................................................................................. 5 Table 2: ITP data battery specifications...................................................................................................... 10 Table 3: Summary of factors affecting the accuracy of conventional Coulomb Counting approach ......... 21
List of figures Figure 1: Flowchart of Coulomb counting method ....................................................................................... 7 Figure 2: NMC1 ITP and Estimated SoC and SoC estimation error .......................................................... 11 Figure 3: NMC2 ITP and estimated SoC and SoC estimation error ........................................................... 12 Figure 4: LIP1 ITP and estimated SoC and SoC estimation error .............................................................. 13 Figure 5: LIP2 ITP and Estimated SoC and SoC estimation error ............................................................. 14 Figure 6: NMC1 ITP and estimated SoC and SoC estimation error using CC method with noisy data ..... 16 Figure 7: NMC2 ITP and estimated SoC and SoC estimation error using CC with noisy data .................. 17 Figure 8:LIP1 ITP and estimated SoC and SoC estimation error using CC with noisy data ...................... 18 Figure 9:LIP2 ITP and estimated SoC and SoC estimation error using CC with noisy data ...................... 19 Figure 10: A flowchart showing the proposed improved CC approach...................................................... 22 Figure 11: A schematic of the adaptive filter .............................................................................................. 24 Figure 12: NMC1 SoC and SoC error using improved CC with noisy data ............................................... 26 Figure 13: NMC2 SoC and SoC error using improved CC with noisy data ............................................... 27 Figure 14: LiP1 SoC and SoC error using improved CC with noisy data .................................................. 28 Figure 15: LIP2 SoC and SoC error using improved CC with noisy data .................................................. 29
List of Abbreviations ABPS
Australian Battery Performance Standard
Ah
Ampere Hour
ANN
Artificial Neural Network
ARENA
Australian Renewable Energy Agency
BMS
Battery Management System
DoD
Depth of discharge
DST
Dynamic stress test
EMS
Energy Management System
ECM
Equivalent circuit model
EM
Empirical model
DDM
Data driven model
ITP
IT Power (Australia) Pty Ltd, trading as ITP Renewables
kW
Kilowatt
kWh
Kilowatt hour
KF
Kalman Filter
EKF
Extended Kalman Filter
OCV
Open circuit voltage
SoC
State of Charge
Executive Summary Precise knowledge of battery capacity is essential for ensuring maximum utilisation of solar energy and longevity for the battery in a PV-Battery system. Inclusion of a generic battery capacity estimation methodology in the Australian Battery Performance Standard (ABPS) project is essential to generalise the standard for all batteries commonly used in conjunction with domestic PV systems. Though different capacity estimation techniques have been reported in the literature, there is no generic capacity estimation technique that can precisely determine the battery capacity regardless of battery chemistry or operating conditions. Based on the comprehensive literature review, as presented in a previous report [1], it has been identified that the dynamic capacity is commonly determined as a function of the state of charge (SoC). Among different existing battery capacity estimation techniques, the Coulomb Counting (CC) approach has been identified to possess the required attributes, namely real time applicability, low complexity, relatively low computational processing power, simpler implementation, and chemistry agnostic nature. However, the accuracy of this approach is severely affected by measurement inaccuracy/noise, inaccurate knowledge of initial SoC, battery self-discharge etc. With a view to propose a generic chemistry agnostic battery capacity estimation approach, development of a modified CC approach is under way. The modified CC approach will enhance the conventional CC approach by overcoming the aforementioned limitations of CC approach. So far, we have modified the CC approach by including features that overcome the issues related to measurement inaccuracy/noise and self-discharge. Further improvements in the developed approach is under progress with an aim to ensure higher estimation accuracy of the CC approach by overcoming the issues related to inaccurate knowledge of initial SoC and changing coulombic efficiency. The accuracy of the so far developed approach has been thoroughly validated using the battery charging and discharging data provided by the ITP renewables, which shows that the approach can overcome the issues with the noisy measurements and provides a good accuracy. The modified CC approach, when further developed by including features to overcome the issues related to inaccurate knowledge of initial SoC and changing coulombic efficiency, will be further validated using the CSIRO battery test data, once available.
