Point of Contact: Matthieu Dubarry
Sponsors: Office of Naval Research (APRISES); SAFT (France); Element Energy; ACCURE (Germany)
Current collaborators: University of Warwick, University of Oviedo, Naval Research Laboratory, Dalhousie University, Oxford University, London Imperial College, Free University of Brussels, University of Bayreuth.
In the last two decades, batteries have become an important part of our lifestyle, from portable electronics and electrified vehicles to renewable energy storage onto the electric grid. The art of battery engineering follows a conventional “design, build, and test” paradigm to deliver products that meet the design goals and specifications for a specific use. To warrant performance and to ensure safety and reliability, BMS (Battery Management Systems) were developed and implemented. Unfortunately, engineers who design BMS usually have little understanding of the electrochemical processes involved in Li-ion batteries that operate with much more complicated chemistries than Ni-MH or lead-acid batteries and thus require much more sophisticated controls. As a result, black-box battery models (derived from empirical correlations) tend to be implemented in BMS for both state of charge (SOC) and state of health (SOH) tracking. Although empirical models work well within the initial use of a battery, they tend to fail when they are needed the most, when cells aged. To mitigate this problem, new strategies needed to be developed. HNEI strategy includes a new measurement technology to visualize and model the internal state of a battery in order to enable monitoring for the entire battery pack.
Battery diagnosis and prognosis is a difficult task. First, lithium-ion batteries are chemically and electrochemically much more complex than traditional batteries. They rely on intercalation processes, and as such, the active materials are often non-conductive oxides or phosphates that swell and contract with lithium intercalation and deintercalation. This leads to much more complex electrode architectures with a need for two percolation frameworks to each grain of active material: a conductive one to bring electrons, and an ionic one to bring lithium ions. Some polymeric binder is also necessary to maintain the mechanical integrity of the electrodes with cycling. Moreover, their high voltage necessitates the use of organic electrolytes that are not stable. As a result, there is a multitude of degradation mechanisms operating in parallel that can all, to some degree and in different combinations, affect the longevity of a battery. Second, battery degradation is path dependent. Different usages for a battery (current, temperature, SOC range, SOC window…, etc.) can exacerbate or inhibit some of the degradation mechanisms. As a result, every battery degradation is unique since no two batteries are used in exactly the same way. This implies that it is nearly impossible to predetermine how a deployed battery will age, and thus that any prognosis must be performed onboard to be effective. Lithium-ion batteries are known to degrade slowly at first before a seemingly random and undetectable change with a rapid acceleration of the degradation. This event is dependent on how the battery was used and its prediction is essential for any useful estimation of the state of a battery. Third, large battery packs are comprised of thousands of cells and BMS have limited computing power. This precludes the practical use of complex models and the use of a multitude of sensors for each cell.
Traditionally, battery diagnosis is handled via two opposite approaches. The academic route aims for maximum accuracy and achieves it by inputting a lot of resources, with post-mortem characterization and extensive modeling. As a result, the analysis of a single battery is long, costly, often destructive and thus inadequate for deployment. The second route — the one usually used on deployed systems — is opposite. It uses as little resources as possible and must not be destructive. As a result, it is often restricted to an extrapolation of the evolution of capacity and resistance and is thus ineffective in predicting the sudden acceleration of capacity fading and thus true SOH. This assessment of state of the art led HNEI to define a third industry-compatible intermediate route to reach an accurate diagnosis with a cost-effective and non-destructive method, using only sensors already available in battery packs while requiring limiting computing power. Research conducted for this project is completed in the PakaLi Battery Laboratory.
Illustration of HNEI approach towards battery diagnosis.
- 2021, M. Dubarry, D. Beck, Analysis of Synthetic Voltage vs. Capacity Datasets for Big Data Li-ion Diagnosis and Prognosis, Energies, Vol. 14, Issue 9, Paper 2371. (Open Access: PDF)
- 2020, M. Dubarry, D. Beck, Big data training data for artificial intelligence-based Li-ion diagnosis and prognosis, Journal of Power Sources, Vol. 479, Paper 228806.
- 2020, D. Anseán, G. Baure, M. González, I. Cameán, A.B. García, M. Dubarry, Mechanistic investigation of silicon-graphite/LiNi0.8Mn0.1Co0.1O2 commercial cells for non-intrusive diagnosis and prognosis, Journal of Power Sources, Vol. 459, Paper 227882.
