The Experts below are selected from a list of 28170 Experts worldwide ranked by ideXlab platform
Seiichi Shiga - One of the best experts on this subject based on the ideXlab platform.
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energy consumption and co2 emissions reduction potential of electric drive Vehicle diffusion in a road freight Vehicle Fleet
Energy Procedia, 2017Co-Authors: Juan Gonzalez C Palencia, Mikiya Araki, Seiichi ShigaAbstract:Abstract Road freight transport is dominated by gasoline- and diesel- fueled internal combustion engine Vehicles, presenting a challenge for decarbonization. There is a growing interest in electric-drive Vehicles as alternative to reduce CO2 and local pollutant emissions in road freight transport. In this research, a Vehicle stock turnover model is used to estimate the potential of electric-drive Vehicles to reduce energy consumption and CO2 emissions in a road freight Vehicle Fleet; focusing on Japan as a case of study. In the Base scenario, tank to wheel CO2 emissions are reduced 51.9% between 2012 and 2050 driven by Vehicle stock reduction, hybrid electric Vehicle diffusion and Vehicle fuel consumption improvement. By 2050, tank to wheel CO2 emissions can be reduced up to 55.8% with the diffusion of battery electric Vehicles and fuel cell electric Vehicles. Despite of aggressive electric-drive Vehicle deployment, fossil fuels account for more than 52% of the energy consumed in all scenarios in 2050.
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Scenario analysis of lightweight and electric-drive Vehicle market penetration in the long-term and impact on the light-duty Vehicle Fleet
Applied Energy, 2017Co-Authors: Juan C. González Palencia, Yuki Otsuka, Mikiya Araki, Seiichi ShigaAbstract:Electric-drive Vehicles, including hybrid electric Vehicles, plug-in hybrid electric Vehicles, battery electric Vehicles, fuel cell electric Vehicles and fuel cell hybrid electric Vehicles, are emerging as less polluting alternatives to internal combustion engine Vehicles. Therefore, it is important to assess their penetration in the Vehicle market in the future. A ‘two-step’ approach is used to estimate the optimum market penetration of lightweight and electric-drive Vehicles in the long-term and the impact on the light-duty Vehicle Fleet, focusing on Japan. First, an optimization model is used to estimate the Vehicle market composition in 2050. Then, a Vehicle stock turnover model is used to estimate light-duty Vehicle Fleet energy and material consumption, CO2 emissions and cost. Internal combustion engine Vehicles and hybrid electric Vehicles dominate in the Base scenario. Fuel cell hybrid electric Vehicles dominate when low cost is prioritized. Shift to battery electric Vehicles occurs when low CO2 emissions are prioritized. CO2 emissions are reduced 56.9% between 2012 and 2050 in the Base scenario. Lightweight mini-sized battery electric Vehicle diffusion has the largest CO2 emissions reductions, 87.3% compared to the 2050 baseline value; with the net cash flow peaking at 10.2 billion USD/year in 2035 and becoming negative after 2044.
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Energy, environmental and economic impact of mini-sized and zero-emission Vehicle diffusion on a light-duty Vehicle Fleet
Applied Energy, 2016Co-Authors: Juan C. González Palencia, Mikiya Araki, Seiichi ShigaAbstract:Diffusion of battery electric Vehicles and fuel cell hybrid electric Vehicles can contribute to reduce passenger light-duty Vehicle Fleet CO2emissions. However, barriers such as higher Vehicle capital cost and lack of electricity and hydrogen infrastructure prevent their deployment. A Vehicle stock turnover model was used to assess the impact of mini-sized and zero-emission Vehicle diffusion on passenger light-duty Vehicle Fleet energy and material consumption, CO2emissions and cost, focusing on Japan. 2050 passenger light-duty Vehicle Fleet energy consumption and tank-to-wheel CO2emissions in the base scenario are 48.7 and 51.9% lower than the 2012 values. Diffusion of mini-sized and battery electric Vehicles provides the largest energy consumption and CO2emissions reductions, 64.7, and 87.8% compared with the 2050 baseline values. Incremental cost of zero emission Vehicles is reduced through downsizing. The 2050 net cash flow for battery electric Vehicles diffusion is reduced from 15.9 to −16.7 billion USD/year if downsizing is applied; while in the case of fuel cell hybrid electric Vehicle diffusion, downsizing reduces the 2050 net cash flow from −12.5 to −47.8 billion USD/year. Thus, shifting to mini-sized zero emission Vehicles provides the quadruple benefit of reducing energy and material consumption, CO2emissions and cost.