1
Introduction One of the most important parameters of a battery is its capacity, accurate estimation of which is imperative for the design of an energy management system (EMS) in a PV-battery system that ensures maximum utilisation of solar energy and longevity of the battery. As a part of the Australian Battery Performance Standard (ABPS) project, a team at the Deakin university has been working towards the development of a generic battery capacity estimation. The aim is to develop an accurate, chemistry agonistic battery state of charge (SoC) estimation technique, which in turn will provide an estimation of the remaining dynamic capacity of a battery. The battery SoC is a reflection of available dynamic capacity of a battery. In formal terms, SoC of a battery indicates the available dynamic capacity in a battery as compared to its nominal capacity (or available capacity). Usually, the SoC is termed as the fuel gauge of a battery, as it indicates how full the battery is. The energy management system (EMS) in a PV-battery system requires precise knowledge about the battery SoC, in order to ensure optimum and efficient operation of the PVbattery system, as well as longer life cycle of the battery. In a previous report submitted as part of the ABPS project [1], a thorough literature review on the existing battery capacity estimation methods has been performed from the aspects of real-time applicability, implementation complexity, being chemistry agnostic, applicability in PV-battery system, etc. From the literature review it has been identified that among the existing approaches, the Coulomb Counting (CC) approach is the most suitable considering the required features. However, it has also been identified that the CC approach suffers from low estimation accuracy when the battery current measurement is erroneous or noisy. The accuracy of the CC approach further degrades when the knowledge of initial battery state of charge (SoC) is inaccurate. In addition, the CC approach accuracy degrades when a battery is subjected to self-discharge. Considering the factors that affect the accuracy of the conventional CC approach, the Deakin team is working toward the development of a modified Coulomb counting approach for battery SoC estimation. The main aim is to ensure that the modified approach will overcome the aforementioned issues related to CC approach. This report presents the current progress of the proposed approach under development. The work so far incorporates an adaptive filter in the conventional CC approach to overcome the inaccuracy issues related to the noisy measurements. 2
Also, the conventional CC approach has been modified to include the self-discharge inherent in a battery. The modified CC approach developed thus far has been validated against the ITP data. The SoC estimation results show the effectiveness of the proposed approach in overcoming the issues associated with noisy measurement data. Currently, research works to improve the accuracy of the CC approach by overcoming the issues related to inaccurate knowledge of initial SoC and changing coulombic efficiency is underway. Details on this will be provided in a future report. The remainder of the report is structured as follows: the next section presents some preliminaries about batteries and a summary of the previous milestone report, followed by sections describing the rationale behind the choice of the CC approach. The fundamentals of the conventional CC approach and its drawbacks are discussed, the modified CC approach that was developed is presented, and validation of the developed approach using battery charging and discharging data provided by ITP is outlined. Finally, a concluding section is presented.
Preliminaries Battery capacity is the measure of the amount of charge which can be extracted from a fully charged battery before its cut-off discharge voltage is reached. However, the battery capacity is not a constant parameter and it varies over battery lifetime due to internal aging of the battery. The factors that affect the battery aging process are charge and discharge current, operating temperature, battery depth of discharge (DoD), number of charge and discharge cycles, calendar life etc. Battery capacity is commonly represented in watt-hours (Wh) or kilowatt-hours (kWh) (both measures of energy), or ampere-hours (Ah). Among the available measures, Ah is the most commonly used measure of battery capacity, which is defined as the number of hours a battery can provide a current equal to the discharge rate. Precise and accurate knowledge of battery capacity is imperative for the optimum energy management of battery storage devices in residential solar PV systems. However, there is no available generic battery SoC estimation methodology that can be used for different battery chemistries and operating conditions such as temperature, ageing, charging and discharging rates. The SoC provides the best indication of actual battery capacity. SoC in simple terms is the percentage representation of the remaining charge in a battery at time t, which may be expressed as:
