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New regulations on pollutants and, specially, on CO2 emissions could restrict the use of the internal combustion engine in automotive applications. This paper presents a review of different technologies under development for meeting such regulations, ranging from new combustion concepts to advanced boosting methods and after-treatment systems. Many of them need an accurate control of the operating conditions and, in many cases, they impose demanding requirements at a system integration level. In this framework, engine control disciplines will be key for the implementation and development of the next generation engines, taking profit of recent advancements in models, methods and sensors. According to authors’ opinion, the internal combustion engine will still be the dominant technology in automotive applications for the next decades.

Highlights • A new mathematical tool is proposed to analyse resonance evolution. • Resonance excitation is analysed in various combustion modes. • Experimental results from four combustion modes are analysed. • The resonance analysis is used to improve the estimation of the trapped mass. Abstract This paper analyses the in-cylinder pressure oscillations in internal combustion engines, which are initiated by combustion and resonate during the expansion stroke. A specific mathematical tool, which takes into account the resonant frequency theory, has been developed to determine the resonance intensity evolution from the in-cylinder pressure signal. Two engines, a conventional spark-ignited (SI) engin modified to perform homogeneous charge compression ignition (HCCI) combustion, and a conventional compression ignited (CI) engine, were used to analyse the resonance excitation in various combustion modes at different operating conditions. The new transformation has been used to characterize resonance in various combustion modes and a previous method developed by the authors to estimate the trapped mass was fed by such knowledge to improve its robustness and accuracy. [ABSTRACT FROM AUTHOR]

This paper addresses the optimal control of a long-haul passenger train to deliver minimum-fuel operations. Contrary to the common Pontryagin minimum principle approach in railroad-related literature, this work addresses this optimal control problem with a direct method of optimization, the use of which is still marginal in this field. The implementation of a particular direct method based on the Euler collocation scheme and its transcription into a nonlinear problem are described in detail. In this paper, this optimization technique is benchmarked with well-known optimization methods in the literature, namely dynamic programming and the Pontryagin minimum principle, by simulating a real route. The results showed that the direct methods are on the same level of optimality compared with other algorithms while requiring reduced computational time and memory and being able to handle very complex dynamic systems. The performance of the direct method is also compared to the real trajectory followed by the train operator and exhibits up to 20% of fuel saving in the example route. [ABSTRACT FROM AUTHOR]

Perfect knowledge of future driving conditions can be rarely assumed on real applications when optimally splitting power demands among different energy sources in a hybrid electric vehicle. Since performance of a control strategy in terms of fuel economy and pollutant emissions is strongly affected by vehicle power requirements, accurate predictions of future driving conditions are needed. This paper proposes different methods to model driving patterns with a stochastic approach. All the addressed methods are based on the statistical analysis of previous driving patterns to predict future driving conditions, some of them employing standard vehicle sensors, while others require non-conventional sensors (for instance, global positioning system or inertial reference system). The different modelling techniques to estimate future driving conditions are evaluated with real driving data and optimal control methods, trading off model complexity with performance. [ABSTRACT FROM PUBLISHER]

Abstract: Nox estimation in diesel engines is an up-to-date problem but still some issues need to be solved. Raw sensor signals are not fast enough for real-time use while control-oriented models suffer from drift and aging. A control-oriented gray box model based on engine maps and calibrated off-line is used as benchmark model for Nox estimation. Calibration effort is important and engine data-dependent. This motivates the use of adaptive look-up tables. In addition to, look-up tables are often used in automotive control systems and there is a need for systematic methods that can estimate or update them on-line. For that purpose, Kalman filter (KF) based methods are explored as having the interesting property of tracking estimation error in a covariance matrix. Nevertheless, when coping with large systems, the computational burden is high, in terms of time and memory, compromising its implementation in commercial electronic control units. However look-up table estimation has a structure, that is here exploited to develop a memory and computationally efficient approximation to the KF, named Simplified Kalman filter (SKF). Convergence and robustness is evaluated in simulation and compared to both a full KF and a minimal steady-state version, that neglects the variance information. SKF is used for the online calibration of an adaptive model for Nox estimation in dynamic engine cycles. Prediction results are compared with the ones of the benchmark model and of the other methods. Furthermore, actual online estimation of Nox is solved by means of the proposed adaptive structure. Results on dynamic tests with a diesel engine and the computational study demonstrate the feasibility and capabilities of the method for an implementation in engine control units. [Copyright &y& Elsevier]

Compressor characterization, either by running experiments in a turbocharger test rig or by detailed CFD modelling, can be expensive and time-consuming. In this work, a novel method is proposed which can be used to build a complete compressor map from a reduced number of measured operating points combined with a previously collected database. The methodology is based on the application of the Singular Value Decomposition (SVD) method to acquire the orthonormal bases of a matrix which contains the information of previous compressor observations. These bases are used along with pivoted QR decomposition to obtain the minimum number of measurement points which are required to implement this technique as well as its optimal placement within the map. The reconstruction of two different compressor maps was made to validate the method. The results show a substantially better trade-off between number of testing points and accuracy compared to standard equidistributed sampling., Competing Interests: The authors declare no conflict of interest., (© 2022 The Authors.)

Aim: To examine the impact of the lockdown caused by the COVID-19 pandemic on both the glycemic control and the daily habits of a group of patients with type 1 diabetes mellitus (T1DM) using flash continuous glucose monitoring devices (flash CGMs)., Methods: Retrospective analysis based on all the information gathered in virtual consultations from a cohort of 50 adult patients with T1DM with follow-up at our site. We compared their CGM metrics during lockdown with their own previous data before the pandemic occurred, as well as the potential psychological and therapeutic changes., Results: We observed a reduction of average glucose values: 160.26 ± 22.55 mg/dL vs 150 ± 20.96 mg/dL, P = .0009; estimated glycosylated hemoglobin: 7.21 ± 0.78% vs 6.83 ± 0.71%, P = .0005; glucose management indicator 7.15 ± 0.57% vs 6.88 ± 0.49%; P = .0003, and glycemic variability: 40.74 ± 6.66 vs 36.43 ± 6.09 P < .0001. Time in range showed an improvement: 57.46 ± 11.85% vs a 65.76 ± 12.09%, P < .0001, without an increase in percentage of time in hypoglycemia., Conclusions: COVID-19 lockdown was associated with an improvement in glycemic control in patients with T1DM using CGMs., (© The Author(s) 2020. Published by Oxford University Press on behalf of the Endocrine Society.)