Hardware implementation of an algorithm based on kalman filtrer for monitoring low capacity Li-ion batteries

In this paper, we introduce an algorithm based on an adaptive Kalman filter algorithm for estimating the state of charge of low capacity Li-ion batteries. Using the first order model with a static characterization, good results have been reached and the algorithm converges even with random initial SoC values and has represented no cumulative error drawbacks. This algorithm has been validated, simulated and implemented on a hardware platform based on a microcontroller for an online SoC estimation for multimedia application.

[1]  Seongjun Lee,et al.  State-of-charge and capacity estimation of lithium-ion battery using a new open-circuit voltage versus state-of-charge , 2008 .

[2]  Gregory L. Plett,et al.  Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 2. Modeling and identification , 2004 .

[3]  C. Moo,et al.  Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries , 2009 .

[4]  Valerie H. Johnson,et al.  Battery performance models in ADVISOR , 2002 .

[5]  Yann Creff,et al.  A Review of Approaches for the Design of Li-Ion BMS Estimation Functions Revue de différentes approches pour l’estimation de l’état de charge de batteries Li-ion , 2013 .

[6]  L.-A. Dessaint,et al.  A Generic Battery Model for the Dynamic Simulation of Hybrid Electric Vehicles , 2007, 2007 IEEE Vehicle Power and Propulsion Conference.

[7]  S. Y. Chen,et al.  Kalman Filter for Robot Vision: A Survey , 2012, IEEE Transactions on Industrial Electronics.

[8]  Jiahao Li,et al.  A comparative study of state of charge estimation algorithms for LiFePO4 batteries used in electric vehicles , 2013 .

[9]  Jean-Michel Vinassa,et al.  Adaptive voltage estimation for EV Li-ion cell based on artificial neural networks state-of-charge meter , 2012, 2012 IEEE International Symposium on Industrial Electronics.

[10]  Dong Wang,et al.  A comparative study of three model-based algorithms for estimating state-of-charge of lithium-ion batteries under a new combined dynamic loading profile , 2016 .

[11]  Jianqiu Li,et al.  A review on the key issues for lithium-ion battery management in electric vehicles , 2013 .

[12]  A. Miraoui,et al.  Review of adaptive systems for lithium batteries State-of-Charge and State-of-Health estimation , 2012, 2012 IEEE Transportation Electrification Conference and Expo (ITEC).

[13]  Joeri Van Mierlo,et al.  Optimization of an advanced battery model parameter minimization tool and development of a novel electrical model for lithium-ion batteries , 2014 .

[14]  Andreas Jossen,et al.  Methods for state-of-charge determination and their applications , 2001 .

[15]  Najoua Essoukri Ben Amara,et al.  $Implementation of a Coulomb counting algorithm for SOC estimation of Li-Ion battery for multimedia applications , 2015, 2015 IEEE 12th International Multi-Conference on Systems, Signals & Devices (SSD15).

[16]  Yan Xia Gao,et al.  SOC Estimation of Lithium-Ion Battery Based on Kalman Filter Algorithm , 2013 .