Abstract
SummaryAccurate estimation of lithium-ion battery health will (a) improve the performance and lifespan of battery packs in electric vehicles, spurring higher adoption rates, (b) determine the actual extent of battery degradation during usage, enabling a health-conscious control, and (c) assess the available battery life upon retiring of the vehicle to re-purpose the batteries for “second-use” applications. In this paper, the real-time validation of an advanced battery health estimation algorithm is demonstrated via electrochemistry, control theory, and battery-in-the-loop (BIL) experiments. The algorithm is an adaptive interconnected sliding mode observer, based on a battery electrochemical model, which simultaneously estimates the critical variables such as the state of charge (SOC) and state of health (SOH). The BIL experimental results demonstrate that the SOC/SOH estimates from the observer converge to an error of 2% with respect to their true values, in the face of incorrect initialization and sensor signal corruption.
Highlights
The expanding global electric vehicle market is an indication of a conscious effort by civilization to reduce the reliance on fossil fuels and steadily replace it with a more environment-friendly energy storage and conversion system
In this work, a low-fidelity reduced-order electrochemical model derived from the P2D model, referred to as the single particle model (SPM), is used with an aim to lend itself to observer design for online state/parameter estimation and to minimize computational effort, thereby enabling the model to be run on real-time embedded controllers that have limited power capability and resources
The cell is subjected to dynamic current profiles, such as the Urban Dynamometer Driving Schedule (UDDS) and the world harmonized light-duty vehicles test procedure (WLTP), through MITS PRO software and the Arbin LBT21024 equipment
Summary
The expanding global electric vehicle market is an indication of a conscious effort by civilization to reduce the reliance on fossil fuels and steadily replace it with a more environment-friendly energy storage and conversion system. Lithium-ion batteries (LIBs) are electrochemical energy storage systems that have found themselves to be the preferred choice for the electrification of the transportation sector and being considered as a storage solution in the renewable energy sector owing to their superior specific energy and power density Despite these benefits, LIBs are known to be susceptible to abuse (such as thermal runaway (Wang et al, 2012)) due to complex degradation mechanisms, which may lead to safety and reliability issues. The Battery Management System (BMS) is tasked with the responsibility of monitoring the critical battery internal variables that represent the current state of charge (SOC) and state of health (SOH) (Rahimi-Eichi et al, 2013) and uses that information for maintaining conditions conducive for a longer battery lifespan. These critical variables are not available for measurement via sensors, and the BMS has to ‘‘estimate’’ these variables from available battery current, voltage, and temperature data
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