Impact of cell variability on pack statistics for different vehicle segments

Abstract Depending on the level of electrification ([PH]EV, [M]HEV) and cell format (pouch, prismatic, cylindrical), the number of individual cells in a vehicle's battery pack can span several orders of magnitude (from tens to thousands). In this paper, we develop a novel analytical framework to investigate the impact of cell-level manufacturing variability on pack performance. Statistical distributions of pack energy and pack power are derived for any NsMp pack configurations and any level of cell-to-cell variability. These distributions are used to develop vehicle-dependent cell-level manufacturing requirements. The degree to which the series direction negatively affects pack statistics, and the degree to which the parallel direction improves pack statistics, is first quantified under a random sampling scenario. PHEV packs (96s1p) are found to have the highest pack-level statistical penalty, while EV packs made of small-format cylindrical cells (96s74p) incur virtually no statistical penalty. Pack-level statistics for Ns1p packs are shown to greatly benefit from the clustering of low-performing cells. Cell binning strategies for NsMp packs present both advantages and disadvantages. The impacts of cell aging and pack-level voltage limits are further discussed. The results of this study apply even in the presence of cell balancing and under partial SOC usage.

[1]  Kenichi Tanaka,et al.  Discharge characteristics of multicell lithium-ion battery with nonuniform cells , 2013 .

[2]  Malte Kuypers,et al.  Application of 48 Volt for Mild Hybrid Vehicles and High Power Loads , 2014 .

[3]  Jonghoon Kim,et al.  Stable Configuration of a Li-Ion Series Battery Pack Based on a Screening Process for Improved Voltage/SOC Balancing , 2012, IEEE Transactions on Power Electronics.

[4]  Markus Lienkamp,et al.  Parameter variations within Li-Ion battery packs – Theoretical investigations and experimental quantification , 2018, Journal of Energy Storage.

[5]  James Marco,et al.  Battery energy storage system modeling: Investigation of intrinsic cell-to-cell variations , 2019, Journal of Energy Storage.

[6]  Charles M. Grinstead,et al.  Introduction to probability , 1999, Statistics for the Behavioural Sciences.

[7]  M. Whittingham,et al.  Narrowing the Gap between Theoretical and Practical Capacities in Li‐Ion Layered Oxide Cathode Materials , 2017 .

[8]  T. Baumhöfer,et al.  Production caused variation in capacity aging trend and correlation to initial cell performance , 2014 .

[9]  Matthew B. Pinson,et al.  Internal resistance matching for parallel-connected lithium-ion cells and impacts on battery pack cycle life , 2014 .

[10]  Michael G. Debije,et al.  Infrared Regulating Smart Window Based on Organic Materials , 2017 .

[11]  James Marco,et al.  Modelling and experimental evaluation of parallel connected lithium ion cells for an electric vehicle battery system , 2016 .

[12]  Giorgio Rizzoni,et al.  A probabilistic approach for prognosis of battery pack aging , 2017 .

[13]  Chunting Chris Mi,et al.  Study of the Characteristics of Battery Packs in Electric Vehicles With Parallel-Connected Lithium-Ion Battery Cells , 2015 .

[14]  A. Jossen,et al.  Experimental investigation of parametric cell-to-cell variation and correlation based on 1100 commercial lithium-ion cells , 2017 .

[15]  W. D. Widanage,et al.  A Study of Cell-to-Cell Interactions and Degradation in Parallel Strings: Implications for the Battery Management System , 2016 .

[16]  Simon F. Schuster,et al.  Lithium-ion cell-to-cell variation during battery electric vehicle operation , 2015 .

[17]  Stephen W. Moore,et al.  2001-01-0959 A Review of Cell Equalization Methods for Lithium Ion and Lithium Polymer Battery Systems , 2001 .

[18]  Brian C. Sisk,et al.  A Simulation Based Analysis of 12V and 48V Microhybrid Systems Across Vehicle Segments and Drive Cycles , 2015 .

[19]  Weige Zhang,et al.  Study on battery pack consistency evolutions and equilibrium diagnosis for serial- connected lithium-ion batteries , 2017 .

[20]  Ulrike Krewer,et al.  Impacts of Variations in Manufacturing Parameters on Performance of Lithium-Ion-Batteries , 2018 .

[21]  Iosu Aizpuru,et al.  Comparative Study and Evaluation of Passive Balancing Against Single Switch Active Balancing Systems for Energy Storage Systems , 2016 .

[22]  M Rosa Palacín,et al.  Understanding ageing in Li-ion batteries: a chemical issue. , 2018, Chemical Society reviews.

[23]  J. Schneider.  48V Boost Recuperation Systems - Golden Gate into the Future , 2019, SAE technical paper series.

[24]  Shriram Santhanagopalan,et al.  Quantifying Cell-to-Cell Variations in Lithium Ion Batteries , 2012 .

[25]  M. Wohlfahrt‐Mehrens,et al.  Ageing mechanisms in lithium-ion batteries , 2005 .

[26]  Roberto Roncella,et al.  Performance comparison of active balancing techniques for lithium-ion batteries , 2014 .

[27]  Weige Zhang,et al.  Recognition of battery aging variations for LiFePO 4 batteries in 2nd use applications combining incremental capacity analysis and statistical approaches , 2017 .

[28]  Jian Xie,et al.  Failure Investigation of LiFePO4 Cells in Over-Discharge Conditions , 2013 .

[29]  Phil Mellor,et al.  Comparison of passive cell balancing and active cell balancing for automotive batteries , 2011, 2011 IEEE Vehicle Power and Propulsion Conference.

[30]  Matthieu Dubarry,et al.  From Li-ion single cell model to battery pack simulation , 2008, 2008 IEEE International Conference on Control Applications.

[31]  Saeed Khaleghi Rahimian,et al.  Exploring the Opportunity Space For High-Power Li-Ion Batteries in Next-Generation 48V Mild Hybrid Electric Vehicles , 2017 .

[32]  Brian C. Sisk,et al.  Estimating the Power Limit of a Lithium Battery Pack by Considering Cell Variability , 2015 .

[33]  Matthieu Dubarry,et al.  Origins and accommodation of cell variations in Li‐ion battery pack modeling , 2010 .

[34]  Xuejiao Zhao,et al.  Reliability Modeling Method for Lithium-ion Battery Packs Considering the Dependency of Cell Degradations Based on a Regression Model and Copulas , 2019, Materials.

[35]  A. Pesaran,et al.  Lower-Energy Requirements for Power-Assist HEV Energy Storage Systems--Analysis and Rationale (Presentation) , 2010 .

[36]  Naehyuck Chang,et al.  A Statistical Model-Based Cell-to-Cell Variability Management of Li-ion Battery Pack , 2015, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

[37]  Sebastian Paul,et al.  Analysis of ageing inhomogeneities in lithium-ion battery systems , 2013 .

[38]  Ken Darcovich,et al.  Modelling the impact of variations in electrode manufacturing on lithium-ion battery modules , 2012 .

[39]  A. Jossen,et al.  Influence of cell-to-cell variations on the inhomogeneity of lithium-ion battery modules , 2018 .

[40]  Chen Li,et al.  Failure statistics for commercial lithium ion batteries: A study of 24 pouch cells , 2017 .

[41]  Xiangming He,et al.  A Facile Consistency Screening Approach to Select Cells with Better Performance Consistency for Commercial 18650 Lithium Ion Cells , 2017 .