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.
Highlights
Lithium-ion batteries (LIBs) are adopted in a wide spectrum of applications and products including portable electronics, transportation electrification, renewable generation support and grid storage
Online monitoring of the temperature of an LIB is a crucial function of a battery management system (BMS)
Fully relying on sensors to monitor the temperature of LIBs is impractical in many applications due to the need to perform regular offline calibrations and maintenance procedures to ensure accurate measurements at any instance
Summary
Lithium-ion batteries (LIBs) are adopted in a wide spectrum of applications and products including portable electronics, transportation electrification, renewable generation support and grid storage. Fully relying on sensors to monitor the temperature of LIBs is impractical in many applications due to the need to perform regular offline calibrations and maintenance procedures to ensure accurate measurements at any instance. Another ANN method is proposed in [9] to estimate the surface temperature of LIB cells. The polynomial based mathematical models are prepared from experimental data with negligible approximations, delivering promising results Inspired by these efforts, this research aims at utilizing polynomial-based modelling to estimate the surface temperature of an LIB at a reduced cost and simplified implementation requirements while maintaining the accuracy and reliability of the estimation process.
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