Guangzhao Luo

TL;DR: In this paper, an adaptive unscented Kalman filters (AUKF) and least square support vector machines (LSSVM) were used to estimate lithium polymer battery state-of-charge (SOC) estimation.

TL;DR: This paper classifies the recently proposed online SOC estimation methods into five categories, and seven nonlinear filters existing in literature are compared in terms of their accuracy, robustness, and execution time as a reference for online implementation.

TL;DR: This paper mainly focuses on battery modeling methods, which have the potential to be used in a model-based SOC estimation structure and 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.

TL;DR: 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.

TL;DR: An optimization process with nondominated sorting genetic algorithm II (NSGA-II) is proposed to establish a more efficient SOH estimator with support vector regression (SVR) and the short-term features from the current pulse test and the degradation features in this article are the knee points at the transfer instants of the voltage.