Energy management of a plug-in fuel cell/battery hybrid vehicle with on-board fuel processing

Tribioli, Laura; Cozzolino, Raffaello; Chiappini, Daniele; Iora, Paolo

doi:10.1016/j.apenergy.2016.10.015

Cited by 88 publications

(30 citation statements)

References 44 publications

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“…The SOC is taken as the first state, because the strong relationship between the SOC and the reward. Since the average velocity has great relationship with the co‐state, it is taken as the second state. Since the driving distance has relationship with the SOC, the normalized driving distance is taken as the last state.…”

Section: The Formulation Of the Self‐learning Energy Managementmentioning

confidence: 99%

Self‐learning energy management for plug‐in hybrid electric bus considering expert experience and generalization performance

Guo¹,

Zhao²,

Guo³

et al. 2020

Int J Energy Res

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A self-learning energy management is proposed for plug-in hybrid electric bus, by combining Q-Learning (QL) and Pontryagin's minimum principle algorithms. Different from the existing strategies, the expert experience and generalization performance are focused in the proposed strategy. The expert experience is designed as the approximately optimal reference state-of-charge (SOC) trajectories, and the generalization performance is enhanced by a multiply driving cycle training method. In specific, an efficient zone of SOC is firstly designed based on the approximately optimal reference SOC trajectories. Then, the agent of the QL is trained off-line by taking the expert experience as reference SOC trajectories.Finally, an adaptive strategy is proposed based on the well-trained agent. Specially, two different reward functions are defined. That is, the reward function in the off-line training mainly considers the tracking performance between the expert experience and the SOC, while mainly considering the punishment in the adaptive strategy. Simulation results show that the proposed strategy has good generalization performance and can improve the fuel economy by 22.49%, compared to a charge depleting-charge sustaining (CDCS) strategy. K E Y W O R D Senergy management, expert experience, generalization performance, plug-in hybrid electric bus, Q-learning

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Section: The Formulation Of the Self‐learning Energy Managementmentioning

confidence: 99%

Self‐learning energy management for plug‐in hybrid electric bus considering expert experience and generalization performance

Guo¹,

Zhao²,

Guo³

et al. 2020

Int J Energy Res

View full text Add to dashboard Cite

show abstract

“…In particular, ellipse centres depend on some FC and B parameters/variables, while ellipse radii depend also on the traction power p M . Consequently, all of the i FC and i B pairs of values that satisfy (33) are:…”

Section: Simplified Emsmentioning

confidence: 99%

“…As a result, the substitution of (31) and (32) in (27) makes the latter a conic as it depends on both quadratic functions of iFC and iB because (1) is simplified as (26), thus highlighting the usefulness of the assumption. Therefore, based on previous considerations, (27) can be rearranged as: (33) in which:…”

Section: Simplified Emsmentioning

confidence: 99%

Modelling and Design of Real-Time Energy Management Systems for Fuel Cell/Battery Electric Vehicles

Serpi

Porru

2019

Energies

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Modelling and design of real-time energy management systems for optimising the operating costs of a fuel cell/battery electric vehicle are presented in this paper. The proposed energy management system consists of optimally sharing the propulsion power demand between the fuel cell and battery by enabling them to support each other for operating cost minimisation. The optimisation is achieved through real-time minimisation of a cost function, which accounts for fuel cell and battery degradation, hydrogen consumption and charge sustaining costs. A detailed analysis of each term of the overall cost function is performed and presented, which enables the development of a real-time, advanced energy management system for improving a previously presented simplified version using more accurate modelling and by considering cost function minimisation over a given time horizon. The performance of the proposed advanced energy management system are verified through numerical simulations over different driving cycles; particularly, simulations were performed in MATLAB-Simulink by considering a hysteresis-based energy management system and both simplified and advanced versions of the proposed energy management system for comparison.

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“…There are alternative proposals in the short and medium term due to the lack of structure of dedicated stations that consists of providing vehicles with a direct conversion system of hydrogen from hydrocarbon. For example, Tribiori et al propose a vehicle equipped by an auto-thermal reformer and, in order to minimize the hydrogen buffer size, the control algorithm is subject to constraints on the maximum hydrogen buffer level [22,23]. The proposal is based on the use of an ATR reformer and a high temperature PEM fuel cell where the fuels used are isooctane (C 8 H 18 ), propane (C 3 H 8 ) and methane (CH 4 ) [24,25].…”

Section: Fuelmentioning

confidence: 99%

Design and Simulation of a Powertrain System for a Fuel Cell Extended Range Electric Golf Car

et al. 2018

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This article analyses the energy behaviour of an electric golf car as the penultimate step to developing a fuel cell electric light-duty vehicle. The configuration used is that of an extended range electric vehicle with a fuel cell (FCEREV). The system includes two energy storage sources to drive the powertrain: the first consists of using energy stored in a lead-acid battery pack and the second consists of hydrogen stored in metal hydrides and its use is based on a 200 W polymer electrolyte membrane (PEM) type fuel cell. The type of system also allows charging the vehicle by connecting it to the electrical grid. The aim of the proposed design is to extend the autonomy of the golf car allowing it to make several trips in one day without having to charge it by connecting it to the electrical grid, considering the large amount of time this would take. The analysis of the performance has been set based on the current regulation and is therefore within the range for these types of vehicles. This arrangement extends autonomy by 38% as opposed to the pure EV electrical mode, which allows for making at least two more trips with a hydrogen tank filled with 0.085 kg H 2 .

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Energy management of a plug-in fuel cell/battery hybrid vehicle with on-board fuel processing

Cited by 88 publications

References 44 publications

Self‐learning energy management for plug‐in hybrid electric bus considering expert experience and generalization performance

Self‐learning energy management for plug‐in hybrid electric bus considering expert experience and generalization performance

Modelling and Design of Real-Time Energy Management Systems for Fuel Cell/Battery Electric Vehicles

Design and Simulation of a Powertrain System for a Fuel Cell Extended Range Electric Golf Car

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