Remaining Useful Life Prediction of Lithium-ion Battery Degradation for a Hybrid Electric Vehicle

Nabil Laayouj*  and Hicham Jamouli

Laboratory of Industrial Engineering and Computer Science (LGII), National School of Applied Sciences Ibn Zohr University Agadir, Morocco

* Corresponding author:  nabil.laayouj@gmail.com, tel: +212662267208

Accepted 16 February 2015

Abstract

Prognostic activity deals with prediction of the remaining useful life (RUL) of physical systems based on their actual health state and their usage conditions. RUL estimation gives operators a potent tool in decision making by quantifying how much time is left until functionality is lost. In addition, it can be used to improve the characterization of the material proprieties that govern damage propagation for the structure being monitored. RUL can be estimated by using three main approaches, namely model-based, data-driven and hybrid approaches. The prognostics methods used later in this paper are hybrid and data-driven approaches, which employ the Particle Filter in the first one and the autoregressive integrated moving average in the second. The performance of the suggested approaches is evaluated in a comparative study on data collected from lithium-ion battery of hybrid electric vehicle.

Keywords: Remaining useful life; prognosis; Particle Filter; ARIMA