Adaptive Estimation of the State of Charge for Lithium-Ion Batteries: Nonlinear Geometric Observer Approach

Abstract

Fang, H.; Sahinoglu, Z.; Wada, T.; Hara, S. TR2014-094 September 2014Abstract This paper considers the state of charge (SoC) and parameter estimation of lithium-ion batteries. Different from various prior arts, where estimation is based on local linearization of a nonlinear battery model, nonlinear geometric observer approach is followed to design adaptive observers for the SoC and parameter estimation based on nonlinear battery models. A major advantage of the proposed approach is the possibility to establish the exponential stability of the resultant error dynamics of state and parameter estimation. The proposed adaptive observers are shown to be robust with respect to unmodeled process uncertainties. Analysis also shows the design tradeoff between the convergence rate and the robustness of the estimation error dynamics with respect to the measurement noise. Simulation and experimental results validate the effectiveness and main advantages of the proposed approach. Error analysis is presented and explains the experimental results. IEEE Transactions on Control Systems TechnologyThis work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved. Abstract-This paper considers the State of Charge (SoC) and parameter estimation of Lithium-ion batteries. Different from various prior art, where estimation is based on local linearization of a nonlinear battery model, nonlinear geometric observer approach is followed to design adaptive observers for the SoC and parameter estimation based on nonlinear battery models. A major advantage of the proposed approach is the possibility to establish the exponential stability of the resultant error dynamics of state and parameter estimation. The proposed adaptive observers are shown to be robust with respect to unmodeled process uncertainties. Analysis also shows the design tradeoff between the convergence rate and the robustness of the estimation error dynamics with respect to the measurement noise. Simulation and experimental results validate the effectiveness and main advantages of the proposed approach. Error analysis is presented and explains the experimental results.

Battery State Estimation for a Single Particle Model With Electrolyte Dynamics

“…Ideally, one seeks to derive a provably stable estimator for the highest fidelity electrochemical battery model possible. The first wave of studies utilize the "single particle model" (SPM) for estimator design [4], [14], [15], [16], [17]. The SPM idealizes each electrode as a single spherical porous particle by neglecting the electrolyte dynamics.…”

A Novel State-of-Charge Estimation Method of Lithium-Ion Batteries Combining the Grey Model and Genetic Algorithms

“…To prepare nonlinear battery models as well as model parameters, researchers proposed an adaptive geometric observer for simplicity. This method was robust in processing uncertainties, and the error dynamics of state and parameter estimation were convergent [19]. To assure the stability of an adaptive observer, Lyapunov theory has been proposed to estimate the SoC with the advantages of no need for a priori knowledge of the model parameters [20].…”

State-of-Charge Estimation and Active Cell Pack Balancing Design of Lithium Battery Power System for Smart Electric Vehicle

“…In addition, there exist many problems with the battery management system (BMS) such as inaccurate state-of-charge (SOC) estimation due to multiple charging and discharge of the cells. Hence, the SOC [6] was one of the essential parameters to estimate in order to prevent damage to the battery. Unfortunately, the estimation of SOC is not a simple process as it depends on factors such as battery's capacitance, resistance, internal temperature, ambient temperature [7,8], and other cell characteristics.…”