The accelerated adoption of electric vehicles (EVs), driven by a growing global market and sustainability laws, has magnified the importance of Battery Thermal Management Systems (BTMS). The efficacy of these systems, however, is challenged by real-world variables, including traffic patterns, ambient conditions, and driver behavior. This work presents a predictive thermal management strategy that integrates driving and charging phases to minimize energy consumption over a complete trip. Leveraging the vehicle route information to anticipate charging stops and the State of Charge (SOC) allows to proactively adapt the control strategy to the current situation. When a charging event is predicted, the controller preconditions the system and transitions to a charging-optimized strategy that manages the thermal stresses associated with fast charging. The strategy was validated experimentally using a 3.7 kWh battery pack, demonstrating a reduction in BTMS energy consumption compared to a conventional rule-based controller across different drive cycles.

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.

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.

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%.

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. Copyright (c) 2025 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)

Driving advisory usually require a-priori information about the route, which can be provided by context awareness technologies, such as vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) connectivity. This study presents a cooperative driving algorithm by formulating an optimal control problem (OCP) and solving it with a non-linear Model Predictive Control (MPC) approach with two steps. First, the OCP is solved by Dynamic Programming (DP), providing the cost-to-go (CTG) from any pair time-state to the end of the problem. The CTG can be used as a terminal cost for the MPC to be solved recursively during the route considering traffic as a disturbance in a short horizon. Secondly, the optimization is solved iteratively: at any time-step the first vehicle is solved assuming that the rest follow the traffic prediction algorithm. The process continues sequentially until all the vehicles have been solved, while the traffic prediction is replaced by the optimal trajectories of the vehicles that have been previously computed. Different prediction horizons, mixed traffic levels and the scheduling policy to solve the different vehicles are evaluated. Results suggest that the cooperative driving algorithm can reduce the fleet fuel consumption by up to 35%, while covering the same route in a similar time.

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.

The rapid adoption of Battery Electric Vehicles (BEVs) has been driven by growing environmental awareness and advancements in energy storage technologies. However, lithiumion 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. Copyright (c) 2025 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)

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The growing call for pollution-free environments has prompted the creation of zero-emission zones (ZEZs) around the world. For regional and national transport, plug-in hybrid electric vehicle (PHEV) are an attractive option, which also offer ZE driving. To address the PHEV challenges of sufficient ZE driving range and of meeting real-world emission targets outside the ZEZs, this work proposes an adaptive supervisory control strategy, which minimizes the total operational costs while complying with tailpipe [Formula: see text] emissions constraints. It combines a Modular Energy Management Strategy (MEMS), for cost-optimal power-split, with an Integrated Emission Management (IEM) strategy for determining the cost-optimal air path setting of the internal combustion engine. A real-time implementable, optimal control strategy is derived based on Pontryagin’s Minimum Principle. To determine the optimal selection of the co-states used in this strategy, a numerical optimization is performed for different route segments and real-world cycles. This study demonstrates that PHEVs can successfully be operated around ZEZs. The best performance is realized with an adaptive supervisory control strategy with different co-states per route segment; compared to the standard strategy with fixed co-states, this proposed strategy was able to achieve cost and [Formula: see text] emission reductions of up to 10% and 22%, respectively, for the studied real-world cycles.

Controlling fuel cell degradation poses a significant challenge for the widespread adoption of Fuel Cell Electric Vehicles (FCEVs) due to the high costs associated with the materials used in these systems. Given the complex powertrain of such vehicles, energy management strategies (EMS) are crucial for their efficiency and energy consumption. This paper develops a predictive EMS for FCEVs by designing an objective function to minimize the impact of fuel cell system degradation and employing offline Dynamic Programming optimization of a previously covered trip. The resulting cost-to-go matrix serves as a proxy for the terminal cost in an iterative finite time horizon to be optimized in a MPC approach. In parallel, an Extended Kalman Filter (EKF) is used for the online adaptation of the fuel cell model utilized in the EMS. This adaptive strategy enhances model accuracy under different driving conditions, correcting bias and drift caused by long-term fuel cell usage. Simulations of real-world driving cycles validate the proposed EMS, highlighting the trade-off between fuel cell lifespan and fuel consumption. The study shows that the fuel cell lifespan can be extended by up to 60% without increasing fuel consumption, or fuel consumption can be reduced by 1.2% while increasing the fuel cell lifespan by 42%, compared to the baseline strategy, which does not employ adaptive calibration and the designed objective function. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)

Battery electric vehicles (BEVs) are gaining market shares due to their ability to employ clean energy, their smooth operation and reduced noise, pollutant emissions and maintenance. Batteries are one of the key technologies in BEV since they strongly affect the vehicle cost and driving range, two of the major concerns of BEV costumers. While the energy storing capabilities of BEVs usually exceed commuting requirements, batteries can be also utilized for home energy management system using bi-directional charging technology. This paper introduces an efficient energy management system for a smart home with BEVs and a bidirectional charger by addressing the corresponding optimal control problem of deciding the battery charging and discharging strategy to minimize energy expenditure and cost. To this aim, the electricity price and expenditure of upcoming weeks is forecasted using data from the present week, and a model of the complete system is used to find the optimal solutions by means of backward induction in a receding horizon approach. The proposed strategy relies on currently available information about the home and vehicle energy expenditure and energy prices in the recent past. The results of the study show a reduction of the electricity cost above 20% in the considered use-case.