Joško Deur - One of the best experts on this subject based on the ideXlab platform.
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A bi-level optimisation framework for electric Vehicle Fleet charging management
Applied Energy, 2016Co-Authors: Branimir Skugor, Joško DeurAbstract:Abstract The paper proposes a bi-level optimisation framework for Electric Vehicle (EV) Fleet charging based on a realistic EV Fleet model including a transport demand sub-model. The EV Fleet is described by an aggregate battery model, which is parameterised by using recorded driving cycle data of a delivery Vehicle Fleet. The EV Fleet model is used within the inner level of the bi-level optimisation framework, where the aggregate charging power is optimised by using the dynamic programming (DP) algorithm. At the superimposed optimisation level, the final State-of-Charge (SoC) values of individual EVs being disconnected from the grid are optimised by using a multi-objective genetic algorithm-based optimisation. In each iteration of the bi-level optimisation algorithm, it is generally needed to recalculate the transport demand sub-model for the new set of final SoC values. In order to simplify this process, the transport demand is modelled by using a computationally efficient response surface method, which is based on naturalistic synthetic driving cycles and agent-based simulations of the EV model. When compared to the single-level charging optimisation approach, which assumes the final SoC values to be equal to 1 (full batteries on departure), the bi-level optimisation provides a degree of optimisation freedom more for more accurate techno-economic analyses of the integrated transport-energy system. The two approaches are compared through a simulation study of the particular delivery Vehicle Fleet transport-energy system.
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a nonlinear charge based model of electric Vehicle Fleet aggregate battery
European Battery Hybrid and Fuel Cell Electric Vehicle Congress (EEVC), 2015Co-Authors: Branimir Skugor, Joško DeurAbstract:This paper presents a novel nonlinear charge- based aggregate battery model representing the Fleet of electric Vehicles (EV) connected to a grid. The proposed aggregate battery model is parameterised and validated against a more accurate, but more complex individual battery- based Fleet model where each EV is modelled separately as a charge storage. Also, the novel nonlinear model is compared with the linear model commonly used in energy planning and charging management studies, where the battery is modelled as energy storage. The functionality of the novel aggregate battery model is demonstrated through a case study related to a delivery Vehicle Fleet for which a comprehensive set of recorded driving cycle data is available. The charging management optimisations of the presented battery models are conducted by using the dynamic programming approach, and the comparative analyses of the optimal charging patterns are given.
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A novel model of electric Vehicle Fleet aggregate battery for energy planning studies
Energy, 2015Co-Authors: Branimir Skugor, Joško DeurAbstract:The paper proposes an aggregate battery modelling approach for an (electric Vehicle) EV Fleet, which is aimed for energy planning studies of EV-grid integration. The proposed model improves on the existing, basic aggregate battery modelling approach by accounting for a variable structure of the aggregate battery systems, variable (state of charge) SoC constraints and specific input time-distributions such as those of average SoC at destination and number of arriving and departing Vehicles. In the particular case-study presented, the input distributions are reconstructed from a large set of delivery Vehicle Fleet driving missions, including simulation of individual Vehicle behaviours over the full set of driving cycles. The charging power input is obtained by using a dynamic programming-based optimisation algorithm aimed at finding a global optimum in terms of minimised electricity cost. For the purpose of proposed model validation and its comparison with the basic model, a distributed Fleet Vehicle model is developed, where a specific algorithm is proposed for distributing the optimised charging power input to charging inputs of individual Vehicles.
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Dynamic programming-based optimisation of charging an electric Vehicle Fleet system represented by an aggregate battery model
Energy, 2015Co-Authors: Branimir Skugor, Joško DeurAbstract:This paper proposes a DP(dynamic programming)-based optimisation method of charging an EV (electric Vehicle) Fleet modelled as a single, so-called aggregate battery. The main advantage of the approach is that it provides a globally optimal solution, with a relatively non-excessive computational load owing to a low order of the aggregate battery model. The method is illustrated through a case study of an isolated, hypothetically electrified delivery truck transport system charged from both grid and RES (renewable energy sources). Two scenarios of energy production from RES (with and without excess in RES production), along with several electricity price models are studied. The DP optimisation results are compared with the results obtained by an existing heuristic charging algorithm used in EnergyPLAN software to illustrate the DP algorithm advantages in minimising the charging energy cost and satisfying the aggregate battery charge sustaining conditions. The proposed DP optimisation method can be used in various energy planning studies, as well as a core of the supervisory/aggregator level of hierarchical EV Fleet charging strategies.
