An Evaluation of the Moving Horizon Estimation Algorithm for Online Estimation of Battery State of Charge and State of Health | Zendy

Bibin Pattel | Zendy; Hoseinali Borhan | Zendy; Sohel Anwar | Zendy

Open Access

An Evaluation of the Moving Horizon Estimation Algorithm for Online Estimation of Battery State of Charge and State of Health

Author(s) -

Bibin Pattel,

Hoseinali Borhan,

Sohel Anwar

Publication year - 2014

Publication title -

purdue university indianapolis (indiana university)

Language(s) - English

Resource type - Conference proceedings

DOI - 10.1115/imece2014-37140

Subject(s) - extended kalman filter , kalman filter , convergence (economics) , state of charge , moving horizon estimation , estimation , state (computer science) , control theory (sociology) , computer science , noise (video) , battery (electricity) , process (computing) , algorithm , state of health , mathematical optimization , engineering , mathematics , artificial intelligence , control (management) , power (physics) , physics , systems engineering , quantum mechanics , economics , image (mathematics) , operating system , economic growth

Moving Horizon Estimation (MHE) has emerged as a powerful technique for tackling the estimation problems of the state of dynamic systems in the presence of constraints, nonlinearities and disturbances. In this paper, the Moving Horizon Estimation approach is applied in estimating the State of Charge (SoC) and State of Health (SoH) of a battery and the results are compared against those for the traditional estimation method of Extended Kalman Filter (EKF). The comparison of the results show that MHE provides improvement in performance over EKF in terms of different state initial conditions, convergence time, and process and sensor noise variations.Copyright © 2014 by ASME

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