Engineering, Control and Systems Engineering, Automotive Engineering
25
Scopus Publications
Scopus Publications
Hydrogen-CNG blends for SI engines: Experimental assessment of performance, emissions and operability Mario Eduardo Santos Martins, André Nakaema Aronis, Juliano Pereira Silveira, Igor Rodrigues dos Santos, Benjamín Pla, Thompson Diórdinis Metzka Lanzanova, Nina Paula Gonçalves Salau International Journal of Hydrogen Energy, 2026 Hydrogen enrichment of compressed natural gas (HCNG) was investigated in a spark-ignition engine using an experimental framework to examine the combined effects of blend composition, load, knock limitation, and lean operability. The study comprised a baseline CNG spark sweep, stoichiometric hydrogen-fraction sweeps from 19.5 to 100 vol% at 3 bar, 6 bar, and full load, and lean-limit exploration. The baseline sweep assessed spark-timing effects on efficiency, peak pressure, and NO x emissions, while the parametric tests were conducted at CA50 ≈ 8 ° aTDCf. Under stoichiometric operation, hydrogen shortened flame development by up to 73.1% and combustion duration by up to 72.9%. Carbon-based emissions decreased, whereas pressure-rise rates and knock propensity increased; full load became knock-limited above 68% H 2 . In lean sweeps, hydrogen extended the stable λ range, and the results show that the most favorable HCNG blend depends on operating condition, supporting variable dual-fuel metering. • High load requires more CNG to suppress knock and maintain volumetric energy. • The optimum H2 fraction depends strongly on load rather than a fixed blend ratio. • NOx can drop to approximately zero with hydrogen enrichment in lean burn. • Efficiency optimum occurs near the λ where NOx approaches zero. • Hydrogen extends the practical lean operating window of CNG combustion.
Traffic information impact on the optimisation of fuel consumption and emissions in an urban driving scenario Benjamín Pla, Pau Bares, André Aronis, Augusto Perin International Journal of Engine Research, 2026 Traffic jams are one of the main causes of city pollution and significantly impact the economic cost of transportation. Context awareness by the traffic players may be key to improving the current control strategies and optimising traffic flow. This study investigates the effect of information availability through vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) connectivity in an urban real-driving route. An optimal control problem (OCP) is formulated to create speed advisory profiles, and it is solved using dynamic programming (DP) to provide the global optimal solution. Experimental engine tests have been used to characterise the fuel consumption and emissions of the engine, while traffic sensors around the city of Valencia have been used to reproduce realistic urban mobility using the traffic simulation software SUMO. The paper quantifies the impact of traffic information on vehicle fuel consumption and emissions. Under normal traffic conditions and assuming total access to the traffic information, the DP algorithm can reduce almost 60% on average the fuel consumption compared to normal driving behaviour provided by the default car-following model of SUMO.
Predictive cruise control based on short traffic forecast in an urban simulation environment Augusto Perin, Benjamín Pla, Pau Bares, André Aronis, Lorenzo Brunelli, Giuseppe Mercurio Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering, 2026 This paper aims to extend the potential of Adaptive Cruise Control (ACC) technology by forecasting the behaviour of the traffic using physical models. A preceding vehicle prediction algorithm is proposed estimating the future kinematics of the vehicles with an extended car-following model. The prediction is tested with two cruise control frameworks: An anticipative car-following (ACF) control where the prediction is used to anticipate the drivers reaction and an economical predictive cruise control algorithm, named as eco-PCC, where the estimation of the preceding vehicle is added as a constraint to an optimal control problem (OCP) to minimise the fuel consumption over a receding horizon. The algorithms are tested in SUMO with a validated simulation environment of the city of Bologna. Results compare the benefits of the ACF approach and the eco-PCC algorithm with a baseline ACC without predictions. Several prediction horizons have been tested, highlighting the trade-off between prediction accuracy and the energy improvements. Experiments in an engine testbed presented benefits up to 8.03% in fuel consumption for the ACF control and up to 24.14% for the eco-PCC algorithm when compared to the baseline ACC without prediction.
Demand-Adapting Charging Strategy for Battery-Swapping Stations Benjamín Pla, Pau Bares, Andre Aronis, Augusto Perin Batteries, 2025 This paper analyzes the control strategy for urban battery-swapping stations by optimizing the charging policy based on real-time battery demand and the time required for a full charge. The energy stored in available batteries serves as an electricity buffer, allowing energy to be drawn from the grid when costs or equivalent CO2 emissions are low. An optimized charging policy is derived using dynamic programming (DP), assuming average battery demand and accounting for both the costs and emissions associated with electricity consumption. The proposed algorithm uses a prediction of the expected traffic in the area as well as the expected cost of electricity on the net. Battery tests were conducted to assess charging time variability, and traffic density measurements were collected in the city of Valencia across multiple days to provide a realistic scenario, while real-time data of the electricity cost is integrated into the control proposal. The results show that incorporating traffic and electricity price forecasts into the control algorithm can reduce electricity costs by up to 11% and decrease associated CO2 emissions by more than 26%.
