The Experts below are selected from a list of 114 Experts worldwide ranked by ideXlab platform
Kosmas Knodler - One of the best experts on this subject based on the ideXlab platform.
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optimal electric vehicle energy efficiency recovery in an intelligent transportation system
19th ITS World CongressERTICO - ITS EuropeEuropean CommissionITS AmericaITS Asia-Pacific, 2012Co-Authors: Stephen Jones, Arno Huss, Emre Kural, Rolf Albrecht, Alexander Massoner, Kosmas KnodlerAbstract:The limited range of Electric Vehicles can be significantly improved by means of optimal operation or control strategies enhanced with off-board data, provided by an Intelligent Transportation System, in addition to on-board information. The development of such strategies, for a twin axle Electric Vehicle, is the key goal of the OpEneR (Optimal Energy efficiency and Recovery) project. Within OpEneR, a highly advanced and beyond state-of-the-art MiL/SiL tool-chain has been developed, which enables the accurate development and validation of advanced energy management and safety strategies. Here a detailed model of the electrified vehicle powertrain equipped with an advanced regenerative braking system, is extended to includes a wide range of on-board and off-board sensors (GPS navigation, car-to-infrastructure, car-to-car data, etc.), the 3D real world driving route, as well as complex traffic and environmental conditions for the Driver to interact with. The paper summarizes the design of the simulation tool-chain used to virtually develop the advanced strategies, for optimization of the torque distribution between the two electric-Drive Axles, energy-optimal route planning and short term traffic maneuvers. Preliminary simulation results obtained under several traffic scenarios are presented in this paper.
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Optimal Electric Vehicle Energy Efficiency & Recovery in an Intelligent Transportation System
2012Co-Authors: Stephen Jones, Arno Huss, Emre Kural, Rolf Albrecht, Alexander Massoner, Kosmas KnodlerAbstract:The limited range of Electric Vehicles can be significantly improved by means of optimal operation or control strategies enhanced with off-board data, provided by an Intelligent Transportation System, in addition to on-board information. The development of such strategies, for a twin axle Electric Vehicle, is the key goal of the OpEneR (Optimal Energy efficiency and Recovery) project. Within OpEneR, a highly advanced and beyond state-of-the-art MiL/SiL tool-chain has been developed, which enables the accurate development and validation of advanced energy management and safety strategies. Here a detailed model of the electrified vehicle powertrain equipped with an advanced regenerative braking system, is extended to includes a wide range of on-board and off-board sensors (GPS navigation, car-to-infrastructure, car-to-car data, etc.), the 3D real world driving route, as well as complex traffic and environmental conditions for the Driver to interact with. The paper summarizes the design of the simulation tool-chain used to virtually develop the advanced strategies, for optimization of the torque distribution between the two electric-Drive Axles, energy-optimal route planning and short term traffic maneuvers. Preliminary simulation results obtained under several traffic scenarios are presented in this paper.
Stephen Jones - One of the best experts on this subject based on the ideXlab platform.
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optimal electric vehicle energy efficiency recovery in an intelligent transportation system
19th ITS World CongressERTICO - ITS EuropeEuropean CommissionITS AmericaITS Asia-Pacific, 2012Co-Authors: Stephen Jones, Arno Huss, Emre Kural, Rolf Albrecht, Alexander Massoner, Kosmas KnodlerAbstract:The limited range of Electric Vehicles can be significantly improved by means of optimal operation or control strategies enhanced with off-board data, provided by an Intelligent Transportation System, in addition to on-board information. The development of such strategies, for a twin axle Electric Vehicle, is the key goal of the OpEneR (Optimal Energy efficiency and Recovery) project. Within OpEneR, a highly advanced and beyond state-of-the-art MiL/SiL tool-chain has been developed, which enables the accurate development and validation of advanced energy management and safety strategies. Here a detailed model of the electrified vehicle powertrain equipped with an advanced regenerative braking system, is extended to includes a wide range of on-board and off-board sensors (GPS navigation, car-to-infrastructure, car-to-car data, etc.), the 3D real world driving route, as well as complex traffic and environmental conditions for the Driver to interact with. The paper summarizes the design of the simulation tool-chain used to virtually develop the advanced strategies, for optimization of the torque distribution between the two electric-Drive Axles, energy-optimal route planning and short term traffic maneuvers. Preliminary simulation results obtained under several traffic scenarios are presented in this paper.
