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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]

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]

Abstract: There are many approaches addressing the problem of optimal energy management in hybrid electric vehicles; however, most of them optimise the control strategy for particular driving cycles. This paper takes into account that the driving cycle is not a priori known to obtain a near-optimal solution. The proposed method is based on analysing the power demands in a given receding horizon to estimate future driving conditions and minimise the fuel consumption while cancelling the expected battery energy consumption after a defined time horizon. Simulations show that the proposed method allows charge sustainability providing near-optimal results. [Copyright &y& Elsevier]