A Stochastic Optimal Control Approach for Power Management in Plug-In Hybrid Electric Vehicles

By Scott Jason Moura, Hosam K. Fathy, Duncan S. Callaway, Jeffrey L. Stein. IEEE Transactions on Control Systems Technology, 2011

601 citations recorded by the source corpus.

Subfields: Automotive Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/1dc10b16ffc29eb6f874e36f21859efec9e6e796

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In Automotive Engineering

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Citations in the corpus

Stochastic MPC With Learning for Driver-Predictive Vehicle Control and its Application to HEV Energy Management (2014, background): “More recently, SDP has been proposed to enable the implementation of DP-based energy management in real driving [20], [23], [39].”
A Comprehensive Study of Key Electric Vehicle (EV) Components, Technologies, Challenges, Impacts, and Future Direction of Development (2017, background): “, explored efficient ways to split power demand among different power sources of mid-sized sedan PHEVs in [175], which can be used for other vehicle configurations as well.”
Adaptive Partial Differential Equation Observer for Battery State-of-Charge/State-of-Health Estimation Via an Electrochemical Model (2014, background): “Such knowledge enables them to efficiently route energy while satisfying power demands and device-level operating constraints [2].”
Classification and Review of Control Strategies for Plug-In Hybrid Electric Vehicles (2011, background): “Stochastic DP optimization is not a real-time solution by nature; however, the control values can be used in real-time implementation since the values are an average result of many different drive cycles [21].”
Velocity Predictors for Predictive Energy Management in Hybrid Electric Vehicles (2015, background): “Markov-chain models are widely used for vehicular velocity modeling [13], [14].”
Influence of driving patterns on life cycle cost and emissions of hybrid and plug-in electric vehicle powertrains (2013, background): “We focus on EREV PHEVs, since the performance of blended-operation PHEVs varies substantially with control strategy parameters (Tulpule et al., 2009; Sciarretta et al., 2004; Sciarretta and Guzzella, 2007; Moura et al., 2011).”