3
������(��) =
(1)
đ??śđ??śđ??ˇđ??ˇđ??ˇđ??ˇđ??ˇđ??ˇđ??ˇđ??ˇđ??ˇđ??ˇđ??ˇđ??ˇđ??ˇđ??ˇ (đ?‘Ąđ?‘Ą) Ă— 100% đ??śđ??śđ?‘ đ?‘ đ?‘ đ?‘ đ?‘ đ?‘ đ?‘ đ?‘ đ?‘ đ?‘ đ?‘ đ?‘ đ?‘ đ?‘
where, đ??śđ??śđ??ˇđ??ˇđ??ˇđ??ˇđ??ˇđ??ˇđ??ˇđ??ˇđ??ˇđ??ˇđ??ˇđ??ˇđ??ˇđ??ˇ = Capacity of the battery under consideration đ??śđ??śđ?‘ đ?‘ đ?‘ đ?‘ đ?‘ đ?‘ đ?‘ đ?‘ đ?‘ đ?‘ đ?‘ đ?‘ đ?‘ đ?‘ = Nominal capacity of the battery
Please note that equation (1) defines the SoC with respect to the nominal capacity of the battery. However, the SoC is sometimes also defined considering the available capacity of a battery, including the capacity fade. There are different battery capacity estimation approaches, which have emerged over the years that represent the battery capacity in terms of the SoC of the battery. As presented in the previous report submitted as part of the ABPS project [1], these approaches are widely classified as (i) direct measurement approach, (ii) bookkeeping approach, (iii) mathematical method based approach, (iv) learning algorithm based approach. A summary of existing battery capacity (in terms of SoC) estimation approaches is shown in table 1. Approach
Direct Measurement based Approach
OCV based approach
Impedance measurement based approach
Coulomb Counting method
How it works?
Uses SoC vs OCV relationship
Uses the relationship between battery ac impedance and SoC. Battery ac Impedance is determined using spectroscopy test.
In this approach, the amount of charge received or given by the battery during a period is determined from measured battery current to estimate as below:
= SoC SoC 0 + Q
1
t1
âˆŤ hcI L dt
Advantages
Issues related to the approach
Can provide SoC estimation with good accuracy.
Offline approach. On line SoC estimation is not possible, as measurement of OCV requires a long rest time. Impedance spectroscopy is an offline test that requires additional complex set up. In addition, it has been reported that this test yields accurate battery impedance measurement in controlled environment in laboratory.
Can provide SoC estimation with good accuracy.
Most commonly used method for practical applications for its simplicity and ease of implementation
ref t 0
4
Affected by current measurement error and error in initial SoC value. Difficulty in determining Columbic efficiency in real time.
Chemistry Agnostic No
No
Yes
Approach Book keeping approach
Model based approach
Enhanced Coulomb Counting method
Equivalent circuit model (ECM) based approach Empirical model (EM) based approach Data driven model (DDM) based approach
Learning algorithm based approach
How it works?
Advantages
Coulomb counting method is improved by including battery OCV vs SoC relationship, depth of discharge (DoD) information and correction factors for charging, discharging and selfdischarge modes in order to update columbic efficiency online.
Overcomes issues with Coulomb counting method by including correction factors to overcome current measurement error and inaccuracy due to error in initial SoC value.
Uses one of the models (ECM, EM and DDM) to derive stochastic filter (Kalman filter, Extended Kalman filter, Unscented Kalman filter, Particle filter or H infinity filter) or observer to determine battery SoC in real-time.
In this approach SoC of a battery is estimated using predetermined nonlinear relationship between battery SoC and measurable battery parameters such as voltage, current and temperature, etc. The nonlinear relationship function is approximated by learning the inherent knowledge embedded in historical or experimental battery data. Commonly used learning algorithms include ANN, SVM, Extreme learning machine, Fuzzy logic, Genetic algorithm etc.
Has the ability to determine battery SoC in real-time with good accuracy.
Has the ability to determine battery SoC in real-time with good accuracy.
Issues related to the approach
Chemistry Agnostic Yes
Added layer of complexity in Coulomb counting method, which complicates the realtime implementation. No It is difficult to determine battery model parameters accurately. It requires prior laboratory experiment to determine the battery model parameters. These approaches are usually computationally expensive.
No
No
No
Large data set is required. Affected by errors/noise in training data set. Difficulty in determining large data set that include data under different battery operating condition. Computationally expensive.