- 2020, M. Dubarry, G. Baure, Perspective on Commercial Li-ion Battery Testing, Best Practices for Simple and Effective Protocols, Electronics, Vol. 9, Issue 1, Paper 152. (Open Access: PDF)
- 2019, A. Barai, K. Uddin, M. Dubarry, L. Somerville, A. McGordon, P. Jennings, I. Bloom, A comparison of methodologies for the non-invasive characterisation of commercial Li-ion cells, Progress in Energy and Combustion Science, Vol. 72, pp. 1-32. (Open Access: PDF)
- 2018, M. Dubarry, Q. Nan, P. Brooker, Calendar aging of commercial Li-ion cells of different chemistries – A review, Current Opinion in Electrochemistry, Vol. 9, pp. 106-113.
- 2018, A. Devie, G. Baure, M. Dubarry, Intrinsic Variability in the Degradation of a Batch of Commercial 18650 Lithium-Ion Cells, Energies, Vol. 11, Issue 5, Paper 1031. (Open Access: PDF)
- 2018, C.T. Love, M. Dubarry, T. Reshetenko, A. Devie, N. Spinner, K.E. Swider-Lyons, R. Rocheleau, Lithium-Ion Cell Fault Detection by Single-Point Impedance Diagnostic and Degradation Mechanism Validation for Series-Wired Batteries Cycled at 0 °C, Energies, Vol. 11, Issue 4, Paper 834. (Open Access: PDF)
- 2017, D. Ansean, M. Dubarry, A. Devie, B.Y. Liaw, V.M. Garcia, J.C. Viera, M. Gonzalez, Operando lithium plating quantification and early detection of a commercial LiFePO4 cell cycled under dynamic driving schedule, Journal of Power Sources, Vol. 356, pp. 36-46.
- 2016, D. Ansean, M. Dubarry, A. Devie, B.Y. Liaw, V.M. Garcia, J.C. Viera, M. Gonzalez, Fast charging technique for high power LiFePO4 batteries: a mechanistic analysis of aging, Journal of Power Sources, Vol. 321, pp. 201-209.
- 2014, M. Dubarry, C. Truchot, B.Y. Liaw, Cell degradation in commercial LiFePO4 cells with high-power and high-energy designs, Journal of Power Sources, Vol. 258, pp. 408-419.
- 2011, M. Dubarry, B.Y. Liaw, M.-S. Chen, S.-S. Chyan, K.-C. Han, W.-T. Sie, S.-H. Wu, Identifying battery aging mechanisms in large format Li ion cells, Journal of Power Sources, Vol. 196, Issue 7, pp. 3420-3425.
- 2010, M. Dubarry, N. Vuillaume, B.Y. Liaw, Origins and accommodation of cell variations in Li-ion battery pack modeling, International Journal of Energy Research, Vol. 34, Issue 2, pp. 216-231.
- 2010, B.Y. Liaw, M. Dubarry, A roadmap to understand battery performance in electric and hybrid vehicle operation, in G. Pistoia (ed.) Electric and Hybrid Vehicles: Power Sources, Models, Sustainability, Infrastructure and the Market, Elsevier, Chapter 15, pp. 375-403.
- 2009, M. Dubarry, B.Y. Liaw, Identify capacity fading mechanism in a commercial LiFePO4 cell, Journal of Power Sources, Vol. 194, Issue 1, pp. 541-549.
- 2007, M. Dubarry, V. Svoboda, R. Hwu, B.Y. Liaw, Capacity loss in rechargeable lithium cells during cycle life testing: The importance of determining state-of-charge, Journal of Power Sources, Vol. 174, Issue 2, pp. 1121-1125.
- 2007, M. Dubarry, V. Svoboda, R. Hwu, B.Y. Liaw, A roadmap to understand battery performance in electric and hybrid vehicle operation, Journal of Power Sources, Vol. 174, Issue 2, pp. 366-372.
- 2007, M. Dubarry, V. Svoboda, R. Hwu, B.Y. Liaw, Capacity and power fading mechanism identification from a commercial cell evaluation, Journal of Power Sources, Vol. 165, Issue 2, pp. 566-572.
