A new offline optimization approach for designing a fuel cell hybrid bus

Ribau, João P.; Viegas, Rita; Angelino, Ana; Moutinho, Alexandra; Silva, Carla M.

doi:10.1016/j.trc.2014.02.012

Cited by 33 publications

(12 citation statements)

References 22 publications

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“…Capital and energy storage system costs are the most important factors for hybrid and electric buses, which are also affected by driving cycles and the scheduled routes (Lajunen, 2014). Ribau et al (2014) use a real-coded GA energy management strategy (EMS) for a hydrogen powered fuel cell hybrid bus, using representative driving cycles for the cities of Lisbon and Porto, Portugal. The results show that although the powertrain assumed is more expensive than conventional diesel buses, the energy efficiency improvement that is achieved (around 35%) leads to significant fuel cost savings.…”

Section: Figure 1: Map Of the Stockholm Bus Network -Bus Routes (Leftmentioning

confidence: 99%

Locating charging infrastructure for electric buses in Stockholm

Xylia

Leduc

Patrizio

et al. 2017

Transportation Research Part C: Emerging Technologies

237

101

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Highlights Presenting an optimization model for the location of electric bus charging stations.  Model application to a large-scale case study for the bus network of Stockholm, Sweden.  Results show that low fuel costs for electricity can balance annual infrastructure costs.  Emissions decrease up to 51% and energy consumption up to 34% with electrification.  The model may assist decision-making for investments in public transport.Keywords electric bus; charging infrastructure; optimization; Mixed Integer Linear Programing; public transport; Sweden AbstractCharging infrastructure requirements are being largely debated in the context of urban energy planning for transport electrification. As electric vehicles are gaining momentum, the issue of locating and securing the availability, efficiency and effectiveness of charging infrastructure becomes a complex question that needs to be addressed. This paper presents the structure and application of a model developed for optimizing the distribution of charging infrastructure for electric buses in the urban context, and tests the model for the bus network of Stockholm. The major public bus transport hubs connecting to the train and subway system show the highest concentration of locations chosen by the model for charging station installation. The costs estimated are within an expected range when comparing to the annual bus public transport costs in Stockholm. The model could be adapted for various urban contexts to promptly assist in the transition to fossil-free bus transport. The total costs for the operation of a partially electrified bus system in both optimization cases considered (cost and energy) differ only marginally from the costs for a 100% biodiesel system. This indicates that lower fuel costs for electric buses can balance the high investment costs incurred in building charging infrastructure, while achieving a reduction of up to 51% in emissions and up to 34% in energy use in the bus fleet.

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Section: Figure 1: Map Of the Stockholm Bus Network -Bus Routes (Leftmentioning

confidence: 99%

Locating charging infrastructure for electric buses in Stockholm

Xylia

Leduc

Patrizio

et al. 2017

Transportation Research Part C: Emerging Technologies

237

101

View full text Add to dashboard Cite

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“…The use of genetic algorithms applied to component optimization in alternative vehicles has been extensively considered in recent works by the author [17][18][19][20], where hybrid urban bus and taxi vehicles were optimized aiming to minimize the cost and the energy consumption of the powertrain in real and synthetic driving conditions. The simultaneous optimization of different objectives using multi-objective algorithms, aiming to minimize the fuel consumption and the GHG emissions [21], has been considered in previous studies by the author [17,22], where besides the cost and efficiency, a life cycle approach is also considered.…”

Section: Road Vehicle Enhancements and Taxi Applicationsmentioning

confidence: 99%

“…Besides performing a LCA analysis some studies conduct also a detailed review on the cost of the vehicle powertrain components [3,31,32]. In addition to the environmental and financial aspects of vehicles and fuels, several studies complemented this analysis with optimization methods aiming to mitigate the transport impact [17,18,20,22,33].…”

Section: Environmental and Financial Burden Of Alternative Vehiclesmentioning

confidence: 99%

Taxi Fleet Renewal in Cities with Improved Hybrid Powertrains: Life Cycle and Sensitivity Analysis in Lisbon Case Study

2015

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Stringent emissions regulations in cities and the high amount of daily miles driven by taxi vehicles enforce the need to renew these fleets with more efficient and cleaner technologies. Hybrid vehicles are potential candidates due to their enhanced powertrain, and slower battery depletion and fewer lifetime issues, relative to full electric vehicles. This paper proposes a methodology to analyze the best theoretical hybrid powertrain candidate with maximum in-use efficiency, minimum life cycle greenhouse gas emissions, and minimum additional cost, for a Lisbon taxi fleet case study. A multi-objective genetic algorithm integrated with a vehicle simulator is used to achieve several trade-off optimal solutions for different driving patterns. Potential improvements in taxi carbon footprint are discussed as a function of its lifetime, urban/extra-urban driving and maintenance/fuel life cycle uncertainty. Hybrid powertrains reveal to be advantageous comparatively to the conventional vehicle, especially in urban conditions. Specifically optimized solutions could reduce in-use energy consumption by 43%-47% in urban driving, and 27%-34% in extra-urban driving conditions, and reduce life cycle emissions by 47%-49% and 34%-36% respectively, relative to the conventional taxi. A financial gain of 50 $/km/fleet in extra-urban and 226 $/km/fleet in urban routes could be achieved by replacing the taxi fleet with the optimal solutions. OPEN ACCESSEnergies 2015, 8 9510

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“…The DP-based energy management strategy belongs to the category of off-line energy management techniques, which are not suitable for online control [11]. However, this approach provides a benchmark for assessing the optimality of other energy management strategies and helps to improve the online strategy [12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Application Study on the Dynamic Programming Algorithm for Energy Management of Plug-in Hybrid Electric Vehicles

et al. 2015

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Abstract:To explore the problems associated with applying dynamic programming (DP) in the energy management strategies of plug-in hybrid electric vehicles (PHEVs), a plug-in hybrid bus powertrain is introduced and its dynamic control model is constructed. The numerical issues, including the discretization resolution of the relevant variables and the boundary issue of their feasible regions, were considered when implementing DP to solve the optimal control problem of PHEVs. The tradeoff between the optimization accuracy when using the DP algorithm and the computational burden was systematically investigated. As a result of overcoming the numerical issues, the DP-based approach has the potential to improve the fuel-savings potential of PHEVs. The results from comparing the DP-based strategy and the traditional control strategy indicate that there is an approximately 20% improvement in fuel economy.

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A new offline optimization approach for designing a fuel cell hybrid bus

Cited by 33 publications

References 22 publications

Locating charging infrastructure for electric buses in Stockholm

Locating charging infrastructure for electric buses in Stockholm

Taxi Fleet Renewal in Cities with Improved Hybrid Powertrains: Life Cycle and Sensitivity Analysis in Lisbon Case Study

Application Study on the Dynamic Programming Algorithm for Energy Management of Plug-in Hybrid Electric Vehicles

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