Table 1: Existing SoC Estimation Techniques The direct measurement approaches use the relationships between measurable battery quantities, such as open circuit voltage, terminal voltage, internal impedance etc. to determine the battery SoC. Though direct measurement approaches are simple to realise and easily implementable, these approaches suffer from several drawbacks such as longer rest time, requirement of additional circuitry and inability to adapt to varying operating conditions such as charging rate, temperature, battery aging etc. The bookkeeping approach counts the amount of charge (Coulombs) gained or lost over time to estimate the change in battery SoC. Since this approach keeps a track of the number of Coulombs in a battery, it is usually termed as the Coulomb counting (CC) approach. The CC approach possesses features such as easy implementation, chemistry agnostic nature, and a good accuracy over a wide spectrum of operational conditions. Nevertheless, the CC approach suffers from 5
estimation inaccuracy due to measurement error/noise and inaccurate knowledge of the initial SoC. Regardless of the shortcomings, the CC approach is the most commonly used approach for battery SoC estimation. The model based approaches, which utilise mathematical models of a battery alongside advanced estimation algorithms, are reported to possess better accuracy and robustness against measurement error. However, this approach requires precise mathematical models of the battery, which is generally not readily available and requires prior experimental data to develop. Moreover, this approach is not chemistry agnostic and real time implementation is a challenge, as this approach requires highly complex mathematical computations. In addition, inaccurate battery models can affect the accuracy of this method. The learning algorithm approaches such as Neural Network (NN), Support Vector Machine (SVM), Fuzzy Logic, Bayesian statistical learning etc. have also been applied in recent years to estimate battery SoC. Similar to the model-based approaches, the artificial intelligence approaches are dependent on battery chemistry, and require a sufficiently large data set for training and validation processes. In addition, this approach is computationally expensive and is not easy to implement. Based on the thorough literature review, it was identified that the CC method possesses the required features to be recommended in the ABPS report for estimating battery capacity. In the following sections, initially the basics of CC approach is presented, followed by the rationales behind the choice of CC approach.
Basics of the coulomb counting (CC) method The Coulomb counting (CC) approach, also known as the ampere-hour integration approach, is one of the most commonly used approaches to determine the current SoC of a battery. For calculation of battery SoC, this approach only requires measurement of the charging and discharging current of a battery together with the knowledge of initial SoC of the battery. The basic equation governing CC approach is as follows:
t 0 +t SoC( t1) = SoC( t 0 ) + ∫ I bat dt × 100% t o 6
(2)
Where, đ?‘†đ?‘†đ?‘†đ?‘†đ?‘†đ?‘†(đ?‘Ąđ?‘Ą0 ) = đ?‘–đ?‘–đ?‘–đ?‘–đ?‘–đ?‘–đ?‘–đ?‘–đ?‘–đ?‘–đ?‘–đ?‘–đ?‘–đ?‘– đ?‘†đ?‘†đ?‘†đ?‘†đ?‘†đ?‘† (%) đ??śđ??śđ?‘›đ?‘› = đ?‘›đ?‘›đ?‘›đ?‘›đ?‘›đ?‘›đ?‘›đ?‘›đ?‘›đ?‘›đ?‘›đ?‘›đ?‘›đ?‘› đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘? (đ??´đ??´â„Ž)
đ??źđ??źđ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘? = đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘? đ?‘?đ?‘?â„Žđ?‘Žđ?‘Žđ?‘Žđ?‘Žđ?‘Žđ?‘Žđ?‘Žđ?‘Žđ?‘Žđ?‘Žđ?‘Žđ?‘Ž/đ?‘‘đ?‘‘đ?‘‘đ?‘‘đ?‘‘đ?‘‘đ?‘‘đ?‘‘â„Žđ?‘Žđ?‘Žđ?‘Žđ?‘Žđ?‘Žđ?‘Žđ?‘Žđ?‘Žđ?‘Žđ?‘Žđ?‘Žđ?‘Ž đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘? (đ??´đ??´)
