The battery voltage prediction is critical to model predictive controls for the safe and efficient operation of battery systems. This paper presents a comprehensive study using a long-short-term-memory-based method to predict the battery voltage with past voltage and forecasted current and SOC information. Unlike prior art using many-to-one architecture, a many-to-many architecture was used with test data representing three temperatures. Battery-controller-accessible inputs were also selected. Further, the effectiveness of normalization for voltage prediction was investigated. The results show the temperature has no noticeable impact on the prediction accuracy. The lowest RMSE obtained from the 0 °C case is 0.0997. With having both inputs and output already on a similar scale, applying data normalization didn't provide any consistent accuracy improvement across the three selected temperatures.
Battery Voltage Prediction Using Neural Networks
2021-06-21
1447127 byte
Conference paper
Electronic Resource
English
Prediction of Flashover Voltage of Contaminated Insulator Using Artificial Neural Networks
Online Contents | 2006
|A Comparative Study of Recurrent Neural Network Architectures for Battery Voltage Prediction
SAE Technical Papers | 2021
|Application of Artificial Neural Networks for Prediction of Voltage Instability
Online Contents | 1997
|