Ala A. Hussein

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In this paper, an artificial neural network (ANN) based approach is proposed to estimate the capacity fade in lithium-ion (Li-ion) batteries for electric vehicles (EVs). Besides its robustness, stability, and high accuracy, the proposed technique can significantly improve the state-of-charge (SOC) estimation accuracy over the lifespan of the battery, which(More)
Battery management systems (BMS) must estimate the state‐of‐charge (SOC) of the battery accu‐ rately to prolong its lifetime and ensure a reliable operation. Since batteries have a wide range of applications, the SOC estimation requirements and methods vary from an application to another. This paper compares two SOC estimation methods, namely extended(More)
Accurate battery state-of-charge (SOC) estimation in real-time is desired in many applications. Among other methods, the extended Kalman filter (EKF) allows for high-accuracy real-time tracking of the SOC. However, an accurate SOC model is needed in order to guarantee convergence. Additionally, the knowledge of the statistics of the process noise and the(More)
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