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Delivering acceptable low end torque and good transient response is a significant challenge for all turbocharged engines. As downsized gasoline engines and Diesel engines make up a larger and larger proportion of the light-duty engines entering the market, the issue takes on greater significance. Several schemes have been proposed to improve torque response in highly boosted engines, including the use of electrical assist turbochargers and compressed air assist. In this paper we examine these methods with respect to their effectiveness in improving transient response and their relative performance along with some of the practical considerations for real world application. Results shown in this paper are from 1-D simulations using the Ricardo WAVE software package.

Recent work has investigated the use of O₂ concentration in the intake manifold as a control variable for diesel engines. It has been recognized as a very good indicator of NOX emissions especially during transient operation, however, much of the work is concentrated on estimating the O₂ concentration as opposed to measuring it. This work investigates Universal Exhaust Gas Oxygen (UEGO) sensors and their potential to be used for such measurements. In previous work it was shown that these sensors can be operated in a controlled pressure environment such that their response time is of the order 10 ms. In this paper, it is shown how the key causes of variation (and therefore potential sources of error) in sensor output, namely, pressure and temperature are largely mitigated by operating the sensors in such an environment. Experiments were undertaken on a representative light-duty diesel engine using modified UEGO sensors in the intake and exhaust system.

A key challenge in achieving good transient performance of highly boosted engines is the difficulty of accelerating the turbocharger from low air flow conditions (“turbo lag”). Multi-stage turbocharging, electric turbocharger assistance, electric compressors and hybrid powertrains are helpful in the mitigation of this deficit, but these technologies add significant cost and integration effort. Air-assist systems have the potential to be more cost-effective. Injecting compressed air into the intake manifold has received considerable attention, but the performance improvement offered by this concept is severely constrained by the compressor surge limit. The literature describes many schemes for generating the compressed gas, often involving significant mechanical complexity and/or cost. In this paper we demonstrate a novel exhaust assist system in which a reservoir is charged during braking.

Behavioral models of traffic actors have a potential of unlocking sophisticated safety features and mitigating several challenges of urban automated driving. Intuitively, volunteers driving on routes of daily commuting in their private vehicles are the preferred source of information to be captured by data collection system. Such dataset can then serve as a basis for identifying efficient methods of context representation and parameterization of behavioral models. This paper describes the experimental setup supporting the development of driver behavioral models within the SIMUSAFE project. In particular, the paper presents an IoT data acquisition and analysis infrastructure supporting self-confrontation interviews with drivers. The proposed retrofit system was installed in private vehicles of volunteers in two European cities. Wherever possible, the setup used open source software and electronic components available on consumer market.

Enhanced perception algorithms are the key requirement for the introduction of more sophisticated active safety functionalities to urban areas. In order to realize the principles of test-driven development for such systems, either a set of representative and comprehensive test-drive logs needs to be available up front or a sufficiently universal simulation environment for virtual maneuvers should be employed. In this article a case study of developing a radar-based estimator for target heading is considered. This relatively standard problem serves as an illustrative example to assess the merits of an inherently simplified stimulus generated from virtual maneuvers and its limitation in comparison to real measurements. It is argued that a convenient modelling approach for a virtual scene simulation applied from an early development stage can further improve the quality and integrity of active safety projects, especially when they involve multiple sensor types.