Bluwave Inc.

Nasrin Sadeghianpourhamami

Mostafa Farrokhabadi

Devashish Paul

Alexander Linchieh

Rahul Gahlawat

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

Improvements in the field of electric vehicles (EV) are provided, including EV telematics estimation. Prior techniques estimate a state of charge (SoC) of a battery of an EV based on electrochemical measurements of the battery, such as battery terminal voltage, battery current, or cell temperature. The present improvements estimate SoC or other parameters based on non-electrochemical variables, referred to as exogenous information, meaning information other than electrochemical parameters of the battery. Example exogenous information includes battery type, and battery capacity, vehicle type or load, driver behaviour, weather conditions, or traffic or road conditions. Exogenous information may be used to enable more accurate estimations of EV SoC by EV related systems.

Methods and systems for electric vehicle telematics estimation

Systems and methods are provided for dynamically selecting a control policy from among several available control policies for controlling an electric vehicle fleet charging system. A control policy may take into account fluctuating local renewable generation and/or time of use electricity pricing. The performance of the selected control policy is monitored and a different control policy may be deployed in its place if the different control policy has a higher chance of providing better performance given the current control environment. Thus, as the control environment changes, the control policy that controls the power system may also be changed in an adaptive manner. In this way, the control policies may be changed as the control environment changes to provide an improved real-time performance compared to the use of a single control policy.

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Ottawa, Canada

Nasrin Sadeghianpourhamami