A-ECMS: An Adaptive Algorithm for Hybrid Electric Vehicle Energy Management

By C. Musardo, Giorgio Rizzoni, Y. Guezennec, B. Staccia. Proceedings of the 44th IEEE Conference on Decision and Control, 2005

1,074 citations recorded by the source corpus.

Subfields: Automotive Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/b075d35633733f90ab0da6ae2ba417eb8d0399f0

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Citations in the corpus

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.”
A Stochastic Optimal Control Approach for Power Management in Plug-In Hybrid Electric Vehicles (2011, methodology): “For example, the equivalent fuel consumption minimization approach [4] [5] [6] uses models of electric powertrain performance to mathematically convert electricity consumption to an equivalent amount of fuel, and then makes real-time power split decisions to minimize net fuel consumption.”
Optimal Control of Hybrid Electric Vehicles Based on Pontryagin's Minimum Principle (2011, methodology): “Real-time applications of ECMS were suggested in [9] and [10].”
Control of Connected and Automated Vehicles: State of the Art and Future Challenges (2018, background): “This requires route information and long-term planning, and is discussed in Section 5.1 Some real-time approaches borrow the ECMS formulation; if the driving schedule is not known in advance, various up-date laws for λ have been proposed, based on historical data and forecasts [90, 91].”
Cost-Optimal Energy Management of Hybrid Electric Vehicles Using Fuel Cell/Battery Health-Aware Predictive Control (2020, methodology): “, equivalent consumption minimization strategy (ECMS) [23], [24], Pontryagin’s Minimum Principle (PMP) [25], [26], and model predictive control (MPC) [27], [28], were adopted.”
Velocity Predictors for Predictive Energy Management in Hybrid Electric Vehicles (2015, methodology): “The ECMS variants, such as telemetric-ECMS and adaptive-ECMS, are proposed to adjust the equivalence factor based on information from telemetry equipment or on-board sensors [7], [8].”