Eco-Driving for Different Electric Powertrain Topologies Considering Motor Efficiency

Koch, Alexander K.; Bürchner, Tim; Herrmann, Thomas; Lienkamp, Markus

doi:10.3390/wevj12010006

Cited by 14 publications

(17 citation statements)

References 27 publications

(48 reference statements)

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“…Therefore, when the transmission is in the first gear, when the velocity is less than 17 km/h, the efficiency of motor 1 is higher; when the velocity is more than 27 km/h, the efficiency of motor 2 is higher; and when the velocity is between 17 and 27 km/h, the efficiency of motor 1 and motor 2 is both relatively high, taking into account that the demand torque of the vehicle in the low-speed zone of 17~27 km/h on the low-gear position is larger, the efficiency of the corresponding motor 1 is higher at this time [20]. Therefore, motor 1 can be considered as a preferred choice.…”

Section: Research On Driving Control Strategymentioning

confidence: 99%

Research on Automatic Optimization of a Vehicle Control Strategy for Electric Vehicles Based on Driver Style

Song,

Xi,

Gao

2024

WEVJ

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In order to reduce energy consumption, improve driving mileage, and make vehicles adopt driver styles, research on automatic optimization of control strategy based on driver style is conducted in this paper. According to the structure of the powertrain, the vehicle control strategy is designed and a driver-style recognition model based on fuzzy recognition is added to the rule-based control strategy to improve the driver adaptation of the vehicle. In order to further improve the energy-saving effect of the strategy, the control strategy based on driver style is automatically optimized by the Isight optimization platform to make the strategy reach optimum. The test results show that the strategy based on driver style is able to adapt to different styles of drivers and the economy of the vehicle is improved by 2.06% compared with pre-optimization, which validates the effectiveness of the strategy.

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Section: Research On Driving Control Strategymentioning

confidence: 99%

Research on Automatic Optimization of a Vehicle Control Strategy for Electric Vehicles Based on Driver Style

Song,

Xi,

Gao

2024

WEVJ

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“…Since we also want to allow for significant deviations from the human trajectory, we define other metrics. As explained in [28], there are three common approaches to optimize energy efficiency: wheel-to-distance, tank-to-distance and the minimisation of the acceleration squared. The first two refer to energy minimization by means of a vehicle model that is not included in the CPM Olympics, in order to keep the task within reasonable bounds.…”

Section: B Evaluating the Performancementioning

confidence: 99%

CPM Olympics: Development of Scenarios for Benchmarking in Networked and Autonomous Driving

Mokhtarian

Schäfer

Alrifaee

2022

2022 IEEE Intelligent Vehicles Symposium (IV)

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The Cyber-Physical Mobility (CPM) Remote project provides web-based access to a simulation service and the CPM Lab itself, which is provided by the Chair of Embedded Software at RWTH Aachen University. With this approach, users no longer need special hardware, software, or physical access to use the simulation tool and the CPM Lab. This reduces the effort required to participate in research compared to the conventional simulation and lab setup.CPM Remote already offers uniform hardware by outsourcing the simulation to our Chair's servers and uniform software through the computer simulation of the CPM Lab. This paper proposes extending the CPM Remote framework by a set of formalized problems and a unified evaluation method. These benchmarks detect whether the vehicles have successfully passed through the scenario and evaluate the quality of the driven trajectories. We compiled the extracted scenarios into an event, the CPM Olympics. Thus, CPM Remote now has a fully defined stack for the objective comparison of algorithms for networked and autonomous driving. I. SUPPLEMENTARY MATERIALThe remote access to the Cyber-Physical Mobility Lab is provided at: www.cpm-remote.de. The construction plans and details on our CPM Lab are available here: https://cpm.embedded.rwth-aachen.de/. We published the source code of this paper on GitHub https://github.com/embedded-softwarelaboratory/Dataset-Converter.

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“…Since driving style will be an important customer-relevant property of an AVC, the dimensions of the driving style must be considered in the property portfolio and the methods to test them should be part of the vehicle concept evaluation. Koch et al (2020) present an eco-driving algorithm to deal with the interdependence of the drivetrain and the driven driving style. They show the energy saving potentials and the downsizing potentials due to a co-optimization of multiple motors and gears.…”

Section: Research On Specific Aspects Of Autonomous Drivingmentioning

confidence: 99%

User Need-Oriented Concept Development of Autonomous Vehicles

et al. 2021

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Vehicle concept development is a domain that has applied and detailed its process over decades. The megatrends of the 21st century of “automation” and “sharing” influence the vehicle concept in such a manner that this well-running process needs an update. The vehicle itself and the customer of the vehicle are changing and therefore the components of the vehicle and the input variables of the useroriented design of the vehicle concept must be changed as well. We present a development process for autonomous vehicle concepts to address these challenges. We are therefore analyzing the current definition of a vehicle concept and its development process. Based on a literature review of a selection of common design methodologies, we update this definition for autonomous vehicle concepts and present a development process that presents design concepts of autonomous vehicle in a user need oriented way. This includes the sharing of models since user needs could be fulfilled by more than one vehicle concept. We believe that the presented process can be a starting point for vehicle concept development of the 21st century.

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Eco-Driving for Different Electric Powertrain Topologies Considering Motor Efficiency

Cited by 14 publications

References 27 publications

Research on Automatic Optimization of a Vehicle Control Strategy for Electric Vehicles Based on Driver Style

Research on Automatic Optimization of a Vehicle Control Strategy for Electric Vehicles Based on Driver Style

CPM Olympics: Development of Scenarios for Benchmarking in Networked and Autonomous Driving

User Need-Oriented Concept Development of Autonomous Vehicles

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