Collecting and Mining Big Data for Electric Vehicle Systems Using Battery Modeling Data

Growth of Electric vehicles (EV) starts to change the way that people transit. Several factors that affact the performance of EVs and environment including energy efficiency, safety, product durability, climate, geographical factor, infrastructure, and grid capacity need to be further investigated to cope with upcoming challenges. These issues mainly involve three fields including information technology, EV design and battery management. With the demand to allow EV-data to connect to clouds, the big data collected from EVs creates an unprecedented opportunity for developing novel ways for transportation and information exchange. In this work, we demonstrate the process of pattern extraction of EV related data based on a battery model and characteristics of EV systems. Furthermore, the proposed approach provides an energy management scheme for drivers to overcome "range anxiety". We utilized the EV system data which is critical to the energy consumption to discover patterns for long-term performance estimation. We formulated driver's behaviors by training the operating data collected from a real EV system with an unsupervised learning algorithm by the GHSOM neural network model. The experimental result shows that our approach has high potential to explore driver's behavioral patterns and estimate the driving range. The proposed framework can be appled to new EV design, intelligent transportation system (ITS), and big data analytics for the fields of internet of vehicle as well as urban computing.