Multicell state estimation using variation based sequential Monte Carlo filter for automotive battery packs

Abstract Accurate state monitoring is required for the high performance of battery management systems (BMS) in electric vehicles. By using model-based observation methods, state estimation of a single cell can be achieved with non-linear filtering algorithms e.g. Kalman filtering and Particle filtering. Considering the limited computational capability of a BMS and its real-time constraint, duplication of this approach to a multicell system is very time consuming and can hardly be implemented for a large number of cells in a battery pack. Several possible solutions have been reported in recent years. In this work, an extended two-step estimation approach is studied. At first, the mean value of the battery state of charge is determined in the form of a probability density function (PDF). Secondly, the intrinsic variations in cell SOC and resistance are identified simultaneously in an extended framework using a recursive least squares (RLS) algorithm. The on-board reliability and estimation accuracy of the proposed method is validated by experiment and simulation using an NMC/graphite battery module.

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

[2]  Xiaoning Jin,et al.  Lithium-ion battery state of health monitoring and remaining useful life prediction based on support vector regression-particle filter , 2014 .

[3]  Jianqiu Li,et al.  Cell state-of-charge inconsistency estimation for LiFePO4 battery pack in hybrid electric vehicles using mean-difference model , 2013 .

[4]  Hongwen He,et al.  Model-based dynamic multi-parameter method for peak power estimation of lithium-ion batteries , 2012 .

[5]  Christian Fleischer,et al.  Adaptive on-line prediction of the available power of lithium-ion batteries , 2013 .

[6]  O. Nelles.  Nonlinear System Identification , 2001 .

[7]  Matthieu Dubarry,et al.  From single cell model to battery pack simulation for Li-ion batteries , 2009 .

[8]  Michael Osterman,et al.  Prognostics of lithium-ion batteries based on DempsterShafer theory and the Bayesian Monte Carlo me , 2011 .

[9]  Qiang Miao,et al.  Prognostics of lithium-ion batteries based on relevance vectors and a conditional three-parameter capacity degradation model , 2013 .

[10]  Wei He,et al.  State of charge estimation of lithium-ion batteries using the open-circuit voltage at various ambient temperatures , 2014 .

[11]  Karl Johan Åström,et al.  Adaptive Control , 1989, Embedded Digital Control with Microcontrollers.

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

[13]  Christian Fleischer,et al.  Critical review of the methods for monitoring of lithium-ion batteries in electric and hybrid vehicles , 2014 .

[14]  Gregory L. Plett,et al.  Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 3. State and parameter estimation , 2004 .

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

[16]  Sebastian Thrun,et al.  Probabilistic robotics , 2002, CACM.

[17]  Gregory L. Plett,et al.  Sigma-point Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 1: Introduction and state estimation , 2006 .

[18]  Jiahao Li,et al.  Sequential Monte Carlo filter for state estimation of LiFePO4 batteries based on an online updated model , 2014 .

[19]  Weijun Gu,et al.  Online cell SOC estimation of Li-ion battery packs using a dual time-scale Kalman filtering for EV applications , 2012 .

[20]  Wen-Yeau Chang,et al.  Estimation of the state of charge for a LFP battery using a hybrid method that combines a RBF neural network, an OLS algorithm and AGA , 2013 .

[21]  Stephen Yurkovich,et al.  Linear parameter varying battery model identification using subspace methods , 2011 .

[22]  Xiaosong Hu,et al.  Adaptive unscented Kalman filtering for state of charge estimation of a lithium-ion battery for elec , 2011 .

[23]  Il-Song Kim,et al.  The novel state of charge estimation method for lithium battery using sliding mode observer , 2006 .

[24]  Jiahao Li,et al.  A comparative study and validation of state estimation algorithms for Li-ion batteries in battery management systems , 2015 .

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

[26]  Simon Schwunk,et al.  Particle filter for state of charge and state of health estimation for lithium–iron phosphate batteries , 2013 .

[27]  Christian Fleischer,et al.  On-line adaptive battery impedance parameter and state estimation considering physical principles in reduced order equivalent circuit battery models: Part 1. Requirements, critical review of methods and modeling , 2014 .

[28]  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 .