Hybrid electric vehicle | EPFL Graph Search

A hybrid electric vehicle (HEV) is a type of hybrid vehicle that combines a conventional internal combustion engine (ICE) system with an electric propulsion system (hybrid vehicle drivetrain). The presence of the electric powertrain is intended to achieve either better fuel economy than a conventional vehicle or better performance. There is a variety of HEV types and the degree to which each function as an electric vehicle (EV) also varies. The most common form of HEV is the hybrid electric car, although hybrid electric trucks (pickups and tractors), buses, boats and aircraft also exist. Modern HEVs make use of efficiency-improving technologies such as regenerative brakes which convert the vehicle's kinetic energy to electric energy, which is stored in a battery or supercapacitor. Some varieties of HEV use an internal combustion engine to turn an electrical generator, which either recharges the vehicle's batteries or directly powers its electric drive motors; this combination is kno

Environomic design of vehicle integrated energy systems

Zlatina Kirilova Dimitrova

To meet the targets of sustainable development and greenhouse emission reduction of the future vehicles fleet, the automotive industry needs to deploy cost-competitive and efficient advanced energy conversion systems for the future commercial personal vehicles. The efficiency improvement needs induce to search new structured methodologies allowing the integration of the efficiency/cost vision for different vehicle energy technologies, in the earlier design stage of the new vehicles and their propulsion systems. The assessment of environmental impacts is also needed. In this context, the aim of this thesis is to develop a generic methodology for the design of the vehicle energy systems, which can consider on a holistic way, the âenvironomicâ criteria. To design and evaluate the competitiveness of energy integrated systems for vehicles, a systematic comparison including thermodynamic, economic and environmental considerations is required. This thesis presents the development of a systematic environomic optimization for vehicle energy systems. The efficiency, economic and environmental performances are assessed for different energy technology options and their integration in advanced vehicles powertrains. The performances indicators are compared and the trade-off are assessed to support decision making and to identify optimal energy systems configurations. The results from the energy integration studies reveal the potential of the combinations of different options of energy recovery and the integration of the energy services on the vehicle efficiency. The additional cost of the ORC installation is estimated to be 40 -55 â¬/kW. The competitiveness and the trade-off of different advanced powertrains (electric, hybrid electric and hybrid pneumatic) are assessed and optimal design configurations are pointed out. It appears that different powertrain configurations are in competition for optimal mobility service depending on the selected environomic criteria.

EPFL2015

Behavioural compass: animal behaviour recognition using magnetometers

Kamiar Aminian, Pritish Chakravarty, Maiki Maalberg

Animal-borne data loggers today often house several sensors recording simultaneously at high frequency. This offers opportunities to gain fine-scale insights into behaviour from individual-sensor as well as integrated multi-sensor data. In the context of behaviour recognition, even though accelerometers have been used extensively, magnetometers have recently been shown to detect specific behaviours that accelerometers miss. The prevalent constraint of limited training data necessitates the importance of identifying behaviours with high robustness to data from new individuals, and may require fusing data from both these sensors. However, no study yet has developed an end-to-end approach to recognise common animal behaviours such as foraging, locomotion, and resting from magnetometer data in a common classification framework capable of accommodating and comparing data from both sensors. We address this by first leveraging magnetometers’ similarity to accelerometers to develop biomechanical descriptors of movement: we use the static component given by sensor tilt with respect to Earth’s local magnetic field to estimate posture, and the dynamic component given by change in sensor tilt with time to characterise movement intensity and periodicity. We use these descriptors within an existing hybrid scheme that combines biomechanics and machine learning to recognise behaviour. We showcase the utility of our method on triaxial magnetometer data collected on ten wild Kalahari meerkats (Suricata suricatta), with annotated video recordings of each individual serving as groundtruth. Finally, we compare our results with accelerometer-based behaviour recognition. The overall recognition accuracy of > 94% obtained with magnetometer data was found to be comparable to that achieved using accelerometer data. Interestingly, higher robustness to inter-individual variability in dynamic behaviour was achieved with the magnetometer, while the accelerometer was better at estimating posture. Magnetometers were found to accurately identify common behaviours, and were particularly robust to dynamic behaviour recognition. The use of biomechanical considerations to summarise magnetometer data makes the hybrid scheme capable of accommodating data from either or both sensors within the same framework according to each sensor’s strengths. This provides future studies with a method to assess the added benefit of using magnetometers for behaviour recognition.

2019

Modeling consideration for electric vehicles and plug-in electric vehicles in car renewal

Maurin Baillif, Michel Bierlaire, Matthieu Marie Cochon de Lapparent, Anna Fernández Antolín

We propose a behaviorally sound model capable of simulating choice set generation for car renewal. It appears at an upper layer of a choice model of a new car. Our model sheds light on potential latent demand for electric cars. We develop a hierarchical latent variable approach to model consideration for electric and plug-in hybrid electric vehicles in car renewal. It drives the observed outcomes that regard how much an individual would account or not for electric cars when planning to renew one. We model it as a latent factor that is defined as a linear combination of attitudes and perceptions. In our application, respondents are asked to whether they would consider or not a 100% electric and a plug-in hybrid electric vehicle. Attitudes and perceptions are also latent factors. These are additional variables that we model. They here are of three types: range anxiety, environmental concerns, and perception about compatibility of use in everyday life. They are measured through a series of questions that concern barriers and motivations to adoption of electric cars. For each of them, respondents are asked to rate on a 5 points Likert scale. We use data from the 2012 Nissan-Renault Alliance survey for application. It covers 5 European countries (France, Germany, Italia, Spain, UK). The sample size is about 5000 observations. In addition to observed outcomes that we model, we have information about individuals, structures of their households, the current cars they own and the way they use them (driven mileages). The measurement model takes the form of a mixed multivariate ordered Logit model. We discuss identification conditions to uncover the structural parameters of our system. It is estimated by maximization of the associated simulated log-likelihood function. Estimates live up to our expectations and are in line with existing literature.

2015