A feedforward deep neural network for predicting the state-of-charge of lithium-ion battery in electric vehicles

This study proposes a feedforward deep neural network to predict the parameters of the lithium-ion battery in electric vehicles. Correlation analysis is used to select the candidate parameters for the proposed model with no categorical variable. A direct feedforward deep artificial neural network is developed to predict a battery's charge state and develop the proposed inverse model. The predicted state-of-charge of the direct model is combined with four virtual functions to form the input variables for the proposed inverse model. Furthermore, virtual functions are incorporated to enhance the predicting capability of the proposed inverse function model. The predicted multi-output variables of the proposed inverse model are speed, mileage, voltage, velocity, and state-of-charge. The proposed inverse model is superior to the feedforward deep neural network previously proposed in the literature because of its multiple output capabilities. Also, the proposed model makes decision-making easier when used for the design simulation than the single-output deep neural networks, which predict the state-of-charge of a battery only. The mean square error is used as the metric for accurate measurement. During the simulation by the proposed inverse model (with virtual functions), accuracy was 44.43 times higher than the traditional inverse deep neural network model. Redefined parameters were used to verify the findings of the model. This result suggests that incorporating virtual functions into a deep neural network model's inverse model can improve the accuracy of battery and electric vehicle parameter predictions.