Supervisory controller for minimizing fuel consumption and NOx emissions of plug-in hybrid electric vehicles operating in zero-emission zones Douglas Uberti Pinto, Benjamín Pla, André Nakaema Aronis, Emmanuele Frasci, Ivan Arsie International Journal of Engine Research, 2025 This paper introduces a supervisory controller designed for Plug-in Hybrid Electric Vehicles (PHEVs) to minimize total energy consumption and tailpipe NOx emissions while adhering to Zero Emission Zone (ZEZ) constraints. The case study exploits historical driving cycle data from an urban bus route in Zurich to analyze trip correlations, where a ZEZ restriction was added to assess the vehicle performance under such conditions. A PHEV bus model was built, integrating the powertrain and After-Treatment System (ATS), where an electric heater is included to mitigate NOx emissions. The supervisory controller is tasked with determining the optimal power split and the electric heater power to ensure adherence to feasible operating conditions along the route. Historical driving cycle data analysis demonstrates that the speed profiles along the selected route exhibit similarities. This observation is leveraged by a Dynamic Programming (DP) optimization, where an arbitrary bus trip is employed to generate a cost-to-go matrix. Then, the resulting cos-to-go matrix was indexed by the position in the route and is used on the real-time controller by applying the one-step look-ahead roll-out algorithm. Simulation results demonstrate the controller effectiveness in addressing ZEZ restrictions, presenting a trade-off between energy consumption and tailpipe NOx emissions. A benchmark was carried out, comparing the results obtained with the DP solution as the baseline, assuming perfect knowledge of driving cycle disturbances, revealing a 7% increase in tailpipe NOx emissions and a 1.3% increase in fuel consumption compared to the theoretical minimum and fulfilling the ZEZ restrictions in all the cases.
Battery heat flow HIL for cooling system testing and optimization Benjamín Pla, Pau Bares, Andre Aronis, Victor Tomanik IFAC Papersonline, 2025 The rapid adoption of Battery Electric Vehicles (BEVs) has been driven by growing environmental awareness and advancements in energy storage technologies. However, lithium-ion cells, central to BEVs, are highly sensitive to temperature variations, requiring effective thermal management to prevent degradation, ensure safety, and optimize performance. This work presents a novel method to replicate the thermal behaviour of a battery on liquid cooling systems using standard system components. By combining a virtual battery model with a physical system, the thermal behaviour of a real battery pack is accurately reproduced. This cost-effective, safety-compliant approach enhances the development of efficient thermal management systems for BEVs. The Hardware In the Loop (HIL) platform was developed with a PXI from National Instruments and a solid-state resistance of 1 kW, while a 4 kWh battery pack prototype refrigerated with a cold plate was used for validation.
Driving speed profile optimization for electric vehicles under variable traffic conditions Benjamín Pla, Pau Bares, Andre Aronis, Augusto Perin, Richard Burke IFAC Papersonline, 2025 The present paper optimizes the driving profile for an electric vehicle, by using an optimal control formulation and route scheduling. The slope, traffic lights timing and the trajectory limitations, i.e., speed limitations and safety speed in turns, are computed along the route for minimising the energy consumption with consideration of the time. The work also analyses how unexpected traffic conditions caused by external drivers might influence the final performance of the controller. The paper uses real experimental data in several routes between Bath and the University of Bath from a HondaE electrical vehicle and reproduces several traffic conditions by using SUMO to validate the proposed methodology and determine its limitations under traffic disturbances.
Optimal control of the power split for a fuel cell vehicle considering air path dynamics B. Pla, P. Bares, A. Aronis, D. Pinto, D. di Blasio, T. Fletcher, R.D. Burke IFAC Papersonline, 2025 Dynamic Programming (DP) is often used to compute the optimal energy management in fuel cell vehicles (FCV) during a priori known driving cycles to benchmark different technologies or provide insight into suitable control strategies to be applied online. Due to the curse of dimensionality, using DP usually involves the use of simplified models that apply the quasi-steady approach for FC modelling, employing a map that provides the net FC power for a given current demand. While electro-chemistry processes inside the FC are much faster than the driving cycle dynamics, the response of the air-path, specially if turbocharging is used may make the quasi-steady hypothesis too optimistic. In this work, a state-of-the-art FCV model with seven states, adapted from the literature, has been calibrated using experimental data for the FC. The performance of using the quasi-steady approach for DP optimization has been assessed, leading to errors in H2 consumption above 13% for the considered cycles. Then, a model order reduction technique based on the Singular Value Decomposition (SVD) is applied to enable the use of DP on a simplified model including FC dynamics with positive results reducing the gap between the global and simplified model to levels lower than 10% and providing benefits in H2 consumption of 3.7 and 1.3% in WLTC and RDE cycles, respectively.
