Real World Operation of a Complex Plug-in Hybrid Electric Vehicle: Analysis of Its CO2 Emissions and Operating Costs

Millo, Federico; Rolando, Luciano; Fuso, Rocco

doi:10.3390/en7074554

Cited by 5 publications

(8 citation statements)

References 23 publications

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“…However, the use of local real-world driving cycles in optimisation models is important, since they can differ strongly from standardised driving cycles or from assuming an average value for energy use of buses. As a result, the real-world heterogeneity in transport systems is neglected (Millo et al 2014;Wang et al 2015;Xu et al 2015;Yay et al 2016;Zhang et al 2014).…”

Section: Literature Reviewmentioning

confidence: 99%

Comparison of management strategies for the charging schedule and all-electric operation of a plug-in hybrid-electric bi-articulated bus fleet

Dreier

Rudin²,

Howells

2020

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This study developed a real-time optimisation (RTO) model that uses real-world bus operation data, i.e. route-specific and time-specific driving cycles. Potentials for energy savings and all-electric operation were estimated for a plug-in hybridelectric bi-articulated bus fleet (PLUG scenario) that can be managed according to different management strategies. Five strategies, A to E, were simulated that manage the charging schedule and all-electric operation with different priorities: PLUG-A, prioritise buses for charging by arrival times at the charging station (first come, first served); PLUG-B, prioritise buses for charging by energy intensities of the bus routes; PLUG-C, minimise the total energy use of the bus fleet; PLUG-D, maximise the total all-electric time of the bus fleet; and PLUG-E, maximise the total all-electric distance of the bus fleet. For comparison, a business-as-usual (BAU) scenario with conventional buses and another scenario with hybrid-electric buses (HYB) were simulated. Two weeks of real-world bus operation data from the city of Curitiba in Brazil were used as input data. The study finds that total energy savings of 17% and 27% in the HYB and PLUG scenarios can be achieved compared to the BAU scenario, respectively. Meanwhile, the average shares of the total all-electric time (TAET) and total all-electric distance (TAED) to the total bus fleet operation amount to 20% and 14% in the HYB scenario. Furthermore, both TAET and TAED in the PLUG scenario depend strongly on the chosen strategy amounting to ranges of 21-64% and 17-61%, respectively. Simultaneous maxima were found for strategy D.

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Section: Literature Reviewmentioning

confidence: 99%

Comparison of management strategies for the charging schedule and all-electric operation of a plug-in hybrid-electric bi-articulated bus fleet

Dreier

Rudin²,

Howells

2020

Public Transp

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“…k m , k g = 1 means discharging state, and k m , k g = −1 means charging state, which can be given by: The SOC and current have an integral relationship, so the dynamics of SOC can be calculated as: (10) where Q batt is the max charge capacity of the battery, which will continuously attenuate in the battery life.…”

Section: Battery Modelmentioning

confidence: 99%

“…Furthermore, many intelligent optimization algorithms have been used for solving the energy management control problem recently [7,8]. The control objectives of these optimal strategies for general hybrid electric vehicles (HEVs) are usually the performance of fuel and electricity consumption [1,4], drivability [9] and emissions [7,10].…”

Section: Introductionmentioning

confidence: 99%

Energy Management Strategy in Consideration of Battery Health for PHEV via Stochastic Control and Particle Swarm Optimization Algorithm

et al. 2017

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This paper presents an energy management strategy for plug-in hybrid electric vehicles (PHEVs) that not only tries to minimize the energy consumption, but also considers the battery health. First, a battery model that can be applied to energy management optimization is given. In this model, battery health damage can be estimated in the different states of charge (SOC) and temperature of the battery pack. Then, because of the inevitability that limiting the battery health degradation will increase energy consumption, a Pareto energy management optimization problem is formed. This multi-objective optimal control problem is solved numerically by using stochastic dynamic programming (SDP) and particle swarm optimization (PSO) for satisfying the vehicle power demand and considering the tradeoff between energy consumption and battery health at the same time. The optimization solution is obtained offline by utilizing real historical traffic data and formed as mappings on the system operating states so as to implement online in the actual driving conditions. Finally, the simulation results carried out on the GT-SUITE-based PHEV test platform are illustrated to demonstrate that the proposed multi-objective optimal control strategy would effectively yield benefits.Keywords: plug-in hybrid electric vehicle (PHEV); battery health; energy management strategy; stochastic dynamic programming (SDP); particle swarm optimization (PSO)

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“…These are universal vehicles designed to produce zero-emission while driving in urban areas, and travel long distances while using the combustion engine. The complex propulsion system helps reduce carbon dioxide emission, in particular in urban areas [22,23].…”

Section: Introductionmentioning

confidence: 99%

The Economic Aspect of Using Different Plug-In Hybrid Driving Techniques in Urban Conditions

et al. 2021

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Plug-in hybrids (PHEV) have become popular due to zero-emission driving, e.g., in urban areas, and using an internal combustion engine on longer distances. Energy consumption by the PHEV depends on many factors which can be either dependent or independent of the driver. The article examines how the driver can use the vehicle’s capabilities to influence its wear. Determining the optimal driving technique, due to the adopted nature of the timetable, is the basic variable that determines the profitability of using a given drive system. Four driving techniques have been selected to determine which one can offer the largest advantages. A vehicle-dedicated application has recorded the drivetrain performance on a predetermined route through an urban area. The analysis of results has demonstrated which of the driving techniques provides measurable effects in terms of reduced energy consumption and the shortest travelling time. The study shows longitudinal acceleration and torque generated by the electric drive. The information included in the study can help any PHEV user reduce the operating cost by applying an appropriate driving technique. The proposed research introduces the possibilities of assessing the influence of the driving style on energy consumption. The innovative side of this research is the observation of stochastic phenomena that are difficult to detect when using approximation modelling.

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Real World Operation of a Complex Plug-in Hybrid Electric Vehicle: Analysis of Its CO2 Emissions and Operating Costs

Cited by 5 publications

References 23 publications

Comparison of management strategies for the charging schedule and all-electric operation of a plug-in hybrid-electric bi-articulated bus fleet

Comparison of management strategies for the charging schedule and all-electric operation of a plug-in hybrid-electric bi-articulated bus fleet

Energy Management Strategy in Consideration of Battery Health for PHEV via Stochastic Control and Particle Swarm Optimization Algorithm

The Economic Aspect of Using Different Plug-In Hybrid Driving Techniques in Urban Conditions

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