Vattenfall R&D, KTH Royal Institute of Technology Lifetime estimation of lithium-ion batteries for stationary energy storage systems Degree Project in Chemical Engineering, KE202X Joakim Andersson, 9310257879 2017-06-13 Supervisors: Longcheng Liu, Jinying Yan. Abbreviations AFM Atomic force microscopy ANN Artificial neural network ARIMA Autoregressive integrated moving average BMS Battery management system CC-CV Constant charge-constant voltage CDKF Central difference Kalman filter DEC Diethyl carbonate DEKF Duel extended Kalman filter DMC Dimethyl carbonate DoD Depth of discharge EC Ethylene carbonate EKF Extended Kalman filter EoL End of life ESS Energy storage system ESVEKF Enhanced state vector extended Kalman filter EV Electric vehicle HEV Hybrid electric vehicle ICA Incremental capacity analysis LCO Lithium cobalt oxide LFP Lithium iron phosphate Li-ion Lithium-ion LMO Lithium manganese oxide LTO Lithium titanate oxide NCA Lithium nickel cobalt aluminum oxide NMC Lithium nickel manganese cobalt oxide OCV Open circuit voltage P2D Pseudo-2D PC Propylene carbonate PDE Partial differential equation RC Resistor-capacitor RLS Recursive least squares RuL Remaining useful life RW Random walk SEI Solid electrolyte interface SNN Structured neural network SoC State of charge SOH State of health SPKF Sigma point Kalman filter SPM Single particle model STEM Scanning transmission electron microscopy SWDEKF Single weight dual extended Kalman filter UKF Unscented Kalman filter VC Vinylene carbonate ABSTRACT With the continuing transition to renewable inherently intermittent energy sources like solar- and wind power, electrical energy storage will become progressively more important to manage energy production and demand. A key technology in this area is Li-ion batteries. To operate these batteries efficiently, there is a need for monitoring of the current battery state, including parameters such as state of charge and state of health, to ensure that adequate safety and performance is maintained. Furthermore, such monitoring is a step towards the possibility of the optimization of battery usage such as to maximize battery lifetime and/or return on investment. Unfortunately, possible online measurements during actual operation of a lithium-ion battery are typically limited to current, voltag e and possibly temperature, meaning that direct measurement of battery status is not feasible. To overcome this, battery modeling and various regression methods may b e used. Several of the most common regression algorithms suggested for estimation of battery state of charge and state of health are based on Kalman filtering. While these methods have shown great promise, there currently exist no thorough analysis of the impact of so-called filter tuning on the effectiveness of these algorithms in Li-ion battery monitoring applications, particularly for state of health estimation. In addition, the effects of only adjusting the cell capacity model parameter for aging effects, a relatively common approach in the literature, on overall state of health estimation accuracy is also in need of investigation. In this work, two different Kalman filtering methods intended for state of charge estimation: the extended Kalman filter and the extended adaptive Kalman filter, as well as three intended for state of health estimation: the dual extended Kalman filer, the enhanced state vector extended Kalman filer, and the single weight dual extended Kalman filer, are compared from accuracy, performance, filter tuning and practical usability standpoints. All algorithms were used with the same simple one resistor- capacitor equivalent circuit battery model. The Li-ion battery data used for battery model development and simulations of filtering algorithm performance was the “Randomized Battery Usage Data Set” obtained from the NASA Prognostics Center of Excellence. It is found that both state of charge estimators performs imilarly in terms of accuracy of state of charge estimation with regards to reference values, easily outperforming the common Coulomb counting approach in terms of precision, robustness and flexibility. The adaptive filter, while computationally more demanding, required less tuning of filter parameters relative to the extended Kalman filter to achieve comparable performance and might therefore be advantageous from a robustness and usability perspective. Amongst the state of health estimators, thee nhanced state vector approach was found to be most robust to initialization and was also least taxing computationally. The single weight filter could be made to achieve comparable results with careful, if time consuming, filter tuning. The full dual extended Kalman filter has the advantage of estimating not only the cell capacity but also the internal resistance parameters. This comes at the price of slow performance and time consuming filter tuning, involving 17 parameters. It is however shown that long-term state of health estimation is superior using this approach, likely due to the online adjustment of internal resistance parameters. This allows the dual extended Kalman filter to accurately estimate the SoH over a full test representing more than a full conventional battery lifetime. The viability of only adjusting the capacity in online monitoring approaches therefore appears questionable. Overall the importance of filter tuning is found to be substantial, especially for case s of very uncertain starting battery states and characteristics . ACKNOWLEDGEMENTS I would like to give my deepest gratitude to all those who made this thesis a reality. To my supervisors Longcheng Liu at the department of Chemical Engineering , KTH and Jinying Yan at Vattenfall R&D for their unrelenting support throughout the work and for the opportunity to take part in the project in the first place. Thank you for all the insightful input and fruitful discussions. My thanks go out to my examiner Matthäus Bäbler for all the help and assistance I ever needed. I would also like to thank everybody at Vattenfall R&D and KTH who helped and supported me in case of need. Thanks to Jonas Ricknell for insightful comments on the work. Finally I would like to thank my mother and my father for always supporting me and for providing motivation during rough times. I could never have made it without you. Joakim Andersson, Stockholm, June 2017. TABLE OF CONTENTS 1 Introduction ..................................................................................................................................... 