Real-Time Implementation of Nonlinear State-of-Charge Estimation Techniques for Hybrid Electric Vehicles

Abstract

Abstract:The problem of State-of-Charge (SOC) estimation is crucial in automotive industry for successful marketing of both electric vehicles (EVs) and hybrid electric vehicles (HEVs). Nowadays the wealth of rechargeable Ni-MH and Li-ion batteries is a great prospective to be used extensively to plug-in hybrid electric vehicles (PHEVs), hybrid electric vehicles (HEVs), and battery electric vehicles (BEVs). In addition the Li-ion batteries are much lighter in weight and of reduced size, therefore much easier to be integrated in the vehicle structure in order to provide more power to boost up the acceleration, and to increase the energy efficiency through on-board battery energy storage by means of regenerative braking and mechanical-electric energy conversion. Gradual capacity reduction and performance decay can be evaluated strictly based on the current knowledge of rechargeable battery technology, and consequently is required a rigorous monitoring and a tight control of the SOC level, that is essential for increasing the operating lifetime of the rechargeable batteries. In this paper we present some preliminary results obtained in our research to implement some of SOC estimation techniques for the rechargeable batteries of different chemistry. We investigate the effectiveness of two nonlinear Kalman Filters estimators (Extended and Unscented versions) in order to find the most suitable estimator, more accurate and to perform better. The contribution of this paper is the implementation of these estimators in real time based on a generic model of the battery by means of extensive simulations in a MATLAB/SIMULINK programming environment.