Evaluation of battery modules state for electric vehicle using artificial neural network and experimental validation

Abstract

This work undertakes research problem on prediction of state of battery modules used in electric vehicle. Past studies focussed extensively on the estimation of state of charge and state of health of a single cell/battery. However, the focus on the estimation of state of entire battery module is hardly studied. During the actual operation of electric vehicle, the environmental conditions (road slope and climate) and factors such as abnormal voltage and temperature conditions causes the deviations among the battery modules from its equilibrium state. As a result, its power efficiency and the life‐cycle decreases. One of the reasons of deviation is the defects in manufacturing of a battery module. For this purpose, the evaluation and estimation of state of battery modules is a priority to have information on which module among the given ones should be discharged first. Therefore, this study proposes the methodology for the evaluation of state of battery modules based on its current and temperature. Firstly, the rules are defined based on experiments to determine the priority for the discharge of each of the six modules. Dataset of 6000 samples obtained comprises of the 12 inputs (temperature and current) and 6 outputs (on‐off state of switch). Each output represents the priority of module to be discharged. To analyze this multi‐input multi‐output dataset, the popular artificial intelligence algorithm namely, artificial neural network with three training algorithms (Levenberg, Scaled conjugate and Bayesian regularization), using a number of neurons from 2 to 20 in the hidden layer is used. It was found that the model obtained using Levenberg algorithm performs the best.

Electric Vehicle Lithium-ion Battery Ageing Analysis under Dynamic Condition: A Machine Learning Approach

“…The SOH estimation by the convolutional neural network, artificial neural network, and recurrent neural network are discussed in [13][14][15] respectively. The neural network has hidden layers, as the number of hidden layers is increased then computational complexity also increases with the model accuracy which is represented in fig.…”

An Effective Deep Neural Network Method for Prediction of Battery State at Cell and Module Level

“…A dataset consisting of 6000 samples obtained from the experiment is utilized for training and testing purposes of the DNN model. In which, each data sample includes 12 input parameters (e.g., temperature (T i ) and current This example has been previously examined by Liang et al [33] In that work, the authors used the Levenberg method to train the ANN model for predicting priority discharge of battery modules. It was found that the ANN model achieved using the Levenberg algorithm has the best with R value of 0.96 and MSE value of 0.17.…”

Big data driven vehicle battery management method: A novel cyber-physical system perspective

“…However, the battery is a complex electrochemical system with hardly observable or simulatable chemical reactions [5,6], so it is challenging to estimate the state of battery accurately with insufficient training data set and limited BMS calculation ability [7]. Fortunately, the development of the calculation and communication technology, especially the cloud platform [8,9] and the artificial intelligence algorithm [10], brings a bright prospect to this issue. The concept of EV cloud was proposed in [11] to collect the battery data and estimate the driving range of EVs.…”