- 2006, M. Dubarry, V. Svoboda, R. Hwu, B.Y. Liaw, Incremental Capacity Analysis and Close-to-Equilibrium OCV Measurements to Quantify Capacity Fade in Commercial Rechargeable Lithium Batteries, Electrochemical and Solid-State Letters, Vol. 9, Issue 10, pp. A454-A457.
- 2019, S. Schindler, G. Baure, M.A. Danzer, M. Dubarry, Kinetics accommodation in Li-ion mechanistic modeling, Journal of Power Sources, Vol. 440, Paper 227117.
- 2019, M. Dubarry, C. Pastor-Fernández, G. Baure, T.F. Yu, W.D. Widanage, J. Marco, Battery energy storage system modeling: Investigation of intrinsic cell-to-cell variations, Journal of Energy Storage, Vol. 23, pp. 19-28. (Open Access: PDF)
- 2019, M. Dubarry, G. Baure, C. Pastor-Fernández, T.F. Yu, W.D. Widanage, J. Marco, Battery energy storage system modeling: A combined comprehensive approach, Journal of Energy Storage, Vol. 21, pp. 172-185. (Open Access: PDF)
- 2017, M. Dubarry, M. Berecibar, A. Devie, D. Ansean, N. Omar, I. Villarreal, State of Health Battery Estimator Enabling Degradation Diagnosis: Model and Algorithm Description, Journal of Power Sources, Vol. 360, pp. 59-69.
- 2016, M. Dubarry, A. Devie, B.Y. Liaw, Cell-balancing currents in parallel strings of a battery system, Journal of Power Sources, Vol. 321, pp. 36-46.
- 2016, M. Berecibar, M. Dubarry, N. Omar, I. Villarreal, J. Van Mierlo, Degradation Mechanism Detection for NMC Batteries based on Incremental Capacity Curves, World Electric Vehicle Journal, Vol. 8, Issue 2, pp. 350-361. (Open Access: PDF)
- 2016, M. Berecibar, F. Devriendt, M. Dubarry, I. Villarreal, N. Omar, W. Verbeke, J. Van Mierloz, Online State of Health estimation on NMC cells based on Predictive Analytics, Journal of Power Sources, Vol. 320, pp. 239-250.
- 2016, M. Berecibar, M. Dubarry, I. Villarreal, N. Omar, J. Van Mierlo, Degradation Mechanisms Detection for HP and HE NMC Cells Based on Incremental Capacity Curves, Proceeding of the IEEE Vehicle Power and Propulsion Conference (VPPC), INSPEC 16558169.
- 2015, M. Berecibar, N. Omar, M. Garmendia, I. Villarreal, P. Van den Bossche, J. Van Mierlo, M. Dubarry, SOH estimation and prediction for NMC cells based on degradation mechanism detection, Proceeding of the IEEE Vehicle Power and Propulsion Conference (VPPC), INSPEC 15678053.
- 2015, M. Dubarry, C. Truchot, A. Devie and B. Y. Liaw, State-of-charge determination in lithium-ion battery packs based on two-point measurements in life, Journal of The Electrochemical Society, Vol. 162, Issue 6, pp. A877-A884. (Open Access: PDF)
- 2014, M. Dubarry, A. Devie, B.Y. Liaw, The value of battery diagnostics and prognostics, Journal of Energy and Power Sources, Vol. 1, Issue 5, pp. 242-249.
- 2014, C. Truchot, M. Dubarry, B.Y. Liaw, State-of-charge estimation and uncertainty for lithium-ion battery strings, Applied Energy, Vol. 119, pp. 218-227.
- 2012, M. Dubarry, C. Truchot, B.Y. Liaw, Synthesize battery degradation modes via a diagnostic and prognostic model, Journal of Power Sources, Vol. 219, pp. 204-216.
- 2010, M.G. Cugnet, M. Dubarry, B.Y. Liaw, Peukert’s Law of a Lead-Acid Battery Simulated by a Mathematical Model, Proceeding of the the ECS MA2009-02 Meeting, ECS Transactions, Vol. 25, Issue 35, pp. 223-233.
- 2009, M. Dubarry, N. Vuillaume, B.Y. Liaw, From single cell model to battery pack simulation for Li-ion batteries, Journal of Power Sources, Vol. 186, Issue 2, pp. 500-507.
- 2007, M. Dubarry, B.Y. Liaw, Development of a universal modeling tool for rechargeable lithium batteries, Journal of Power Sources, Vol. 174, Issue 2, pp. 856-860.