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Optimal Electric Vehicle Energy Efficiency & Recovery in an Intelligent Transportation System
2012Co-Authors: Stephen Jones, Arno Huss, Emre Kural, Rolf Albrecht, Alexander Massoner, Kosmas KnodlerAbstract:The limited range of Electric Vehicles can be significantly improved by means of optimal operation or control strategies enhanced with off-board data, provided by an Intelligent Transportation System, in addition to on-board information. The development of such strategies, for a twin axle Electric Vehicle, is the key goal of the OpEneR (Optimal Energy efficiency and Recovery) project. Within OpEneR, a highly advanced and beyond state-of-the-art MiL/SiL tool-chain has been developed, which enables the accurate development and validation of advanced energy management and safety strategies. Here a detailed model of the electrified vehicle powertrain equipped with an advanced regenerative braking system, is extended to includes a wide range of on-board and off-board sensors (GPS navigation, car-to-infrastructure, car-to-car data, etc.), the 3D real world driving route, as well as complex traffic and environmental conditions for the Driver to interact with. The paper summarizes the design of the simulation tool-chain used to virtually develop the advanced strategies, for optimization of the torque distribution between the two electric-Drive Axles, energy-optimal route planning and short term traffic maneuvers. Preliminary simulation results obtained under several traffic scenarios are presented in this paper.
S Burtt - One of the best experts on this subject based on the ideXlab platform.
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Optimization of the Tractive Performance of Four-Wheel-Drive Tractors - Correlation between Analytical Predictions and Experimental Data
SAE transactions, 2000Co-Authors: Jo Yung Wong, Zhiwen Zhao, N. B. Mclaughlin, Jianqiao Li, S BurttAbstract:Analytical studies reveal that for a four-wheel-Drive tractor with rigidly coupled Drive Axles to achieve the optimum tractive performance under a given operating condition, the theoretical speed (the product of angular speed and free rolling radius) of the front tires must be equal to that of the rear tires, or the theoretical speed ratio must be one. This paper presents tractive performance test data obtained using an instrumented four-wheel-Drive tractor with seven different sets of tires at various theoretical speed ratios. Field data confirm the analytical findings that when the theoretical speed ratio is equal to one, the slip efficiency and tractive efficiency reach their respective peaks, the fuel efficiency (the ratio of drawbar power to fuel consumed per hour) reaches a maximum, and the overall tractive performance is at an optimum. It is concluded that to achieve optimum tractive performance in the field, proper matching of front and rear tire sizes and careful control of the inflation pressure and normal load of the tires to ensure the theoretical speed ratio equal or close to one are of practical importance. Copyright
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Optimization of the tractive performance of four-wheel-Drive tractors: Theoretical analysis and experimental substantiation:
Proceedings of the Institution of Mechanical Engineers Part D: Journal of Automobile Engineering, 1998Co-Authors: Jo Yung Wong, Ž. Knežević, N. B. Mclaughlin, S BurttAbstract:AbstractThe results of a theoretical analysis reveal that, for a four-wheel-Drive tractor to achieve the optimum tractive performance under a given operating condition, the thrust (or driving torque) distribution between the front and rear Axles should be such that the slips of the front and rear tyres are equal. For four-wheel-Drive tractors with rigidly coupled front and rear Drive Axles, this can be achieved if the theoretical speed (the product of the angular speed and the free-rolling radius of the tyre) of the front and that of the rear wheels are equal or the theoretical speed ratio is equal to 1.Field test data confirm the theoretical findings that, when the theoretical speed ratio is equal to 1, the efficiency of slip and tractive efficiency reach their respective peaks, the fuel consumption per unit drawbar power reaches a minimum, and the overall tractive performance is at an optimum.
Rolf Albrecht - One of the best experts on this subject based on the ideXlab platform.
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optimal electric vehicle energy efficiency recovery in an intelligent transportation system
19th ITS World CongressERTICO - ITS EuropeEuropean CommissionITS AmericaITS Asia-Pacific, 2012Co-Authors: Stephen Jones, Arno Huss, Emre Kural, Rolf Albrecht, Alexander Massoner, Kosmas KnodlerAbstract:The limited range of Electric Vehicles can be significantly improved by means of optimal operation or control strategies enhanced with off-board data, provided by an Intelligent Transportation System, in addition to on-board information. The development of such strategies, for a twin axle Electric Vehicle, is the key goal of the OpEneR (Optimal Energy efficiency and Recovery) project. Within OpEneR, a highly advanced and beyond state-of-the-art MiL/SiL tool-chain has been developed, which enables the accurate development and validation of advanced energy management and safety strategies. Here a detailed model of the electrified vehicle powertrain equipped with an advanced regenerative braking system, is extended to includes a wide range of on-board and off-board sensors (GPS navigation, car-to-infrastructure, car-to-car data, etc.), the 3D real world driving route, as well as complex traffic and environmental conditions for the Driver to interact with. The paper summarizes the design of the simulation tool-chain used to virtually develop the advanced strategies, for optimization of the torque distribution between the two electric-Drive Axles, energy-optimal route planning and short term traffic maneuvers. Preliminary simulation results obtained under several traffic scenarios are presented in this paper.
