A prediction method for voltage and lifetime of lead–acid battery by using machine learning

• Main Authors: Zhi-Hao Wang, Hendrick, Gwo-Jiun Horng, Hsin-Te Wu, Gwo-Jia Jong
• Format: Article
• Language: English
• Published: SAGE Publishing 2020-01-01
• Series: Energy Exploration & Exploitation
• Online Access: https://doi.org/10.1177/0144598719881223

Lead–acid battery is the common energy source to support the electric vehicles. During the use of the battery, we need to know when the battery needs to be replaced with the new one. In this research, we proposed a prediction method for voltage and lifetime of lead–acid battery. The prediction models were formed by three kinds mode of four-points consecutive voltage and time index.The first mode was formed by four fixed voltages value during four weeks, namely M1. The second mode was formed by four previous voltage values from prediction time, namely M2. Third mode was formed by the combinations of four previous data with the last predicted data, namely M3. The training data were recorded from 10 lead–acid batteries. We separated between training data and testing data. Data collection for training were recorded in 155 weeks. The examined data for the model was captured in 105 weeks. Three of batteries were selected for prediction. Machine learning methods were used to create the batteries model of voltage and lifetime prediction. Convolutional Neural Network was selected to train and predict the battery model. To compare our model performance, we also performed Multilayer Perceptron with the same data procedure. Based on experiment, M1 model did not achieve the correct prediction besides the linear case. M2 model successfully predicted the battery voltage and lifetime. The M2 curve was almost the same with real-time measurement, but the curve was not fitting smoothly. M3 model achieved the high prediction with smooth curve. According to our research on lead–acid battery voltage prediction, we give the following conclusions and suggestions to be considered. The accuracy of prediction is affected by the number of input parameters is used in prediction. The input parameters need to have time consecutive.