15,670 research outputs found
An investigation on the effect of driver style and driving events on energy demand of a PHEV
Environmental concerns, security of fuel supply and CO2 regulations are driving innovation in the automotive industry towards electric and hybrid electric vehicles. The fuel economy and emission performance of hybrid electric vehicles (HEVs) strongly depends on the energy management system (EMS). Prior knowledge of driving information could be used to enhance the performance of a HEV. However, how the necessary information can be obtained to use in EMS optimisation still remains a challenge. In this paper the effect of driver style and driving events like city and highway driving on plug in hybrid electric vehicle (PHEV) energy demand is studied.
Using real world driving data from three drivers of very different driver style, a simulation has been exercised for a given route having city and highway driving. Driver style and driving events both affect vehicle energy demand. In both driving events considered, vehicle energy demand is different due to driver styles. The major part of city driving is reactive driving influenced by external factors and driver leading to variation in vehicle speed and hence energy demand. In free highway driving, the driver choice of cruise speed is the only factor affecting vehicle energy demand
Electric vehicle battery model identification and state of charge estimation in real world driving cycles
This paper describes a study demonstrating a new method of state-of-charge (SoC) estimation for batteries in real-world electric vehicle applications. This method combines realtime model identification with an adaptive neuro-fuzzy inference system (ANFIS). In the study, investigations were carried down on a small-scale battery pack. An equivalent circuit network model of the pack was developed and validated using pulse-discharge experiments. The pack was then subjected to demands representing realistic WLTP and UDDS driving cycles obtained from a model of a representative electric vehicle, scaled match the size of the battery pack. A fast system identification technique was then used to estimate battery parameter values. One of these, open circuit voltage, was selected as suitable for SoC estimation, and this was used as the input to an ANFIS system which estimated the SoC. The results were verified by comparison to a theoretical Coulomb-counting method, and the new method was judged to be effective. The case study used a small 7.2 V NiMH battery pack, but the method described is applicable to packs of any size or chemistry
A novel Big Data analytics and intelligent technique to predict driver's intent
Modern age offers a great potential for automatically predicting the driver's intent through the increasing miniaturization of computing technologies, rapid advancements in communication technologies and continuous connectivity of heterogeneous smart objects. Inside the cabin and engine of modern cars, dedicated computer systems need to possess the ability to exploit the wealth of information generated by heterogeneous data sources with different contextual and conceptual representations. Processing and utilizing this diverse and voluminous data, involves many challenges concerning the design of the computational technique used to perform this task. In this paper, we investigate the various data sources available in the car and the surrounding environment, which can be utilized as inputs in order to predict driver's intent and behavior. As part of investigating these potential data sources, we conducted experiments on e-calendars for a large number of employees, and have reviewed a number of available geo referencing systems. Through the results of a statistical analysis and by computing location recognition accuracy results, we explored in detail the potential utilization of calendar location data to detect the driver's intentions. In order to exploit the numerous diverse data inputs available in modern vehicles, we investigate the suitability of different Computational Intelligence (CI) techniques, and propose a novel fuzzy computational modelling methodology. Finally, we outline the impact of applying advanced CI and Big Data analytics techniques in modern vehicles on the driver and society in general, and discuss ethical and legal issues arising from the deployment of intelligent self-learning cars
Human Motion Trajectory Prediction: A Survey
With growing numbers of intelligent autonomous systems in human environments,
the ability of such systems to perceive, understand and anticipate human
behavior becomes increasingly important. Specifically, predicting future
positions of dynamic agents and planning considering such predictions are key
tasks for self-driving vehicles, service robots and advanced surveillance
systems. This paper provides a survey of human motion trajectory prediction. We
review, analyze and structure a large selection of work from different
communities and propose a taxonomy that categorizes existing methods based on
the motion modeling approach and level of contextual information used. We
provide an overview of the existing datasets and performance metrics. We
discuss limitations of the state of the art and outline directions for further
research.Comment: Submitted to the International Journal of Robotics Research (IJRR),
37 page
Forecasting Recharging Demand to Integrate Electric Vehicle Fleets in Smart Grids
Electric vehicle fleets and smart grids are two growing technologies. These technologies
provided new possibilities to reduce pollution and increase energy efficiency.
In this sense, electric vehicles are used as mobile loads in the power grid. A distributed
charging prioritization methodology is proposed in this paper. The solution is based
on the concept of virtual power plants and the usage of evolutionary computation
algorithms. Additionally, the comparison of several evolutionary algorithms, genetic
algorithm, genetic algorithm with evolution control, particle swarm optimization, and
hybrid solution are shown in order to evaluate the proposed architecture. The proposed
solution is presented to prevent the overload of the power grid
The State-of-the-art of Coordinated Ramp Control with Mixed Traffic Conditions
Ramp metering, a traditional traffic control strategy for conventional
vehicles, has been widely deployed around the world since the 1960s. On the
other hand, the last decade has witnessed significant advances in connected and
automated vehicle (CAV) technology and its great potential for improving
safety, mobility and environmental sustainability. Therefore, a large amount of
research has been conducted on cooperative ramp merging for CAVs only. However,
it is expected that the phase of mixed traffic, namely the coexistence of both
human-driven vehicles and CAVs, would last for a long time. Since there is
little research on the system-wide ramp control with mixed traffic conditions,
the paper aims to close this gap by proposing an innovative system architecture
and reviewing the state-of-the-art studies on the key components of the
proposed system. These components include traffic state estimation, ramp
metering, driving behavior modeling, and coordination of CAVs. All reviewed
literature plot an extensive landscape for the proposed system-wide coordinated
ramp control with mixed traffic conditions.Comment: 8 pages, 1 figure, IEEE INTELLIGENT TRANSPORTATION SYSTEMS CONFERENCE
- ITSC 201
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