Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Smart Core and Surface Temperature Estimation Techniques for Health-conscious Lithium-ion Battery Management Systems: A Model-to-Model Comparison

Version 1 : Received: 3 July 2021 / Approved: 5 July 2021 / Online: 5 July 2021 (10:11:31 CEST)

How to cite: Surya, S.; Samanta, A.; Williamson, S. Smart Core and Surface Temperature Estimation Techniques for Health-conscious Lithium-ion Battery Management Systems: A Model-to-Model Comparison. Preprints 2021, 2021070087 (doi: 10.20944/preprints202107.0087.v1). Surya, S.; Samanta, A.; Williamson, S. Smart Core and Surface Temperature Estimation Techniques for Health-conscious Lithium-ion Battery Management Systems: A Model-to-Model Comparison. Preprints 2021, 2021070087 (doi: 10.20944/preprints202107.0087.v1).

Abstract

Estimation of core and surface temperature is one of the crucial functionalities of the lithium-ion Battery Management System (BMS) towards providing effective thermal management, fault detection and operational safety. While, it is impractical to measure core temperature using physical sensors, implementing a complex estimation strategy in on-board low-cost BMS is challenging due to high computational cost and the cost of implementation. Typically, a temperature estimation scheme consists of a heat generation model and a heat transfer model. Several researchers have already proposed ranges of thermal models having different levels of accuracy and complexity. Broadly, there are first-order and second-order heat capacitor-resistor-based thermal models of lithium-ion batteries (LIBs) for core and surface temperature estimation. This paper deals with a detailed comparative study between these two models using extensive laboratory test data and simulation study to access suitability in online prediction and onboard BMS. The aim is to guide whether it’s worth investing towards developing a second-order model instead of a first-order model with respect to prediction accuracy considering modelling complexity, experiments required and the computational cost. Both the thermal models along with the parameter estimation scheme are modelled and simulated using MATLAB/Simulink environment. Models are validated using laboratory test data of a cylindrical 18650 LIB cell. Further, a Kalman Filter with appropriate process and measurement noise levels are used to estimate the core temperature in terms of measured surface and ambient temperatures. Results from the first-order model and second-order models are analyzed for comparison purposes.

Subject Areas

Electric Vehicles; Stationary Battery Energy Storage System; Battery Automated System; Online State Estimation; Thermal Modeling; First-order model; Second-order Model; Kalman Filtering

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