Some drivers of electric vehicles are prone to range anxiety – the fear that a vehicle has insufficient energy storage to cover the road distance needed to reach its intended destination. This presentation will illustrate how the analysis of sensor data from a fleet of vehicles operating in real-world conditions, across different climates, with diverse driving styles and usage patterns, can identify which parameters (such as driving style, distance traveled, periods of inactivity, ambient temperature, etc) significantly affect energy consumption in an electric vehicle.

• Engineering analysis of vehicle CAN data – from raw data to decision
• Use engineering algorithms to reduce huge amounts of sensor data to indicators
• Use the calculated engineering indicators for trend analysis, comparisons, correlation, etc
• Easily search for sensor data in a data lake containing usage data from thousands of vehicles