Forecasting of Lead Acid Battery Capacity Based on Levenberg Marquardt Neural Network
Date
2016-07-15Author
KALOKO, Bambang Sri
M. UDIN, M.Udin
A. H. LOKA, A. H. Loka
Metadata
Show full item recordAbstract
One of the discussion in the research of electric vehicle is the energy source or battery. Due to mature
technology, environmental friendliness, and low cost, the lead acid battery has been widely accepted in
electric vehicle. It is necessary to forecast battery capacity in electric car in order to know when the time to
recharge the battery or replace it. The Levenberg Marquardt algorithm is chosen to adaptively optimize
weights at each epoch so as to accommodate time-varying system conditions. In a state of nominal current
of 1.2 A, battery discharge graph has 1.8617 Ah and 1.55 hours compared to simulation results 2 Ah and
1.38 hours thus 10.96 % of error is obtained. When the load is 8 W, showed 1.5 Ah of battery capacity and
3.1 hours with small error 1.58 % compared to the current load on the nominal load is equal to 20 W
produced a greater error 27.27 % with 1.86 Ah and 0.8 hours. This means that the system made it would be
better if used under a nominal load of the above nominal load. But it needs to design better system to
maintain more accurate results of the battery capacity and the time.
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- LSP-Jurnal Ilmiah Dosen [7292]