1 1.1 Background .............................................................................................................................. 2 1.1.1 Historical perspective and outlook on lithium-ion batteries .......................................... 2 1.1.2 Lithium-ion batteries in stationary applications ............................................................. 5 1.1.3 Main components of Li-ion batteries .............................................................................. 6 1.1.4 Chemistry of Li-ion batteries ........................................................................................... 7 1.1.4.1 Cathode materials ....................................................................................................... 8 1.1.4.2 Anode materials ........................................................................................................ 12 1.1.4.3 Additives .................................................................................................................... 13 1.1.5 Battery management systems ....................................................................................... 13 1.2 Lithium-ion battery aging ...................................................................................................... 14 1.2.1 Aging of electrolyte ....................................................................................................... 16 1.2.2 Aging mechanisms at anode .......................................................................................... 16 1.2.3 Aging mechanisms at cathode ....................................................................................... 19 1.2.3.1 Chemistry-specific cathode aging processes ............................................................. 20 1.2.4 Main aging impact factors ............................................................................................. 22 1.2.4.1 Calendar aging ........................................................................................................... 23 1.2.4.2 Cycle aging ................................................................................................................. 25 1.2.5 Summary of Li-ion battery aging impact factors ........................................................... 26 1.3 Lithium-ion battery modeling ................................................................................................ 28 1.3.1 Empirical models ........................................................................................................... 29 1.3.2 Electrochemical models ................................................................................................ 29 1.3.3 Equivalent circuit models .............................................................................................. 30 1.4 State of charge estimation .................................................................................................... 32 1.5 State of health and remaining useful life estimation ............................................................ 34 2 Scope and aim of thesis ................................................................................................................. 38 3 Methodology ................................................................................................................................. 39 3.1 Equivalent circuit battery model development..................................................................... 39 3.1.1 Capacity determination ................................................................................................. 40 3.1.2 Open circuit voltage – state of charge expression ........................................................ 42 3.1.3 Identification of equivalent circuit model parameters ................................................. 46 3.2 State of health estimation validation and considerations .................................................... 50 3.3 Kalman filtering ..................................................................................................................... 53 3.3.1 Extended Kalman filter .................................................................................................. 57 3.3.2 Adaptive extended Kalman filter ................................................................................... 59 3.3.3 Dual extended Kalman filter .......................................................................................... 59 3.3.4 Enhanced state vector extended Kalman filter ............................................................. 63 3.3.5 Single weight dual extended Kalman filter .................................................................... 64 4 Results and discussion ................................................................................................................... 65 4.1 Extended Kalman filter .......................................................................................................... 65 4.2 Adaptive extended Kalman filter ........................................................................................... 76 4.3 Dual extended Kalman filter .................................................................................................. 79 4.4 Enhanced state vector extended Kalman filter ..................................................................... 88 4.5 Single weight dual extended Kalman filter ............................................................................ 92 4.6 Comparison of algorithms and summary .............................................................................. 95 4.6.1 State of charge algorithms ............................................................................................ 95 4.6.2 State of health estimation algorithms ........................................................................... 96 5 Conclusions and future work ....................................................................................................... 103 6 References ................................................................................................................................... 