State of Charge Estimation in Li-ion Batteries Using an Isothermal Pseudo Two-Dimensional Model

Abstract The dynamics of Li-ion batteries are often defined by a set of coupled nonlinear partial differential equations called the pseudo two-dimensional model. It is widely accepted that this model, while accurate, is too complex for estimation and control. As such, the literature is replete with numerous approximations of this model. For the first time, an algorithm for state-of-charge estimation using the original pseudo two-dimensional model is provided. A discrete version of the model is reformulated into a state-space model by separating linear, nonlinear, and algebraic states. This model is high dimensional (of the order of tens to hundreds of states) and consists of implicit nonlinear algebraic equations. The degeneracy problems with high-dimensional state estimation are circumvented by developing a particle filter algorithm that sweeps in time and spatial coordinates independently. The implicit algebraic equations are handled by ensuring the presence of a ‘tether’ particle in the algorithm. The approach is illustrated through simulations.

[1]  K. Smith Electrochemical Modeling, Estimation and Control of Lithium Ion Batteries , 2006 .

[2]  Changsun Ahn,et al.  Robust estimation of road friction coefficient , 2011, Proceedings of the 2011 American Control Conference.

[3]  Rolf Findeisen,et al.  Electrochemical Model Based Observer Design for a Lithium-Ion Battery , 2013, IEEE Transactions on Control Systems Technology.

[4]  Mohammad Farrokhi,et al.  State-of-Charge Estimation for Lithium-Ion Batteries Using Neural Networks and EKF , 2010, IEEE Transactions on Industrial Electronics.

[5]  A. Stefanopoulou,et al.  Lithium-Ion Battery State of Charge and Critical Surface Charge Estimation Using an Electrochemical Model-Based Extended Kalman Filter , 2010 .

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

[7]  Venkatasailanathan Ramadesigan,et al.  Coordinate Transformation, Orthogonal Collocation, Model Reformulation and Simulation of Electrochemical-Thermal Behavior of Lithium-Ion Battery Stacks , 2011 .

[8]  G. Pistoia,et al.  Lithium batteries : science and technology , 2003 .

[9]  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 .

[10]  Richard D. Braatz,et al.  Modeling and Simulation of Lithium-Ion Batteries from a Systems Engineering Perspective , 2010 .

[11]  C. M. Doyle Design and simulation of lithium rechargeable batteries , 2010 .

[12]  Ralph E. White,et al.  Online Estimation of the State of Charge of a Lithium Ion Cell , 2006 .