Charge analysis for Li-ion battery pack state of health estimation for electric and hybrid vehicles

The aging monitoring of Lithium-ion batteries (LIBs) represents a critical point for electrified vehicle applications. Consumers may expect their vehicle to have a steady autonomy range and available power throughout the lifetime of their cars. Several embedded solutions exist in the literature. In this paper two new approaches are suggested to enrich the existing solutions. To that extent, capacity fading is studied using exchanged energy during charging events. What's more, power fading is assessed using direct current resistance (DCR) and voltage measurement at the beginning of charge events. Both solutions produce reliable state of health measurements SoH with significantly good accuracy.

[1]  Elie Riviere,et al.  Détermination in-situ de l'état de santé de batteries lithium-ion pour un véhicule électrique , 2016 .

[2]  Pan Chaofeng,et al.  On-board state of health estimation of LiFePO4 battery pack through differential voltage analysis , 2016 .

[3]  A. Eddahech,et al.  Determination of lithium-ion battery state-of-health based on constant-voltage charge phase , 2014 .

[4]  Jean-Michel Vinassa,et al.  Strategy for lithium-ion battery performance improvement during power cycling , 2013, IECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society.

[5]  Lucia Gauchia,et al.  Deterministic models of Li-ion battery aging: It is a matter of scale , 2018, Journal of Energy Storage.

[6]  Dirk Uwe Sauer,et al.  Experimental investigation of the lithium-ion battery impedance characteristic at various conditions and aging states and its influence on the application , 2013 .

[7]  Jay Lee,et al.  A review on prognostics and health monitoring of Li-ion battery , 2011 .

[8]  Jean-Michel Vinassa,et al.  Remaining useful life prediction of lithium batteries in calendar ageing for automotive applications , 2012, Microelectron. Reliab..

[9]  Didier Dumur,et al.  Li-Ion Battery Pack SoC Estimation for Electric Vehicles , 2018, IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society.

[10]  A. Eddahech,et al.  Ageing monitoring of lithium-ion cell during power cycling tests , 2011, Microelectron. Reliab..

[11]  Valérie Sauvant-Moynot,et al.  Development of an empirical aging model for Li-ion batteries and application to assess the impact of Vehicle-to-Grid strategies on battery lifetime , 2016 .

[12]  D. Depernet,et al.  Online impedance spectroscopy of lead acid batteries for storage management of a standalone power plant , 2012 .

[13]  U. Troeltzsch,et al.  Characterizing aging effects of lithium ion batteries by impedance spectroscopy , 2006 .

[14]  Jasim Ahmed,et al.  Algorithms for Advanced Battery-Management Systems , 2010, IEEE Control Systems.

[15]  Eric Prada.  Aging modeling and lifetime optimization of Li-ion LiFePO4-graphite batteries according to the vehicle use. , 2012 .

[16]  Andreas Jossen,et al.  Calendar Aging of NCA Lithium-Ion Batteries Investigated by Differential Voltage Analysis and Coulomb Tracking , 2017 .

[17]  Xuezhe Wei,et al.  Practical On-Board Measurement of Lithium Ion Battery Impedance Based on Distributed Voltage and Current Sampling , 2018 .

[18]  Kai Goebel,et al.  Comparison of prognostic algorithms for estimating remaining useful life of batteries , 2009 .

[19]  Haritza Camblong,et al.  A critical review on self-adaptive Li-ion battery ageing models , 2018, Journal of Power Sources.

[20]  A. Eddahech,et al.  Performance comparison of four lithium–ion battery technologies under calendar aging , 2015 .

[21]  M. Dubarry,et al.  Calendar aging of commercial Li-ion cells of different chemistries – A review , 2018, Current Opinion in Electrochemistry.

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

[23]  Dylan Dah-Chuan Lu,et al.  Incremental capacity analysis and differential voltage analysis based state of charge and capacity estimation for lithium-ion batteries , 2018 .