Bluwave Inc.

Alexander Linchieh

Mostafa Farrokhabadi

Devashish Paul

Nasrin Sadeghianpourhamami

Methods and systems are provided relating to energy management of controllable assets in a system, such as vehicles in a fleet. Where the vehicle fleet has little or no historical data for a particular type of vehicle, for example electric vehicles, a data-driven based predictor for the fleet may be trained using third party data for that particular type of vehicle. This enables a data-driven control approach even when the fleet has little or no historical data of a give type. Historical data may include information relating to journeys travelled by vehicles on roads. A specific journey of a given vehicle may be subdivided into segments, and a segment signature data structure may be created and populated for each segment. The predictor(s) may be trained using data in a global repository of segment signatures. A fleet specific signature repository may be created for use by the fleet by selecting a subset of the signatures from the global repository.

Systems and methods for data-driven energy management of a vehicle fleet with electric vehicles

Methods and systems relating to improvements in controlling power grid systems are provided. Improvements include dynamic tuning of compromise optimization control in power grid systems. The controlling of assets associated with a power grid system may include optimizing for several conflicting objectives. The performance of the optimization with respect to each objective may be monitored in real-time or near real-time and based on streaming and historic data relating to the system. The optimization may be adjusted in real-time or near real-time when it is determined that the performance of the optimization is not meeting specific levels of performance in regard to one or more of the conflicting objectives. Further, user input may be provided to the system to assign priority levels to one or more of the conflicting objectives.

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Ottawa, Canada

Alexander Linchieh