• Energy Research

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 This research has been partially financed by the Spanish Ministerio de Economia y Competitividad, through project TRA2013-40853-R PRELIMIT.

[EN] This article proposes a diesel oxidation catalyst diagnostics strategy based on the exothermic process generated by exhaust gas species oxidation in the catalyst. The diagnostics strategy is designed to be applied on-board and respecting real-time electronic control unit computational limitations. Diagnostics purposes are fulfilled by means of the comparison of the passive model temperature, which represents the outlet temperature of a non-impregnated diesel oxidation catalyst, and the measurement provided by the on-board catalyst-out temperature sensor. Thus, the presented diagnostics strategy uses only two production grade temperature sensors and the measurements of air and fuel mass flows from the electronic control unit. Passive diagnostics is based on the oxidation of engine-raw emissions, whilst active diagnostics is based on the oxidation of requested post injected fuel. Post-injection strategy is also discussed for active diagnosis. Then, the diagnostics strategy is able to discern whether the diesel oxidation catalyst is able to oxidise or not. (C) 2016 Elsevier Ltd. All rights reserved. This research has been partially financed by the Spanish Ministerio de Economia y Competitividad, through project TRA2013-40853-R 'Desarrollo de nuevas tecnicas de limitacion de la perdida de presion en DPFs para reducir las emisiones y el consumo de los motores diesel (PRELIMIT)'.

Due to the growing air quality concern in urban areas and rising fuel prices, urban bus fleets are progressively turning to hybrid electric vehicles (HEVs) which show higher efficiency and lower emissions in comparison with conventional vehicles. HEVs can reduce fuel consumption and emissions by combining different energy sources (i.e., fuel and batteries). In this sense, the performance of HEVs is strongly dependent on the energy management strategy (EMS) which coordinates the energy sources available to exploit their potential. While most EMSs are calibrated for general driving conditions, this paper proposes to adapt the EMS to the specific driving conditions on a particular bus route. The proposed algorithm relies on the fact that partial information on the driving cycle can be assumed since, in the case of a urban bus, the considered route is periodically covered. According to this hypothesis, the strategy presented in this paper is based on estimating the driving cycle from a previous trip of the bus in the considered route. This initial driving cycle is used to compute the theoretical optimal solution by dynamic programming. The obtained control policy (particularly the cost-to-go matrix) is stored and used in the subsequent driving cycles by applying one-step look-ahead roll out, then, adapting the EMS to the actual driving conditions but exploiting the similarities with previous cycles in the same route. To justify the proposed strategy, the paper discusses the common patterns in different driving cycles of the same bus route, pointing out several metrics that show how a single cycle captures most of the key parameters for EMS optimization. Then, the proposed algorithm (off-line dynamic programming optimization and one-step look-ahead rollout) is described. Results obtained by simulation show that the proposed method is able to keep the battery charge within the required range and achieve near-optimal performance, with only a 1.9% increase in fuel consumption with regards to the theoretical optimum. As a reference for comparison, the equivalent consumption minimization strategy (ECMS), which is the most widespread algorithm for HEV energy management, produces an increase in fuel consumption with respect to the optimal solution of 11%.

[EN] This paper presents a model for on-line NOx estimation. The method uses both, low frequency components and high frequency components of in-cylinder pressure signal: it harnesses in-cylinder pressure resonance to estimate the trapped mass, and based on this measurement, a NOx model is adapted to estimate NOx emissions cycle by cycle. In addition of the in-cylinder pressure signal, the procedure only requires from lambda and air mass flow to estimate NOx, so it can give a direct estimation of NOx or improve transient response and aging of current NOx sensors. The method was validated on a CI engine with high pressure EGR loop under steady and transient conditions showing errors below 10% and cycle by cycle time response. (C) 2016 Elsevier Ltd. All rights reserved.

[EN] In-cylinder pressure is the most important variable to analyze the combustion process in internal combustion engines, and can be used as feedback signal for closed-loop combustion control and diagnostics. However, pressure sensors are still affected by challenges such as durability and cost, which prevent their use in massproduction vehicles. Therefore, this work presents a model-based approach to estimate the in-cylinder pressure by means of the combination of a control oriented model and information from the set of sensors available in current production engines for automotive applications. Pressure peak location of each cylinder is estimated through the knock sensor signal, and used as feedback to improve the model. An extended Kalman filter is used to adapt the model to the information from the knock sensor signal. The adaptive model is implemented in a four cylinder light-duty engine and compared with the open loop model, ensuring a continuous estimation of incylinder pressure signal, and an improvement for the estimation of different cycle by cycle combustion parameters and cylinder to cylinder variations. Finally, the proposed approach is applied with different fuels showing that the proposed method can be applied independently on the fuel used. Irina A. Jimenez received a funding through the grant 132GRISO-LIAP/2018/132 from the Generalitat Valenciana and the European Social Fund.

The effort to implement more environmental-friendly fuels has been enhanced not only by the desire to reduce the greenhouse effects but also for public health issues. This paper studies the effects on pollutant emissions from a light-duty Euro 6 vehicle with four types of fuel: diesel (fossil origin, used as reference), biodiesel (renewable origin), Gas-to-Liquid (fossil origin) and farnesane (renewable origin). Both stationary engine and real-world driving cycles are studied. First, each fuel was tested in stationary modes in a vehicle test-bench and then tested in a realistic driving cycle with the same vehicle. This allows the separation the transient effects of the driving cycle from stationary results. Stationary tests lead to engine emission maps and driving cycle tests allow weighting the importance of each stationary condition during a realistic route. Instantaneous and cumulative CO, THC (total hydrocarbon), NOx and PN (particle number) emissions on route were obtained. The fuel that presented a highest level of emissions at stationary conditions was, for CO, diesel, for THC, diesel, for NOx, biodiesel and for PN, diesel. The behaviour of fuels during the driving cycles, from less pollutant to more pollutant, was: for CO, diesel, farnesane, GTL and biodiesel; for THC, GTL, farnesane, biodiesel, diesel. For NOx, farnesane and diesel (very similar values), GTL and biodiesel; for PN, GTL, biodiesel, farnesane and diesel.

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.

[EN] Knock phenomenon reduces the thermal efficiency and restricts performance improvement in spark-ignited engines. Reliable and rapid knock recognition is crucial for the engine knock control. Among the wide set of knock detection techniques, those based on in-cylinder pressure sensors provide the most precise recognition. However, pressure sensors are still affected by challenges such as durability and cost. For on-board applications, knock is usually detected by vibration signal, but the accuracy is limited due to natural vibration and external noises.In this paper, a novel knock recognition method based on knock sensor signal is proposed. The method consists of the comparison of a resonance index obtained through the knock sensor signal and a combustion model capable of estimating the fraction of mass burned, and thus being able to estimate if the amplitude in the knock sensor signal is produced by the auto-ignition of certain amount of fuel. The proposed method was compared with a fixed threshold for knock sensor resonance intensity, the improvements were quantified by using as reference a high sensitive knock recognition method based on cylinder pressure. Results show that the proposed method is able to improve the accuracy in over a 10 % of knock detection than using one set threshold over the entire cycle. Acknowledgments Irina A. Jimenez received a funding through the grant 132GRISOLIAP/2018 from the Generalitat Valenciana, Spain and the European Social Fund.