đ?‘Ąđ?‘Ą0 , đ?‘Ąđ?‘Ą1 = đ?‘Ąđ?‘Ąđ?‘Ąđ?‘Ąđ?‘Ąđ?‘Ą đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘? đ?‘Ąđ?‘Ąđ?‘Ąđ?‘Ąđ?‘Ąđ?‘Ąđ?‘Ąđ?‘Ą đ?‘–đ?‘–đ?‘–đ?‘–đ?‘–đ?‘–đ?‘–đ?‘–đ?‘Žđ?‘Žđ?‘Žđ?‘Žđ?‘Žđ?‘Žđ?‘Žđ?‘Žđ?‘Žđ?‘Ž đ?‘Ąđ?‘Ą = đ?‘Ąđ?‘Ąđ?‘Ąđ?‘Ąđ?‘Ąđ?‘Ąđ?‘Ąđ?‘Ą đ?‘–đ?‘–đ?‘–đ?‘–đ?‘–đ?‘–đ?‘–đ?‘–đ?‘–đ?‘–đ?‘–đ?‘–đ?‘–đ?‘–đ?‘–đ?‘– đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘? đ?‘Ąđ?‘Ą0 đ?‘Žđ?‘Žđ?‘Žđ?‘Žđ?‘Žđ?‘Ž đ?‘Ąđ?‘Ą1
From equation (2) it can be seen that this approach requires the calculation of the amount of charge gained or lost in a given period of time (t) to determine the battery SoC. A flow chart illustrating the CC approach is shown in Figure 1. Remark: The polarity of the battery current depends on whether the battery is charging or discharging. If the battery is charging, the polarity of the current is defined as positive, and if the battery is discharging, then the polarity of the current is defined as negative.
Figure 1: Flowchart of Coulomb counting method 7
8
Rationale behind the choice of Coulomb counting (CC) approach •
Chemistry Agnostic Feature:
The feature that makes the CC approach a good candidate for generic battery capacity estimation is its chemistry agnostic property. Most of the existing methods in the literature are chemistry dependent and require detailed chemistry specific models to calculate SoC. Compared to other approaches the CC approach requires only the measurement of battery charging or discharging currents to estimate the battery SoC, which is independent of the battery chemistry. •
Ease of implementation:
Another notable feature of the CC approach is that this approach is relatively easy to implement compared to other existing approaches. The computational requirement of this approach is very low as it only requires simple arithmetic calculations to determine the battery SoC, which makes it suitable for real time implementation. •
Capability to Operate over a wide spectrum of operating conditions:
Since this approach only requires the measurement of charging or discharging currents of a battery under any operating condition of the battery (varying charging/discharging rates, operating temperatures, aging, etc.) to determine battery SoC, the estimation accuracy of this approach is unaffected by the changes in operating conditions.
Validation of Coulomb counting approach using ITP renewable data This section presents validation of the CC approach using the charging and discharging data provided by ITP renewables. Table 1 presents the details of the batteries which were used for the purpose of validating the Coulomb counting approach. Battery chemistry
Nominal voltage (V)
1
Nickel manganese cobalt (NMC1)
51.8
9.6
185
2
Lithium-ion-phosphate (LIP1)
51.2
10.24
200
3
Nickel manganese cobalt (NMC2)
51.8
8.3
160.23
9
Nominal rated capacity (kWh) (Ah)
4
Battery chemistry
Nominal voltage (V)
Lithium-ion-phosphate (LIP2)
51.2
Nominal rated capacity (kWh) (Ah) 9.6
187.5
Table 2: ITP data battery specifications
The ITP data used for SoC estimation is from June 2016 to Dec 2017. The data contains measurements such as temperature, current, voltage, power, SoC and time for all four types of batteries. The data is recorded every minute. The data has missing values which can be seen as empty spaces or with hash tags. Each battery is charged and discharged 3 cycles per day at constant current rate. The SoC reported by the battery management system provided in the data is used to compare the SoC calculated using Coulomb counting method. The CC approach for battery SoC estimation was implemented in MATLAB following the algorithm shown in Figure 1. The implemented CC approach was fed with charge / discharge data of the aforementioned batteries to estimate the SoC. The estimated SoC of the batteries were then compared with the battery SoC data provided by ITP renewables to determine the accuracy of the method relative to the ITP renewables data. The results of the comparison of SoC provided by ITP renewables with the estimated SoC using the CC approach are shown in Figures 2-5. The figures further present SoC estimation error by the CC approach.
