Author_Institution :
Electr. Eng. Dept., Nat. Kaohsiung Univ. of Appl. Sci., Kaohsiung, Taiwan
Abstract :
In this paper, we address a big-data analysis method for estimating the driving range of an electric vehicle (EV), allowing drivers to overcome range anxiety. First, we present an estimating approach to project the life of battery pack for 1600 cycles (i.e., 8 years/160 000 km) based on the data collected from a cycle-life test. This approach has the merit of simplicity. In addition, it considers several critical issues that occur inside battery packs, such as the dependence of internal resistance and the state-of-health. Subsequently, we describe our work on driving pattern analysis of an EV, using a machine-learning approach, namely growing hierarchical self-organizing maps, to cluster the collected EV big data. This paper contains the analysis of energy consumption and driving range estimation for EVs, including powertrain simulation and driving behavior analysis. The experimental results, including both simulating battery degradation and analysis of driving behaviors, demonstrate a feasible solution for improving driving range estimation by the EV big data.
Keywords :
Big Data; battery powered vehicles; data analysis; data mining; estimation theory; learning (artificial intelligence); pattern clustering; traffic engineering computing; Big Data modeling method; EV big data clustering; EV driving pattern analysis; battery packs; big-data analysis method; driving behavior analysis; electric vehicle driving range estimation; energy consumption; growing hierarchical self-organizing maps; machine-learning approach; powertrain simulation; Batteries; Big data; Data mining; Driving range; Electric vehicles; Modeling; EV big-data; Electric vehicle; battery modeling method; data mining; electric vehicle; range estimation;