The purpose of this study is to enhance control strategies for selective catalytic reduction (SCR) and ammonia slip catalyst (ASC) systems, aiming to effectively reduce NOx emissions from automotive engines during realistic driving cycles. Despite the effectiveness of these after-treatment systems (ATS), their dynamic and non-linear characteristics present significant challenges in achieving precise control. Therefore, this research proposes a hybrid approach that combines backward induction (BI) as the primary optimization technique with model predictive control (MPC) framework for real-time application. The article introduces a reduced-state control-oriented model of the SCR + ASC system, which is embedded into the BI algorithm to calculate optimal control actions within a finite horizon. Additionally, it is proposed an alternative approach for adapting the grid of model states within the BI algorithm, effectively reducing the computational cost. This adjustment enables the algorithm to operate in real-time with near-optimal results, as confirmed by experimental validation. Lastly, the study explores how different degrees of knowledge regarding system disturbances impact the strategy's performance, examining three distinct scenarios: constant prediction horizon, probabilistic description, and full knowledge of the prediction horizon.

After-treatment systems (ATS) in vehicle powertrains are developed to achieve maximum pollutant abatement efficiency under design operating conditions. However, anomalies in the ATS may appear during real operation, such as reducing agent supply failure and catalyst ageing. As in the case of an ATS composed of a selective catalytic reduction (SCR) system plus an ammonia slip catalyst (ASC), injection failure leads to insufficient ammonia levels to reduce nitrogen oxide (NOx), and ageing decreases the ammonia storage capacity and NOx conversion efficiency, these drawbacks drive both pollutants to levels above those expected and allowed by governmental regulations. To address this problem, a novel methodology was proposed that simultaneously estimates the operational state of the ATS in terms of ammonia injection failure and ASC catalyst ageing. The approach through control-oriented models based on an artificial neural network (ANN) and sensor signal analysis (SSA), as well as an extended Kalman filter (EKF) observer, compares the current emission levels estimated by the observer in a two-dimensional probabilistic plan and a variable time-window, with those expected if the ATS were operating under normal conditions. The proposed methodology was evaluated in real driving cycles with the ATS subjected to several undesired operating conditions. As a result, the model was able to accurately estimate the levels of ammonia injection failure and catalyst ageing state. Therefore, enabling the control system to modify the ammonia injection strategy to achieve desired levels of NOx and NH3 emissions.

To ensure that after-treatment systems (ATS) reduce emissions to the levels for which they were designed, it is essential that the ATS control can rely on the feedback signals from the sensors and actuators that are part of the system. Knowing that the amount of ammonia injected into the catalyst governs the Nitrogen oxides (NOx) reduction, this work addresses the impact of the ammonia injection failure in the Selective Catalytic Reduction (SCR) on the exhaust emissions and describes a model-based fault diagnosis strategy. The proposed approach is based on an artificial neural network (ANN) and a sensor signal analysis (SSA) model of the catalyst, as well as an observer to merge the models and accurately estimate the emissions. The proposed diagnostic strategy is based on the comparison of the observed NOx and ammonia (NH3) emissions of the actual system with those expected in the system without ammonia injection failure. Experimental results show that the proposed strategy can detect failures in ammonia injection above 10%. Once the degradation level is detected, a correction strategy is applied by increasing the ammonia injector opening time according to the estimated degradation to increase the injected ammonia up to levels similar to faultless conditions. When the injection failure was corrected, the proposed strategy was able to mitigate the impact on NOx emissions, reducing them by 23.33% and approaching the NOx levels without injection failure (5.35% increase).

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 NOx abatement which shows complex dynamics and requires urea injection as reducing agent. Current urea injection strategies usually rely on the NOx emissions feedback. This work presents a model for the on-line simultaneous prediction of NOx and NH3 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 NOx and NH3 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 NO(x )and NH3 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).

The incoming emission regulations for internal combustion engines are gradually introducing new pollutant species, which requires greater complexity of the exhaust gas aftertreatment systems concerning layout, control and diagnostics. This is the case of ammonia, which is already regulated in heavy-duty vehicles and to be included in the emissions standards applied to passenger cars. The ammonia is injected into the exhaust gas through urea injections for NOx abatement in selective catalytic reduction (SCR) systems and can be also generated in other aftertreatment systems as three-way catalysts. However, ammonia slip may require removal on a dedicated catalyst called ammonia slip catalyst (ASC). The set consisting of the urea injection system, SCR and ASC requires control and on-board diagnostic tools to ensure high NOx conversion efficiency and minimization of the ammonia slip under real driving conditions. These tasks are based on the use of NOx sensors ZrO2 pumping cell-based, which present as a drawback high cross-sensitivity to ammonia. Consequently, the presence of this species can affect the measurement of NOx and compromise SCR-ASC control strategies. In the present work, a methodology to predict ammonia and NOx tailpipe emissions is proposed. For this purpose, a control-oriented ASC model was developed to use its ammonia slip prediction to determine the cross-sensitivity correction of the NOx sensor placed downstream of the ASC. The model is based on a simplified solution of the transport equations of the species involved in the main ASC reactions. The ammonia slip model was calibrated using steady- and quasi-steady-state tests performed in a Euro 6c diesel engine. Finally, the performance of the proposed methodology to predict NOx and ammonia emissions was evaluated against experimental data corresponding to Worldwide harmonized Light vehicles Test Cycles (WLTC) applying different urea dosing strategies.

This work presents the development of a model to capture the NOx sensors cross sensitivity behavior based on [Formula: see text] sensor cell temperature, as well as a model do predict the slip of the NOx and NH3 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 NOx 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 NOx 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 NOx and 45.5% for NH3 slip.