Journal Article10.1016/j.apenergy.2024.123264
Variable horizon multivariate driving pattern recognition framework based on vehicle-road two-dimensional information for electric vehicle
Huimin Liu,Cheng Lin,Xiao Yu,Zhenyi Tao,Jiaqi Xu +4 more
About: This article is published in Applied Energy. The article was published on 01 Jul 2024.
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References
Pontryagin’s Minimum Principle based model predictive control of energy management for a plug-in hybrid electric bus
TL;DR: A stochastic model predictive controller (MPC) based on Pontryagin’s Minimum Principle (PMP), which differs from widely used dynamic programming (DP)-based predictive methods is proposed, demonstrating the promising performance and computational efficiency of the proposed methodology.
349
Multi-mode predictive energy management for fuel cell hybrid electric vehicles using Markov driving pattern recognizer
TL;DR: The development of an adaptive energy management strategy is presented, including a driving pattern recognizer and a multi-mode model predictive controller, which can reduce the average fuel cell power transients by over 87.00% under multi-pattern test cycles with a decrement of (at least) 2.07% hydrogen consumption.
189
Energy management for a power-split hybrid electric bus via deep reinforcement learning with terrain information
TL;DR: Improvements in energy management method are made and terrain information is systematically integrated into the energy management strategy for a power-split hybrid electric bus based on a deep reinforcement learning approach: the deep deterministic policy gradient algorithm.
163
Intelligent energy management strategy of hybrid energy storage system for electric vehicle based on driving pattern recognition
TL;DR: The DPR uses cluster analysis to classify driving cycles into different patterns according to the features extracted from the historical driving data sampling window and utilizes pattern recognition to identify real-time driving patterns to effectively decrease the maximum charge/discharge current of battery and improve the battery lifetime and the vehicle endurance range.
157
Fuzzy Optimal Energy Management for Fuel Cell and Supercapacitor Systems Using Neural Network Based Driving Pattern Recognition
Ridong Zhang,Jili Tao,Huiyu Zhou +2 more
TL;DR: A novel adaptive energy management strategy is proposed for real-time power split between fuel cells and supercapacitors in a hybrid electric vehicle, demonstrating that less current fluctuations and fuel consumption can be achieved under various driving conditions.