An Adaptive Autotuned Polynomial-Based Extended Kalman Filter for Sensorless Surface Temperature Estimation of Li-Ion Battery Cells

Abstract

This paper proposes an adaptive filter for estimating the surface temperature of lithium-ion battery cells in real time. The proposed temperature sensorless method aims to achieve a highly accurate temperature estimation at a relatively low implementation cost. The method employs a system dynamic and measurement models derived using polynomial curve fitting and implemented in the proposed adaptive autotuned extended Kalman filter (AA-EKF). Derivation of the proposed technique followed by experimental verification are demonstrated.

Keywords

• Battery management system (BMS)
• extended Kalman filter (EKF)
• lithium-ion battery (LIB)
• polynomial fitting
• electric vehicle