104 7 Appendices .................................................................................................................................. 111 Appendix 1: Code for plotting and organization of battery diagnostic data ................................... 111 Appendix 2: Code for extended Kalman filter ................................................................................. 115 Appendix 3: Code for adaptive extended Kalman filter .................................................................. 117 Appendix 4: Code for dual extended Kalman filter ......................................................................... 119 Appendix 5: Code for enhanced state vector extended Kalman filter ............................................ 122 Appendix 6: Code for single weight dual extended Kalman filter ................................................... 123 List of figures Figure 1: Price development of Li-ion batteries 2005-2030 [21]. ........................................................... 4 Figure 2: Gravimetric energy density and specific power of different available battery technologies [2]. ................................................................................................................................................................. 4 Figure 3: Simple illustration of the original LiCoO 2-based Li-ion cell. Electrodes, current collectors and separator are all submerged in the electrolyte solution [36]. ................................................................ 8 Figure 4: The three crystal structures of common Li -ion battery cathode materials. Green dots represent Li-ions or Li-ion intercalation sites [37]. ................................................................................. 9 Figure 5: Comparison of market share and size of Li -ion battery cathode materials in 1995 and 2010 [30]. ......................................................................................................................................................... 9 Figure 6: Composition dependence for performance characteristics of NMC cathodes [46]. ............. 11 Figure 7: Graphical summary of the most important Li-ion battery degradation mechanisms [68]. ... 15 Figure 8: Example of anode side reaction leading to SEI formation via formation of DMDOHC [73]. . 17 Figure 9: Main degradation mechanisms at graphite-based anodes [40]. ........................................... 18 Figure 10: Main Li-ion battery cathode aging processes [40]. .............................................................. 20 Figure 11: Mechanism of the dissolution of manganese from LMO cathode [40] ............................... 21 Figure 12: Aging mechanisms of layered transition metal oxides [40]. ................................................ 22 Figure 13: Effect of storage temperature on aging in terms of percentage of capacity loss. All batteries were stored at 50 % SoC during testing [85]. ........................................................................................ 23 Figure 14: Arrhenius plot for high and low temperature behavior. r is the rate of reaction [45] ....... 23 Figure 15: Calendar aging of LTO anode compared to graphite anode. ASI=area specific impedance. The impedance can be seen as the equivalent of resistance for alternating current [82] ......................... 25 Figure 16: A simple equivalent circuit model consisting of a voltage source, dependent on the SoC, and a resistor in series. ................................................................................................................................. 30 Figure 17: n resistor-capacitor equivalent circuit model. ..................................................................... 31 Figure 18: A 1 resistor-capacitor equivalent circuit model. .................................................................. 32 Figure 19: Determining RuL by extrapolating SoH measurements [64]. ............................................... 36 Figure 20: Reference discharge voltage profiles for determination of cell capacity changes over the course of battery testing for RW9. ........................................................................................................ 40 Figure 21: Capacity degradation during testing of RW9. Note the total timespan of around half a year. ............................................................................................................................................................... 42 Figure 22: OCV-time relationship for low current discharge test of RW9. ........................................... 42 Figure 23: OCV-SoC for RW9 battery. .................................................................................................... 43 Figure 24: Comparison of polynomial and combined model for OCV-SoC curve fitting. ...................... 45 Figure 25: Fitted OCV-SoC polynomial is clearly stable while the combined model “spikes”. ............. 45 Figure 26: All pulsed discharge tests for determination of ECM parameter values for RW9. .............. 46 Figure 27: Ballpark estimation of ECM parameters from pulsed discharge ......................................... 47 Figure 28: 1 RC ECM model fitting results for first pulsed discharge test of RW9 ............................... 48 Figure 29: Modeling error in first pulsed discharge test of RW9 using 1 RC ECM ................................ 49 Figure 30: Difference in modelling error when not adjusting for changing capacitance ..................... 50 Figure 31: Voltage, current and temperature over the first 100 RW steps for RW9. ........................... 52 Figure 32: Negative SoC by Coulomb counting in RW phase 10 for battery RW9 ................................ 52 Figure 33: The Kalman filtering principle [114]. .................................................................................... 54 Figure 34: The DEKF algorithm. Solid lines represent the paths of state- and weight vectors while dashed lines are the flows of the covariance matrices [123]............................................................... 63 Figure 35: SoC estimated by EKF over first 100 RW steps of battery RW9 .......................................... 66 Figure 36: SoC estimated EKF during end of first RW phase of battery RW9. ...................................... 