By Anton Shvets, Jaichitra Balakrishnan, & Paul Tluczek
The BMW ChargeForward project in collaboration with California-based utility PG&E is designed to optimize electric vehicle (EV) charging.
It also explores the following topics:
- Using vehicle telematics to measure performance data
- Evaluating charging events to determine if they offer additional grid benefits beyond overnight charging
- Exploring additional optimization opportunities using renewable energy sources
Our analysis is divided into following sections:
- User Energy Consumption Analysis
- Area Average Consumption Statistical Distribution
- User Energy Consumption Segmentation
- EV Charge Time Predictive Modeling
- Vehicle Driving Behavior Modeling
- Vehicle Plug-in Pattern Statistical Modeling
- EV Charge and Grid Optimizer Model Using Renewable Energy Sources
Activities in the project include:
- Data Scraping, Data Wrangling, Data Cleaning, Web Scraping
- Statistical Data Analysis and Fitting Distributions
- Geolocation Analysis
- Pattern Identification Algorithm
- Machine Learning Modeling – Classification
- Machine Learning Modeling – Regression
- Machine Learning Modeling – Clustering
Our analysis and research gives BMW insight for implementations of EV charging recommendation engines. The next phase of BMW ChargeForward applies our findings to advance their real-time vehicle technical information with predictions of travel behavior, grid load, and energy market price analysis. This strategically enables managing a vehicle’s charging through its current and projected charging events.