10
Figure 2: NMC1 ITP and Estimated SoC and SoC estimation error
11
Figure 3: NMC2 ITP and estimated SoC and SoC estimation error
12
Figure 4: LIP1 ITP and estimated SoC and SoC estimation error
13
Figure 5: LIP2 ITP and Estimated SoC and SoC estimation error
Fig.2 shows the ITP SoC and the estimated SoC using the Coulomb counting method of the NMC1 battery. From the figure, it can be seen that, during the initial time-period the estimated SoC by 14
CC approach matches exactly with the ITP SoC data, and the plot for SoC estimation by CC approach overlaps with the plot obtained from ITP SoC data. However, as time progresses, the plots do not coincide and start to deviate from each other. The deviation keeps increasing because of the accumulation of error in the SoC estimation process by the CC approach, which may occur due to current measurement error, measurement noise or inaccurate knowledge of initial the SoC etc. Fig.2 further shows the error in SoC estimation by the CC approach as compared to the ITP data. Initially, the error is zero as the SoC estimation by the CC approach matches with the SoC data provided in the ITP data. As the time progresses, the error increases. Fig. 3 -5presents SoC estimation by CC approach for NMC2, LIP1 and LIP2 batteries, compared to the battery SoC data provided in the ITP data set. Similar to the NMC1 battery results presented in Fig. 2, the SoC estimation by the CC approach matches with the ITP data initially. However, as the time progresses, due to issues related to CC approach (current measurement error, measurement noise or inaccurate knowledge of initial the SoC, etc.) SoC estimation by the CC approach deviates from the SoC data provided by the ITP renewables. In order to further investigate the impact of inaccurate and noisy measurements on the SoC estimation accuracy of the CC approach, the battery charging and discharging data provided by the ITP renewables were superimposed with random noise of mean and variance of 0 and 1 respectively. The noisy ITP data was used to further evaluate the performance of the CC approach to SoC estimation. The SoC estimation using CC approach from the noisy ITP data is shown in Figures 6-9. The results show that CC approach is very sensitive to error in the data and the estimation accuracy degrades if the current measurements are noisy. If there is an error in the current data, then the error gets carried over by integration and moves forward until the end of the iteration. Thus, if the error persists on every current sample measured, then the error will be summed after each iteration and will result in a large misalignment with the actual SoC as reported by the battery. The possible sources of measurement error are noises in the surrounding of the measuring unit while measuring and inaccuracy of the measuring unit.
15
Figure 6: NMC1 ITP and estimated SoC and SoC estimation error using CC method with noisy data
16
Figure 7: NMC2 ITP and estimated SoC and SoC estimation error using CC with noisy data
17
Figure 8:LIP1 ITP and estimated SoC and SoC estimation error using CC with noisy data
18
Figure 9:LIP2 ITP and estimated SoC and SoC estimation error using CC with noisy data
Apart from inaccuracy due to measurement noise, other notable factors that affect the accuracy of the CC approach are listed below. •
Initial SoC data 19
According to the governing equation (refer to equation (2)) of the CC approach, at every instant in time, the previous SoC of the battery is required to calculate the current SoC of the battery. Hence, any error in the knowledge of the initial SoC of the battery will adversely affect the accuracy of the calculated current SoC of the battery. Therefore, a very accurate knowledge of initial SoC of a battery is required for accurate calculation of SoC using the CC approach. •
Self-discharge
All batteries undergo a self-discharging process when they are in idle mode (neither charging nor discharging). The CC approach does not take into consideration the self-discharge characteristics of a battery. To understand the effect of self-discharge on the SoC estimation, a battery is charged and kept idle for a period of time before it is discharged. The CC approach does not consider the reduction in SoC due to the self- discharging process. It uses the final SoC determined during the charging cycle as the initial SoC while calculating the SoC during the discharging cycle, disregarding any self-discharge that may have occurred. This causes a further error in calculating the battery SoC, which accumulates as the CC approach continues to estimate battery SoC. •
Coulombic efficiency
Another drawback of the CC approach is that it does not account for the variations in the coulombic efficiency of batteries, which changes due to variations in the charging/discharging rates and operating temperature of a battery. In the conventional CC approach, it is assumed that the total battery capacity does not experience any reduction over time; that is, the total battery capacity at any point in time is the same as the initial total battery capacity. However, due to the change in coulombic efficiency of batteries as a result of aging and variations in battery operating conditions, battery capacity does reduce over time. As a result, this assumption causes further errors in the battery SoC calculation using the conventional CC approach. Table 3 presents a summary of the factors that affect the accuracy of the conventional CC approach. Issues Impact on SoC calculation
Initial SoC Very high
Measurement error High
20
Self-discharge Little
Coulombic Efficiency Moderate
Issues
How the SoC estimation is affected
Initial SoC Can severely affect the SoC calculation during both charging and discharging cycles of a battery, as the CC approach calculates the change in battery SoC during a sample period. Together with the knowledge of initial SoC, the updated battery SoC is calculated. If there is an error in the knowledge of the initial SoC, this error will propagate in the SoC calculation of the battery, during both the charging/discharging cycles.
