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%.

The control and diagnosis of selective catalytic reduction (SCR) system require from a precise estimation of NOx reduction in order to properly control the NH3 injection. This paper aims to resolve the cross-sensitivity of current NOx sensors at the outlet of the SCR, by providing the control unit with an estimation of NOx and ammonia slip. The problem of discerning between NOx and ammonia slip is solved by identifying an intermediate variable representing the SCR load. The SCR load is estimated by combining the mass conservation principle between the inlet and the outlet of the SCR and a NOx reduction model, via an Extended Kalman filter. Current models and observers have several limitations to represent the real behaviour of the SCR along all the operating conditions: on one hand, when relying on the mass conservation, small errors at models are integrated, leading to important bias on the SCR load, on the other hand, the dynamics at the SCR difficult the observation and the adaptation of the model. The main focus of the developed algorithm is to use a simplified model which might be used for ammonia slip estimation, being aware of current limitations of SCR models in real operation. Experimental results in a EURO 6 compression ignited (CI) engine show the potential of such observation in transient conditions and an adequate correlation with external ammonia measurements provided by additional sensors available on the test bench.

To reach the emission limits imposed by governments and reduce the negative impact on the environment, the use of aftertreatment systems has become essential for internal combustion engine (ICE) based powertrains. In particular, the selective catalytic reduction (SCR) system is a widespread aftertreatment technology with high efficiency for $\mathrm{N}{\mathrm{O}}_{\mathrm{x}}$ abatement which shows complex dynamics and requires urea injection as reducing agent. Current urea injection strategies usually rely on the $\mathrm{N}{\mathrm{O}}_{\mathrm{x}}$ emissions feedback. This work presents a model for the on-line simultaneous prediction of $\mathrm{N}{\mathrm{O}}_{\mathrm{x}}$ and $\mathrm{N}{\mathrm{H}}_3$ emissions after the SCR catalyst, allowing the emissions estimation even in conditions of urea injector failure, when it is not possible to rely on the injector feedback signal. The proposed model is based on state of the art on-board after-treatment instrumentation and proposes an extended Kalman filter (EKF) to combine a data-based model and the analysis of sensor signals to provide a reliable estimation of $\mathrm{N}{\mathrm{O}}_{\mathrm{x}}$ and $\mathrm{N}{\mathrm{H}}_3$ slip. The proposed strategy is experimentally assessed in dynamic driving cycles, such as Worldwide harmonised Light vehicles Test Cycle (WLTC) and Standardised Random Test (RTS). The proposed method is evaluated in standard conditions (without failures) and with urea injection failures of 25% and 120% of the nominal injection amount. As a result, the prediction on $\mathrm{N}{\mathrm{O}}_{\mathrm{x}}$ and $\mathrm{N}{\mathrm{H}}_3$ slip has been improved in all injection failure conditions, by an overall average of 47.8% and 61.8%, respectively, when compared to state-of-the-art control oriented models (physically based zero dimensional model or data-based).

This work presents the development of a model to capture the NO _x sensors cross sensitivity behavior based on N O x sensor cell temperature, as well as a model do predict the slip of the NO _x and NH ₃ after the SCR catalyst, as a way to reduce the error in the exhaust emissions estimation needed for feedback SCR control strategies. The emissions prediction model is based on the different cross sensitivity behavior of two distinct NO _x sensors. The proposed models were tested and compared on a fully instrumented engine test bench when applied in a Worldwide harmonized Light vehicles Test Cycle (WLTC) and a full map cycle. As a result, the proposed model showed for NO _x sensors cross sensitivity estimation an overall improvement of 34.8% for sensor 1 and 31.0% for sensor 2, and in terms of emissions prediction an overall improvement of 36.3% for NO _x and 45.5% for NH ₃ slip.

A control-oriented model of spark ignition combustion is presented. The model makes use of avaliable signals, such as spark advance, air mass, intake pressure, and lambda, to characterize not only the average combustion evolution but also the cycle-to-cycle variability. The conventional turbulent flame propagation model with two states, namely entrained mass and burnt mass, is improved by look-up tables at some parameters, and the cycle-to-cycle variability is estimated by propagation of an exogenous noise with a normal probabilistic distribution at the turbulent and laminar flame speed, which intends to simulate the unknowns at turbulent flow, temperature distribution, or initial kernel distribution. The model is able to estimate which is the expected variability during the combustion evolution and might be used online for characterizing the time response of closed-loop control actions or it can be used offline to improve the control strategies without large experimental test campaigns. Experimental data from a four-stroke commercial engine was used for calibration and validation purposes, demonstrating the capabilities of the model in steady and transient conditions.

The potential of e-bus transportation to improve air quality and reduce noise pollution in cities is significant. In order to improve efficiency and extend the useful life of these vehicles, there is a growing need to investigate improvements for the thermal management system of electric city buses. In electric vehicles, there are several systems whose thermal behaviors need to be regulated, such as batteries, electric machines, power electronics, air conditioning, and cabin. In this study, a 0D/1D model of an electric city bus is developed that integrates all sub-models of the powertrain, auxiliaries, and thermal management system. This model is used to evaluate different configurations and thermal management strategies of the electric urban bus by simulating public transport driving cycles in Valencia, Spain, under winter conditions. First, the original thermal–hydraulic circuit of the bus was modified, resulting in an improvement in the battery energy consumption with savings of 11.4% taking advantage of the heat produced in the electric motors to heat the battery. Then, the original PTC heating system of the bus was compared with a proposed heat pump system in terms of battery power consumption. The heat pump system achieved an energy savings of 3.9% compared to the PTC heating system.