67 Figure 37: SoC estimation by EKF with unadjusted ECM parameters for RW phase 5, battery RW9. .. 68 Figure 38: EKF converging to correct SoC value despite incorrect starting values. .............................. 69 Figure 39: Differences in convergence rate to Coulomb counting reference value for varying starting SoC covariance. ..................................................................................................................................... 70 Figure 40: Eventual convergence of EKF SoC estimate despite poor choice of initial SoC covariance .70 Figure 41: EKF SoC estimation with different measurement noise covariances................................... 72 Figure 42: Overall trend of rms SoC error versus logarithm of measurement noise covariance for cases of noisy signals and correct starting SoC for the EKF. ........................................................................... 72 Figure 43: Cell voltage predictions of the EKF for the case of a noisy voltage signal using different measurement noise covariance matrices. ............................................................................................ 73 Figure 44: Error bounds per (20) of SoC estimate using EKF................................................................ 75 Figure 45: The stabilization of SoC covariance in the EKF, indicating estimation convergence ........... 75 Figure 46: SoC estimation for 100 first RW steps using AEKF with Coulomb counting reference ....... 76 Figure 47: Rapid convergence of AEKF SoC estimate for various initial process noise covariance matrices. ................................................................................................................................................ 77 Figure 48: The quick converging of the AEKF for various starting measurement covariances ............ 78 Figure 49: Capacity estimation over first full RW phase of RW9. ......................................................... 80 Figure 50: SoC- (left) and capacity (right) estimates by DEKF when initial guesses of both SoC and capacity are poor for 100 first RW steps of first RW phase of RW9. .................................................... 81 Figure 51: Rapid convergence of both internal resistance parameter estimates using the DEKF despite poor initial guesses. ............................................................................................................................... 82 Figure 52: Measured cell voltage for RW cycling using CC-CV mode charging for RW2. Note the periods of constant voltage at 4.2 V. ................................................................................................................. 83 Figure 53: Current over time for cycling of battery RW2. ..................................................................... 83 Figure 54: SoC by Coulomb counting and DEKF for CC-CV mode charge cycling of RW2. .................... 84 Figure 55: Convergence of capacity estimates despite large disparity in starting values using DEKF and data from battery RW2. ........................................................................................................................ 85 Figure 56: Quick convergence of capacity estimates with different initial guesses of capacity for DEKF using battery data from RW2. ............................................................................................................... 85 Figure 57: Capacity estimation using the DEKF over the first RW phase of RW9 with the capacity process noise covariance set too high. Internal filter parameters are like in table 12 except for capacity process -8 noise covariance=1∙10 . ........................................................................................................................ 87 Figure 58: Convergence of estimates of internal resistance parameters for varying values of process noise covariance in the DEKF. ............................................................................................................... 88 Figure 59: Capacity estimation over entire first RW phase of battery RW9 using ESVEKF .................. 89 Figure 60: SoC estimation using the ESVEKF for a poor initial guess of cell capacity ........................... 89 Figure 61: Capacity estimation using the ESVEKF converging to measured value independently of starting guess. ....................................................................................................................................... 90 Figure 62: Convergence of SoC using the ESVEKF despite poor guesses of both SoC and capacity ..... 91 Figure 63: Rapid convergence of capacity estimates with three different initial guesses using ESVEKF and data from battery RW2 .................................................................................................................. 91 Figure 64: Convergence of capacity estimate for SWDEKF despite poor initial guess ......................... 92 Figure 65: Convergence of capacity estimates to measured value for RW9 for different starting guesses in SWDEKF. ............................................................................................................................................ 93 Figure 66: Capacity estimates for battery RW2 using the SWDEKF for different starting guesses of capacity.................................................................................................................................................. 94 Figure 67: Capacity estimates using SWDEKF for RW2 for high initial capacity covariance ................ 94 Figure 68: Comparison of long-term capacity estimation of SoH algorithms. ...................................... 97 Figure 69: Principle of "accidental" convergence of SoC estimation for faulty concurrent estimation of capacity. The red line represents an SoC estimate with a low capacity, the green line high capacity, and the blue line is the in-between case. .................................................................................................. 101 List of tables Table 1: Comparison of current battery technologies [4]. ...................................................................... 5 Table 2: Common components of commercial Li -ion batteries. ............................................................. 