Measurement error Any noise in the battery current measurement can affect the calculation of the battery SoC using conventional CC approach. This is because of the fact that the current measurements are integrated over a sampling time period to determine the amount of charge gained/lost in that time period. If there is measurement noise, it will accumulate and increase the battery SoC calculation error, due to the integration operation carried out for every instance of calculating battery SoC using conventional CC.
Self-discharge Every battery goes through selfdischarging when the battery is in idle mode, i.e., not charging or discharging. As a result, the battery SoC decreases during the idle period. As the conventional CC approach does not include the effect of self-discharging; it can affect the accuracy of the conventional CC approach. This effect is more pronounced when the battery is in idle mode for long periods of time.
Coulombic Efficiency Dissimilar charging and discharging rate makes identification of coulombic efficiency harder and can reduce the accuracy of the SoC determined. An increased coulombic efficiency means reduced losses between a consecutive charging and discharging.
Table 3: Summary of factors affecting the accuracy of conventional Coulomb Counting approach
Proposed Improved Coulomb Counting approach It has been clearly described in the previous section that issues such as noise in the current measurements, error in the knowledge of initial SoC, self-discharging effects, coulombic efficiency etc. can severely affect the calculation of the battery SoC using conventional CC approach. In this section, progress on the development of improved CC approach for battery SoC estimation is presented. So far, the improved CC approach overcomes the issues related to noise in the current measurements and self-discharging. Further improvements to overcome the issues such as error in the knowledge of initial SoC, and coulombic efficiency are under progress, and will be included in the future reports. A modified CC approach has been developed, and is described in the flowchart for battery SoC estimation in Figure 10.
21
Figure 10: A flowchart showing the proposed improved CC approach
One of the main features of the proposed approach is the inclusion of an adaptive filter in the SoC estimation algorithm with the aim to filter out noise in the current measurements. The structure of the adaptive filter is shown in Error! Reference source not found., which takes the noisy current measurements as an input X n and returns the filtered noise free current measurements Y n . In the adaptive filter, the noisy current measurement X n is delayed by D samples to create a delayed version of the noisy current measurements X n − D , which is de-correlated from the noisy measurements X n . The adaptive filter computes the noise free current measurement as follows:
= Y n
L
∑Wi n X n − D − i
i =1
22
(3)
where L represents the filter length, Wi n is the i th weighting parameters of the adaptive filter, which are automatically tuned using the least mean square (LMS) algorithm, n ∈ 0,N − 1 with N being the sampled data length of the current measurements. The filter length L depends on the sampling rate of the current measurements, usually L is chosen to be the integer multiple of the sampling rate. The number of filter weights Wi n is same as the length (L) of the filter. The filter weight Wi n is calculated as follows using LMS algorithm: Wi n += 1 Wi n + me n x n − D − i
(4)
n Y n − X n , m is called the convergence factor, which controls how well the where e=
filter weights converges to the optimum weights of the filter. In order to guarantee stability of the filter the convergence factor m is chosen to be: 0 < m<
2
(5)
l max
T l max is the maximum eigenvalue of the matrix R[n], where R n = E X n X n and
X n − D − 1 . . . X n − D − L . For further reading on LMS adaptive filters X n= please refer to [2].
