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• A method for trapped mass estimation from in-cylinder pressure resonance is used. • An iterative algorithm is developed for reducing the computational burden. • An outlier rejection procedure is presented for improving the robustness. • An observer is designed to improve the estimation in a multi-sensor scenario. A novel technique of trapped mass determination, based on the in-cylinder pressure resonance, has been recently published by the authors. However, the method only works when sufficient resonance intensity exists and the current formulation might preclude its implementation in real-time due to excessive computational burden. The present paper proposes an iterative algorithm for reducing the number of operations, an adaptive filter to identify faulty measurements and a Kalman filter that combines several sensors and models, currently used in commercial light-duty engines, to ensure a continous estimation of trapped mass, air mass, and exhaust gas recirculation (EGR). The filter is implemented using experimental data of a EURO 6 light-duty engine in a world harmonize light-duty test cycle (WLTC), showing the potential of being implemented in real driving conditions with robustness and harnessing a new measurement to improve the accuracy and response of current estimations. [ABSTRACT FROM AUTHOR]

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: The paper presents a formulation of the energy management problem for Hybrid Electrical Vehicles and Plug-in Hybrid Electrical Vehicles alike, which permits to consider different cost indexes like fuel consumption, total and primary energy consumption, economic cost or CO2 footprint. In-depth analysis of the problem optimal solution is done by means of the application of the method, which also permits the optimal tuning of other implementable control strategies. Such an approach is used to understand the effect of the selected cost index, the regional energetic share, the driving conditions, and for deriving rules for battery sizing. [Copyright &y& Elsevier]

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]

Abstract: Look-up tables are commonly used in the automotive field for handling operating point variations. However, constant maps cannot cope with systems variations and ageing. Methods, such as Kalman filter or Extended Kalman filter for non-linear cases, can be used for table adaptation providing an optimal solution to the problem. But these methods are computationally intensive, making difficult to implement them on commercial engine control units. The current paper proposes a learning method for online updating of look-up tables or maps. This algorithm uses precalculated membership functions based on a standard Kalman filter observer for weighting the adaptation. The main contribution of the method is the derivation of a steady-state Kalman filter observer that lowers the calculation burden and simplifies the implementation against the standard Kalman filter implementation that requires higher computational cost. As far as table is updated online while engine runs, this allows correcting drift errors and the unit-to-unit dispersion. The method is illustrated for mapping engine variables such as and in a Diesel engine by using an adaptive look-up table, and its characteristics make it suitable for implementing in commercial engine electronic control units for online purposes. [Copyright &y& Elsevier]

With the tightening of on-board diagnostics requirements, accuracy of sensors is essential to monitor the efficiency and ensure a proper control of the after-treatment systems. Temperature sensors are commonly used in the exhaust line at the diesel oxidation catalyst-inlet of turbocharged diesel engines for control and diagnosis of the after-treatment system. In particular, negative temperature constant sensors are used for this purpose. However, due to the necessary robustness that on-board sensors must fulfil, thermal inertia causes significant differences during engine transient operating conditions in temperature measurements. A Kalman filter is proposed in this paper for the on-line dynamic estimation of the catalyst-inlet temperature, which combines a slow but accurate measurement of the on-board temperature sensor with a fast but drifted temperature model. A fast research-grade thermocouple is used as reference of the actual exhaust gas temperature as well as a frequency analysis is performed in order to calibrate the model and analyse results of the signal reconstruction. Results of the algorithm are then successfully proved in experimental transient tests and typical European approval test cycles. [ABSTRACT FROM AUTHOR]