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Dynamic programming-based optimization of electric Vehicle Fleet charging
2014 IEEE International Electric Vehicle Conference (IEVC), 2014Co-Authors: Branimir Skugor, Joško DeurAbstract:The paper deals with charging optimization for delivery electric Vehicle Fleets based on dynamic programming method. Charging of each individual Vehicle within the Fleet is optimized separately, thus providing globally optimal solution on the Vehicle level. By posing an upper constraint on the grid power used for charging, the individual charging optimizations are coupled together on the Fleet level in a suboptimal way. Consecutive optimizations for each Vehicle within the Fleet are conducted for different orders of individual Vehicle optimizations. In this way the sensitivity of optimization results with respect to ordering of charging optimizations can be analysed and a solution closer to global optimum can be found. The obtained optimization results are used for the purpose of validation of previously developed aggregate battery models and corresponding heuristic method dealing with distribution of optimal aggregate power over individual Vehicles.
Frances Sprei - One of the best experts on this subject based on the ideXlab platform.
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objective functions for plug in hybrid electric Vehicle battery range optimization and possible effects on the Vehicle Fleet
Transportation Research Part C-emerging Technologies, 2018Co-Authors: Lars Henrik Bjornsson, Sten Karlsson, Frances SpreiAbstract:While a hybrid electric Vehicle (HEV) mainly runs on the same fuel as a conventional combustion engine, a plug-in hybrid electric Vehicle (PHEV) has the potential to replace most of that fuel with electricity from the grid. Further, the driving-range limitations associated with a pure battery electric Vehicle (BEV) do not apply to the PHEV. This makes the PHEV an interesting option for reducing greenhouse gas (GHG) emissions and local air pollutants as well as energy dependence, without sacrificing performance. However, how large fuel reduction that could be expected from PHEVs strongly depends on the battery range and driving and charging patterns (Bjornsson and Karlsson, 2015). To maximize fuel reduction, battery capacity should be designed to reach a high share of electric driving. However, maximizing fuel reduction might not be the main objective for all stakeholders when optimizing battery range. Car owners could be more interested in reaching a low total cost of ownership (TCO), while manufacturers might focus on a battery range that suits as many potential buyers as possible. In this study, we analyze how the optimal battery range for the PHEV and the resulting Vehicle Fleet properties vary with the choice of objective function under various techno-economic conditions and policy options.
Branimir Skugor - One of the best experts on this subject based on the ideXlab platform.
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A bi-level optimisation framework for electric Vehicle Fleet charging management
Applied Energy, 2016Co-Authors: Branimir Skugor, Joško DeurAbstract:Abstract The paper proposes a bi-level optimisation framework for Electric Vehicle (EV) Fleet charging based on a realistic EV Fleet model including a transport demand sub-model. The EV Fleet is described by an aggregate battery model, which is parameterised by using recorded driving cycle data of a delivery Vehicle Fleet. The EV Fleet model is used within the inner level of the bi-level optimisation framework, where the aggregate charging power is optimised by using the dynamic programming (DP) algorithm. At the superimposed optimisation level, the final State-of-Charge (SoC) values of individual EVs being disconnected from the grid are optimised by using a multi-objective genetic algorithm-based optimisation. In each iteration of the bi-level optimisation algorithm, it is generally needed to recalculate the transport demand sub-model for the new set of final SoC values. In order to simplify this process, the transport demand is modelled by using a computationally efficient response surface method, which is based on naturalistic synthetic driving cycles and agent-based simulations of the EV model. When compared to the single-level charging optimisation approach, which assumes the final SoC values to be equal to 1 (full batteries on departure), the bi-level optimisation provides a degree of optimisation freedom more for more accurate techno-economic analyses of the integrated transport-energy system. The two approaches are compared through a simulation study of the particular delivery Vehicle Fleet transport-energy system.
