Implementation of ANN Based SOC Estimation for Lithium-Ion Battery

Battery technology is the bottleneck of the Electric Vehicle. The SOC is one of the crucial parameters in Lithium-ion battery. This project presents an improved nonlinear characteristic in SOC estimation of Li-ion battery by using Artificial Neural Network (ANN). However, the accuracy of Artificial Neural Network depends on the amount of Input order, output order, and Hidden layer neurons. The contributions are brief as the computational ability of ANN model which does not need battery model and parameters somewhat than only desires current, voltage and temperature sensors. The technique contribution of the improved ANN based SOC estimation is developed by using new innovative soft computing method of RAO algorithm. The contributions are summarized to computational capability of ANN technique require the parameters rather than only needs voltage, current and temperature sensor. The performance of the proposed model is Back propagation neural network using dspic30F4011 controller. The consequences show that the projected ANN attains higher correctness with less computational time than other standing SOC algorithm under diverse Electrical vehicle drive cycle.