7 Table 3: Qualitative comparison of common cathode materials [47-49]. ............................................ 11 Table 4: Advantages, disadvantages and applications of common Li-ion battery cathode materials [9]. ............................................................................................................................................................... 12 Table 5: Factors affecting calendar and cycle aging. ............................................................................. 26 Table 6: Impact factors and effects of aging mechanisms [68]. ............................................................ 27 Table 7: Measured cell capacities for RW9. .......................................................................................... 41 Table 8: Development of ECM parameters with aging for battery RW9. ............................................. 49 Table 9: Internal EKF parameters for the results in figures 35 and 36. ................................................. 66 Table 10: Internal filter parameters for AEKF for figure 46. ................................................................. 76 Table 11: The effect of moving estimation window size on AEKF SoC estimation accuracy. ............... 78 Table 12: Internal filter parameter values for initialization of DEKF in figure 49 ................................. 80 Table 13: Internal filter parameters of DEKF for results in figure 51 .................................................... 81 Table 14: Differences in temperature between pulsed discharge tests and RW phases for RW9 ....... 82 Table 15: Internal filter parameters of the ESVEKF for the results in figures 59 and 60 ...................... 90 Table 16: Initial ECM parameters of battery RW2. ............................................................................... 91 Table 17: Internal filter parameters for SWDEKF results in figure 64 .................................................. 93 Table 18: Internal filter parameters for quantitative comparison of EKF and AEKF algorithms. .......... 95 Table 19: Results of comparison of EKF and AEKF for SoC estimation ................................................. 95 Table 20: Internal filter parameters for comparison of long-term capacity estimation performance of SoH algorithms. ..................................................................................................................................... 97 Table 21: Root-mean-square capacity estimation errors for results in figure 65 for investigated SoH estimation algorithms. .......................................................................................................................... 98 Table 22: Root-mean-square error over first four measurement points of long-term capacity estimation test. ........................................................................................................................................................ 98 Table 23: Execution times of SoH estimation algorithms for simulations of 300 RW cycles ............. 100 Table 1: Comparison of current battery technologies [4]. ...................................................................... 5 Table 2: Common components of commercial Li -ion batteries. ............................................................. 7 Table 3: Qualitative comparison of common cathode materials [47 -49]. ............................................ 11 Table 4: Advantages, disadvantages and applications of common Li-ion battery cathode materials [9]. ............................................................................................................................................................... 12 Table 5: Factors affecting calendar and cycle aging. ............................................................................. 26 Table 6: Impact factors and effects of aging mechanisms [68]. ............................................................ 27 Table 7: Measured cell capacities for RW9. .......................................................................................... 41 Table 8: Development of ECM parameters with aging for battery RW9. ............................................. 49 Table 9: Internal EKF parameters for the results in figures 35 and 36. ................................................. 66 Table 10: Internal filter parameters for AEKF for figure 46. ................................................................. 76 Table 11: The effect of moving estimation window size on AEKF SoC estimation accuracy. ............... 78 Table 12: Internal filter parameter values for initialization of DEKF in figure 49 ................................. 80 Table 13: Internal filter parameters of DEKF for results in figure 51 .................................................... 81 Table 14: Differences in temperature between pulsed discharge tests and RW phases for RW9. ...... 82 Table 15: Internal filter parameters of the ESVEKF for the results in figures 59 and 60 ...................... 90 Table 16: Initial ECM parameters of battery RW2. ............................................................................... 91 Table 17: Internal filter parameters for SWDEKF results in figure 64 .................................................. 93 Table 18: Internal filter parameters for quantitative comparison of EKF and AEKF algorithms. .......... 95 Table 19: Results of comparison of EKF and AEKF for SoC estimation ................................................. 95 Table 20: Internal filter parameters for comparison of long-term capacity estimation performance of SoH algorithms. ..................................................................................................................................... 97 Table 21: Root-mean-square capacity estimation errors for results in figure 65 for investigated SoH estimation algorithms. .......................................................................................................................... 98 Table 22: Root-mean-square error over first four measurement points of long-term capacity estimation test. ........................................................................................................................................................ 98 Table 23: Execution times of SoH estimation algorithms for simulations of 300 RW cycles ............. 100