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a nonlinear charge based model of electric Vehicle Fleet aggregate battery
European Battery Hybrid and Fuel Cell Electric Vehicle Congress (EEVC), 2015Co-Authors: Branimir Skugor, Joško DeurAbstract:This paper presents a novel nonlinear charge- based aggregate battery model representing the Fleet of electric Vehicles (EV) connected to a grid. The proposed aggregate battery model is parameterised and validated against a more accurate, but more complex individual battery- based Fleet model where each EV is modelled separately as a charge storage. Also, the novel nonlinear model is compared with the linear model commonly used in energy planning and charging management studies, where the battery is modelled as energy storage. The functionality of the novel aggregate battery model is demonstrated through a case study related to a delivery Vehicle Fleet for which a comprehensive set of recorded driving cycle data is available. The charging management optimisations of the presented battery models are conducted by using the dynamic programming approach, and the comparative analyses of the optimal charging patterns are given.
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A novel model of electric Vehicle Fleet aggregate battery for energy planning studies
Energy, 2015Co-Authors: Branimir Skugor, Joško DeurAbstract:The paper proposes an aggregate battery modelling approach for an (electric Vehicle) EV Fleet, which is aimed for energy planning studies of EV-grid integration. The proposed model improves on the existing, basic aggregate battery modelling approach by accounting for a variable structure of the aggregate battery systems, variable (state of charge) SoC constraints and specific input time-distributions such as those of average SoC at destination and number of arriving and departing Vehicles. In the particular case-study presented, the input distributions are reconstructed from a large set of delivery Vehicle Fleet driving missions, including simulation of individual Vehicle behaviours over the full set of driving cycles. The charging power input is obtained by using a dynamic programming-based optimisation algorithm aimed at finding a global optimum in terms of minimised electricity cost. For the purpose of proposed model validation and its comparison with the basic model, a distributed Fleet Vehicle model is developed, where a specific algorithm is proposed for distributing the optimised charging power input to charging inputs of individual Vehicles.
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Dynamic programming-based optimisation of charging an electric Vehicle Fleet system represented by an aggregate battery model
Energy, 2015Co-Authors: Branimir Skugor, Joško DeurAbstract:This paper proposes a DP(dynamic programming)-based optimisation method of charging an EV (electric Vehicle) Fleet modelled as a single, so-called aggregate battery. The main advantage of the approach is that it provides a globally optimal solution, with a relatively non-excessive computational load owing to a low order of the aggregate battery model. The method is illustrated through a case study of an isolated, hypothetically electrified delivery truck transport system charged from both grid and RES (renewable energy sources). Two scenarios of energy production from RES (with and without excess in RES production), along with several electricity price models are studied. The DP optimisation results are compared with the results obtained by an existing heuristic charging algorithm used in EnergyPLAN software to illustrate the DP algorithm advantages in minimising the charging energy cost and satisfying the aggregate battery charge sustaining conditions. The proposed DP optimisation method can be used in various energy planning studies, as well as a core of the supervisory/aggregator level of hierarchical EV Fleet charging strategies.
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Dynamic programming-based optimization of electric Vehicle Fleet charging
2014 IEEE International Electric Vehicle Conference (IEVC), 2014Co-Authors: Branimir Skugor, Joško DeurAbstract:The paper deals with charging optimization for delivery electric Vehicle Fleets based on dynamic programming method. Charging of each individual Vehicle within the Fleet is optimized separately, thus providing globally optimal solution on the Vehicle level. By posing an upper constraint on the grid power used for charging, the individual charging optimizations are coupled together on the Fleet level in a suboptimal way. Consecutive optimizations for each Vehicle within the Fleet are conducted for different orders of individual Vehicle optimizations. In this way the sensitivity of optimization results with respect to ordering of charging optimizations can be analysed and a solution closer to global optimum can be found. The obtained optimization results are used for the purpose of validation of previously developed aggregate battery models and corresponding heuristic method dealing with distribution of optimal aggregate power over individual Vehicles.
Mikiya Araki - One of the best experts on this subject based on the ideXlab platform.
