Our connected energy management system consists of a suite of models and algorithms operating on a cloud server.
About
Exergi Predictive has developed a proprietary connected energy management system (C-EMS) consisting of a suite of models and algorithms operating on a cloud server that samples a small set of historical vehicle data from individual vehicles collected every second during a trip. These data are stored in a secure spatial database and used by advanced algorithms and vehicle simulations developed to predict battery state of charge more accurately, expected range on a future route, and on-route charging requirements than current onboard predictions. All-electric vehicle manufacturers (Class 4-8) already report an expected driving range in their vehicles, but these can be inaccurate based on driver behavior, if the vehicle is highly loaded, or if there are inclement weather conditions. The C-EMS technology for hybrid trucks was tested on trucks in a major commercial delivery fleet, resulting in over 20% improvement in in-use fuel economy. Exergi is currently expanding its product offering from an application programming interface (or API) for plug-in hybrid vehicles to all battery electric vehicles (Class 4-8) and we expect to have a proof of concept completed in a BEV operating in an actual delivery fleet by the first quarter of 2021.
Key Benefits
The effective range of vehicles improves due to increased certainty Reduced vehicle range uncertainty to a slower charge and a lower max state of charge (SOC) Improved energy efficiency and responsible impacts
Applications
Commercial Auto Manufacturers (Classes 4-8) Delivery Fleets Charging Infrastructure Utility Companies