Indiana University-Purdue University Indianapolis (IUPUI) / Moving Horizon Estimation (MHE) is a powerful estimation technique for tackling
the estimation problems of the state of dynamic systems in the presence of constraints,
nonlinearities and disturbances and measurement noises. In this work, 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. An equivalent circuit battery model is used to capture the dynamics of the
battery states, experimental data is used to identify the parameters of the battery
model. MHE based state estimation technique is applied to estimates the states
of the battery model, subjected to various estimated initial conditions, process and
measurement noises and the results are compared against the traditional EKF based
estimation method. Both experimental data and simulations are used to evaluate the
performance of the MHE. The results shows that MHE performs better than EKF
estimation even with unknown initial state of the estimator, MHE converges faster
to the actual states,and also MHE is found to be robust to measurement and process
noises.
| Identifer | oai:union.ndltd.org:IUPUI/oai:scholarworks.iupui.edu:1805/6293 |
| Date | January 2014 |
| Creators | Bibin Nataraja, Pattel |
| Contributors | Anwar, Sohel |
| Source Sets | Indiana University-Purdue University Indianapolis |
| Language | en_US |
| Detected Language | English |
| Type | Thesis |
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