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energy consumption and co2 emissions reduction potential of electric drive Vehicle diffusion in a road freight Vehicle Fleet
Energy Procedia, 2017Co-Authors: Juan Gonzalez C Palencia, Mikiya Araki, Seiichi ShigaAbstract:Abstract Road freight transport is dominated by gasoline- and diesel- fueled internal combustion engine Vehicles, presenting a challenge for decarbonization. There is a growing interest in electric-drive Vehicles as alternative to reduce CO2 and local pollutant emissions in road freight transport. In this research, a Vehicle stock turnover model is used to estimate the potential of electric-drive Vehicles to reduce energy consumption and CO2 emissions in a road freight Vehicle Fleet; focusing on Japan as a case of study. In the Base scenario, tank to wheel CO2 emissions are reduced 51.9% between 2012 and 2050 driven by Vehicle stock reduction, hybrid electric Vehicle diffusion and Vehicle fuel consumption improvement. By 2050, tank to wheel CO2 emissions can be reduced up to 55.8% with the diffusion of battery electric Vehicles and fuel cell electric Vehicles. Despite of aggressive electric-drive Vehicle deployment, fossil fuels account for more than 52% of the energy consumed in all scenarios in 2050.
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Scenario analysis of lightweight and electric-drive Vehicle market penetration in the long-term and impact on the light-duty Vehicle Fleet
Applied Energy, 2017Co-Authors: Juan C. González Palencia, Yuki Otsuka, Mikiya Araki, Seiichi ShigaAbstract:Electric-drive Vehicles, including hybrid electric Vehicles, plug-in hybrid electric Vehicles, battery electric Vehicles, fuel cell electric Vehicles and fuel cell hybrid electric Vehicles, are emerging as less polluting alternatives to internal combustion engine Vehicles. Therefore, it is important to assess their penetration in the Vehicle market in the future. A ‘two-step’ approach is used to estimate the optimum market penetration of lightweight and electric-drive Vehicles in the long-term and the impact on the light-duty Vehicle Fleet, focusing on Japan. First, an optimization model is used to estimate the Vehicle market composition in 2050. Then, a Vehicle stock turnover model is used to estimate light-duty Vehicle Fleet energy and material consumption, CO2 emissions and cost. Internal combustion engine Vehicles and hybrid electric Vehicles dominate in the Base scenario. Fuel cell hybrid electric Vehicles dominate when low cost is prioritized. Shift to battery electric Vehicles occurs when low CO2 emissions are prioritized. CO2 emissions are reduced 56.9% between 2012 and 2050 in the Base scenario. Lightweight mini-sized battery electric Vehicle diffusion has the largest CO2 emissions reductions, 87.3% compared to the 2050 baseline value; with the net cash flow peaking at 10.2 billion USD/year in 2035 and becoming negative after 2044.
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Energy, environmental and economic impact of mini-sized and zero-emission Vehicle diffusion on a light-duty Vehicle Fleet
Applied Energy, 2016Co-Authors: Juan C. González Palencia, Mikiya Araki, Seiichi ShigaAbstract:Diffusion of battery electric Vehicles and fuel cell hybrid electric Vehicles can contribute to reduce passenger light-duty Vehicle Fleet CO2emissions. However, barriers such as higher Vehicle capital cost and lack of electricity and hydrogen infrastructure prevent their deployment. A Vehicle stock turnover model was used to assess the impact of mini-sized and zero-emission Vehicle diffusion on passenger light-duty Vehicle Fleet energy and material consumption, CO2emissions and cost, focusing on Japan. 2050 passenger light-duty Vehicle Fleet energy consumption and tank-to-wheel CO2emissions in the base scenario are 48.7 and 51.9% lower than the 2012 values. Diffusion of mini-sized and battery electric Vehicles provides the largest energy consumption and CO2emissions reductions, 64.7, and 87.8% compared with the 2050 baseline values. Incremental cost of zero emission Vehicles is reduced through downsizing. The 2050 net cash flow for battery electric Vehicles diffusion is reduced from 15.9 to −16.7 billion USD/year if downsizing is applied; while in the case of fuel cell hybrid electric Vehicle diffusion, downsizing reduces the 2050 net cash flow from −12.5 to −47.8 billion USD/year. Thus, shifting to mini-sized zero emission Vehicles provides the quadruple benefit of reducing energy and material consumption, CO2emissions and cost.