King County Metro’s maintenance protocol for their hybrid electric bus fleet is currently ineffective due to unpredictable battery service requirements. My research seeks to develop a battery and vehicle model, that utilizes geographic information system (GIS) data to predict the state of health of lithium-ion battery packs. Predicative maintenance will rely on parameters such as road grade, stop-start frequency, velocity, acceleration, date, and weather conditions. My goal this year is to develop a model that links cumulative cycling behavior to the battery’s state of health and necessary maintenance requirements. I use GIS data and ArcMap to visualize the spatial parameters and will use Python to develop the model and analyze data in real time. A physics-based diagnostic model combined with geospatial information will lower the operating and maintenance cost of hybrid buses and allow for better route planning.
Advisor: Daniel Schwartz – Chemical Engineering