Feed Forward Neural Network Sebagai Algoritma Estimasi State of Charge Baterai Lithium Polymer

Prasetyo, Mohammad Imron Dwi; Tjahjono, Anang; Windarko, Novie Ayub

doi:10.20527/klik.v7i1.290

Cited by 1 publication

(2 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Finding the correct number of hidden layers and neurons is crucial for the FFNN training test because it has an effect not only on mse and mae value but also on SoC estimation accuracy [16] .…”

Section: Fig 5 Plotting Of Ffnn Training Resultsmentioning

confidence: 99%

“…The Feed Forward Neural Network or FFNN method gives a very strong simulation performance when applied to Lithium Polymer Batteries to estimate the SoC value. The number of hidden layers is very crucial to the results of the training data [16] . The FFNN used to predict SoC in various types of battery such as Li-Po, Li-Ion, and Ni-MH gave a 98% accuracy.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The State of Charge Estimation of LiFePO4 Batteries Performance Using Feed Forward Neural Network Model

Dyartanti,

Jamaluddin,

Akshya

et al. 2024

AMM

View full text Add to dashboard Cite

Lithium-ion batteries like LiFePO4 become a new choice for electrical energy sources in the world and can be used on electric vehicles. Battery packs monitoring by Battery Management System in electric vehicles require accurate monitoring. The inaccuracy of monitoring such property can lead to low safety, low efficiency and battery’s life reduction. Estimating state of charge (SoC) to prevent battery damage from overcharging and over discharging. Some of the methods used to estimate SoC such as Coulomb Counting have errors during the charge and discharge process. This research proposes a counting method for measuring SoC with the artificial neural networks (ANN) to provide more precise estimation. Feed Forward Neural Network (FFNN) is an ANN model that can give an accurate estimation of SoC by learning data of the charge-discharge process performed on sample batteries. The sample batteries are tested with a battery analyzer to get its charging-discharging data consisting of variables such as voltage, current, capacity, and time with C-Rate variations. These variables data are then learned by the modeled FFNN to predict SoC value. The FFNN model consisted of 16 neurons in the first layer, 8 neurons in the second layer, and 4 neurons in the third layer. The predicted SoC value from FFNN has a similar value with its real SoC value. The relationship between SoC and battery voltage is plotted in a curve and shows an identical characteristic with how the SoC-Voltage curve of a battery should be and have a low mae value. This FFNN model can be applied further such as in electric vehicles to maintain its safety and for longer use.

show abstract

Section: Fig 5 Plotting Of Ffnn Training Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%