Developing an Inference Engine for Estimating State of Charge of the Lead Acid Battery

Abstract

Abstract Lead acid battery efficiency is affected by huge uncertainty features. An inference engine is required to monitor the uncertainty of the battery state of charge. The objective of the research is to design an inference system to predict the lead acid battery state of charge. A Relief algorithm and Pearson correlation were applied to pre-process the real-world dataset. A fuzzy inference system was adopted to design the inference engine of the state of charge. This research found four main features which had significant impact to lead acid state of charge, including: export power, temperature, volt per cell and ampere. These features had different directions of correlation and furtherly set as inference system’s output. This research had successfully developed an inference engine for lead acid state of charge with Mamdani fuzzy type and centroid defuzzification. In the future, it needs expert validation of the developed rules in the inference system.