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Optimal Electric Vehicle Energy Efficiency & Recovery in an Intelligent Transportation System
2012Co-Authors: Stephen Jones, Arno Huss, Emre Kural, Rolf Albrecht, Alexander Massoner, Kosmas KnodlerAbstract:The limited range of Electric Vehicles can be significantly improved by means of optimal operation or control strategies enhanced with off-board data, provided by an Intelligent Transportation System, in addition to on-board information. The development of such strategies, for a twin axle Electric Vehicle, is the key goal of the OpEneR (Optimal Energy efficiency and Recovery) project. Within OpEneR, a highly advanced and beyond state-of-the-art MiL/SiL tool-chain has been developed, which enables the accurate development and validation of advanced energy management and safety strategies. Here a detailed model of the electrified vehicle powertrain equipped with an advanced regenerative braking system, is extended to includes a wide range of on-board and off-board sensors (GPS navigation, car-to-infrastructure, car-to-car data, etc.), the 3D real world driving route, as well as complex traffic and environmental conditions for the Driver to interact with. The paper summarizes the design of the simulation tool-chain used to virtually develop the advanced strategies, for optimization of the torque distribution between the two electric-Drive Axles, energy-optimal route planning and short term traffic maneuvers. Preliminary simulation results obtained under several traffic scenarios are presented in this paper.
Arno Huss - One of the best experts on this subject based on the ideXlab platform.
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optimal electric vehicle energy efficiency recovery in an intelligent transportation system
19th ITS World CongressERTICO - ITS EuropeEuropean CommissionITS AmericaITS Asia-Pacific, 2012Co-Authors: Stephen Jones, Arno Huss, Emre Kural, Rolf Albrecht, Alexander Massoner, Kosmas KnodlerAbstract:The limited range of Electric Vehicles can be significantly improved by means of optimal operation or control strategies enhanced with off-board data, provided by an Intelligent Transportation System, in addition to on-board information. The development of such strategies, for a twin axle Electric Vehicle, is the key goal of the OpEneR (Optimal Energy efficiency and Recovery) project. Within OpEneR, a highly advanced and beyond state-of-the-art MiL/SiL tool-chain has been developed, which enables the accurate development and validation of advanced energy management and safety strategies. Here a detailed model of the electrified vehicle powertrain equipped with an advanced regenerative braking system, is extended to includes a wide range of on-board and off-board sensors (GPS navigation, car-to-infrastructure, car-to-car data, etc.), the 3D real world driving route, as well as complex traffic and environmental conditions for the Driver to interact with. The paper summarizes the design of the simulation tool-chain used to virtually develop the advanced strategies, for optimization of the torque distribution between the two electric-Drive Axles, energy-optimal route planning and short term traffic maneuvers. Preliminary simulation results obtained under several traffic scenarios are presented in this paper.
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Optimal Electric Vehicle Energy Efficiency & Recovery in an Intelligent Transportation System
2012Co-Authors: Stephen Jones, Arno Huss, Emre Kural, Rolf Albrecht, Alexander Massoner, Kosmas KnodlerAbstract:The limited range of Electric Vehicles can be significantly improved by means of optimal operation or control strategies enhanced with off-board data, provided by an Intelligent Transportation System, in addition to on-board information. The development of such strategies, for a twin axle Electric Vehicle, is the key goal of the OpEneR (Optimal Energy efficiency and Recovery) project. Within OpEneR, a highly advanced and beyond state-of-the-art MiL/SiL tool-chain has been developed, which enables the accurate development and validation of advanced energy management and safety strategies. Here a detailed model of the electrified vehicle powertrain equipped with an advanced regenerative braking system, is extended to includes a wide range of on-board and off-board sensors (GPS navigation, car-to-infrastructure, car-to-car data, etc.), the 3D real world driving route, as well as complex traffic and environmental conditions for the Driver to interact with. The paper summarizes the design of the simulation tool-chain used to virtually develop the advanced strategies, for optimization of the torque distribution between the two electric-Drive Axles, energy-optimal route planning and short term traffic maneuvers. Preliminary simulation results obtained under several traffic scenarios are presented in this paper.