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Figure 11: A schematic of the adaptive filter
Another improvement in the proposed approach is the inclusion of the effect of battery selfdischarging. As presented in the flowchart of the updated approach in Figure 4, the proposed approach detects when the battery is in idle mode of operation and continues to keep track of the change (decrease) in battery SoC. The idle mode of operation is determined using the measured current value. When the current value is measured to be close to zero then the battery is considered to be in the idle mode of operation. The duration of idle operation mode is monitored, and using the self-discharge rate of battery supplied by the manufacturer, the change in SoC for that duration will be calculated. This can reduce the error in the knowledge of initial SoC, which can affect the battery SoC estimation during the charging/discharging cycle after the idle period of the battery. In the following section, the effectiveness of the proposed approach in calculating battery SoC from noisy current measurements is presented. The battery charging and discharging data provided by ITP renewables has been used as the test data.
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Performance Evaluation of the Proposed Approach using ITP data In order to validate the effectiveness of the proposed modified CC approach in overcoming the effect of noisy current measurements, the battery charging and discharging data provided by the ITP renewables was superimposed with random noises of mean and variance of 0 and 1 respectively to create noisy current measurements. The resultant noisy ITP data was fed into the improved Coulomb counting approach to estimate the battery SoC. The estimate of the battery SoC using the improved Coulomb counting approach with the noisy ITP data for the batteries listed in Table 1 are presented in Fig 12-15. The figures show the SoC values obtained from ITP data and estimated SoC using modified CC approach for NMC1, NMC2, LIP1 and LIP2 batteries. It can be seen from the figures that the proposed approach can accurately estimate the battery SoC even when using the noisy current measurements, which aligns very well with the SoC data provided by ITP renewables. The error which is the difference in SoC measurement between ITP and proposed Coulomb counting is less than that of the error in case of conventional CC approach.
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Figure 12: NMC1 SoC and SoC error using improved CC with noisy data
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Figure 13: NMC2 SoC and SoC error using improved CC with noisy data
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Figure 14: LiP1 SoC and SoC error using improved CC with noisy data
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Figure 15: LIP2 SoC and SoC error using improved CC with noisy data
Future work
This report presents a modified CC approach that overcomes the issues related to measurement error/noise and self-discharge in the conventional CC method. The modified CC approach 29
incorporates an adaptive filter and takes into account the battery self-discharge. Further work to extend the modified CC approach to overcome other issues related to conventional CC approach will include: 1) Development of an approach to estimate initial battery SoC. 2) Improve the proposed SoC estimation approach by including the effect of columbic efficiency and a measure of battery capacity fade. 3) Include the impact of battery trickle charge on SoC estimation. 4) Further, validate the updated proposed SoC estimation approach using ITP renewables data. 5) Validate the updated proposed SoC estimation approach using the data from the CSIRO
battery testing activities when available.
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Conclusion The main deliverable of this milestone within the ABPS is progress on the development of a generic battery capacity estimation methodology. It should possess attributes such as low computation complexity, ease of implementation, and a high accuracy across a range of battery chemistries and operating conditions. From the thorough analysis of existing battery capacity estimation approaches, it has been identified that the Coulomb counting (CC) approach possesses most of the aforementioned attributes. However, it was identified that the accuracy of the CC approach is severely affected by noise in the current measurement, inaccurate knowledge of initial SoC and self-discharging of batteries during idle periods. In order to overcome these issues with the CC approach, an improved CC approach has been presented, which includes an adaptive filter, capable of rectifying the noisy current measurements and improving the estimation accuracy of the battery state of charge. In addition, the proposed approach incorporates the effects of battery self-discharge to avoid inaccuracies in the battery state of charge estimation during charge / discharge cycles occurring after an idle period. The effectiveness of the proposed approach has been validated using the battery charging and discharging data provided by ITP renewables. The proposed approach will be further validated when the battery charging and discharging data is available from CSIRO battery tests being undertaken as part of the ABPS.
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References
[1] S.Saha, ME. Haque, S. Islam. A Comprehensive Review on Battery Capacity Estimation Methods. Milestone 1 report, ABPS project, December 2018 [2] Widrow, Bernard. Least-mean-square adaptive filters. Eds. Simon S. Haykin, and Bernard Widrow. Vol. 31. New York: Wiley-Interscience, 2003.
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