Identification of Lead-Acid Battery Parameters by Kalman Filter Using Various Battery Models
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The conventional methods applied to identify the battery’s parameters consist in estimating the state of charge (SOC), based on an electrical or empirical models developed with fixed parameters, to establish an appropriate command to charge or to discharge the battery. These methods are adapted for uncritical basic system (cellular phone, battery of cars with an internal combustion) which the battery can be changed easily without any mission or cost impacts. Whoever the methods are inefficient in some fields like spacecraft, stand-alone photovoltaic system (PV), electric (EV) and hybrid Vehicle (HEV) which depends, totally or in majority, of the stored electric energy.
Trying to increase the effectiveness, we use a new approach of identification that combined the conventional methods with adaptive and dynamic techniques. This approach is already used in other domains and proved their efficiency with a remarkable robustness. From the estimation methods, the Kalman filter (KF) is chosen because of its reputation as an optimal estimator in the field of tracking parameters.
This work illustrates new approach to identify Lead Acid (LA) Battery parameters using KF estimator, followed by a simulation to validate the algorithms, also we use an experimental PV system developed in our laboratory to approve the results
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