Multi-Level Energy Management—Part II: Implementation and Validation

Reeven, Vital van; Hofman, Théo

doi:10.3390/vehicles1010003

Cited by 5 publications

(5 citation statements)

References 11 publications

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“…The elevation profile of this cycle is shown in Figure 16, and contains four slopes of respectively +6%, −12%, +12% and -6%. This elevation profile is also available as a physical test track as described in [28]. The maximum feasible speed on this track is 25 km/h.…”

Section: Multi-level Iteration Short Cyclementioning

confidence: 99%

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Multi-Level Energy Management for Hybrid Electric Vehicles—Part I

Reeven

Hofman

2019

Vehicles

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The fuel economy of a hybrid electric vehicle (HEV) is improved, by taking the energy relevant system states into account in the energy management system (EMS). With an increasing number of states and decision variables, energy optimizing algorithms in the EMS can be prohibitive for real-time implementation. In part I of this work, a model-based, multi-level approach is taken to subdivide the original (large) optimization problem into computational efficient sub-problems, based on optimal control techniques using a preview. The resulting EMS solves the problem of power-split between engine and motor/generator, mode and gear switching including switching costs, with battery energy constraints. The superior energy efficiency of the multi-level EMS is simulated on a representative heavy duty drive cycle, where it saves 7.0% fuel, compared to a conventional vehicle, where the baseline EMS for the HEV saves 5.8%. In part II, real-world validation of the EMS is performed.

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Section: Multi-level Iteration Short Cyclementioning

confidence: 99%

“…The conclusions are drawn in Section 8. The real-world validation of the multi-level EMS is described in [28].…”

mentioning

confidence: 99%

Multi-Level Energy Management for Hybrid Electric Vehicles—Part I

Reeven

Hofman

2019

Vehicles

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“…DP is known to deliver globally optimal solutions that can be used to understand the best possible approach or as a benchmark for evaluation of other low computation strategies if a priori drive cycle information is known, while its real-time implementation is not commonly seen [257,258]. In multi-level energy management systems (EMS), DP can be applied to the actual powertrain in supervisor layers using over-the-air route information to calculate optimal set-points for battery SoC, component temperatures, co-state equivalence factors and multi-objective weights by running calculations at much larger time steps than required for real-time control and consuming lower computational efforts [259]. Another application could be the offline tuning of heuristic, and rule-based strategies, which are then implemented to online powertrain control [260].…”

Section: Dynamic Programming (Dp)mentioning

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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“…The length is the prediction horizon. Therefore, the value of β is updated by combining past and predicted data as follows [32]:…”

Section: Improved Ecms Based On Driving Cycle Predictionmentioning

confidence: 99%

Driving-Cycle-Aware Energy Management of Hybrid Electric Vehicles Using a Three-Dimensional Markov Chain Model

Zhao

Hofman

2019

Automot. Innov.

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Multi-Level Energy Management—Part II: Implementation and Validation

Cited by 5 publications

References 11 publications

Multi-Level Energy Management for Hybrid Electric Vehicles—Part I

Multi-Level Energy Management for Hybrid Electric Vehicles—Part I

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

Driving-Cycle-Aware Energy Management of Hybrid Electric Vehicles Using a Three-Dimensional Markov Chain Model

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