Electric Vehicle NiMH Battery State of Charge Estimation Using Artificial Neural Networks of Backpropagation and Radial Basis

Abstract

The state of charge of a battery depends on many magnitudes, but only voltage and intensity are included in mathematical equations because other variables are complex to integrate into. The contribution of this work was to obtain a model to determine the state of charge with these complex variables. This method was developed considering four models, the multilayer feed-forward backpropagation models of two and three input variables used supervised training, with the variable-learning-rate backpropagation training function, five and seven neurons in the hidden layer, respectively, achieving an optimal training. Meanwhile, the radial basis neural network models of two and three input variables were trained with the hybrid method, the propagation constant with a value of 1 and 80 neurons in the hidden layer. As a result, the radial basis neural network with the variable-learning-rate training function, considering the discharge temperature, was the one with the best performance, with a correlation coefficient of 0.99182 and a confidence interval of 95% (0.98849; 0.99516). It is then concluded that artificial neural networks have high performance when modeling nonlinear systems, whose parameters are difficult to measure with time variation, so estimating them in formulas where they are omitted is no longer necessary, which means an accurate SOC.

Energy Management Systems’ Modeling and Optimization in Hybrid Electric Vehicles

“…If the error value sometimes increases during training, this is usually an indication of getting away from the solution, and training stops when the validation error increases repeatedly for certain number of iterations [96]. The learning rate is a measure of the size of the step towards the minimum of a loss function or the size of the change in weight at each iteration [108]. In the histogram, the highest error density is the smallest range of data from all errors and is an indication of the performance of the ANN.…”

“…In the histogram, the highest error density is the smallest range of data from all errors and is an indication of the performance of the ANN. If the ANN is not trained on many epochs, no significant overfitting is expected in the epoch with the best validation performance [108]. The validation phase is a critical process that involves the identification and reduction of bias in the training data and evaluation of the system's accuracy, while the testing phase is where test data are provided to each of the trained models to verify the best prediction [108].…”