• FCREx dynamic & operation limits effect on performance & durability was evaluated. • Performance and durability for FCREx vehicles in driving cycle were correlated. • Maximum lifetime (+110%) was achieved with |di/dt| max = 0.01 A/cm2s i min = 0.15 A/cm2. • |di/dt| max = 0.001 A/cm2s or i min = 0.2 A/cm2 do not allow charge-sustained mode. • Recommendations for FCREx vehicle and FC stack manufacturers were elaborated. Aiming at increasing fuel cell (FC) stack durability in driving conditions, part of the scientific community has focused its efforts on developing energy management strategies (EMS) for fuel cell hybrid vehicles (FCV). Nonetheless, most of these studies do not explicitly explain the effect of constraining the EMS in both degradation and performance when acting on the FC system dynamics or operational space nor consider the FC range-extender (FCREx) architecture for passenger car application. This study evaluates the potential of FCREx architecture to maximize FC stack durability and performance through control strategy dynamic and operational space limitations. For that purpose, a FCV modeling platform was developed and integrated together with an EMS optimizer algorithm and a semi-empirical advanced FC stack degradation model for driving cycle conditions. The resulting modeling platform was then simulated in WLTC 3b driving cycle to predict FC degradation and H 2 consumption with different dynamic and operational restrictions. Practical limits for EMS constraining were identified as —di/dt— max = 0.001 A/cm2s or i min = 0.2 A/cm2 since they prevented the EMS from fulfilling the constant state-of-charge constraint in high-dynamic driving condition. In this sense, —di/dt— max = 0.01 A/cm2s and i min = 0.15 A/cm2 were recommended as the combination of constraints that maximizes FC stack durability (+110%) without affecting the FCV operability with only an increase in of 4.7% in H 2 consumption. From these results, a set of recommendations and guidelines for FCREx vehicle manufacturers and FC stack developers were elaborated based on the benefits of understanding the dynamics and operational constraints that the FC system is going to be subjected to under real operation. [ABSTRACT FROM AUTHOR]

In this study, durability and performance prediction were integrated in the sizing process of the FC stack of a fuel cell range-extender (FCREx) vehicle together with the design of a dynamics-limited control strategy. For that purpose, a FCREx vehicle model integrating a FC stack, balance of plant, battery, H 2 tank and vehicle body (C-class SUV) validated in previous studies was used. To predict FC stack degradation rate, a novel semi-empirical multi-layered degradation modeling framework for automotive application is proposed and developed. Degradation rates are calculated based on reference degradation rates measured at reference and known conditions (1st layer) and scaled with the electrochemical phenomena (2nd layer) and the operating conditions (3rd layer) through scaling functions based on physical tendencies. Results show how increasing the FC stack power decreases H 2 consumption but increases durability, while increasing the dynamic limitations on the control strategy increases both H 2 consumption and durability. The isolated effect of sizing implied a decrease in H 2 consumption of ∼ 3% and an increase in FC stack durability of ∼ 53% when comparing the 40 kW and 100 kW designs. In contrast, the effect of dynamic limitations was significantly perceived in the 40 kW design which implied an increase in H 2 consumption close to 8% and an increase in durability of 294% when comparing the infinite dynamics and the highest dynamically restricted cases. Nevertheless, the effect of sizing is neglected under high dynamic limitation and limiting the current density change rate to 0.001 A/cm2 s may prevent the control strategy from fulfilling the charge sustaining mode in aggressive driving. Based on these results, a set of recommendations were elaborated for FC stack and FCV manufacturers aiming to apply FCREx architecture to passenger car vehicles. • Higher FC stack power decreases H 2 consumption and increases durability. • Higher dynamic limitation increases H 2 consumption and durability. • FC stack sizing effect becomes negligible under high dynamic limitations • A novel semi-empirical degradation model for automotive applications was developed. • Degradation rates are scaled with electrochemical phenomena and physical conditions [ABSTRACT FROM AUTHOR]

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]

Fuel cell (FC) technologies for mobility are gaining interest as promising options to decarbonize the transport sector in line with the current progress towards the H 2 economy. Previous studies show how the fuel cell range extender (FCREx) powertrain architecture can offer flexible and efficient operation along with the potentially low total cost of ownership (TCO) in passenger car applications. Cradle-to-grave emissions of these vehicles have not been estimated, nor their variation with the components sizing or the H 2 production pathway analyzed. In this study, the life cycle assessment (LCA) and sizing methodologies were combined to address these knowledge gaps. The design spaces were generated by varying the FC maximum power, the battery capacity and the H 2 tank capacity and by simulating the resulting designs with the WLTC 3b driving cycle. Then, the lifetime H 2 and energy consumption results and design parameters were calculated and used as inputs to estimate the greenhouse gases (GHG) and NO X emissions on the manufacturing and fuel production cycles. From the results, it was proven how considering steam methane reforming (SMR) with carbon capture and storage (CCS) as the H 2 production pathway could decrease by 60% and 38% GHG-100 and NO X emissions respectively, with respect to electrolysis where electricity is generated with the EU mix. The optimum design, in terms of emissions, was found to be with low-moderate battery capacity and moderate-high FC maximum power in contrast to the optimum design for performance, which had high battery capacity and high FC stack power. • A novel approach using sizing and LCA methodologies is used for passenger FCREx. • Design spaces for FCREx vehicles showing GHG-100, NO X and performance are generated. • Production pathways for H 2 were electrolysis with the EU mix, SMR and SMR with CCS. • GHG-100 & NO X emissions may vary up to 10% when comparing the worst and best designs. • Emissions-wise and performance-wise optimum FCREx designs are compared [ABSTRACT FROM AUTHOR]

