Forecasting of Lead Acid Battery Capacity Based on Levenberg Marquardt Neural Network

Abstract

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.