IEEE VTS Motor Vehicles Challenge 2020 - Energy Management of a Fuel Cell/Ultracapacitor/Lead-Acid Battery Hybrid Electric Vehicle

Solano, Javier; Jemeï, Samir; Boulon, Loïc; Silva, Luis I.; Hissel, Daniel; Péra, Marie‐Cécile

doi:10.1109/vppc46532.2019.8952246

Cited by 18 publications

(7 citation statements)

References 18 publications

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“…The discretization values of the state space and the control actions are listed in Table 5. To evaluate the designed EMS performance, a real driving profile [52] from a heavy-duty HFCV is used throughout the training procedure, as demonstrated in Figure 9. Since the maximum power of the driving profile is higher than the limitations of the modular small-scale test bench, the driving profile has been scaled down.…”

Section: Simulation Results Analysis and Discussionmentioning

confidence: 99%

“…To analyze the performance of the suggested Dec-RL EMS, a modular test bench based on a heavy-duty HFCV [52,53] is established, as shown in Figure 3 and FPGA is utilized for measuring the data and to execute highly efficient data processing. The real-time controller contains a microprocessor unit for implementing the control strategies.…”

Section: General Powertrain Structurementioning

confidence: 99%

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A Decentralized Multi-agent Energy Management Strategy Based on a Look-Ahead Reinforcement Learning Approach

Khalatbarisoltani¹,

Kandidayeni²,

Boulon³

et al. 2021

SAE Int. J. Elec. Veh.

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An energy management strategy (EMS) has an essential role in ameliorating the efficiency and lifetime of the powertrain components in a hybrid fuel cell vehicle (HFCV). The EMS of intelligent HFCVs is equipped with advanced data-driven techniques to efficiently distribute the power flow among the power sources, which have heterogeneous energetic characteristics. Decentralized EMSs provide higher modularity (plug and play) and reliability compared to the centralized data-driven strategies. Modularity is the specification that promotes the discovery of new components in a powertrain system without the need for reconfiguration. Hence, this paper puts forward a decentralized reinforcement learning (Dec-RL) framework for designing an EMS in a heavy-duty HFCV. The studied powertrain is composed of two parallel fuel cell systems (FCSs) and a battery pack. The contribution of the suggested multi-agent approach lies in the development of a fully decentralized learning strategy composed of several connected local modules. The performance of the proposed approach is investigated through several simulations and experimental tests. The results indicate the advantage of the established Dec-RL control scheme in convergence speed and optimization criteria.

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Section: Simulation Results Analysis and Discussionmentioning

confidence: 99%

Section: General Powertrain Structurementioning

confidence: 99%

A Decentralized Multi-agent Energy Management Strategy Based on a Look-Ahead Reinforcement Learning Approach

Khalatbarisoltani¹,

Kandidayeni²,

Boulon³

et al. 2021

SAE Int. J. Elec. Veh.

View full text Add to dashboard Cite

show abstract

“…With the purpose to attract more researchers and engineers to improve EVs' performance, an annual Motor Vehicle Challenge (MVC) was launched in IEEE VPPC 2016 and the Challenge was supported by IEEE Vehicular Technology Society (VTS). In previous editions of the MVC, the participants were invited to propose an energy management strategy (EMS) to reduce operation cost of an hybrid vehicle [1], its fuel consumption and the battery recharging cost [2], hydrogen consumption of a dual-mode locomotive [3] and heavy-duty track [4], battery state-of-charge of a dual-motor all-wheel drive [5], and sizing of a commercial EV with hybrid dualenergy storage systems [6].…”

Section: Introductionmentioning

confidence: 99%

IEEE VTS Motor Vehicles Challenge 2024 - Energy and Powertrain Losses Management of an e-Racing Vehicle

Li,

Vo-Duy

2023

2023 IEEE Vehicle Power and Propulsion Conference (VPPC)

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Motor Vehicle Challenge, supported by IEEE Vehicular Technology Society, is an annual activity to find an appropriate energy management strategy to improve electric vehicles' performance. In Challenge 2024, power losses and efficiency of power converters using silicon carbide MOSFET and an internal permanent magnet (IPM) motor are modelled and implemented into an electric vehicle powertrain model for a racing vehicle with hybrid energy storage sources. The objective of the developed energy management strategy is to minimise both the size, energy consumption of the energy storage sources and the losses and cost of all the power electronics converters. Participants from both academia and industry are welcome to participate and submit the solutions for the Challenge.

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“…In order to stimulate advances in vehicular energy and power management, the IEEE Vehicular Technology Society (VTS) initiated the VTS Motor Vehicles Challenge in 2016. The competition's themes have focused on energy management of hybrid energy storage systems (fuel cells/batteries/supercapacitors [1] [2] [3] [4]) and their applications to cars ( [5] [6]), trucks [7] and trains [8]. These competitions provide benchmark problems where researchers can evaluate and compare the performance of their energy management algorithms against other research groups.…”

Section: Introductionmentioning

confidence: 99%

IEEE VTS Motor Vehicles Challenge 2023: A Multi-physical Benchmark Problem for Next Generation Energy Management Algorithms

Brembeck

Castro

Tobolar

et al. 2022

2022 IEEE Vehicle Power and Propulsion Conference (VPPC)

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This article presents a benchmark problem that researchers can use to evaluate the performance of energy management algorithms for multi-energy source and multimotor electric vehicles. The model makes use of Modelica, an open source, a-causal, object-oriented language for modeling cyber-physical systems in multidomains (e.g. electrical, mechanical, thermal) of the vehicle components. The model also includes the aspect of three-dimensional mechanics, which enables completely new degrees of freedom in the controller design in comparison to one-dimensional approaches. To support interoperability among multiple design tools, the Modelica vehicle model is provided as a Functional Mockup Unit, an industry standard for exchange of simulation models. A set of standardized input-output interfaces and key performance metrics is also provided in the benchmarking problem, enabling the systematic ranking of multiple energy management strategies.

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IEEE VTS Motor Vehicles Challenge 2020 - Energy Management of a Fuel Cell/Ultracapacitor/Lead-Acid Battery Hybrid Electric Vehicle

Cited by 18 publications

References 18 publications

A Decentralized Multi-agent Energy Management Strategy Based on a Look-Ahead Reinforcement Learning Approach

A Decentralized Multi-agent Energy Management Strategy Based on a Look-Ahead Reinforcement Learning Approach

IEEE VTS Motor Vehicles Challenge 2024 - Energy and Powertrain Losses Management of an e-Racing Vehicle

IEEE VTS Motor Vehicles Challenge 2023: A Multi-physical Benchmark Problem for Next Generation Energy Management Algorithms

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