Velocity Predictors for Predictive Energy Management in Hybrid Electric Vehicles

By Chao Sun, Xiaosong Hu, Scott Jason Moura, Fengchun Sun. IEEE Transactions on Control Systems Technology, 2015

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Energy saving potentials of connected and automated vehicles (2018, background): “Predicted velocity transitions and road grade can reduce energy use also via predictive power split in hybrid powertrains [48], fuel cut-off [49] and cylinder deactivation [50] in combustion”
Cost-Optimal Energy Management of Hybrid Electric Vehicles Using Fuel Cell/Battery Health-Aware Predictive Control (2020, background): “For example, refer [63] and [64] for more related details.”
Pontryagin’s Minimum Principle based model predictive control of energy management for a plug-in hybrid electric bus (2019, methodology): “Novel application-oriented ECMSs, such as adaptive [24] and map-based ECMS [2, 25], or artificial neural network-enhanced EMCS were further proposed [11], in which the equivalent factor is dynamically tuned based on environmental information.”
Energy Management in Plug-in Hybrid Electric Vehicles: Recent Progress and a Connected Vehicles Perspective (2017, methodology): “They claimed to obtain 92% fuel optimality using NN-based predictor when compared with the MPC benchmark solution using DP in simulation environment [82].”