Aiming to reduce global warming and emissions in general, cleaner technologies are the spotlight of research and industry development. Among them, fuel cell vehicles (FCV) are gaining interest to decarbonize the transport sector. Plug-in FCV or FCV in range-extender configuration (FCREx) is an interesting option to reduce the total cost of ownership (TCO) and the energy usage per km. The aim of this study was to generate design spaces of FCREx by varying the FC stack maximum power output, the battery capacity, and the H 2 tank capacity to understand the implications of this architecture in range, consumption, and cost (estimated with a WLTP driving cycle). Unlike other studies, the approach was focused on a novel architecture for passenger vehicles and was focused on the development of the validated FC system model and the energy management strategy (EMS) optimization for each design, based on the Pontryagin Minimum Principle (PMP). Consumption was found to decrease with increasing battery capacity and FC maximum power due to the higher efficiency of the systems. The design spaces showed how with 5 kg of H 2 and ≥ 50 kWh of battery capacity the maximum range of FCREx could be over 700 km. The results of this study showed how FCREx architecture could provide overall energy consumption saving up to 6.8% and H 2 consumption saving ranging from 16.8% to 25%, compared to current commercial FCVs. The optimum FCREx design, not only based on performance, should have ∼ 30 kWh of battery capacity and ≥ 80 kW of FC maximum power to minimize manufacturing costs while maximizing efficiency. • Passenger FCREx components sizing and evaluation was performed for the first time. • As a novelty, the optimization comprises both the EMS and the FC system operation. • Space designs for plug-in FCV (FCREx) were generated. • Equivalent-in-range FCREx designs were compared to commercial FCV. • FCREx architecture potentially increases FCV range and efficiency (16.8%–25%). [ABSTRACT FROM AUTHOR]

• OC-SVM and CNN are compared for knock detection in a heavy-duty engine. • The STFT of the knock sensor signal is used as the main input in both methods. • The effect of various sensor configurations over each method is analysed. • The classification performance of each method is discussed. Knock detection is critical to engine control as it prevents damage and ensures optimal performance. However, it still presents significant challenges, particularly in alternative combustion systems, due to its complex nature. This study compares two machine learning (ML) approaches (supervised and unsupervised) for detecting knock in a heavy-duty engine, using various knock-sensor setups. It focuses on using accelerometer data as the primary input, a widely used on-road sensor whose compatibility with ML approaches requires further exploration. The analysis includes One-Class Support Vector Machine (OC-SVM) and Convolutional Neural Networks (CNN). Both methods demonstrated their ability to detect engine knock effectively, achieving sensitivity levels over 80% compared to MAPO (maximum amplitude pressure oscillation) index. The sensor placement revealed different effects over the results according to the method used, whereas the number of sensors showed a minor influence on the outcomes. The advantages and disadvantages of each method are discussed. [ABSTRACT FROM AUTHOR]

Abstract: The use of biodiesel is under evaluation as a possible fuel alternative to fossil fuels. Despite the literature available concerning the effect of biodiesel on engine performance and emissions under steady operation, there are not many research studies which cope with the effect of biodiesel during transient operation. Moreover, it is interesting to study the repercussions that the use of biodiesel has on complete vehicle performance and emissions. In this sense, the part 1 of the present paper deals with a preliminary analysis where the test facilities and procedures are explained, and the effects of the physicochemical properties of biodiesel on fuel injection are studied. To cope with the repercussion of the use of biodiesel in engine emissions during the MVEG-A cycle in this part of the paper a deep analysis of the pollutant emissions obtained during tests is done. Based on this analysis the paper also presents an approach to improve the engine control calibration to take advantage of utilising biodiesel. [Copyright &y& Elsevier]