On-Line Detection of State-of-Charge in Lead Acid Battery Using Both Neural Network and On-Line Identi cation

Estimating the state-of-charge of a lead-acid battery at remote seismic nodes is a key factor in managing the available power. Optimal management enables the continuous acquisition of seismic data of an area. This paper presents the management of lead-acid batteries at remote seismic nodes, using the Neural Network model's historical data to estimate the battery's state-of-charge. Powersim (PSIM) simulation tool was used to implement photovoltaic energy harvesting system with a buck mode converter and maximum power point tracking algorithm to acquire historical data. A backpropagation neural network technique for training the historical dataset of hourly points in 500 days on the Matlab platform is adopted, and a feedforward neural network is employed due to the irregularities of the input data. The neural network model's hidden layer contains the transfer function of the Tansig Function to produce the model output of state-of-charge estimations. Besides, this paper is based on the management of estimating the state-of-charge of the lead-acid battery near-realtime instead of relying on the vendor's lifecycle information. The simulated results show the simplicity and optimal estimations of state-of-charge of the lead-acid battery with RMSE of 0.023%.

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MAPPING OF ELECTROCHEMISTRY AND NEURAL NETWORK MODEL APPLIED IN STATE OF CHARGE ESTIMATION FOR LEAD ACID BATTERY USED IN ELECTRIC VEHICLE

Indonesian Journal of Chemistry ◽

10.22146/ijc.21401 ◽

2011 ◽

Vol 11 (2) ◽

pp. 140-147

Author(s):

Bambang Sri Kaloko ◽

Soebagio Soebagio ◽

Mauridhi H. Purnomo

Keyword(s):

Neural Network ◽

Network Model ◽

Neural Network Model ◽

State Of Charge ◽

Analytical Models ◽

Lead Acid Battery ◽

The Neural Network ◽

Battery Systems ◽

Product Realization ◽

Lead Acid

Analytical models have been developed to diminish test procedures for product realization, but they have only been partially successful in predicting the performance of battery systems consistently. The complex set of interacting physical and chemical processes within battery systems has made the development of analytical models of significant challenge. Advanced simulation tools are needed to be more accurately model battery systems which will reduce the time and cost required for product realization. As an alternative approach begun, the development of cell performance modeling using non-phenomenological models for battery systems were based on artificial neural networks (ANN) using Matlab 7.6.0(R2008b). ANN has been shown to provide a very robust and computationally efficient simulation tool for predicting state of charge for Lead Acid cells under a variety of operating conditions. In this study, the analytical model and the neural network model of lead acid battery for electric vehicle were used to determinate the battery state of charge. A precision comparison between the analytical model and the neural network model has been evaluated. The precise of the neural network model has error less than 0.00045 percent.

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