An accurate algorithm for lithium polymer battery SOC estimation is proposed based on adaptive unscented Kalman filters (AUKF) and least square support vector machines (LSSVM). A novel approach using the moving window method is applied, with AUKF and LSSVM to accurately establish the battery model with limited initial training samples. The effectiveness of the moving window modeling method is validated by both simulations and lithium polymer battery experimental results. The measurement equation of proposed AUKF method is established by the LSSVM battery model, and AUKF has the advantage of adaptively adjusting noise covariance during the estimation process. In addition, the developed LSSVM model is continuously updated online with new samples during the battery operation, in order to minimize the influence of the changes in battery internal characteristics on modeling accuracy and estimation results after a period of operation. Finally, a comparison of accuracy and performance between AUKF and UKF is made. Simulation and experiment results indicate that the proposed algorithm is capable of predicting lithium battery SOC with a limited number of initial training samples.Index Terms-Lithium polymer battery, moving window method, modeling, least square support vector machine (LSSVM), adaptive unscented Kalman filter (AUKF), state of charge (SOC).  Manuscript

As a critical indictor in the Battery Management System (BMS), State of Charge (SOC) is closely related to the reliable and safe operation of lithium-ion (Li-ion) batteries. Model-based methods are an effective solution for accurate and robust SOC estimation, the performance of which heavily relies on the battery model. This paper mainly focuses on battery modeling methods, which have the potential to be used in a model-based SOC estimation structure. Battery modeling methods are classified into four categories on the basis of their theoretical foundations, and their expressions and features are detailed. Furthermore, the four battery modeling methods are compared in terms of their pros and cons. Future research directions are also presented. In addition, after optimizing the parameters of the battery models by a Genetic Algorithm (GA), four typical battery models including a combined model, two RC Equivalent Circuit Model (ECM), a Single Particle Model (SPM), and a Support Vector Machine (SVM) battery model are compared in terms of their accuracy and execution time.

With the popularity of Electrical Vehicles (EVs), Lithium-ion battery industry is developing rapidly. To ensure the battery safe usage and to reduce its average lifecycle cost, an accurate State of Charge (SOC) tracking algorithms for real-time implementation are required for different applications. Many SOC estimation methods have been proposed in the literature. However, only a few of them consider the real-time applicability. This paper classifies the recently proposed online SOC estimation methods into five categories. Their principal features are illustrated, and the main pros and cons are provided. The SOC estimation methods are compared and discussed in terms of accuracy, robustness, and computation burden. Afterward, as the most popular type of model based SOC estimation algorithms, seven nonlinear filters existing in literature are compared in terms of their accuracy and execution time as a reference for online implementation.

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The performance of model based State-of-Charge (SOC) estimation method relies on an accurate battery model. Nonlinear models are thus proposed to accurately describe the external characteristics of the Lithium-ion (Li-ion) battery. The nonlinear estimation algorithms and online parameter identification methods are needed to guarantee the accuracy of the model based SOC estimation with nonlinear battery models. A new approach forming a dynamic linear battery model is proposed in this paper, which enables the application of the linear Kalman filter for SOC estimation and also avoids the usage of online parameter identification methods. With a moving window technology, Partial Least Squares (PLS) regression is able to establish a series of piecewise linear battery models automatically. One element state space equation is then obtained to estimate the SOC from the linear Kalman filter. The experiments on a LiFePO4 battery prove the effectiveness of the proposed method compared with the Extended Kalman Filter (EKF) with two Resistance and Capacitance (RC) Equivalent Circuit Model (ECM) and the Adaptive Unscented Kalman Filter (AUKF) with Least Squares Support Vector Machines (LSSVM). Index Terms-State-of-charge estimation, partial least squares regression, Kalman filter, Lithium-ion battery. I. INTRODUCTION  ith the significant progress of the battery technology, Lithium-ion (Li-ion) batteries have become a promising choice for Electrical Vehicle (EV) [1] and Battery Energy Storage System (BESS) [2], [3]. The extensive usage of the Li-ion batteries is mainly because of their superior properties including long lifespan, high energy density, low self-discharge 

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