Intelligent energy management agent for a parallel hybrid vehicle-part I: system architecture and design of the driving situation identification process

By Reza Langari, Jong-Seob Won. IEEE Transactions on Vehicular Technology, 2005

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Control Strategies for Hybrid Electric Vehicles: Evolution, Classification, Comparison, and Future Trends (2007, background): “variations, as reported in [19] and [20].”
Velocity Predictors for Predictive Energy Management in Hybrid Electric Vehicles (2015, background): “On the other hand, the predicted power demand relies not only on the present vehicle states, but also on the historical values [16].”
Driving style estimation via inertial measurements (2010, unknown): “Largari and Jong-Seob [ 5 ], [6] classify the driving style in 3 categories (aggressive, normal and calm) according to the ratio of the standard deviation and the average acceleration within a specified window and they use the information for the power management system.”
An investigation on the effect of driver style and driving events on energy demand of a PHEV (2012, methodology): “In intelligent energy management agent (IEMA) presented [13], driver behaviour was classified based on average acceleration and standard deviation of acceleration over a specific driving range.”
Driver's style classification using jerk analysis (2009, methodology): “Langari, R.; Jong-Seob Won [ 5 , 6] attempted the driver style classification by using the ratio of the standard deviation and the average acceleration extracted from the acceleration profile within a specified window.”