A Novel Multi-Mode Adaptive Energy Consumption Minimization Strategy for P1-P2 Hybrid Electric Vehicle Architectures

Haubmann, Mike; Barroso, Daniel; Vidal, Carlos; Bruck, Lucas; Emadi, Ali

doi:10.1109/itec.2019.8790525

Cited by 14 publications

(5 citation statements)

References 10 publications

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“…Supervised learning trains using labelled data and can then apply learnt rules to new data while also extrapolating the logic to unknown cases, whereas unsupervised learning is used to recognise patterns in unlabelled data [202]. Learning based on recurrent neural networks (RNN) can be used for more complex applications such as efficient powertrain mode selection depending on driver behaviour instead of mission profile [262]. Reinforcement learning (RL) is known for interacting with the plant (vehicle-driver-environment) for maximising instantaneous and value function-based estimated rewards.…”

Section: Learning Based Strategiesmentioning

confidence: 99%

A Review of Fuel Cell Powertrains for Long-Haul Heavy-Duty Vehicles: Technology, Hydrogen, Energy and Thermal Management Solutions

et al. 2022

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Long-haul heavy-duty vehicles, including trucks and coaches, contribute to a substantial portion of the modern-day European carbon footprint and pose a major challenge in emissions reduction due to their energy-intensive usage. Depending on the hydrogen fuel source, the use of fuel cell electric vehicles (FCEV) for long-haul applications has shown significant potential in reducing road freight CO2 emissions until the possible maturity of future long-distance battery-electric mobility. Fuel cell heavy-duty (HD) propulsion presents some specific characteristics, advantages and operating constraints, along with the notable possibility of gains in powertrain efficiency and usability through improved system design and intelligent onboard energy and thermal management. This paper provides an overview of the FCEV powertrain topology suited for long-haul HD applications, their operating limitations, cooling requirements, waste heat recovery techniques, state-of-the-art in powertrain control, energy and thermal management strategies and over-the-air route data based predictive powertrain management including V2X connectivity. A case study simulation analysis of an HD 40-tonne FCEV truck is also presented, focusing on the comparison of powertrain losses and energy expenditures in different subsystems while running on VECTO Regional delivery and Longhaul cycles. The importance of hydrogen fuel production pathways, onboard storage approaches, refuelling and safety standards, and fleet management is also discussed. Through a comprehensive review of the H2 fuel cell powertrain technology, intelligent energy management, thermal management requirements and strategies, and challenges in hydrogen production, storage and refuelling, this article aims at helping stakeholders in the promotion and integration of H2 FCEV technology towards road freight decarbonisation.

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Section: Learning Based Strategiesmentioning

confidence: 99%

A Review of Fuel Cell Powertrains for Long-Haul Heavy-Duty Vehicles: Technology, Hydrogen, Energy and Thermal Management Solutions

et al. 2022

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“…A-ECMS was applied to realize the adaptation of EFs by the prediction results, and the driving behavior was then adjusted. In Haußmann et al (2019), an ITS-based EMS established on driving behavior recognition was proposed. The learning method was used to classify the historical driving data, and the driving conditions were divided into four categories.…”

Section: Its-based Emss Established Using Static Datamentioning

confidence: 99%

Review article: A comprehensive review of energy management strategies for hybrid electric vehicles

Zhu

Liu

et al. 2022

Mech. Sci.

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Abstract. In order to prevent the aggravation of global environmental problems, all industries are facing the challenge of green development. In the automotive field, the development of “new-energy vehicles” (plug-in electric vehicles) is particularly necessary. Hybrid electric vehicles (HEVs) have been proven to be an efficient way of solving environmental and energy problems. As the core of HEVs, the energy management strategy (EMS) plays an important role in fuel economy, power performance, and drivability. However, considering the randomness of actual driving conditions, there are great challenges involved in the establishment of an EMS. Therefore, it is critical to develop an efficient and adaptable EMS. This paper presents a systematic review of EMSs for HEVs. First, different issues that can affect the performance of EMSs are summarized. Second, recent studies on EMSs for HEVs are reviewed. Third, the advantages and disadvantages of different categories of EMSs are compared in detail. Finally, promising EMS research topics for future study are put forward.

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“…Furthermore, in [117] and [118], a learning vector quantization network for driving cycle recognition is developed. In [173], driving modes are switched based on the detection of the driving behavior with long short-term memory in a recurrent neural network (RNN). In contrast to supervised learning, unsupervised learning is used to identify structures or patterns in unlabeled data.…”

Section: Learning-based Strategiesmentioning

confidence: 99%

Toward Holistic Energy Management Strategies for Fuel Cell Hybrid Electric Vehicles in Heavy-Duty Applications

et al. 2021

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The increasing need to slow down climate change for environmental protection demands further advancements toward regenerative energy and sustainable mobility. While individual mobility applications are assumed to be satisfied with improving battery electric vehicles (BEVs), the growing sector of freight transport and heavy-duty applications requires alternative solutions to meet the requirements of long ranges and high payloads. Fuel cell hybrid electric vehicles (FCHEVs) emerge as a capable technology for high-energy applications. This technology comprises a fuel cell system (FCS) for energy supply combined with buffering energy storages, such as batteries or ultracapacitors. In this article, recent successful developments regarding FCHEVs in various heavyduty applications are presented. Subsequently, an overview of the FCHEV drivetrain, its main components, and different topologies with an emphasis on heavy-duty trucks is given. In order to enable system layout optimization and energy management strategy (EMS) design, functionality and modeling

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A Novel Multi-Mode Adaptive Energy Consumption Minimization Strategy for P1-P2 Hybrid Electric Vehicle Architectures

Cited by 14 publications

References 10 publications

A Review of Fuel Cell Powertrains for Long-Haul Heavy-Duty Vehicles: Technology, Hydrogen, Energy and Thermal Management Solutions

A Review of Fuel Cell Powertrains for Long-Haul Heavy-Duty Vehicles: Technology, Hydrogen, Energy and Thermal Management Solutions

Review article: A comprehensive review of energy management strategies for hybrid electric vehicles

Toward Holistic Energy Management Strategies for Fuel Cell Hybrid Electric Vehicles in Heavy-Duty Applications

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